query: Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis

positive: ImageNet Classification with Deep Convolutional Neural Networks We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

positive: Towards an intelligent framework for multimodal affective data analysis An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.

positive: Opensmile: the munich versatile and fast open-source audio feature extractor We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.

positive: A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.

positive: Convolutional recurrent neural networks: Learning spatial dependencies for image representation In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.

negative: EventRiver: Visually Exploring Text Collections with Temporal References Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.

negative: Traffic Light Control System for Emergency Vehicles Using Radio Frequency Traffic congestion problem is a phenomena which contributed huge impact to the transportation system in country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light controller system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Integrated Circuit (PIC) 16F877A microcontroller to change the sequence back to the normal sequence before the emergency mode was triggered. This system will reduce accidents which often happen at the traffic light intersections because of other vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the wireless communication; the radio frequency (RF) transmission in the traffic light control system for emergency vehicles. The prototype of this project is using the frequency of 434 MHz and function with the sequence mode of traffic light when emergency vehicles passing by an intersection and changing the sequence back to the normal sequence before the emergency mode was triggered. In future, this prototype system can be improved by controlling the real traffic situation, in fact improving present traffic light system technology.

negative: The Ease of Doing Business Index as a tool for Investment location decisions The Ease of Doing Business Index (EDBI) uses 41 variables to compare the business environment of different countries. It is widely used by policy makers, researchers and multinational companies. This paper aims to assess EDBI’s consistency and validity in representing the business environment by using factor analysis. It is found that the EDBI presents a limited consistency and descriptive power of a country’s business environment. The consequence of these findings is that multinational firms should handle carefully the EDBI in their investment decisions. GEE The Ease of Doing Business Index as a tool for Investment location decisions – João Zambujal-Oliveira, Ricardo Pinheiro-Alves

negative: Literature Review on Automatic Text Summarization: Single and Multiple Summarizations The online information available on world wide web is in enormous amount. Search engines like Google, Yahoo were developed to retrieve information from the databases. But actual results were not obtained as the electronic information is increasing day by day. Thus automatic summarization came into demand. Automatic summarization gathers several documents as input and provides the shorter summarized version as output which is informative, unambiguous, save valuable time. Research was done on a single document and moved towards multiple documents. This review categorizes single and multiple summarization methods.

negative: AN OVERVIEW OF MASSIVE MIMO SYSTEM IN 5 G 4G is proving good speeds up to 1Gbps. Then why do we need anything more. The problem is that it is not able to provide real time applications. 5G is the name given to the next generation of mobile data connectivity. It will definitely provide great speeds between 10Gbps to 100Gbps and it will have enough capacity. But the thing that separated 5G from 4G is latency; the latency provided by 4G is between 40ms to 60ms, whereas in 5G it will provide ultra latency between 1ms to 10ms. The standards for 5G will be set till 2020 and it will be applicable by 2022/23. In this paper we have discussed about 5G and its advantages, a probable architecture for 5G and some challenges in 5G. The main technology that maybe used in 5G are massive MIMO, millimetre wave communication, device to device communication, beam division multiple access etc. In this paper we have discussed about massive MIMO, channel estimate in massive MIMO, beam division multiple access technique to be used in massive MIMO, antenna selection in massive MIMO, capacity and energy efficiency in massive MIMO. In future 5G is going to be a technology which will be invisible, I will be just there everywhere just like electricity. It is a very good area for research as standards and frequency band for 5G are yet to be standardised.

negative: Contextual Dueling Bandits We consider the problem of learning to choose actions using contextual information when provided with limited feedback in the form of relative pairwise comparisons. We study this problem in the dueling-bandits framework of Yue et al. (2009), which we extend to incorporate context. Roughly, the learner’s goal is to find the best policy, or way of behaving, in some space of policies, although “best” is not always so clearly defined. Here, we propose a new and natural solution concept, rooted in game theory, called a von Neumann winner, a randomized policy that beats or ties every other policy. We show that this notion overcomes important limitations of existing solutions, particularly the Condorcet winner which has typically been used in the past, but which requires strong and often unrealistic assumptions. We then present three efficient algorithms for online learning in our setting, and for approximating a von Neumann winner from batch-like data. The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space. The other two algorithms require time and space only logarithmic in the size of the policy space when provided access to an oracle for solving classification problems on the space.

negative:
Beyond Slideware: How a Free-form Presentation Medium Stimulates Free-form Thinking in the Classroom We investigate how presentation in a free-form medium stimulates free-form thinking and discussion in the classroom. Most classroom presentations utilize slideware (e.g. PowerPoint). Yet, slides add intrusive segregations that obstruct the flow of information. In contrast, in a free-form medium of presentation, content is not separated into rigid slide compartments. Instead, it is visually arranged and transformed in a continuous space.
 We develop a case study that investigates student experiences authoring, presenting, viewing, and discussing free-form presentations in a graduate seminar class. We analyze interviews, present a sampling of student presentations, and develop findings: free-form presentation stimulates free-form thinking, spontaneous discussion, and emergent ideation.

negative: Efficient Restoration Method for Images Corrupted with Impulse Noise This paper proposes a two-stage adaptive method for restoration of images corrupted with impulse noise. In the first stage, the pixels which are most likely contaminated by noise are detected based on their intensity values. In the second stage, an efficient average filtering algorithm is used to remove those noisy pixels from the image. Only pixels which are determined to be noisy in the first stage are processed in the second stage. The remaining pixels of the first stage are not processed further and are just copied to their corresponding locations in the restored image. The experimental results for the proposed method demonstrate that it is faster and simpler than even median filtering, and it is very efficient for images corrupted with a wide range of impulse noise densities varying from 10% to 90%. Because of its simplicity, high speed, and low computational complexity, the proposed method can be used in realtime digital image applications, e.g., in consumer electronic products such as digital televisions and cameras.

negative: A Simple Parallel Algorithm for the Maximal Independent Set Problem Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intelligence. One of the main contributions of this paper is the development of powerful and general techniques for converting Monte Carlo algorithms into deterministic algorithms. These techniques are used to convert the Monte Carlo algorithm for the MIS problem into a simple deterministic algorithm with the same parallel running time.

negative: Interruptions on software teams: a comparison of paired and solo programmers This study explores interruption patterns among software developers who program in pairs versus those who program solo. Ethnographic observations indicate that interruption length, content, type, occurrence time, and interrupter and interruptee strategies differed markedly for radically collocated pair programmers versus the programmers who primarily worked alone. After presenting an analysis of 242 interruptions drawn from more than 40 hours of observation data, we discuss how team configuration and work setting influenced how and when developers handled interruptions. We then suggest ways that CSCW systems might better support pair programming and, more broadly, provide interruption-handling support for workers in knowledge-intensive occupations.

