query: Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

positive: Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique D EEP learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 [1]. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [2]. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications. Promising results are emerging. In medical imaging, the accurate diagnosis and/or assessment of a disease depends on both image acquisition and image interpretation. Image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment and to support the expert’s workflow. Among these tools, deep learning is rapidly proving to be the state-of-the-art foundation, leading to improved accuracy. It has also opened up new frontiers in data analysis with rates of progress not before experienced.

positive: Going deeper with convolutions We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

positive:
Caffe: Convolutional Architecture for Fast Feature Embedding Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
 Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

positive: Fully convolutional networks for semantic segmentation

negative: Supplementary Material : Video Summarization with Long Short-term Memory – Section 1: converting between different formats of ground-truth annotations (Section 3.1 in the main text) – Section 2: details of the datasets (Section 4.1 in the main text) – Section 3: details of our LSTM-based models, including the learning objective for dppLSTM and the generating process of shot-based summaries for both vsLSTM and dppLSTM (Section 3.4 and 3.5 in the main text) – Section 4: comparing different network structures for dppLSTM (Section 3.4 in the main text) – Section 5: Other implementation details – Section 6: Additional discussions on video summarization

negative: A multiscale retinex for bridging the gap between color images and the human observation of scenes Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.

negative: When Professionals Become Mothers , Warmth Doesn ’ t Cut the Ice Working moms risk being reduced to one of two subtypes: homemakers—viewed as warm but incompetent, or female professionals—characterized as competent but cold. The current study ( N = 122 college students) presents four important findings. First, when working women become mothers, they trade perceived competence for perceived warmth. Second, working men don’t make this trade; when they become fathers, they gain perceived warmth and maintain perceived competence. Third, people report less interest in hiring, promoting, and educating working moms relative to working dads and childless employees. Finally, competence ratings predict interest in hiring, promoting, and educating workers. Thus, working moms’ gain in perceived warmth does not help them, but their loss in perceived competence does hurt them.

negative: Dealing with the new user cold-start problem in recommender systems: A comparative review The Recommender System (RS) is an efficient tool for decision makers that assists in the selection of appropriate items according to their preferences and interests. This system has been applied to various domains to personalize applications by recommending items such as books, movies, songs, restaurants, news articles and jokes, among others. An important issue problem, which occurs when there is a new user that has been registered to the system and no prior rating of this user is found in the rating table. In this paper, we first present a classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format. Next, some typical algorithms of these groups, such as MIPFGWC-CS, NHSM, FARAMS and HU–FCF, are described. Finally, these algorithms are implemented and validated on some benchmark RS datasets under various settings of the new user cold start. The experimental results indicate that NHSM achieves better accuracy and computational time than the relevant methods. & 2014 Elsevier Ltd. All rights reserved.

negative: Tracking-assisted Weakly Supervised Online Visual Object Segmentation in Unconstrained Videos This paper tackles the task of online video object segmentation with weak supervision, i.e., labeling the target object and background with pixel-level accuracy in unconstrained videos, given only one bounding box information in the first frame. We present a novel tracking-assisted visual object segmentation framework to achieve this. On the one hand, initialized with a given bounding box in the first frame, the auxiliary object tracking module guides the segmentation module frame by frame by providing motion and region information, which is usually missing in semi-supervised methods. Moreover, compared with the unsupervised approach, our approach with such minimum supervision can focus on the target object without bringing unrelated objects into the final results. On the other hand, the video object segmentation module also improves the robustness of the visual object tracking module by pixel-level localization and objectness information. Thus, segmentation and tracking in our framework can mutually help each other in an online manner. To verify the generality and effectiveness of the proposed framework, we evaluate our weakly supervised method on two cross-domain datasets, i.e., the DAVIS and VOT2016 datasets, with the same configuration and parameter setting. Experimental results show the top performance of our method, which is even better than the leading semi-supervised methods. Furthermore, we conduct the extensive ablation study on our approach to investigate the influence of each component and main parameters.

