query: Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

positive: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in earlier iterations to perfo rm more informative gradient-based learning. Metaphorically, the adaptation allows us to find n eedles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems fro m recent advances in stochastic optimization and online learning which employ proximal funct ions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adap tively modifying the proximal function, which significantly simplifies setting a learning rate nd results in regret guarantees that are provably as good as the best proximal function that can be cho sen in hindsight. We give several efficient algorithms for empirical risk minimization probl ems with common and important regularization functions and domain constraints. We experimen tally study our theoretical analysis and show that adaptive subgradient methods outperform state-o f-the-art, yet non-adaptive, subgradient algorithms.

positive: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different for different languages, the feature transformations can be shared across languages. In this paper we propose a shared-hidden-layer multilingual DNN (SHL-MDNN), in which the hidden layers are made common across many languages while the softmax layers are made language dependent. We demonstrate that the SHL-MDNN can reduce errors by 3-5%, relatively, for all the languages decodable with the SHL-MDNN, over the monolingual DNNs trained using only the language specific data. Further, we show that the learned hidden layers sharing across languages can be transferred to improve recognition accuracy of new languages, with relative error reductions ranging from 6% to 28% against DNNs trained without exploiting the transferred hidden layers. It is particularly interesting that the error reduction can be achieved for the target language that is in different families of the languages used to learn the hidden layers.

positive: Communication-Efficient Learning of Deep Networks from Decentralized Data Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10–100× as compared to synchronized stochastic gradient descent.

positive: Federated Meta-Learning for Recommendation Recommender systems have been widely studied from the machine learning perspective, where it is crucial to share information among users while preserving user privacy. In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, instead of model or data adopted in previous approaches. In this framework, user-specific recommendation models are locally trained by a shared parameterized algorithm, which preserves user privacy and at the same time utilizes information from other users to help model training. Interestingly, the model thus trained exhibits a high capacity at a small scale, which is energyand communicationefficient. Experimental results show that recommendation models trained by meta-learning algorithms in the proposed framework outperform the state-of-the-art in accuracy and scale. For example, on a production dataset, a shared model under Google Federated Learning (McMahan et al., 2017) with 900,000 parameters has prediction accuracy 76.72%, while a shared algorithm under federated meta-learning with less than 30,000 parameters achieves accuracy of 86.23%.

positive: Neural networks and principal component analysis: Learning from examples without local minima We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization methods, such as back propagation, with respect to the usual quadratic error function E of the connection weights. Our main result is a complete description of the landscape attached to E in terms of principal component analysis. We show that E has a unique minimum corresponding to the projection onto the subspace generated by the first principal vectors of a covariance matrix associated with the training patterns. All the additional critical points of E are saddle points (corresponding to projections onto subspaces generated by higher order vectors). The auto-associative case is examined in detail. Extensions and implications for the learning algorithms are discussed.

negative: A CORDIC processor for FFT computation and its implementation using gallium arsenide technology In this paper, the architecture and the implementation of a complex fast Fourier transform (CFFT) processor using 0.6 m gallium arsenide (GaAs) technology are presented. This processor computes a 1024-point FFT of 16 bit complex data in less than 8 s, working at a frequency beyond 700 MHz, with a power consumption of 12.5 W. The architecture of the processor is based on the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the use of conventional multiplicationand-accumulation (MAC) units, but evaluates the trigonometric functions using only add and shift operations. Improvements to the basic CORDIC architecture are introduced in order to reduce the area and power of the processor. This together with the use of pipelining and carry save adders produces a very regular and fast processor. The CORDIC units were fabricated and tested in order to anticipate the final performance of the processor. This work also demonstrates the maturity of GaAs technology for implementing ultrahigh-performance signal processors.

negative: A current-starved inverter-based differential amplifier design for ultra-low power applications As silicon feature sizes decrease, more complex circui try arrays can now be contrived on a single die. This increase in the number of on-chip devices per unit area results in increased power dissipation per unit area. In order to meet certain power and operating temperature specifications, circuit design necessitates a focus on power efficiency, which is especially important in systems employing hundreds or thousands of instances of the same device. In large arrays, a slight increase in the power efficiency of a single component is heightened by the number of instances of the device in the system. This paper proposes a fully differential, low-power current-starving inverter-based amplifier topology designed in a commercial 0.18μm process. This design achieves 46dB DC gain and a 464 kHz uni ty gain frequency with a power consumption of only 145.32nW at 700mV power supply vol tage for ultra-low power, low bandwidth applications. Higher bandwidth designs are also proposed, including a 48dB DC gain, 2.4 MHz unity-gain frequency amplifier operating at 900mV wi th only 3.74μW power consumption.

negative: Conditional connectivity For a noncomplete graph G , the traditional definition of its connectivity is the minimum number of points whose removal results in a disconnected subgraph with components H , , . . . , H k . The conditional connectivity of G with respect to some graphtheoretic property P is the smallest cardinality of a set S of points, if any, such that every component Hi of the disconnected graph G S has property P. A survey of promising properties P is presented. Questions for various P-connectivities are listed in analogy with known results on connectivity and line-connectivity.