negative: Metric Learning and Manifolds Metric Learning and Manifolds : Preserving the Intrinsic Geometry A variety of algorithms exist for performing non-linear dimension reduction, but these algorithms do not preserve the original geometry of the data except in special cases. In general, in the low-dimensional representations obtained, distances are distorted, as well as angles, areas, etc. This paper proposes a generic method to estimate the distortion incurred at each point of an embedding, and subsequently to correct distances and other intrinsic geometric quantities back to their original values (up to sampling noise). Our approach is based on augmenting the output of an embedding algorithm with geometric information embodied in the Riemannian metric of the manifold. The Riemannian metric allows one to compute geometric quantities (such as angle, length, or volume) for any coordinate system or embedding of the manifold. In this work, we provide an algorithm for estimating the Riemannian metric from data, consider its consistency, and demonstrate the uses of our approach in a variety of examples.

negative: Specialized Support Vector Machines for open-set recognition Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. Sometimes, some of these classes may be ill-sampled, not sampled at all or undefined. In such cases, we need to think of robust classification methods able to deal with the “unknown” and properly reject samples belonging to classes never seen during training. Notwithstanding, almost all existing classifiers to date were mostly developed for the closed-set scenario, i.e., the classification setup in which it is assumed that all test samples belong to one of the classes with which the classifier was trained. In the open-set scenario, however, a test sample can belong to none of the known classes and the classifier must properly reject it by classifying it as unknown. In this work, we extend upon the wellknown Support Vector Machines (SVM) classifier and introduce the Specialized Support Vector Machines (SSVM), which is suitable for recognition in open-set setups. SSVM balances the empirical risk and the risk of the unknown and ensures that the region of the feature space in which a test sample would be classified as known (one of the known classes) is always bounded, ensuring a finite risk of the unknown. The same cannot be guaranteed by the traditional SVM formulation, even when using the Radial Basis Function (RBF) kernel. In this work, we also highlight the properties of the SVM classifier related to the open-set scenario, and provide necessary and sufficient conditions for an RBF SVM to have bounded open-space risk. We also indicate promising directions of investigation of SVM-based methods for open-set scenarios. An extensive set of experiments compares the proposed method with existing solutions in the literature for open-set recognition and the reported results show its effectiveness.

negative: Gaze- vs. hand-based pointing in virtual environments This paper contributes to the nascent body of literature on pointing performance in Virtual Environments (VEs), comparing gaze- and hand-based pointing. Contrary to previous findings, preliminary results indicate that gaze-based pointing is slower than hand-based pointing for distant objects.

negative: Toward preprototype user acceptance testing of new information systems: implications for software project management Errors in requirements specifications have been identified as a major contributor to costly software project failures. It would be highly beneficial if information systems developers could verify requirements by predicting workplace acceptance of a new system based on user evaluations of its specifications measured during the earliest stages of the development project, ideally before building a working prototype. However, conventional wisdom among system developers asserts that prospective users must have direct hands-on experience with at least a working prototype of a new system before they can provide assessments that accurately reflect future usage behavior after workplace implementation. The present research demonstrates that this assumption is only partially true. Specifically, it is true that stable and predictive assessments of a system's perceived ease of use should be based on direct behavioral experience using the system. However, stable and behaviorally predictive measures of perceived usefulness can be captured from target users who have received information about a system's functionality, but have not had direct hands-on usage experience. This distinction is key because, compared to ease of use, usefulness is generally much more strongly linked to future usage intentions and behaviors in the workplace. Two longitudinal field experiments show that preprototype usefulness measures can closely approximate hands-on based usefulness measures, and are significantly predictive of usage intentions and behavior up to six months after workplace implementation. The present findings open the door toward research on how user acceptance testing may be done much earlier in the system development process than has traditionally been the case. Such preprototype user acceptance tests have greater informational value than their postprototype counterparts because they are captured when only a relatively small proportion of project costs have been incurred and there is greater flexibility to modify a new system's design attributes. Implications are discussed for future research to confirm the robustness of the present findings and to better understand the practical potential and limitations of preprototype user acceptance testing.

negative: Value co-creation between firms and customers: The role of big data-based cooperative assets To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms. ã 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

negative: Online 3D acquisition and model integration This paper presents a system which yields complete 3D models in a fast and inexpensive way. The major building blocks are a high-speed structured light range scanner and a registration module. The former generates ’raw’ range data whereas the latter performs a fast registration (ICP) and renders the partially integrated model as a preview of the final result. As the scanner uses only a single image to make a reconstruction, it is possible to scan while the object is moved. The use of an adaptive projection pattern gives a more robust behaviour. This allows the system to deal more easily with complicated geometry and texture than most other systems. During the scanning process the model is built up incrementally and rendered on the screen. This real-time visual feedback allows the user to check the current state of the 3D model. Holes or other flaws can be detected during the acquisition process itself. This speeds up the model building process, and solves indirectly the problem of view planning. The whole real-time pipeline comprising acquisition, merging and visualization only uses off-the-shel f hardware. A regular desktop PC connected to a camera and LCD projector is turned into a high-speed scanner and modeler. The current implementation has a throughput of approx. 5 fps.

negative:
The TypTop System: Personalized Typo-Tolerant Password Checking Password checking systems traditionally allow login only if the correct password is submitted. Recent work on typo-tolerant password checking suggests that usability can be improved, with negligible security loss, by allowing a small number of typographical errors. Existing systems, however, can only correct a handful of errors, such as accidentally leaving caps lock on or incorrect capitalization of the first letter in a password. This leaves out numerous kinds of typos made by users, such as transposition errors, substitutions, or capitalization errors elsewhere in a password. Some users therefore receive no benefit from existing typo-tolerance mechanisms.
 We introduce personalized typo-tolerant password checking. In our approach, the authentication system learns over time the typos made by a specific user. In experiments using Mechanical Turk, we show that 45% of users would benefit from personalization. Therefore, we design a system, called TypTop, that securely implements personalized typo-tolerance. Underlying TypTop is a new stateful password-based encryption scheme that can be used to store recent failed login attempts. Our formal analysis shows that security in the face of an attacker that obtains the state of the system reduces to the difficulty of a brute-force dictionary attack against the real password. We implement TypTop for Linux and Mac OS login and report on a proof-of-concept deployment.

negative: Super-Resolution with Deep Convolutional Sufficient Statistics Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its lowresolution corrupted observation. When the scaling ratio is small, point estimates achieve impressive performance, but soon they suffer from the regression-to-themean problem, result of their inability to capture the multi-modality of this conditional distribution. Modeling high-dimensional image and audio distributions is a hard task, requiring both the ability to model complex geometrical structures and textured regions. In this paper, we propose to use as conditional model a Gibbs distribution, where its sufficient statistics are given by deep convolutional neural networks. The features computed by the network are stable to local deformation, and have reduced variance when the input is a stationary texture. These properties imply that the resulting sufficient statistics minimize the uncertainty of the target signals given the degraded observations, while being highly informative. The filters of the CNN are initialized by multiscale complex wavelets, and then we propose an algorithm to fine-tune them by estimating the gradient of the conditional log-likelihood, which bears some similarities with Generative Adversarial Networks. We evaluate experimentally the proposed approach in the image superresolution task, but the approach is general and could be used in other challenging ill-posed problems such as audio bandwidth extension.