negative: A 77-GHz MMIC power amplifier for automotive radar applications A MMIC 77-GHz two-stage power amplifier (PA) is reported in this letter. This MMIC chip demonstrated a measured small signal gain of over 10 dB from 75 GHz to 80 GHz with 18.5-dBm output power at 1 dB compression. The maximum small signal gain is above 12 dB from 77 to 78 GHz. The saturated output power is better than 21.5 dBm and the maximum power added efficiency is 10% between 75 GHz and 78 GHz. This chip is fabricated using 0.1-/spl mu/m AlGaAs/InGaAs/GaAs PHEMT MMIC process on 4-mil GaAs substrate. The output power performance is the highest among the reported 4-mil MMIC GaAs HEMT PAs at this frequency and therefore it is suitable for the 77-GHz automotive radar systems and related transmitter applications in W-band.

negative: Paralinguistics in speech and language - State-of-the-art and the challenge Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants’ predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language. © 2012 Elsevier Ltd. All rights reserved.

negative: The Ensembl gene annotation system The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail.Database URL: http://www.ensembl.org/index.html.

negative: 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.

negative: Building upon RouteFlow : a SDN development experience RouteFlow is a platform for providing virtual IP routing services in OpenFlow networks. During the first year of development, we came across some use cases that might be interesting pursuing in addition to a number of lessons learned worth sharing. In this paper, we will discuss identified requirements and architectural and implementation changes made to shape RouteFlow into a more robust solution for Software Defined networking (SDN). This paper addresses topics of interest to the SDN community, such as development issues involving layered applications on top of network controllers, ease of configuration, and network visualization. In addition, we will present the first publicly known use case with multiple, heterogeneous OpenFlow controllers to implement a centralized routing control function, demonstrating how IP routing as a service can be provided for different network domains under a single central control. Finally, performance comparisons and a real testbed were used as means of validating the implementation.

negative: LocationSpark: A Distributed In-Memory Data Management System for Big Spatial Data We present LocationSpark, a spatial data processing system built on top of Apache Spark, a widely used distributed data processing system. LocationSpark offers a rich set of spatial query operators, e.g., range search, kNN, spatio-textual operation, spatial-join, and kNN-join. To achieve high performance, LocationSpark employs various spatial indexes for in-memory data, and guarantees that immutable spatial indexes have low overhead with fault tolerance. In addition, we build two new layers over Spark, namely a query scheduler and a query executor. The query scheduler is responsible for mitigating skew in spatial queries, while the query executor selects the best plan based on the indexes and the nature of the spatial queries. Furthermore, to avoid unnecessary network communication overhead when processing overlapped spatial data, We embed an efficient spatial Bloom filter into LocationSpark’s indexes. Finally, LocationSpark tracks frequently accessed spatial data, and dynamically flushes less frequently accessed data into disk. We evaluate our system on real workloads and demonstrate that it achieves an order of magnitude performance gain over a baseline framework.

negative: Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit nonlinear and nonstationary behaviors. These characteristics tend to undermine the reliability of existing hand-crafted EEG features that ignore time-varying information and impair the performances of classification models. In this paper, we propose a novel method that can automatically capture the nonstationary dynamics of EEG signals for diverse classification tasks. It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG classification with the extracted features. We compare the performance of our proposed method and the state-of-the-art methods in two EEG classification tasks, subject, and event classification. Results show that our approach outperforms the others by averages of <inline-formula><tex-math notation="LaTeX">$\text{15}\%\pm \text{6.3}$</tex-math> </inline-formula> (p-value <inline-formula><tex-math notation="LaTeX">$<\text{0.05}$</tex-math></inline-formula>) and <inline-formula><tex-math notation="LaTeX">$\text{22}\%\pm \text{5.7}$</tex-math></inline-formula> (p-value <inline-formula><tex-math notation="LaTeX">$<\text{0.05}$</tex-math></inline-formula>) for subject and event classifications, respectively.