negative: A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects From half a million hectares at the turn of the century, Philippine mangroves have declined to only 120,000 ha while fish/shrimp culture ponds have increased to 232,000 ha. Mangrove replanting programs have thus been popular, from community initiatives (1930s–1950s) to government-sponsored projects (1970s) to large-scale international development assistance programs (1980s to present). Planting costs escalated from less than US$100 to over $500/ha, with half of the latter amount allocated to administration, supervision and project management. Despite heavy funds for massive rehabilitation of mangrove forests over the last two decades, the long-term survival rates of mangroves are generally low at 10–20%. Poor survival can be mainly traced to two factors: inappropriate species and site selection. The favored but unsuitable Rhizophora are planted in sandy substrates of exposed coastlines instead of the natural colonizers Avicennia and Sonneratia. More significantly, planting sites are generally in the lower intertidal to subtidal zones where mangroves do not thrive rather than the optimal middle to upper intertidal levels, for a simple reason. Such ideal sites have long been converted to brackishwater fishponds whereas the former are open access areas with no ownership problems. The issue of pond ownership may be complex and difficult, but such should not outweigh ecological requirements: mangroves should be planted where fishponds are, not on seagrass beds and tidal flats where they never existed. This paper reviews eight mangrove initiatives in the Philippines and evaluates the biophysical and institutional factors behind success or failure. The authors recommend specific protocols (among them pushing for a 4:1 mangrove to pond ratio recommended for a healthy ecosystem) and wider policy directions to make mangrove rehabilitation in the country more effective.

negative: What characterizes a shadow boundary under the sun and sky? Despite decades of study, robust shadow detection remains difficult, especially within a single color image. We describe a new approach to detect shadow boundaries in images of outdoor scenes lit only by the sun and sky. The method first extracts visual features of candidate edges that are motivated by physical models of illumination and occluders. We feed these features into a Support Vector Machine (SVM) that was trained to discriminate between most-likely shadow-edge candidates and less-likely ones. Finally, we connect edges to help reject non-shadow edge candidates, and to encourage closed, connected shadow boundaries. On benchmark shadow-edge data sets from Lalonde et al. and Zhu et al., our method showed substantial improvements when compared to other recent shadow-detection methods based on statistical learning.

negative: 3D distance fields: a survey of techniques and applications A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement?, and What is the complexity and runtime of such methods?.

negative: Large-Scale Supervised Multimodal Hashing with Semantic Correlation Maximization Due to its low storage cost and fast query speed, hashing has been widely adopted for similarity search in multimedia data. In particular, more and more attentions have been payed to multimodal hashing for search in multimedia data with multiple modalities, such as images with tags. Typically, supervised information of semantic labels is also available for the data points in many real applications. Hence, many supervised multimodal hashing (SMH) methods have been proposed to utilize such semantic labels to further improve the search accuracy. However, the training time complexity of most existing SMH methods is too high, which makes them unscalable to large-scale datasets. In this paper, a novel SMH method, called semantic correlation maximization (SCM), is proposed to seamlessly integrate semantic labels into the hashing learning procedure for large-scale data modeling. Experimental results on two real-world datasets show that SCM can significantly outperform the state-of-the-art SMH methods, in terms of both accuracy and scalability.

negative: Noise radar using random phase and frequency modulation Pulse compression radar is used in a great number of applications. Excellent range resolution and high electronic counter-countermeasures performance is achieved by wideband long pulses, which spread out the transmitted energy in frequency and time. By using a random noise waveform, the range ambiguity is suppressed as well. In most applications, the random signal is transmitted directly from a noise-generating microwave source. A sine wave, which is phase or frequency modulated by random noise, is an alternative, and in this paper, the ambiguity function and the statistical characteristics of the correlation output for the latter configuration are further analyzed. Range resolution is then improved because the noise bandwidth of the modulated carrier is wider than that of the modulating signal, and the range sidelobes are also further suppressed. Random biphase modulation gives a 4-dB (/spl pi//sup 2//4) improvement, but much higher sidelobe suppression could be achieved using continuous phase/frequency modulation. Due to the randomness of the waveform, the output correlation integral is accompanied by a noise floor, which limits the possible sidelobe suppression as determined by the time-bandwidth product. In synthetic aperture radar (SAR) applications with distributed targets, this product should be large compared with the number of resolution elements inside the antenna main beam. The advantages of low range sidelobes and enhanced range resolution make frequency/phase-modulated noise radar attractive for many applications, including SAR mapping, surveillance, altimetry, and scatterometry. Computer algorithms for reference signal delay and compression are discussed as replacements for the classical delay line implementation.

negative: Collusive Piracy Prevention in P2P Content Delivery Networks Collusive piracy is the main source of intellectual property violations within the boundary of a P2P network. Paid clients (colluders) may illegally share copyrighted content files with unpaid clients (pirates). Such online piracy has hindered the use of open P2P networks for commercial content delivery. We propose a proactive content poisoning scheme to stop colluders and pirates from alleged copyright infringements in P2P file sharing. The basic idea is to detect pirates timely with identity-based signatures and time-stamped tokens. The scheme stops collusive piracy without hurting legitimate P2P clients by targeting poisoning on detected violators, exclusively. We developed a new peer authorization protocol (PAP) to distinguish pirates from legitimate clients. Detected pirates will receive poisoned chunks in their repeated attempts. Pirates are thus severely penalized with no chance to download successfully in tolerable time. Based on simulation results, we find 99.9 percent prevention rate in Gnutella, KaZaA, and Freenet. We achieved 85-98 percent prevention rate on eMule, eDonkey, Morpheus, etc. The scheme is shown less effective in protecting some poison-resilient networks like BitTorrent and Azureus. Our work opens up the low-cost P2P technology for copyrighted content delivery. The advantage lies mainly in minimum delivery cost, higher content availability, and copyright compliance in exploring P2P network resources.