negative: Markov Logic Networks for Natural Language Question Answering Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisy nature of these automatically extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. In the first, we simply use the extracted science rules directly as MLN clauses. Unlike typical MLN applications, our domain has long and complex rules, leading to an unmanageable number of groundings. We exploit the structure present in hard constraints to improve tractability, but the formulation remains ineffective. In the second approach, we instead interpret science rules as describing prototypical entities, thus mapping rules directly to grounded MLN assertions, whose constants are then clustered using existing entity resolution methods. This drastically simplifies the network, but still suffers from brittleness. Finally, our third approach, called Praline, uses MLNs to align the lexical elements as well as define and control how inference should be performed in this task. Our experiments, demonstrating a 15% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.

negative: The Acquisition , Transfer , and Depreciation of Knowledge in Service Organizations : Productivity in Franchises Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

negative: Massively Parallel NUMA-aware Hash Joins Driven by the two main hardware trends increasing main memory and massively parallel multi-core processing in the past few years, there has been much research e ort in parallelizing well-known join algorithms. However, the non-uniform memory access (NUMA) of these architectures to main memory has only gained limited attention in the design of these algorithms. We study recent proposals of main memory hash join implementations and identify their major performance problems on NUMA architectures. We then develop a NUMA-aware hash join for massively parallel environments, and show how the speci c implementation details a ect the performance on a NUMA system. Our experimental evaluation shows that a carefully engineered hash join implementation outperforms previous high performance hash joins by a factor of more than two, resulting in an unprecedented throughput of 3/4 billion join argument tuples per second.

negative: Reducing the effect of email interruptions on employees It is generally assumed that because it is not necessary to react to email messages when they arrive, employees will read their messages in their own time with minimum interruption to their work. This research has shown that email messages do have some disruptive effect by interrupting the user. Employees at the Danwood Group in the UK were monitored to see how they used email. It was found that most employees had their email software check for incoming messages every 5min and responded to the arrival of a message within 6 s. A recovery time between finishing reading the email and returning to normal work also existed though it was shorter than published recovery times for a telephone interrupt. This analysis has suggested that a number of methods can be employed to reduce this interrupt effect. Employee training, changing the settings and modes of using the email software and the introduction of a one line email facility are all shown to have beneficial effects. This has led to a series of recommendations that will enable the Danwood Group to make better use of email communication and increase employee effectiveness. r 2003 Elsevier Science Ltd. All rights reserved.

negative: Immersion, presence and performance in virtual environments: an experiment with tri-dimensional chess This paper describes an experiment to assess the influence of immersion on performance in immersive virtual environments. The task involved Tri-Dimensional Chess, and required subjects to reproduce on a real chess board the state of the board learned from a sequence of moves witnessed in a virtual environment. Twenty four subjects were allocated to a factorial design consisting of two levels of immersion (exocentric screen based, and egocentric HMD based), and two kinds of environment (plain and realistic. The results suggest that egocentric subjects performed better than exocentric, and those in the more realistic environment performed better than those in the less realistic environment. Previous knowledge of chess, and amount of virtual practice were also significant, and may be considered as control variables to equalise these factors amongst the subjects. Other things being equal, males remembered the moves better than females, although female performance improved with higher spatial ability test score. The paper also attempts to clarify the relationship between immersion, presence and performance, and locates the experiment within such a theoretical framework.

negative: Probabilistic graph and hypergraph matching We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyper-edges. A match between the feature sets is then modeled as a hypergraph matching problem. We derive the hyper-graph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat soft matching as a mere relaxation of the hard matching problem. Second, the model induces an algebraic relation between the hyper-edge weight matrix and the desired vertex-to-vertex probabilistic matching. Third, the model explains some of the graph matching normalization proposed in the past on a heuristic basis such as doubly stochastic normalizations of the edge weights. A key benefit of the model is that the global optimum of the matching criteria can be found via an iterative successive projection algorithm. The algorithm reduces to the well known Sinkhorn [15] row/column matrix normalization procedure in the special case when the two graphs have the same number of vertices and a complete matching is desired. Another benefit of our model is the straight-forward scalability from graphs to hyper-graphs.

negative: A Review of Non-Invasive Sensory Feedback Methods for Transradial Prosthetic Hands Any implant or prosthesis replacing a function or functions of an organ or group of organs should be biologically and sensorily integrated with the human body in order to increase their acceptance with their user. If this replacement is for a human hand, which is an important interface between humans and their environment, the acceptance issue and developing sensory-motor embodiment will be more challenging. Despite progress in prosthesis technologies, 50–60% of hand amputees wear a prosthetic device. One primary reason for the rejection of the prosthetic hands is that there is no or negligibly small feedback or tactile sensation from the hand to the user, making the hands less functional. In fact, the loss of a hand means interrupting the closed-loop sensory feedback between the brain (motor control) and the hand (sensory feedback through the nerves). The lack of feedback requires significant cognitive efforts from the user in order to do basic gestures and daily activities. To this aim, recently, there has been significant development in the provision of sensory feedback from transradial prosthetic hands, to enable the user take part in the control loop and improve user embodiment. Sensory feedback to the hand users can be provided via invasive and non-invasive methods. The latter includes the use of temperature, vibration, mechanical pressure and skin stretching, electrotactile stimulation, phantom limb stimulation, audio feedback, and augmented reality. This paper provides a comprehensive review of the non-invasive methods, performs their critical evaluation, and presents challenges and opportunities associated with the non-invasive sensory feedback methods.

negative: Visual Event Recognition in Videos by Learning from Web Data We propose a visual event recognition framework for consumer videos by leveraging a large amount of loosely labeled web videos (e.g., from YouTube). Observing that consumer videos generally contain large intraclass variations within the same type of events, we first propose a new method, called Aligned Space-Time Pyramid Matching (ASTPM), to measure the distance between any two video clips. Second, we propose a new transfer learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time features and static SIFT features) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web video domain and consumer video domain). For each pyramid level and each type of local features, we first train a set of SVM classifiers based on the combined training set from two domains by using multiple base kernels from different kernel types and parameters, which are then fused with equal weights to obtain a prelearned average classifier. In A-MKL, for each event class we learn an adapted target classifier based on multiple base kernels and the prelearned average classifiers from this event class or all the event classes by minimizing both the structural risk functional and the mismatch between data distributions of two domains. Extensive experiments demonstrate the effectiveness of our proposed framework that requires only a small number of labeled consumer videos by leveraging web data. We also conduct an in-depth investigation on various aspects of the proposed method A-MKL, such as the analysis on the combination coefficients on the prelearned classifiers, the convergence of the learning algorithm, and the performance variation by using different proportions of labeled consumer videos. Moreover, we show that A-MKL using the prelearned classifiers from all the event classes leads to better performance when compared with A-MKL using the prelearned classifiers only from each individual event class.