negative: Collaboration with Lean Media: how open-source software succeeds Open-source software, usually created by volunteer programmers dispersed worldwide, now competes with that developed by software firms. This achievement is particularly impressive as open-source programmers rarely meet. They rely heavily on electronic media, which preclude the benefits of face-to-face contact that programmers enjoy within firms. In this paper, we describe findings that address this paradox based on observation, interviews and quantitative analyses of two open-source projects. The findings suggest that spontaneous work coordinated afterward is effective, rational organizational culture helps achieve agreement among members and communications media moderately support spontaneous work. These findings can imply a new model of dispersed collaboration.

negative: Face Detection By Finding The Facial Features And The Angle of Inclination of Tilted Face Face is a popular topic of research in computer vision, image analysis and understanding. This paper proposes a method to detect straight posed face as well as tilted face in colour images. Algorithm first detects skin colour region and then skin region is further processed by finding connected components and holes. Each connected components is tested to extract facial features and Y-value of each holes. If features exist in the connected component then the component is concluded as face. The Y-value of both eyes are compared to find whether the face is straight or tilted. For tilted face, the angle of tilt is determined and the input image is rotated by the angle of tilt to straighten up the face. After straightening the face, the rectangle is drawn to enclose the face. The dimension of drawn rectangle is obtained by using the distance parameter of the line between two eyes.

negative: Quality Attributes of Web Software Applications In only four or five years, the world wide web has changed from a static collection of HTML web pages to a dynamic engine that powers e-commerce, collaborative work, and distribution of information and entertainment. These exciting changes have been fueled by many changes in software technology, the software development process, and how software is deployed. Although the word “heterogeneous” is commonly used to describe web software, we might easily forget to notice in how many ways it can be applied. In fact, the synonymous term “diverse” is more general and familiar, and may be more appropriate. Web software applications use diverse types of hardware, they include a diverse collection of types of implementation languages (including traditional programs, HTML, interpreted scripts, and databases), they are composed of software written in diverse languages, and they are built by collections of people with very diverse sets of skills. Although these changes in how web applications are built are interesting and fascinating, one of the most unique aspects of web software applications is in terms of the needs they must satisfy. Web applications have very high requirements for a number of quality attributes. Some of these quality attributes have been important in other (mostly relatively small) segments of the industry, but some of them are relatively new. This paper discusses some of the unique technological aspects of building web software applications, the unique requirements of quality attributes, and how they can be achieved.

negative: Small-Size Hybrid Loop/Open-Slot Antenna for the LTE Smartphone A small-size hybrid loop/open-slot antenna for the LTE smartphone is presented. The antenna is disposed in a small notch of 7 × 40 mm2 in the system ground plane of the smartphone and has a thickness of 5.8 mm to be capable of embedding within most modern smartphones. The antenna mainly comprises a capacitively fed loop antenna and a ground strip embedded therein to create an open-slot antenna so as to generate additional resonant modes for bandwidth enhancement. The antenna can provide two wide operating bands to cover the LTE low band (698-960 MHz) and the LTE middle/high bands (1710-2690/3400-3800 MHz). In this study, the creation of the open-slot antenna using a ground strip embedded in the ground clearance region for the coupled-fed loop antenna is addressed. The proposed antenna is also fabricated and tested. Both the simulation study and the experimental results are presented.

negative: Word segmentation for the Myanmar language This study reports the development of a Myanmar word segmentation method using Unicode standard encoding. Word segmentation is an essential step prior to natural language processing in the Myanmar language, because a Myanmar text is a string of characters without explicit word boundary delimiters. The proposed method has two phases: syllable segmentation and syllable merging. A rule-based heuristic approach was adopted for syllable segmentation, and a dictionary-based statistical approach for syllable merging. Evaluation of test results showed that the method is very effective for the Myanmar language.