negative: RanDroid: Android malware detection using random machine learning classifiers The growing polularity of Android based smartphone attracted the distribution of malicious applications developed by attackers which resulted the need for sophisticated malware detection techniques. Several techniques are proposed which use static and/or dynamic features extracted from android application to detect malware. The use of machine learning is adapted in various malware detection techniques to overcome the mannual updation overhead. Machine learning classifiers are widely used to model Android malware patterns based on their static features and dynamic behaviour. To address the problem of malware detection, in this paper we have proposed a machine learning-based malware detection system for Android platform. Our proposed system utilizes the features of collected random samples of goodware and malware apps to train the classifiers. The system extracts requested permissions, vulnerable API calls along with the existence of app's key information such as; dynamic code, reflection code, native code, cryptographic code and database from applications, which was missing in previous proposed solutions and uses them as features in various machine learning classifiers to build classification model. To validate the performance of proposed system, "RanDroid" various experiments have been carriedout, which show that the RanDroid is capable to achieve a high classification accuracy of 97.7 percent.

negative: Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly. In order to achieve further speedup we introduce a technique that delays gradient updates effectively increasing the mini-batch size. Unfortunately with the increase of mini-batch size we worsen the stale gradient problem in asynchronous stochastic gradient descent (SGD) which makes the model convergence poor. We introduce local optimizers which mitigate the stale gradient problem and together with fine tuning our momentum we are able to train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU.

negative: A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.

negative: Robust Cross-lingual Hypernymy Detection using Dependency Context Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BISPARSE-DEP can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.1

negative: Broadband and compact impedance transformers for microwave circuits Most microwave circuits and antennas use impedance transformers. In a conventional power amplifier, large number of inductors and capacitor or transmission line sections are use to realize an impedance transformer. This article presents an asymmetric broadside-coupled microstrip TLT (transmission line transformer) on the GaAs substrate. This matching network can be easily implemented using any monolithic microwave integrated circuit (MMIC) process supporting multilevel metallization

negative: Seeing Behind the Camera: Identifying the Authorship of a Photograph We introduce the novel problem of identifying the photographer behind a photograph. To explore the feasibility of current computer vision techniques to address this problem, we created a new dataset of over 180,000 images taken by 41 well-known photographers. Using this dataset, we examined the effectiveness of a variety of features (low and high-level, including CNN features) at identifying the photographer. We also trained a new deep convolutional neural network for this task. Our results show that high-level features greatly outperform low-level features. We provide qualitative results using these learned models that give insight into our method's ability to distinguish between photographers, and allow us to draw interesting conclusions about what specific photographers shoot. We also demonstrate two applications of our method.

negative: Participatory Air Pollution Monitoring Using Smartphones Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on off-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the effect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.

negative: Bluetooth based home automation system The past decade has seen significant advancement in the field of consumer electronics. Various ‘intelligent’ appliances such as cellular phones, air-conditioners, home security devices, home theatres, etc. are set to realize the concept of a smart home. They have given rise to a Personal Area Network in home environment, where all these appliances can be interconnected and monitored using a single controller. Busy families and individuals with physical limitation represent an attractive market for home automation and networking. A wireless home network that does not incur additional costs of wiring would be desirable. Bluetooth technology, which has emerged in late 1990s, is an ideal solution for this purpose. This paper describes an application of Bluetooth technology in home automation and networking environment. It proposes a network, which contains a remote, mobile host controller and several client modules (home appliances). The client modules communicate with the host controller through Bluetooth devices. q 2002 Elsevier Science B.V. All rights reserved.

negative: OCNet: Object Context Network for Scene Parsing In this paper, we address the problem of scene parsing with deep learning and focus on the context aggregation strategy for robust segmentation. Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we introduce an object context pooling (OCP) scheme, which represents each pixel by exploiting the set of pixels that belong to the same object category with such a pixel, and we call the set of pixels as object context. Our implementation, inspired by the self-attention approach, consists of two steps: (i) compute the similarities between each pixel and all the pixels, forming a socalled object context map for each pixel served as a surrogate for the true object context, and (ii) represent the pixel by aggregating the features of all the pixels weighted by the similarities. The resulting representation is more robust compared to existing context aggregation schemes, e.g., pyramid pooling modules (PPM) in PSPNet and atrous spatial pyramid pooling (ASPP), which do not differentiate the context pixels belonging to the same object category or not, making the reliability of contextually aggregated representations limited. We empirically demonstrate our approach and two pyramid extensions with state-ofthe-art performance on three semantic segmentation benchmarks: Cityscapes, ADE20K and LIP. Code has been made available at: https://github.com/PkuRainBow/ OCNet.pytorch.

negative: RETOS: Resilient, Expandable, and Threaded Operating System for Wireless Sensor Networks This paper presents the design principles, implementation, and evaluation of the RETOS operating system which is specifically developed for micro sensor nodes. RETOS has four distinct objectives, which are to provide (1) a multithreaded programming interface, (2) system resiliency, (3) kernel extensibility with dynamic reconfiguration, and (4) WSN-oriented network abstraction. RETOS is a multithreaded operating system, hence it provides the commonly used thread model of programming interface to developers. We have used various implementation techniques to optimize the performance and resource usage of multithreading. RETOS also provides software solutions to separate kernel from user applications, and supports their robust execution on MMU-less hardware. The RETOS kernel can be dynamically reconfigured, via loadable kernel framework, so a application-optimized and resource-efficient kernel is constructed. Finally, the networking architecture in RETOS is designed with a layering concept to provide WSN-specific network abstraction. RETOS currently supports Atmel ATmega128, TI MSP430, and Chipcon CC2430 family of microcontrollers. Several real-world WSN applications are developed for RETOS and the overall evaluation of the systems is described in the paper.

negative: Color Balance and Fusion for Underwater Image Enhancement We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.