negative: Zara The Supergirl: An Empathetic Personality Recognition System Zara the Supergirl is an interactive system that, while having a conversation with a user, uses its built in sentiment analysis, emotion recognition, facial and speech recognition modules, to exhibit the human-like response of sharing emotions. In addition, at the end of a 5-10 minute conversation with the user, it can give a comprehensive personality analysis based on the user’s interaction with Zara. This is a first prototype that has incorporated a full empathy module, the recognition and response of human emotions, into a spoken language interactive system that enhances human-robot understanding. Zara was shown at the World Economic Forum in Dalian in September 2015.

negative: When saliency meets sentiment: Understanding how image content invokes emotion and sentiment Sentiment analysis is crucial for extracting social signals from social media content. Due to the prevalence of images in social media, image sentiment analysis is receiving increasing attention in recent years. However, most existing systems are black-boxes that do not provide insight on how image content invokes sentiment and emotion in the viewers. On the other hand, psychological studies have confirmed that salient objects in an image often invoke emotions. In this work, we investigate more fine-grained and more comprehensive interaction between visual saliency and visual sentiment. In particular, we partition images in several meta-level scene-type dimensions that are relevant to most images, including: open-closed, natural-manmade, indoor-outdoor, and face-noface. Facilitated by state of the art saliency detection algorithm and sentiment classification algorithm, we examine how the sentiment of the salient region(s) in an image relates to the overall sentiment of the image. The experiments on a representative image emotion dataset have shown interesting correlation between saliency and sentiment in different scene types and shed light on the mechanism of visual sentiment evocation.

negative: Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments.

negative: A Computational Analysis of Mahabharata Indian epics have not been analyzed computationally to the extent that Greek epics have. In this paper, we show how interesting insights can be derived from the ancient epic Mahabharata by applying a variety of analytical techniques based on a combination of natural language processing, sentiment/emotion analysis and social network analysis methods. One of our key findings is the pattern of significant changes in the overall sentiment of the epic story across its eighteen chapters and the corresponding characterization of the primary protagonists in terms of their sentiments, emotions, centrality and leadership attributes in the epic saga.

negative: Common Sense Knowledge Based Personality Recognition from Text Past works on personality detection has shown that psycho-linguistic features, frequency based analysis at lexical level , emotive words and other lexical clues such as number of first person or sec ond person words carry major role to identify personality associated with the te xt. In this work, we propose a new architecture for the same task using common sen se knowledge with associated sentiment polarity and affective labels. To extract the common sense knowledge with sentiment polarity scores and affect iv labels we used Senticnet which is one of the most useful resources for opinion mining and sentiment analysis. In particular, we combined comm n sense knowledge based features with phycho-linguistic features and freque ncy based features and later the features were employed in supervised classifier . We designed five SMO based supervised classifiers for five personality t raits. We observe that the use of common sense knowledge with affective and sentim ent information enhances the accuracy of the existing frameworks which use o nly psycho-linguistic features and frequency based analysis at lexical le ve .

negative: Musical Instrument Recognition in User-generated Videos using a Multimodal Convolutional Neural Network Architecture This paper presents a method for recognizing musical instruments in user-generated videos. Musical instrument recognition from music signals is a well-known task in the music information retrieval (MIR) field, where current approaches rely on the analysis of the good-quality audio material. This work addresses a real-world scenario with several research challenges, i.e. the analysis of user-generated videos that are varied in terms of recording conditions and quality and may contain multiple instruments sounding simultaneously and background noise. Our approach does not only focus on the analysis of audio information, but we exploit the multimodal information embedded in the audio and visual domains. In order to do so, we develop a Convolutional Neural Network (CNN) architecture which combines learned representations from both modalities at a late fusion stage. Our approach is trained and evaluated on two large-scale video datasets: YouTube-8M and FCVID. The proposed architectures demonstrate state-of-the-art results in audio and video object recognition, provide additional robustness to missing modalities, and remains computationally cheap to train.


query: BIDaaS: Blockchain Based ID As a Service

positive: Practical byzantine fault tolerance and proactive recovery Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.

positive: Blockchain-based secure firmware update for embedded devices in an Internet of Things environment Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device’s firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated.

positive: ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER The blockchain paradigm when coupled with cryptographically-secured transactions has demonstrated its utility through a number of projects, with Bitcoin being one of the most notable ones. Each such project can be seen as a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. Ethereum implements this paradigm in a generalised manner. Furthermore it provides a plurality of such resources, each with a distinct state and operating code but able to interact through a message-passing framework with others. We discuss its design, implementation issues, the opportunities it provides and the future hurdles we envisage.

positive: How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions] ^ IEEE ConsumEr ElECtronICs magazInE 19 By early 2016, Pabellon achieved another major milestone: a first touchfree control panel using solely the body’s electrolytes to control a matrix of magnetic pixels. Unlike conventional passive near-field communication technology that relies on receiver loop antennas for detection and identification, the magnetic pixel needs only our body salts to work. Each pixel represents a discrete sensing element. When arrayed or matrixed, the collection of pixels can be used to position index fingers approaching but not touching the surface. Pabellon’s powertransfer emitter turns into a touch-free pad. Unlike motion and other opticalsensing methods, the position of objects like fingers can be resolved by small changes in the magnetic near field. The ability to finely control the vast amount of things we contact on a daily basis may not require contact alone. The magnetic pixel has a very exciting future in the look-but-don’t-touch world that Pabellon envisions.

positive: Asynchronous Consensus and Broadcast Protocols A consensus protocol enables a system of n asynchronous processes, some of which are faulty, to reach agreement. There are two kinds of faulty processes: fail-stop processes that can only die and malicious processes that can also send false messages. The class of asynchronous systems with fair schedulers is defined, and consensus protocols that terminate with probability 1 for these systems are investigated. With fail-stop processes, it is shown that ⌈(n + 1)/2⌉ correct processes are necessary and sufficient to reach agreement. In the malicious case, it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to reach agreement. This is contrasted with an earlier result, stating that there is no consensus protocol for the fail-stop case that always terminates within a bounded number of steps, even if only one process can fail. The possibility of reliable broadcast (Byzantine Agreement) in asynchronous systems is also investigated. Asynchronous Byzantine Agreement is defined, and it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to achieve it.

negative: Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.

negative: Supply Chain Risk Management: Literature Review and Future Research Supply Chain Risk Management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2006 will be analyzed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. The literature review will provide the basis for outlining future research opportunities in this field.

negative: HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC FPER relationship.