negative: Quasi-Resonant (QR) Controller With Adaptive Switching Frequency Reduction Scheme for Flyback Converter A quasi-resonant (QR) controller with an adaptive frequency reduction scheme has been developed for a flyback converter. While maintaining the valley switching, the QR controller reduces the switching frequency for lighter load by skipping some valleys to reduce the power loss and thereby achieve better light-load efficiency. If the QR controller cannot detect any valley due to the damped oscillation of switch voltage, then the valley switching is given up and the nonvalley switching is employed to keep reducing the switching frequency for lighter load. The proposed QR controller has been implemented in a 0.35-μm 700-V BCDMOS process and applied to a 40-W flyback converter. The power efficiency of the flyback converter is improved by up to 3.0% when the proposed QR controller is employed compared to the one employing the conventional QR controller.

negative: Accelerating Big Data Analytics Using FPGAs Emerging big data analytics applications require a significant amount of server computational power. As chips are hitting power limits, computing systems are moving away from general-purpose designs and toward greater specialization. Hardware acceleration through specialization has received renewed interest in recent years, mainly due to the dark silicon challenge. To address the computing requirements of big data, and based on the benchmarking and characterization results, we envision a data-driven heterogeneous architecture for next generation big data server platforms that leverage the power of field-programmable gate array (FPGA) to build custom accelerators in a Hadoop MapReduce framework. Unlike a full and dedicated implementation of Hadoop MapReduce algorithm on FPGA, we propose the hardware/software (HW/SW) co-design of the algorithm, which trades some speedup at a benefit of less hardware. Considering communication overhead with FPGA and other overheads involved in Hadoop MapReduce environment such as compression and decompression, shuffling and sorting, our experimental results show significant potential for accelerating Hadoop MapReduce machine learning kernels using HW/SW co-design methodology.

negative: The Romance of Learning from Disagreement. The Effect of Cohesiveness and Disagreement on Knowledge Sharing Behavior and Individual Performance Within Teams PURPOSE: The purpose of this study was to explore the effects of disagreement and cohesiveness on knowledge sharing in teams, and on the performance of individual team members. DESIGN/METHODOLOGY/APPROACH: Data were obtained from a survey among 1,354 employees working in 126 teams in 17 organizations. FINDINGS: The results show that cohesiveness has a positive effect on the exchange of advice between team members and on openness for sharing opinions, whereas disagreement has a negative effect on openness for sharing opinions. Furthermore, the exchange of advice in a team has a positive effect on the performance of individual team members and acts as a mediator between cohesiveness and individual performance. IMPLICATIONS: Managers who want to stimulate knowledge sharing processes and performance within work teams may be advised to take measures to prevent disagreement between team members and to enhance team cohesiveness. ORIGINALITY/VALUE: Although some gurus in organizational learning claim that disagreement has a positive effect on group processes such as knowledge sharing and team learning, this study does not support this claim.

negative: Measurement of carotid blood pressure and local pulse wave velocity changes during cuff induced hyperemia We present a prototype design of dual element photoplethysmograph (PPG) probe along with associated measurement system for carotid local pulse wave velocity (PWV) evaluation in a non-invasive and continuous manner. The PPG probe consists of two identical sensing modules placed 23 mm apart. Simultaneously measured blood pulse waveforms from these arterial sites were processed and the pulse transit time delay was resolved using the developed application-specific software. The ability of developed PPG probe and associated measurement system to detect acute changes in carotid local PWV due to blood pressure (BP) variations was experimentally validated by an in-vivo study. Intra-subject carotid BP elevation was achieved by an upper arm cuff based occlusion, which offered a controlled way of local PWV escalation. The elevated carotid BP values were also recorded by a calibrated pressure tonometer prior to the study, and was used as a reference. A significant increment (1.0 – 2.6 m/s) in local PWV was observed and was proportional to the BP increment induced by the occlusive reactive hyperemia. Study results demonstrated the feasibility of real-time signal acquisition and reliable local PWV evaluation under normal and elevated BP conditions using the developed measurement system.

negative: Distributed Compressed Sensing off the Grid This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery.