negative: MRTouch: Adding Touch Input to Head-Mounted Mixed Reality We present MRTouch, a novel multitouch input solution for head-mounted mixed reality systems. Our system enables users to reach out and directly manipulate virtual interfaces affixed to surfaces in their environment, as though they were touchscreens. Touch input offers precise, tactile and comfortable user input, and naturally complements existing popular modalities, such as voice and hand gesture. Our research prototype combines both depth and infrared camera streams together with real-time detection and tracking of surface planes to enable robust finger-tracking even when both the hand and head are in motion. Our technique is implemented on a commercial Microsoft HoloLens without requiring any additional hardware nor any user or environmental calibration. Through our performance evaluation, we demonstrate high input accuracy with an average positional error of 5.4 mm and 95% button size of 16 mm, across 17 participants, 2 surface orientations and 4 surface materials. Finally, we demonstrate the potential of our technique to enable on-world touch interactions through 5 example applications.

negative: Aneka Cloud Application Platform and Its Integration with Windows Azure Aneka is an Application Platform-as-a-Service (Aneka PaaS) for Cloud Computing. It acts as a framework for building customized applications and deploying them on either public or private Clouds. One of the key features of Aneka is its support for provisioning resources on different public Cloud providers such as Amazon EC2, Windows Azure and GoGrid. In this chapter, we will present Aneka platform and its integration with one of the public Cloud infrastructures, Windows Azure, which enables the usage of Windows Azure Compute Service as a resource provider of Aneka PaaS. The integration of the two platforms will allow users to leverage the power of Windows Azure Platform for Aneka Cloud Computing, employing a large number of compute instances to run their applications in parallel. Furthermore, customers of the Windows Azure platform can benefit from the integration with Aneka PaaS by embracing the advanced features of Aneka in terms of multiple programming models, scheduling and management services, application execution services, accounting and pricing services and dynamic provisioning services. Finally, in addition to the Windows Azure Platform we will illustrate in this chapter the integration of Aneka PaaS with other public Cloud platforms such as Amazon EC2 and GoGrid, and virtual machine management platforms such as Xen Server. The new support of provisioning resources on Windows Azure once again proves the adaptability, extensibility and flexibility of Aneka.

negative: Distributional Semantics Resources for Biomedical Text Processing The openly available biomedical literature contains over 5 billion words in publication abstracts and full texts. Recent advances in unsupervised language processing methods have made it possible to make use of such large unannotated corpora for building statistical language models and inducing high quality vector space representations, which are, in turn, of utility in many tasks such as text classification, named entity recognition and query expansion. In this study, we introduce the first set of such language resources created from analysis of the entire available biomedical literature, including a dataset of all 1to 5-grams and their probabilities in these texts and new models of word semantics. We discuss the opportunities created by these resources and demonstrate their application. All resources introduced in this study are available under open licenses at http://bio.nlplab.org.

negative: Optimization algorithm using scrum process Scrum process is methodology for software development. Members in a scrum team have self-organizing team by planning and sharing knowledge. This paper introduces optimization algorithm using the population as scrum team doing the scrum process to find an optimum solution. The proposed algorithm maintains the level of exploration and the exploitation search by specific of the scrum-team. The experiment has compared the proposed approach with GA and PSO by finding an optimal solution of five numerical functions. The experiment result indicates that the proposed algorithm provides the best solution and finds the result quickly.

negative: Evolving a Heuristic Function for the Game of Tetris

negative: Bachelor Degree Project Hierarchical Temporal Memory Software Agent Artificial general intelligence is not well defined, but attempts such as the recent list of “Ingredients for building machines that think and learn like humans” are a starting point for building a system considered as such [1]. Numenta is attempting to lead the new era of machine intelligence with their research to re-engineer principles of the neocortex. It is to be explored how the ingredients are in line with the design principles of their algorithms. Inspired by Deep Minds commentary about an autonomyingredient, this project created a combination of Numentas Hierarchical Temporal Memory theory and Temporal Difference learning to solve simple tasks defined in a browser environment. An open source software, based on Numentas intelligent computing platform NUPIC and Open AIs framework Universe, was developed to allow further research of HTM based agents on customized browser tasks. The analysis and evaluation of the results show that the agent is capable of learning simple tasks and there is potential for generalization inherent to sparse representations. However, they also reveal the infancy of the algorithms, not capable of learning dynamic complex problems, and that much future research is needed to explore if they can create scalable solutions towards a more general intelligent system.

negative: The Arcade Learning Environment: An Evaluation Platform for General Agents In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.

negative: Programming challenges of chatbot: Current and future prospective In the modern Era of technology, Chatbots is the next big thing in the era of conversational services. Chatbots is a virtual person who can effectively talk to any human being using interactive textual skills. Currently, there are many cloud base Chatbots services which are available for the development and improvement of the chatbot sector such as IBM Watson, Microsoft bot, AWS Lambda, Heroku and many others. A virtual person is based on machine learning and Artificial Intelligence (AI) concepts and due to dynamic nature, there is a drawback in the design and development of these chatbots as they have built-in AI, NLP, programming and conversion services. This paper gives an overview of cloud-based chatbots technologies along with programming of chatbots and challenges of programming in current and future Era of chatbot.

negative: Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.

negative: Representation Learning: A Review and New Perspectives The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.

negative: Relation Networks for Object Detection Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.

negative: Deep Network Guided Proof Search Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go. Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification. Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved. Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.


query: Using contours to detect and localize junctions in natural images

positive: Feature Detection with Automatic Scale Selection The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is presented for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure. Support for the proposed approach is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by integration with different types of early visual modules, including experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation. In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottleneck. It is argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to automatically analyse complex unknown environments.