negative: Towards cloud based mobile ad hoc network simulation Simulations are considered as the most convenient approach for validating any research proposal. A simulator attempts to model a real-world setting in order to analyze the behavior of any algorithm. A large number of simulation software for ad hoc network has been proposed in literature. These simulators are based on execution of simulation on standalone computers. This paper introduces the notion of `simulation as a service' for mobile ad hoc network's simulation based on cloud computing. Cloud computing is an emerging discipline that enables outsourcing computing task remotely based on pay-as-you-go model. The paper provides a generalized architecture to perform ad hoc network simulations on cloud platforms. Similar to different service models provided by cloud, some service models have been introduced for cloud based simulation. The proposed approach provides different advantages such as dynamic service provisioning to support large scale simulation, sharing of simulation models and reduced cost for simulation etc. The proposed tool has been implemented on Heroku platform that demonstrates the scalability of proposed approach.

negative:
Comparison of orthodontic treatment outcomes in adults with skeletal open bite between conventional edgewise treatment and implant-anchored orthodontics. INTRODUCTION
Open bites are known to be difficult malocclusions to treat. Generally, with conventional edgewise treatment, incisor extrusion rather than molar intrusion is observed. Recently, the use of miniscrews as anchorage has markedly increased. In this study, orthodontic treatment outcomes after conventional edgewise treatment and implant-anchored treatment were investigated by cephalometric analysis and several occlusal indexes. In addition, the stability of these patients 2 years after the retention phase was also analyzed.

METHODS
Thirty adults (15 for nonimplant treatment [non-IA group] and 15 for implant-anchored treatment [IA group]) were our subjects. Cephalometric analysis, peer assessment rating, discrepancy index, and objective grading system were used.

RESULTS
From the cephalometric values in the non-IA group, open-bite patients were generally treated by extrusion of the maxillary and mandibular incisors that resulted in clockwise rotation of the mandibular plane angle. In the IA group, intrusion of the maxillary and mandibular molars that resulted in counterclockwise rotation was noted. Furthermore, in the IA group, the soft-tissue analysis showed decreases in the facial convexity and the inferior labial sulcus angle that resulted in the disappearance of incompetent lips. In the retention phase, extrusion of the mandibular molars was observed in the IA group. From the objective grading system evaluation, significant reductions of overbite in canines and premolars were observed in both groups. Furthermore, less stability was observed in the IA group compared with the non-IA group according to the total objective grading system score.

CONCLUSIONS
Ideal occlusion can be achieved in adults with severe open bite with both conventional edgewise and implant-anchored orthodontic treatment. However, absolute intrusion of the molars and improvement in esthetics might be achieved more effectively by using miniscrews as an anchorage device. In addition, since a significant amount of tooth movement occurs with miniscrews, careful attention is required during the retention phase.

negative: Parametric Optimization for Practical Control Systems Design In this contribution, calculation machinery and code to compute different performance indices, which are necessary for the parametric optimization, are proposed. Moreover, a short review about parametric optimization is carried out. It is highlighted that normally only the ISE (Integral Square Error) performance index is used for control system design. One reason for that is the existence of several numerical algorithms to compute it along the infinite trajectory at once. Finally, an optimal design of a PI controller is used to illustrate and compare the new algorithms.

negative:
Embedding Factorization Models for Jointly Recommending Items and User Generated Lists Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced.
 Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.

negative: Promoting Transfer through Complex Systems Principles Understanding scientific phenomena in terms of complex systems principles is both scientifically and pedagogically important. Situations from different disciplines of science are often governed by the same principle, and so promoting knowledge transfer across disciplines makes valuable cross-fertilization and scientific unification possible. Although evidence for this kind of transfer has been historically controversial, experiments and observations of students suggest pedagogical methods to promote transfer of complex systems principles. One powerful strategy is for students to actively interpret the elements and interactions of perceptually grounded scenarios. Such interpretation can be facilitated through the presentation of cases alongside general principles, and by students exploring and constructing computational models of cases. The resulting knowledge can be both concretely grounded yet highly perspective-dependent and generalizeable. We discuss methods for coordinating computational and mental models of complex systems, the roles of idealization and concreteness in fostering understanding and generalization, and other complementary theoretical approaches to transfer. Understanding Complex Systems 3 Promoting Transfer through Complex Systems Principles When and how do students transfer what they have learned to new situations? This is one of the most important questions confronting education and cognitive science. Addressing it has crucial practical consequence, while also touching on deep basic research issues related to learning, analogical reasoning, and conceptual representation. Considerable research suggests that students do not spontaneously transfer what they have learned, at least not to superficially dissimilar domains (Detterman, 1993; Gick & Holyoak, 1980; 1983). This is disturbing because teachers choose content with the hope that students will apply what they have learned to relevant new situations. We believe that students can transfer scientific principles across superficially dissimilar domains, and we are not alone in this belief (Bransford & Schwartz, 1999; Jacobson, 2001; Judd, 1908; Simon, 1980). To present our case, we will describe kinds of transfer that are worth “fighting for.” Identifying these turns out not to be only an educational question, but a scientific question as well. Accordingly, we will describe a novel approach toward science that seeks to unite phenomena from disparate domains according to general principles that govern complex systems. This complex systems approach to science offers unique educational opportunities for imparting scientific understanding that is both concretely grounded yet transportable. The notion of a grounded generalization may sound like an oxymoron, but it is key to our account of transfer. The time-honored method for conveying generalizations has been to use symbolic formalisms such as predicate logic or algebra. These formalisms can enable a student to transcend the specifics of a situation, but they also run the risk of disconnecting the resulting abstraction from an intuitive understanding of the situation. Instead, we propose learning and teaching methods that promote situation construals that are concrete insofar as they are perceptually, temporally, and spatially grounded. However, they are still idealizations in that many elements of a situation are ignored or highly simplified. In this paper, we will develop an approach to achieving grounded generalizations through the following steps: 1) Describe the 1 See also the notion of ‘situated abstraction’ in the section “comparison to other approaches to transfer.” Understanding Complex Systems 4 nature of complex systems accounts of science, 2) provide examples of general complex systems principles that appear in several case studies, 3) Describe pedagogical benefits of teaching science through complex systems, 4) Discuss the importance of transfer and generalization in relation to complex systems, 5) present a method for achieving generalization through perceptually grounded yet interpreted simulations, 6) compare generalization from grounded simulations to formalism-centered strategies, and 7) draw parallels between computational and mental models, with the aim of identifying design principles that allow the two kinds of models to mesh. Connecting Science with Complex Systems Principles One way to advance science is to progressively flesh out theories, adding experimental details and elaborating mechanistic accounts. By this account, the “devil is in the details” and the proper occupation of scientists is to pursue these details. This vision of science was most emphatically painted by John Horgan in his 1996 book “The End of Science.” He argued that the age of fundamental scientific theorizing and discoveries has passed, and that all that is left to be done is refining the details of theories already laid down by the likes of Einstein, Darwin, and Newton. The rapid rate of scientific specialization seems to support Horgan’s argument. We have gone from an era when the only major scientific journals were “Nature” and “Science” to an era with specialized journals such as “Journal of Contaminant Hydrology” and “Journal of Shoulder and Elbow Surgery,” each an umbrella outlet for several distinct sub-specializations. Well over half of the respondents to a Science Advisory Board believe that biologists are loosing track of the big picture by specializing in narrowly defined biological fields. Yet, many scientists feel compelled to specialize to still further degrees due to the sheer volume of scholarly output that competes for their eyes and minds. A small subset of scientists have chosen to reverse the trend toward increasing specialization. They have instead pursued principles that apply to many scientific domains, from physics to biology to social sciences. The principles are not applied to these domains in a metaphoric or vague fashion, as the terms “chaos” and “fractal” often were applied to art or interpersonal relations. Rather, the claim of complex system researchers Understanding Complex Systems 5 is that the same specific principle, sometimes expressible as equations or as sets of computational rules, can describe seemingly different phenomena. By complex systems, we refer to systems that contain numerous distinct elements that interact locally, resulting in a system globally changing over time. Over the past few decades, the field of complex systems theory has been rapidly developing (Bar-Yam, 1997; Holland, 1995; Kauffman, 1993; Wolfram, 2002). Complex systems theory and methods are now endemic to the sciences and can be found in just about every academic discipline and profession (Amaral & Ottino, 2004; Barabasi, 2002; Diermeir & Merlo, 2000; Epstein & Axtell 1996; Wolfram, 1986). It may at first seem that complex systems represent a small fraction of natural and social phenomena. But, this would be a misunderstanding of the field. What makes something a complex system or complex phenomenon is a matter of perspective. If you take a complex systems perspective, focusing on the interaction of system elements, then almost every system in nature and in society can be described as a complex system. Seen from this perspective, the system can then be analyzed using complex systems principles and methods. Complex systems theory has described a number of quite general principles that can describe natural and social systems across a wide variety of traditional disciplines. A few examples are in order. A commonly found system architecture in nature is: Pattern 1: An entity causes more entities like itself to be produced. At the same time, it causes another kind of entity to be produced that eliminates the