negative: CallRank: Combating SPIT Using Call Duration, Social Networks and Global Reputation The growing popularity of IP telephony systems has made them attractive targets for spammers. Voice call spam, also known as Spam over Internet Telephony (SPIT), is potentially a more serious problem than email spam because of the real time processing requirements of voice packets. We explore a novel mechanism that uses duration of calls between users to combat SPIT. CallRank, the scheme proposed by us, uses call duration to establish social network linkages and global reputations for callers, based on which call recipients can decide whether the caller is legitimate or not. CallRank has been implemented within a VoIP system simulation and our results show that we are able to achieve a false negative rate of 10% and a false positive rate of 3% even in the presence of a significant fraction of spammers.

negative: Soft gelatin capsules (softgels). It is estimated that more than 40% of new chemical entities (NCEs) coming out of the current drug discovery process have poor biopharmaceutical properties, such as low aqueous solubility and/or permeability. These suboptimal properties pose significant challenges for the oral absorption of the compounds and for the development of orally bioavailable dosage forms. Development of soft gelatin capsule (softgel) dosage form is of growing interest for the oral delivery of poorly water soluble compounds (BCS class II or class IV). The softgel dosage form offers several advantages over other oral dosage forms, such as delivering a liquid matrix designed to solubilize and improve the oral bioavailability of a poorly soluble compound as a unit dose solid dosage form, delivering low and ultra-low doses of a compound, delivering a low melting compound, and minimizing potential generation of dust during manufacturing and thereby improving the safety of production personnel. However, due to the very dynamic nature of the softgel dosage form, its development and stability during its shelf-life are fraught with several challenges. The goal of the current review is to provide an in-depth discussion on the softgel dosage form to formulation scientists who are considering developing softgels for therapeutic compounds.

negative: Fast human segmentation using color and depth Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.

negative: Age and gender estimation using deep residual learning network In this paper, we propose a deep residual learning model for age and gender estimation. Our method detects faces in input images, and then the age and gender of each face are estimated. The estimation method consists of three deep neural networks where we adopt residual learning methods. We train the model with IMDB-WIKI database [4]. However, since the database has only a small number of face images under the age of 20, we augment the set by collecting the images on the Internet. Experimental results show that the proposed model with residual learning yields improved performance.

negative:
Conservative surgical management of subungual (matrix derived) melanoma: report of seven cases and literature review. BACKGROUND
Subungual melanoma (SUM) is a rare entity, comprising approximately 0·7-3·5% of all melanoma subtypes. SUM histopathologically belongs to the acral lentiginous pathological subtype of malignant melanoma. Its diagnosis is helped by dermoscopy but pathological examination of doubtful cases is required. Classical management of SUM is based on radical surgery, namely distal phalanx amputation. Conservative treatment with nonamputative wide excision of the nail unit followed by a skin graft has been insufficiently reported in the medical literature even though it is performed in many centres.

OBJECTIVES
To report a series of patients with in situ or minimally invasive SUM treated by conservative surgery, to investigate the postoperative evolution and to evaluate the outcome with a review of the literature.

METHODS
We performed a retrospective extraction study from our melanoma register of all patients with in situ and minimally invasive SUM treated with conservative surgery in the University Hospital Department of Dermatology, Lyon, France from 2004 to 2009. The patient demographics, disease presentation, delay to diagnosis, histopathology and postoperative evolution were reviewed.

RESULTS
Seven cases of SUM treated as such were identified in our melanoma database. All cases had a clinical presentation of melanonychia striata. The mean delay to diagnosis was 2years. Surgical excision of the entire nail unit with a 5-10mm safety margin without bone resection followed by full-thickness skin graft taken from the arm was performed in all cases. No recurrence was observed with a mean follow-up of 45months. Functional results were found satisfactory by all patients and their referring physicians. Sixty-two other cases have been found in the literature and are also discussed.