positive: A Combined Corner and Edge Detector The problem we are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work. For example, we desire to obtain an understanding of natural scenes, containing roads, buildings, trees, bushes, etc., as typified by the two frames from a sequence illustrated in Figure 1. The solution to this problem that we are pursuing is to use a computer vision system based upon motion analysis of a monocular image sequence from a mobile camera. By extraction and tracking of image features, representations of the 3D analogues of these features can be constructed.

positive: Singularity detection and processing with wavelets Most of a signal information is often carried by irregular structures and transient phenomena. The mathematical characterization of singularities with Lipschitz exponents is explained. Theorems are reviewed that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations have a particular behavior that is studied separately. The local frequency of such oscillations are measured from the wavelet transform modulus maxima. It has been shown numerically that oneand two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two-dimensions, the wavelet transform maxima indicate the location of edges in images. The denoising algorithm is extended for image enhancement.

positive: Modeling Visual Attention via Selective Tuning A model for aspects of visual attention based on the concept of selective tuning is presented. It provides for a solution to the problems of selection in an image, information routing through the visual processing hierarchy and task-specific attentional bias. The central thesis is that attention acts to optimize the search procedure inherent in a solution to vision. It does so by selectively tuning the visual processing network which is accomplished by a top-down hierarchy of winner-take-all processes embedded within the visual processing pyramid. Comparisons to other major computational models of attention and to the relevant neurobiology are included in detail throughout the paper. The model has been implemented; several examples of its performance are shown. This model is a hypothesis for primate visual attention, but it also outperforms existing computational solutions for attention in machine vision and is highly appropriate to solving the problem in a robot vision system.

positive: 3D positional integration from image sequences An explicit three-dimensional representation is constructed from feature-points extracted from a sequence of images taken by a moving camera. The points are tracked through the sequence, and their 3D locations accurately determined by use of Kalman Filters. The ego-motion of the camera is also solved for.

negative: SIDES: a cooperative tabletop computer game for social skills development This paper presents a design case study of SIDES: Shared Interfaces to Develop Effective Social Skills. SIDES is a tool designed to help adolescents with Asperger's Syndrome practice effective group work skills using a four-player cooperative computer game that runs on tabletop technology. We present the design process and evaluation of SIDES conducted over six months with a middle school social group therapy class. Our findings indicate that cooperative tabletop computer games are a motivating and supportive tool for facilitating effective group work among our target population and reveal several design lessons to inform the development of similar systems.

negative: On Controllable Sparse Alternatives to Softmax Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.

negative: S-CNN: Subcategory-Aware Convolutional Networks for Object Detection The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

negative: Revealing protein–lncRNA interaction Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.

negative:
gHull: A GPU algorithm for 3D convex hull A novel algorithm is presented to compute the convex hull of a point set in ℝ3 using the graphics processing unit (GPU). By exploiting the relationship between the Voronoi diagram and the convex hull, the algorithm derives the approximation of the convex hull from the former. The other extreme vertices of the convex hull are then found by using a two-round checking in the digital and the continuous space successively. The algorithm does not need explicit locking or any other concurrency control mechanism, thus it can maximize the parallelism available on the modern GPU.
 The implementation using the CUDA programming model on NVIDIA GPUs is exact and efficient. The experiments show that it is up to an order of magnitude faster than other sequential convex hull implementations running on the CPU for inputs of millions of points. The works demonstrate that the GPU can be used to solve nontrivial computational geometry problems with significant performance benefit.

negative: gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data Clustering is an important technique in microarray data analysis, and mining three-dimensional (3D) clusters in gene-sample-time (simply GST) microarray data is emerging as a hot research topic in this area. A 3D cluster consists of a subset of genes that are coherent on a subset of samples along a segment of time series. This kind of coherent clusters may contain information for the users to identify useful phenotypes, potential genes related to these phenotypes and their expression rules. TRICLUSTER is the state-of-the-art 3D clustering algorithm for GST microarray data. In this paper, we propose a new algorithm to mine 3D clusters over GST microarray data. We term the new algorithm gTRICLUSTER because it is based on a more general 3D cluster model than the one that TRICLUSTER is based on. gTRICLUSTER can find more biologically meaningful coherent gene clusters than TRICLUSTER can do. It also outperforms TRICLUSTER in robustness to noise. Experimental results on a real-world microarray dataset validate the effectiveness of the proposed new algorithm.

negative: Validation of the Dutch Version of the CDC Core Healthy Days Measures in a Community Sample An important disadvantage of most indicators of health related quality of life used in public health surveillance is their length. In this study the authors investigated the reliability and validity of a short indicator of health related quality of life, the Dutch version of the four item ‘CDC Core Healthy Days Measures’ (CDC HRQOL-4). The reliability was evaluated by calculating Cronbach’s alpha of the CDC HRQOL-4. The concurrent validity was tested by comparing the CDC HRQOL-4 with three other indicators of health related quality of life, the SF-36, the WHOQoL-BREF and the GHQ-12. The construct validity was evaluated by assessing the ability of the CDC HRQOL-4 to discriminate between respondents with and without a (non-mental) chronic condition, depression, a visit to the general practitioner and use of prescription drugs. Randomly sampled respondents from the city of Utrecht were asked to fill in a questionnaire. 659 respondents (response rate 45%) completed the questionnaire. Participants represented the adult, non-institutionalised population of the city of Utrecht, the Netherlands: 58% women; mean age 41 years; 15% of non-Dutch origin. The reliability of the CDC HRQOL-4 was good. Cronbach’s alpha of three of the four CDC HRQOL-4-items was 0.77 which is good for internal consistent scales. The concurrent validity was good. The four items of the CDC HRQOL-4 showed higher correlations with their corresponding domains of the other instrument than the other domains. Comparison of respondents with or without a chronic condition, depression, visit to the GP and use of prescription drugs produced evidence for an excellent construct validity of the CDC HRQOL-4.