negative: Wireless Body Sensor Network With Adaptive Low-Power Design for Biometrics and Healthcare Applications A four-levels hierarchical wireless body sensor network (WBSN) system is designed for biometrics and healthcare applications. It also separates pathways for communication and control. In order to improve performance, a communication cycle is constructed for synchronizing the WBSN system with the pipeline. A low-power adaptive process is a necessity for long-time healthcare monitoring. It includes a data encoder and an adaptive power conserving algorithm within each sensor node along with an accurate control switch system for adaptive power control. The thermal sensor node consists of a micro control unit (MCU), a thermal bipolar junction transistor sensor, an analog-to-digital converter (ADC), a calibrator, a data encoder, a 2.4-GHz radio frequency transceiver, and an antenna. When detecting ten body temperature or 240 electrocardiogram (ECG) signals per second, the power consumption is either 106.3 ¿W or 220.4 ¿W. By switching circuits, multi sharing wireless protocol, and reducing transmission data by data encoder, it achieves a reduction of 99.573% or 99.164% in power consumption compared to those without using adaptive and encoding modules. Compared with published research reports and industrial works, the proposed method is 69.6% or 98% lower than the power consumption in thermal sensor nodes which consist only of a sensor and ADC (without MCU, 2.4-GHz transceiver, modulator, demodulator, and data encoder) or wireless ECG sensor nodes which selected Bluetooth, 2.4-GHz transceiver, and Zigbee as wireless protocols.

negative: Active torque control of electric power steering system using composite nonlinear feedback control Electric power steering (EPS) system plays an important role in assisting the driver to achieve better handling, steering feel and response in securing vehicle stability. In EPS control system, controlling the torque of the EPS motor is crucial where tracking of the robust command of motor angle to generate desired assist torque is very challenging. In this paper, a reference model is adopted to generate an ideal motor angle that can guarantee the desired performance and composite nonlinear feedback (CNF) control is applied to achieve a faster transient response in tracking the robust reference motor angle. The H2 state optimization is used to obtain the linear control parameters whereas Hooke-Jeeves auto-tuning algorithm is used to fine-tune the nonlinear control parameters to achieve the best transient performance. The proposed control strategy is validated with CarSim Software by interfacing Simulink model with CarSim full nonlinear vehicle model under different test procedures and compared to other controllers based on ITAE performance criteria.

negative: Correlates of Young Women ’ s Interest in Obtaining Cosmetic Surgery This study examined predictors of young women’s interest in obtaining cosmetic surgery. The sample investigated was comprised of 101 college undergraduates residing in the northeastern U.S. (M age=19.99, SD=4.79). Participants’ weight status (M BMI=24.52, SD=5.69), body dissatisfaction, internalization of media messages, reports of physical appearance teasing, and interest in obtaining cosmetic surgery were assessed. Results indicated that all of the predictors examined were correlated with women’s interest in cosmetic surgery. Further, body dissatisfaction mediated the relations between weight status, internalization of media messages, reports of teasing, and women’s interest in cosmetic surgery. Findings are discussed in terms of the importance of understanding cosmetic surgery trends and young women’s susceptibility to body dissatisfaction.

negative: VMVM: unit test virtualization for Java As software evolves and grows, its regression test suites tend to grow as well. When these test suites become too large, they can eventually reach a point where they become too length to regularly execute. Previous work in Test Suite Minimization has reduced the number of tests in such suites by attempting to identify those that are redundant (e.g. by a coverage metric). Our approach to ameliorating the runtime of these large test suites is complementary, instead focusing on reducing the overhead of running each test, an approach that we call Unit Test Virtualization. This Tool Demonstration presents our implementation of Unit Test Virtualization, VMVM (pronounced "vroom-vroom") and summarizes an evaluation of our implementation on 20 real-world Java applications, showing that it reduces test suite execution time by up to 97% (on average, 62%). A companion video to this demonstration is available online, at https://www.youtube.com/watch?v=sRpqF3rJERI.

negative: AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our systemutilizes Redundant ResidueNumber System (RRNS)with newmethod of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS,we introduce AR-RRNSmethod for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data. © 2017 Elsevier B.V. All rights reserved.

negative: Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the metacognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students' learning outcomes.