CONCLUSIONS
Conservative surgical management in patients with in situ or minimally invasive SUM is a procedure with good cosmetic and functional outcome and, in our cases as well as in the literature, the prognosis is not changed.

negative: Bangladeshi banknote recognition by neural network with axis symmetrical masks Automated banknote recognition system can be a very good utility in banking systems and other field of commerce. It can also aid visually impaired people. Although in Bangladesh, bill money recognition machines are not common but it is used in other countries. In this paper, for the first time, we have proposed a Neural Network based recognition scheme for Bangladeshi banknotes. The scheme can efficiently be implemented in cheap hardware which may be very useful in many places. The recognition system takes scanned images of banknotes which are scanned by low cost optoelectronic sensors and then fed into a Multilayer Perceptron, trained by Backpropagation algorithm, for recognition. Axis Symmetric Masks are used in preprocessing stage which reduces the network size and guarantees correct recognition even if the note is flipped. Experimental results are presented which show that this scheme can recognize currently available 8 notes (1, 2, 5, 10, 20, 50, 100 & 500 Taka) successfully with an average accuracy of 98.57%.

negative: Automated detection of smuggled high-risk security threats using Deep Learning The security infrastructure is ill-equipped to detect and deter the smuggling of non-explosive devices that enable terror attacks such as those recently perpetrated in western Europe. The detection of so-called “small metallic threats” (SMTs) in cargo containers currently relies on statistical risk analysis, intelligence reports, and visual inspection of X-ray images by security officers. The latter is very slow and unreliable due to the difficulty of the task: objects potentially spanning less than 50 pixels have to be detected in images containing more than 2 million pixels against very complex and cluttered backgrounds. In this contribution, we demonstrate for the first time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning, to automate the detection of SMTs in fullsize X-ray images of cargo containers. Novel approaches for dataset augmentation allowed to train CNNs from-scratch despite the scarcity of data available. We report fewer than 6% false alarms when detecting 90% SMTs synthetically concealed in streamof-commerce images, which corresponds to an improvement of over an order of magnitude over conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers potentially super-human performance for a fraction of the time it would take for a security officers to carry out visual inspection (processing time is approximately 3.5s per container image).

negative: An efficient method of license plate location License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method. 2005 Elsevier B.V. All rights reserved.

negative: Dermoscopy in differentiating palmar syphiloderm from palmar papular psoriasis. Palmar syphiloderm is one of the most common presentations of secondary syphilis and its recognition is of utmost importance in order to promptly identify such a disease and initiate appropriate workup/management. However, the differential diagnosis with palmar papular psoriasis often poses some difficulties, with consequent possible diagnostic errors/delays and prescription of improper therapies. In this report, we underline the role of dermoscopy as a supportive tool to facilitate the non-invasive recognition of palmar syphiloderm and its distinction from palmar papular psoriasis.

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: Honeypot Frameworks and Their Applications: A New Framework

positive: Peer-to-Peer Botnets: Overview and Case Study Botnets have recently been identified as one of the most important threats to the security of the Internet. Traditionally, botnets organize themselves in an hierarchical manner with a central command and control location. This location can be statically defined in the bot, or it can be dynamically defined based on a directory server. Presently, the centralized characteristic of botnets is us eful to security professionals because it offers a central point of failure for the botnet. In the near future, we believe attackers will move to more resilient architectures. In particular, one class of botnet structure that has entered initial stages of development is peer-to-peer based architectures. In this paper, we present an overview of peer-to-peer botnets. We also present a case study of a Kademlia-based Trojan.Peacomm bot.

positive: Gatekeeper: Monitoring Auto-Start Extensibility Points (ASEPs) for Spyware Management Spyware is a rapidly spreading problem for PC users causing significant impact on system stability and privacy concerns. It attaches to extensibility points in the system to ensure the spyware will be instantiated when the system starts. Users may willingly install free versions of software containing spyware as an alternative to paying for it. Traditional anti-virus techniques are less effective in this scenario because they lack the context to decide if the spyware should be removed. In this paper, we introduce Auto-Start Extensibility Points (ASEPs) as the key concept for modeling the spyware problem. By monitoring and grouping ‘‘hooking’’ operations made to the ASEPs, our Gatekeeper solution complements the traditional signature-based approach and provides a comprehensive framework for spyware management. We present ASEP hooking statistics for 120 real-world spyware programs. We also describe several techniques for discovering new ASEPs to further enhance the effectiveness of our solution.