negative: Hardware/Software Codesign: The Past, the Present, and Predicting the Future Hardware/software codesign investigates the concurrent design of hardware and software components of complex electronic systems. It tries to exploit the synergy of hardware and software with the goal to optimize and/or satisfy design constraints such as cost, performance, and power of the final product. At the same time, it targets to reduce the time-to-market frame considerably. This paper presents major achievements of two decades of research on methods and tools for hardware/software codesign by starting with a historical survey of its roots, by highlighting its major research directions and achievements until today, and finally, by predicting in which direction research in codesign might evolve in the decades to come.

negative: Analysis of Audio-Visual Features for Unsupervised Speech Recognition Research on “zero resource” speech processing focuses on learning linguistic information from unannotated, or raw, speech data, in order to bypass the expensive annotations required by current speech recognition systems. While most recent zero-resource work has made use of only speech recordings, here, we investigate the use of visual information as a source of weak supervision, to see whether grounding speech in a visual context can provide additional benefit for language learning. Specifically, we use a dataset of paired images and audio captions to supervise learning of low-level speech features that can be used for further “unsupervised” processing of any speech data. We analyze these features and evaluate their performance on the Zero Resource Challenge 2015 evaluation metrics, as well as standard keyword spotting and speech recognition tasks. We show that features generated with a joint audiovisual model contain more discriminative linguistic information and are less speaker-dependent than traditional speech features. Our results show that visual grounding can improve speech representations for a variety of zero-resource tasks.

negative: Organizational behavior: affect in the workplace. The study of affect in the workplace began and peaked in the 1930s, with the decades that followed up to the 1990s not being particularly fertile. Whereas job satisfaction generally continues to be loosely but not carefully thought of and measured as an affective state, critical work in the 1990s has raised serious questions about the affective status of job satisfaction in terms of its causes as well as its definition and measurement. Recent research has focused on the production of moods and emotions at work, with an emphasis, at least conceptually, on stressful events, leaders, work groups, physical settings, and rewards/punishment. Other recent research has addressed the consequences of workers' feelings, in particular, a variety of performance outcomes (e.g., helping behaviors and creativity). Even though recent interest in affect in the workplace has been intense, many theoretical and methodological opportunities and challenges remain.

negative: Personality Traits Recognition on Social Network - Facebook For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.

negative: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

negative: A Hybrid Gripper With Soft Material and Rigid Structures Various robotic grippers have been developed over the past several decades for robotic manipulators. Especially, the soft grippers based on the soft pneumatic actuator (SPA) have been studied actively, since it offers more pliable bending motion, inherent compliance, and a simple morphological structure. However, few studies have focused on simultaneously improving the fingertip force and actuation speed within the specified design parameters. In this study, we developed a hybrid gripper that incorporates both soft and rigid components to improve the fingertip force and actuation speed simultaneously based on three design principles: first, the degree of bending is proportional to the ratio of the rigid structure; second, a concave chamber design is preferred for large longitudinal strain; and third, a round shape between soft and rigid materials increases the fingertip force. The suggested principles were verified using the finite element methods. The improved performance of the hybrid gripper was verified experimentally and compared with the performance of a conventional SPAs. The ability of the hybrid gripper to grasp different objects was evaluated and was applied in a teleoperated system.

negative: Finger ECG signal for user authentication: Usability and performance Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, “off-the-person” ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.

negative: The future of parking: A survey on automated valet parking with an outlook on high density parking In the near future, humans will be relieved from parking. Major improvements in autonomous driving allow the realization of automated valet parking (AVP). It enables the vehicle to drive to a parking spot and park itself. This paper presents a review of the intelligent vehicles literature on AVP. An overview and analysis of the core components of AVP such as the platforms, sensor setups, maps, localization, perception, environment model, and motion planning is provided. Leveraging the potential of AVP, high density parking (HDP) is reviewed as a future research direction with the capability to either reduce the necessary space for parking by up to 50 % or increase the capacity of future parking facilities. Finally, a synthesized view discussing the remaining challenges in automated valet parking and the technological requirements for high density parking is given.

negative: Web Application Migration with Closure Reconstruction Due to its high portability and simplicity, web application (app) based on HTML/JavaScript/CSS has been widely used for various smart-device platforms. To take advantage of its wide platform pool, a new idea called app migration has been proposed for the web platform. Web app migration is a framework to serialize a web app running on a device and restore it in another device to continue its execution. In JavaScript semantics, one of the language features that does not allow easy app migration is a closure. A JavaScript function can access variables defined in its outer function even if the execution of the outer function is terminated. It is allowed because the inner function is created as a closure such that it contains the outer function’s environment. This feature is widely used in web app development because it is the most common way to implement data encapsulation in web programming. Closures are not easy to serialize because environments can be shared by a number of closures and environments can be created in a nested way. In this paper, we propose a novel approach to fully serialize closures. We created mechanisms to extract information from a closure’s environment through the JavaScript engine and to serialize the information in a proper order so that the original relationship between closures and environments can be restored properly. We implemented our mechanism on the WebKit browser and successfully migrated Octane benchmarks and seven real web apps which heavily exploit closures. We also show that our mechanism works correctly even for some extreme, closure-heavy cases.