negative: Capacity of Neural Networks for Lifelong Learning of Composable Tasks We investigate neural circuits in the exacting setting that (i) the acquisition of a piece of knowledge can occur from a single interaction, (ii) the result of each such interaction is a rapidly evaluatable subcircuit, (iii) hundreds of thousands of such subcircuits can be acquired in sequence without substantially degrading the earlier ones, and (iv) recall can be in the form of a rapid evaluation of a composition of subcircuits that have been so acquired at arbitrary different earlier times.We develop a complexity theory, in terms of asymptotically matching upper and lower bounds, on the capacity of a neural network for executing, in this setting, the following action, which we call {\it association}: Each action sets up a subcircuit so that the excitation of a chosen set of neurons A will in future cause the excitation of another chosen set B.% As model of computation we consider the neuroidal model, a fully distributed model in which the quantitative resources n, the neuron numbers, d, the number of other neurons each neuron is connected to, and k, the inverse of the maximum synaptic strength, are all accounted for.A succession of experiences, possibly over a lifetime, results in the realization of a complex set of subcircuits. The composability requirement constrains the model to ensure that, for each association as realized by a subcircuit, the excitation in the triggering set of neurons A is quantitatively similar to that in the triggered set B, and also that the unintended excitation in the rest of the system is negligible. These requirements ensure that chains of associations can be triggeredWe first analyze what we call the Basic Mechanism, which uses only direct connections between neurons in the triggering set A and the target set B. We consider random networks of n neurons with expected number d of connections to and from each. We show that in the composable context capacity growth is limited by d^2, a severe limitation if the network is sparse, as it is in cortex. We go on to study the Expansive Mechanism, that additionally uses intermediate relay neurons which have high synaptic weights. For this mechanism we show that the capacity can grow as dn, to within logarithmic factors. From these two results it follows that in the composable regime, for the realistic cortical estimate of d=n^{\frac{1}{2}, superlinear capacity of order n^{\frac{3}{2}} in terms of the neuron numbers can be realized by the Expansive Mechanism, instead of the linear order n to which the Basic Mechanism is limited. More generally, for both mechanisms, we establish matching upper and lower bounds on capacity in terms of the parameters n, d, and the inverse maximum synaptic strength k.The results as stated above assume that in a set of associations, a target B can be triggered by at most one set A. It can be shown that the capacities are similar if the number m of As that can trigger a B is greater than one but small, but become severely constrained if m exceeds a certain threshold.

negative: A survey of unmanned aerial vehicles (UAVs) for traffic monitoring The focus of this paper is on surveying UAV-based systems for traffic monitoring and management. Although there has been voluminous research on the subject, unmanned aerial vehicles (UAVs) are proven to be a viable and less time-consuming alternative to real-time traffic monitoring and management, providing the eye-in-the-sky solution to the problem.

negative: On Training Classifiers for Linking Event Templates The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events.

negative: Spatial, Structural and Temporal Feature Learning for Human Interaction Prediction Human interaction prediction, i.e., the recognition of an ongoing interaction activity before it is completely executed, has a wide range of applications such as human-robot interaction and the prevention of dangerous events. Due to the large variations in appearance and the evolution of scenes, interaction prediction at an early stage is a challenging task. In this paper, a novel structural feature is exploited as a complement together with the spatial and temporal information to improve the performance of interaction prediction. The proposed structural feature is captured by Long Short Term Memory (LSTM) networks, which process the global and local features associated to each frame and each optical flow image. A new ranking score fusion method is then introduced to combine the spatial, temporal and structural models. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for human interaction prediction on the BIT-Interaction, the TV Human Interaction and the UT-Interaction datasets.

negative: BOSS: Building Operating System Services Commercial buildings are attractive targets for introducing innovative cyber-physical control systems, because they are already highly instrumented distributed systems which consume large quantities of energy. However, they are not currently programmable in a meaningful sense because each building is constructed with vertically integrated, closed subsystems and without uniform abstractions to write applications against. We develop a set of operating system services called BOSS, which supports multiple portable, fault-tolerant applications on top of the distributed physical resources present in large commercial buildings. We evaluate our system based on lessons learned from deployments of many novel applications in our test building, a four-year-old, 140,000sf building with modern digital controls, as well as partial deployments at other sites.

negative: Research on deep neural network's hidden layers in phoneme recognition In spite of the great success of the deep neural network (DNN) in speech processing, it is still unclear what kind of underlying mechanisms are involved in this achievement. This preliminary study attempts to find an answer by investigating the functions of DNN's hidden layers in representing speech articulations. Two sets of experiments are performed on the hidden layers in speech recognition. The layer removing experiment is conducted on the English TIMIT database, and the layer replacing experiment is to substitute a layer in an English DNN by the corresponding layer in a Japanese DNN. It is found that the different layers seem to be responsible for different phoneme groups according to the place of articulation. The lower layers are responsible for the back vowels, and the higher layers are responsible for the front vowels. The second layer (i.e. the first hidden layer) of the seven-layer network has major responsibility for more than half of the consonants with the constriction located in the front of the vocal tract, while the other consonants rely on the middle and higher layers. The layer replacing experiment demonstrated that the above relation was language independent. It is necessary to design elaborate studies to discover more details in the future.

negative: Mobile application usability testing in quasi-real conditions The paper presents the setup and results of a mobile application usability test using mobile eye-tracking glasses. Quasi-real conditions of the natural environment were assured for the testing in order to avoid incidental disorders possible in natural conditions and the weaknesses of a fully lab experiment. The experiment methodology was developed and used to carry out a usability test of the mobile part of a Sale Force Automation (SFA) application in terms of its basic functionality. The paper's outlines are the following: the methodology used, arrangement and realisation of the usability testing experiment, test results and answers to the research questions. A list of general conclusions rounds off the report.

negative: Accelerator-centric deep learning systems for enhanced scalability, energy-efficiency, and programmability Deep learning (DL) has been successfully deployed in various application domains ranging from computer vision, speech recognition, to natural language processing. As the network models and the datasets used to train these models scale, system architects are faced with new challenges in designing a scalable and energy-efficient high-performance computing (HPC) system for training DL algorithms. One of the key obstacles that DL researchers are facing is the memory capacity bottleneck, where the limited physical memory size of the PCIe-attached DL accelerator (whether it be a discrete GPU or ASIC accelerator like Google's Tensor Processing Unit) constrains the algorithm that can be studied. In this paper, and the associated invited special session talk, we first discuss recent research literature geared towards designing scalable HPC systems for DL. In this context, we then discuss the memory capacity wall problem and introduce the work on virtualized deep neural networks, a memory virtualization solution that systematically reduces the memory consumption of DNN training. We conclude by providing projections on future challenges DNN memory virtualization will encounter upon and suggest accelerator-centric DL system as a promising research direction for the development of a scalable and energy-efficient deep learning system architecture.

negative: Topographical and temporal diversity of the human skin microbiome. Human skin is a large, heterogeneous organ that protects the body from pathogens while sustaining microorganisms that influence human health and disease. Our analysis of 16S ribosomal RNA gene sequences obtained from 20 distinct skin sites of healthy humans revealed that physiologically comparable sites harbor similar bacterial communities. The complexity and stability of the microbial community are dependent on the specific characteristics of the skin site. This topographical and temporal survey provides a baseline for studies that examine the role of bacterial communities in disease states and the microbial interdependencies required to maintain healthy skin.