positive: Detecting stealth software with Strider GhostBuster Stealth malware programs that silently infect enterprise and consumer machines are becoming a major threat to the future of the Internet. Resource hiding is a powerful stealth technique commonly used by malware to evade detection by computer users and anti-malware scanners. In this paper, we focus on a subclass of malware, termed "ghostware", which hide files, configuration settings, processes, and loaded modules from the operating system's query and enumeration application programming interfaces (APIs). Instead of targeting individual stealth implementations, we describe a systematic framework for detecting multiple types of hidden resources by leveraging the hiding behavior as a detection mechanism. Specifically, we adopt a cross-view diff-based approach to ghostware detection by comparing a high-level infected scan with a low-level clean scan and alternatively comparing an inside-the-box infected scan with an outside-the-box clean scan. We describe the design and implementation of the Strider GhostBuster tool and demonstrate its efficiency and effectiveness in detecting resources hidden by real-world malware such as rootkits, Trojans, and key-loggers.

positive: A multifaceted approach to understanding the botnet phenomenon The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic - 27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnet-related spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.

positive: Modeling Botnet Propagation Using Time Zones Time zones play an important and unexplored role in malware epidemics. To understand how time and location affect malware spread dynamics, we studied botnets, or large coordinated collections of victim machines (zombies) controlled by attackers. Over a six month period we observed dozens of botnets representing millions of victims. We noted diurnal properties in botnet activity, which we suspect occurs because victims turn their computers off at night. Through binary analysis, we also confirmed that some botnets demonstrated a bias in infecting regional populations. Clearly, computers that are offline are not infectious, and any regional bias in infections will affect the overall growth of the botnet. We therefore created a diurnal propagation model. The model uses diurnal shaping functions to capture regional variations in online vulnerable populations. The diurnal model also lets one compare propagation rates for different botnets, and prioritize response. Because of variations in release times and diurnal shaping functions particular to an infection, botnets released later in time may actually surpass other botnets that have an advanced start. Since response times for malware outbreaks is now measured in hours, being able to predict short-term propagation dynamics lets us allocate resources more intelligently. We used empirical data from botnets to evaluate the analytical model.

negative: A survey of automated material handling systems in 300-mm SemiconductorFabs The fast-paced developments and technological breakthroughs in the semiconductor manufacturing industry elevates the importance of optimum utilization of resources. The newer 300-mm wafers fabs place a high level of emphasis on increasing yield and reducing cycle times. Automated material handling systems are importanttools that help us achieve these objectives. In addition, due to the increased weight and size of 300-mm wafers, an automated material handling system isa must for a 300-mm manufacturing facility. This paper discusses various approaches for automated materials handling in semiconductor manufacturing industries.