negative: A 0 . 5V Ultra-Low-Power OTA With Improved Gain and Bandwidth In the context of ultra-low-power and ultra-low-voltage operational transconductance amplifiers (OTAs), there is the inconvenience of low unity-gain frequency and DC gain. In this paper, we presented a 0.5-V improved ultra-low-power OTA using the transistors operate in weak inversion. The proposed topology based on a bulk-driven input differential pair employed an under-weak-inversion gain-stage in the Miller capacitor feedback path to improve the “pole-splitting” effect. Simulations in a standard 0.18 μm CMOS process resulted on considerable enhancement in the unity-gain bandwidth and the DC gain as well. The topology presents rail-to-rail input and output swings and consumes only 1 μW.

negative: Performance analysis of supervised machine learning algorithms for text classification The demand of text classification is growing significantly in web searching, data mining, web ranking, recommendation systems and so many other fields of information and technology. This paper illustrates the text classification process on different dataset using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applied these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analysis the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.

negative: From the ephemeral to the enduring: how approach-oriented mindsets lead to greater status. We propose that the psychological states individuals bring into newly formed groups can produce meaningful differences in status attainment. Three experiments explored whether experimentally created approach-oriented mindsets affected status attainment in groups, both immediately and over time. We predicted that approach-oriented states would lead to greater status attainment by increasing proactive behavior. Furthermore, we hypothesized that these status gains would persist longitudinally, days after the original mindsets had dissipated, due to the self-reinforcing behavioral cycles the approach-oriented states initiated. In Experiment 1, individuals primed with a promotion focus achieved higher status in their newly formed groups, and this was mediated by proactive behavior as rated by themselves and their teammates. Experiment 2 was a longitudinal experiment and revealed that individuals primed with power achieved higher status, both immediately following the prime and when the groups were reassembled 2 days later to work on new tasks. These effects were mediated by independent coders' ratings of proactive behavior during the first few minutes of group interaction. Experiment 3 was another longitudinal experiment and revealed that priming happiness led to greater status as well as greater acquisition of material resources. Importantly, these immediate and longitudinal effects were independent of the effects of a number of stable dispositional traits. Our results establish that approach-oriented psychological states affect status attainment, over and above the more stable characteristics emphasized in prior research, and provide the most direct test yet of the self-reinforcing nature of status hierarchies. These findings depict a dynamic view of status organization in which the same group may organize itself differently depending on members' incoming psychological states.

negative: A risk management ontology for Quality-by-Design based on a new development approach according GAMP 5.0 A new approach to the development of a risk management ontology is presented. This method meets the requirements of a pharmaceutical Quality by Design approach, good manufacturing practice and good automated manufacturing practice. The need for a risk management ontology for a pharmaceutical environment is demonstrated, and the term ‘‘ontology’’ is generally defined and described with regard to the knowledge domain of quality risk management. To fulfill software development requirements defined by good manufacturing practice regulations and good automated manufacturing practice 5.0 for the novel development approach, we used a V-model as a process model, which is discussed in detail. The development steps for the new risk management ontology, such as requirement specification, conceptualization, formalization, implementation and validation approach, are elaborated. 2012 Elsevier Ltd. All rights reserved.

negative: Low-voltage low-power fast-settling CMOS operational transconductance amplifiers for switched-capacitor applications This paper presents a new fully differential operational transconductance amplifier (OTA) for low-voltage and fast-settling switched-capacitor circuits in digital CMOS technology. The proposed two-stage OTA is a hybrid class A/AB that combines a folded cascode as the first stage with active current mirrors as the second stage. It employs a hybrid cascode compensation scheme, merged Ahuja and improved Ahuja style compensations, for fast settling.

negative: Focal loss dense detector for vehicle surveillance Deep learning has been widely recognized as a promising approach in different computer vision applications. Specifically, one-stage object detector and two-stage object detector are regarded as the most important two groups of Convolutional Neural Network based object detection methods. One-stage object detector could usually outperform two-stage object detector in speed; However, it normally trails in detection accuracy, compared with two-stage object detectors. In this study, focal loss based RetinaNet, which works as one-stage object detector, is utilized to be able to well match the speed of regular one-stage detectors and also defeat two-stage detectors in accuracy, for vehicle detection. State-of-the-art performance result has been showed on the DETRAC vehicle dataset.

negative: Online Parameter Estimation Technique for Adaptive Control Applications of Interior PM Synchronous Motor Drives This paper proposes an online parameter estimation method based on a discrete-time dynamic model for the interior permanent-magnet synchronous motors (IPMSMs). The proposed estimation technique, which takes advantage of the difference in dynamics of motor parameters, consists of two affine projection algorithms. The first one is designed to accurately estimate the stator inductances, whereas the second one is designed to precisely estimate the stator resistance, rotor flux linkage, and load torque. In this paper, the adaptive decoupling proportional-integral (PI) controllers with the maximum torque per ampere operation, which utilize the previously identified parameters in real time, are chosen to verify the effectiveness of the proposed parameter estimation scheme. The simulation results via MATLAB/Simulink and the experimental results via a prototype IPMSM drive system with a TI TMS320F28335 DSP are presented under various conditions. A comparative study with the conventional decoupling PI control method is carried out to demonstrate the better performances (i.e., faster dynamic response, less steady-state error, more robustness, etc.) of the adaptive decoupling PI control scheme based on the proposed online parameter estimation technique.