negative: Adaptive Decentralized Droop Controller to Preserve Power Sharing Stability of Paralleled Inverters in Distributed Generation Microgrids This paper addresses the low-frequency relative stability problem in paralleled inverter-based distributed generation (DG) units in microgrids. In the sense of the small-signal dynamics of a microgrid, it can be shown that as the demanded power of each inverter changes, the low-frequency modes of the power sharing dynamics drift to new locations and the relative stability is remarkably affected, and eventually, instability can be yielded. To preserve the power sharing stability, an adaptive decentralized droop controller of paralleled inverter-based DG units is presented in this paper. The proposed power sharing strategy is based on the static droop characteristics combined with an adaptive transient droop function. Unlike conventional droop controllers, which yield 1-DOF tunable controller, the proposed droop controller yields 2-DOF tunable controller. Subsequently, the dynamic performance of the power sharing mechanism can be adjusted, without affecting the static droop gain, to damp the oscillatory modes of the power sharing controller. To account for the power modes immigration at different loading conditions, the transient droop gains are adaptively scheduled via small-signal analysis of the power sharing mechanism along the loading trajectory of each DG unit to yield the desired transient and steady-state response. The gain adaptation scheme utilizes the filtered active and reactive powers as indices; therefore, a stable and smooth power injection performance can be obtained at different loading conditions. The adaptive nature of the proposed controller ensures active damping of power oscillations at different operating conditions, and yields a stable and robust performance of the paralleled inverter system.

negative:
Analyzing and predicting viral tweets Twitter and other microblogging services have become indispensable sources of information in today's web. Understanding the main factors that make certain pieces of information spread quickly in these platforms can be decisive for the analysis of opinion formation and many other opinion mining tasks.
 This paper addresses important questions concerning the spread of information on Twitter. What makes Twitter users retweet a tweet? Is it possible to predict whether a tweet will become "viral", i.e., will be frequently retweeted? To answer these questions we provide an extensive analysis of a wide range of tweet and user features regarding their influence on the spread of tweets. The most impactful features are chosen to build a learning model that predicts viral tweets with high accuracy. All experiments are performed on a real-world dataset, extracted through a public Twitter API based on user IDs from the TREC 2011 microblog corpus.

negative: Cloudlet-based just-in-time indexing of IoT video As video cameras proliferate, the ability to scalably capture and search their data becomes important. Scalability is improved by performing video analytics on cloudlets at the edge of the Internet, and only shipping extracted index information and meta-data to the cloud. In this setting, we describe interactive data exploration (IDE), which refers to human-in-the-loop content-based retrospective search using predicates that may not have been part of any prior indexing. We also describe a new technique called just-in-time indexing (JITI) that improves response times in IDE.

negative: Who, where, when and what: discover spatio-temporal topics for twitter users Micro-blogging services, such as Twitter, and location-based social network applications have generated short text messages associated with geographic information, posting time, and user ids. The availability of such data received from users offers a good opportunity to study the user's spatial-temporal behavior and preference. In this paper, we propose a probabilistic model W4 (short for Who+Where+When+What) to exploit such data to discover individual users' mobility behaviors from spatial, temporal and activity aspects. To the best of our knowledge, our work offers the first solution to jointly model individual user's mobility behavior from the three aspects. Our model has a variety of applications, such as user profiling and location prediction; it can be employed to answer questions such as ``Can we infer the location of a user given a tweet posted by the user and the posting time?" Experimental results on two real-world datasets show that the proposed model is effective in discovering users' spatial-temporal topics, and outperforms state-of-the-art baselines significantly for the task of location prediction for tweets.

negative: A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System Intrusion detection plays a vital role in maintaining the stability of any network. The major requirements for any intrusion detection system are speed, accuracy and less memory. Though various intrusion detection methods are available, they excel at some points while lack in the others. This paper presents a comprehensive survey of the technologies that are used for detecting intrusions. It analyzes the pros and cons of each technology and the literature works that utilizes these technologies. Challenges faced by the current IDS and the requirements for IDS in the current network scenario are discussed in detail. A detailed study on the datasets that can be used for effective building of an IDS is discussed. The research framework is proposed and a discussion of the various technologies that can be used for improving the efficiency of the IDS is provided.

negative: UTILIZING A GAME THEORETICAL APPROACH TO PREVENT COLLUSION AND INCENTIVIZE COOPERATION IN Author: Arash Golchubian Title: Utilizing a Game Theoretical Approach to Prevent Collusion and Incentivize Cooperation in Cybersecurity Contexts Institution: Florida Atlantic University Thesis Advisor: Dr. Mehrdad Nojoumian Degree: Master of Science Year: 2017 In this research, a new reputation-based model is utilized to disincentivize collusion of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize dishonest mining strategies in Blockchain. In the context of SDN, the model uses the reputation values assigned to each entity to disincentivize collusion with an attacker. Our analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased model within a repeated game setting. In the context of Blockchain and mining, we illustrate that by using the same socio-rational model, miners not only are incentivized to conduct honest mining but also disincentivized to commit to any malicious activities against other mining pools. We therefore show that honest mining strategies become Nash Equilibrium in our setting. This thesis is laid out in the following manner. In chapter 2 an introduction to game theory is provided followed by a survey of previous works in game theoretic network security, in chapter 3 a new reputation-based model is introduced to be used within the context of a Software Defined Network (SDN), in chapter 4 a reputation-based solution concept is introduced to force cooperation by each mining entity in Blockchain, and finally, in chapter 5, the concluding remarks and future works are presented. vii To: My loving wife, Sanaz. You inspire me to better myself. and My mother, Mina. You showed me that nothing is impossible. UTILIZING A GAME THEORETICAL APPROACH TO PREVENT COLLUSION AND INCENTIVIZE COOPERATION IN CYBERSECURITY CONTEXTS List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

negative: An exploratory study of backtracking strategies used by developers Developers frequently backtrack while coding. They go back to an earlier state by removing inserted code or by restoring removed code for various reasons. However, little is known about when and how the developers backtrack, and modern IDEs do not provide much assistance for backtracking. As a first step towards gathering baseline knowledge about backtracking and designing more robust backtracking assistance tools in modern IDEs, we conducted an exploratory study with 12 professional developers and a follow-up online survey. Our study revealed several barriers they faced while backtracking. Subjects often manually commented and uncommented code, and often had difficulty finding relevant parts to backtrack. Backtracking was reported to be needed by 3/4 of the developers at least "sometimes".

negative: VINERy: A Visual IDE for Information Extraction Information Extraction (IE) is the key technology enabling analytics over unstructured and semi-structured data. Not surprisingly, it is becoming a critical building block for a wide range of emerging applications. To satisfy the rising demands for information extraction in real-world applications, it is crucial to lower the barrier to entry for IE development and enable users with general computer science background to develop higher quality extractors. In this demonstration, we present VINERY, an intuitive yet expressive visual IDE for information extraction. We show how it supports the full cycle of IE development without requiring a single line of code and enables a wide range of users to develop high quality IE extractors with minimal efforts. The extractors visually built in VINERY are automatically translated into semantically equivalent extractors in a state-of-the-art declarative language for IE. We also demonstrate how the auto-generated extractors can then be imported into a conventional Eclipse-based IDE for further enhancement. The results of our user studies indicate that VINERY is a significant step forward in facilitating extractor development for both expert and novice IE developers.