negative: Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose, illumination, expression, resolution, and motion. While a variety of feature learning and dictionary learning methods have been proposed in recent years and some of them have been successfully applied to image set-based face recognition, most of them learn features and dictionaries for facial image sets individually, which may not be powerful enough because some discriminative information for dictionary learning may be compromised in the feature learning stage if they are applied sequentially, and vice versa. To address this, we propose a SFDL method to learn discriminative features and dictionaries simultaneously from raw face pixels so that discriminative information from facial image sets can be jointly exploited by a one-stage learning procedure. To better exploit the nonlinearity of face samples from different image sets, we propose a deep SFDL (D-SFDL) method by jointly learning hierarchical non-linear transformations and class-specific dictionaries to further improve the recognition performance. Extensive experimental results on five widely used face data sets clearly shows that our SFDL and D-SFDL achieve very competitive or even better performance with the state-of-the-arts.

negative: Sentence level emotion recognition based on decisions from subsentence segments Emotion recognition from speech plays an important role in developing affective and intelligent systems. This study investigates sentence-level emotion recognition. We propose to use a two-step approach to leverage information from subsentence segments for sentence level decision. First we use a segment level emotion classifier to generate predictions for segments within a sentence. A second component combines the predictions from these segments to obtain a sentence level decision. We evaluate different segment units (words, phrases, time-based segments) and different decision combination methods (majority vote, average of probabilities, and a Gaussian Mixture Model (GMM)). Our experimental results on two different data sets show that our proposed method significantly outperforms the standard sentence-based classification approach. In addition, we find that using time-based segments achieves the best performance, and thus no speech recognition or alignment is needed when using our method, which is important to develop language independent emotion recognition systems.

negative: A chaincode based scheme for fingerprint feature extraction A feature extraction method using the chaincode representation of fingerprint ridge contours is presented for use by Automatic Fingerprint Identification Systems. The representation allows efficient image quality enhancement and detection of fine minutiae feature points. For image enhancement a given fingerprint image is first binarized after a quick averaging to generate its chaincode representation. The direction field is estimated from a set of selected chaincodes. The original gray scale image is then enhanced using connected component analysis and a dynamic filtering scheme that takes advantage of the knowledge gained from the estimated direction flow of the contours. For feature extraction, the enhanced fingerprint image is carefully binarized using a local-global binarization algorithm for generating the chaincode representation. The minutiae are generated using a sophisticated ridge contour following procedure. Visual inspection of the experiment images shows that the method is very effective.

negative: Flat-Region Detection and False Contour Removal in the Digital TV Display Bit-depth reduction in digital displays results in false contours in the image. Moreover some of video enhancement processing in the digital TV display, such as histogram equalization, contrast enhancement, and increasing sharpness, etc., make false contours more visible. Bit-depth reduction comes from various display limitations such as video memory constraints, physical characteristics of the display, display drivers, and coarse MPEG quantization, etc. Daly, SJ et al., (2004). We present an efficient method for detecting and segmenting flat-region in the image, and a technique for bit-depth extension to effectively remove false contours. We have simulated bit-depth reduction followed by video enhancement processing that cause false contours with various video image sequences including simple patterns. Our result shows that false contours are effectively removed in the flat-region in the image while the sharpness of object edges is preserved

negative: A New Mura Defect Inspection Way for TFT-LCD Using Level Set Method Mura is a typical vision defect of LCD panel, appearing as local lightness variation with low contrast and blurry contour. This letter presents a new machine vision inspection way for Mura defect based on the level set method. First, a set of real Gabor filters are applied to eliminating the global textured backgrounds. Then, the level set method is employed for image segmentation with a new region-based active contours model, which is an improvement of the Chan-Vese's model so that it more suitable to the segmentation of Mura. Using some results from the level set based segmentation, the defects are quantified based on the SEMU method. Experiments show that the proposed method has better performance for Mura detection and quantification.

negative: Automated detection of euro pallet loads by interpreting PMD camera depth images In this study, a novel approach for the detection of parcel loading positions on a pallet is presented. This approach was realized as a substantial change in comparison with traditional system design of contour detection in de-palletizing processes. Complex 3D-vision systems, costly laser scanners or throughput decreasing local sensor solutions integrated in grippers are substituted by a lowcost photonic mixing device (PMD) camera. By combining PMD technology and a predetermined model of loading situations, stored during assembling the pallet, this approach can compensate for the drawbacks of each respective system. An essential part of the approach are computer-graphics methods specific to the given problem to both detect the deviation between the nominal and the actual loading position and if necessary an automated correction of the packaging scheme. From an economic point of view, this approach can decrease the costs of mandatory contour checking in automated de-palletizing processes.

negative: Text Detection and Character Extraction in Natural Scene Images Detecting text regions in natural scene images has become an important area due to its varies applications. Text Information Extraction (TIE) System involves detecting text regions in a given image, localizing it, extracting the text part and recognizing text using OCR. This work basically concentrates on the detection and extraction of text in natural scene images. In this work, the test image will be preprocessed using RGB to Gray conversion, binarization, Edge Detection method and Geometric based Noise removal method. The features from the pre-processed image are extracted. The extracted features are used by the trained SVM classifier to detect the text regions. After detecting text regions, characters are extracted and finally displayed. Keywords— Text Information Extraction (TIE); Natural Scene Images; Text Detection; Text Extraction; Support Vector Machine (SVM).

negative: Optimized Edge Detection Algorithm for Face Recognition Face recognition is one of the most challenging tasks in the field of image processing. This paper presents an optimized edge detection algorithm for the task of face recognition. In this method a gradient based filter using a wide convolution kernel is applied on the image to extract the edges. Later a thinning algorithm optimized for the wide convolution kernel is applied on the extracted edges. The advantages of this method over other gradient based methods is its ability to find the missing edges more successfully and boost the significant edges of the overall facial contour.

negative: Learning to detect and localize many objects from few examples The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.