negative: Structural description and classification of signature images -This, paper introduces a new method for describing signature images. The description involves features of the signatures and relations among its parts organized in a hierarchical structure. The description is used to classify the signatures and globally analyse their structure. Description Segmentation Feature extraction Classification Signature analysis I. I N T R O D U C T I O N Although a lot of research on signatures has been reported in the past 20 years (t) they are mainly confined to verification in which a set of goal oriented features is used to give the decision about the authenticity of the claimed signature. However, it is interesting to be able to access a signature database using a compact and meaningful description derived from its pictorial content. Constructing such a signature database, requires an automatic description of signature images. Researches on signature description are very limited and appeared implicitly in verification researches. In on-line systems, Lamarche and Plamondon (z) reconsidered the theoretical model of the handwriting process introduced by Verdenberg and Koster <3) to describe the pen tip speed and orientation and used this description for feature extraction. In off-line systems, Nagel and Rosenfeld (̀ ~ modified the model introduced by Eden (~) for describing upright handwriting by adding the slant parameter and used the parameters of the modified model as a basis for choosing their features for verification, and Brocklehurst (6) used data reduction techniques to encode the signatures into a list of 7 features (called encoded description) and used them for verification. In all cases, neither a structural description of the signature as a 2-D image nor a conceptualization of the features was proposed. In this paper, we introduce a method for signature description and a reliable approach that can describe digitized signature images by this method. The description uses a hierarchical approach, i.e. the signature is at the top level and each lower level gives more detail about the higher one. Since the range of the natural variation of signature writing will change with time as found by Kapoor <~ and Evett, (s) and the ratios of the writing parameters for different samples of the same individual's writing remain essentially constant over a wide range of the writing surface and space available, ('~ features and relations of o u r description are extracted in a relative way (e.g. the length is measured by its ratio to the width, and so on) to attempt to make it insensitive to the natural variation in the genuine signatures. The signature is described using a global description suitable for purposes like classifying the signature data and studying its nature, and a local description convenient for applications which require more detailed information about the signature like signature analysis or similarity oriented retrieval. The global description is represented by a character string, and the local description is represented by a tree structure. In Section 2, the general approach for describing signature images is introduced, and Section 3 introduces the practical method of the description. Section 4 presents the application of the global description to classifying and analysing the signature data, and finally, Section 5 contains the closing remarks. 2. SIGNATURE DESCRIPTION Signatures are a special case of handwriting in which special characters and flourishes are available, and the ratio between the middle zone width and the letter heights is sometimes abnormal. In many cases, the signature is not readable even by a human. Therefore, we deal with the signature as an image that has the principal zones (upper, middle and lower zones) of the handwriting, not as strokes, letters and words. As indicated by Rosenfeld, (9) description of a picture involves properties of the picture or its parts and relationships among them. Our description involves features like slant, and relations among signature properties like the comparison between the widths of the zones. The description is not reversible, that is, the signature cannot be reconstructed from its description. However, the description involves sufficient information to characterize the signature and its parts. Features and relations are extracted quantitatively, and then mapped into a conceptual

negative: Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms The mobile platform of a parallel cable-driven mechanism is connected in parallel to a base by lightweight links, such as cables. Since the cables can only work in tension, the set of poses of the mobile platform for which the cables can balance any external wrench, i.e., for which the platform of the mechanism is fully constrained, is often limited or even nonexistent. Thus, the study and determination of this set of poses, called the wrench-closure workspace (WCW), is an important issue for parallel cable-driven mechanisms. In this paper, the case of planar parallel cable-driven mechanisms is addressed. Theorems that characterize the poses of the WCW are proposed. Then, these theorems are used to disclose the parts of the reachable workspace which belong to the WCW. Finally, an efficient algorithm that determines the constant-orientation cross-sections of these parts is introduced.

negative: Additive lattice kirigami Kirigami uses bending, folding, cutting, and pasting to create complex three-dimensional (3D) structures from a flat sheet. In the case of lattice kirigami, this cutting and rejoining introduces defects into an underlying 2D lattice in the form of points of nonzero Gaussian curvature. A set of simple rules was previously used to generate a wide variety of stepped structures; we now pare back these rules to their minimum. This allows us to describe a set of techniques that unify a wide variety of cut-and-paste actions under the rubric of lattice kirigami, including adding new material and rejoining material across arbitrary cuts in the sheet. We also explore the use of more complex lattices and the different structures that consequently arise. Regardless of the choice of lattice, creating complex structures may require multiple overlapping kirigami cuts, where subsequent cuts are not performed on a locally flat lattice. Our additive kirigami method describes such cuts, providing a simple methodology and a set of techniques to build a huge variety of complex 3D shapes.

negative: Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method Within-subject ANOVAs are a powerful tool to analyze data because the variance associated to differences between the participants is removed from the analysis. Hence, small differences, when present for most of the participants, can be significant even when the participants are very different from one another. Yet, graphs showing standard error or confidence interval bars are misleading since these bars include the between-subject variability. Loftus and Masson (1994) noticed this fact and proposed an alternate method to compute the error bars. However, i) their approach requires that the ANOVA be performed first, which is paradoxical since a graph is an aid to decide whether to perform analyses or not; ii) their method provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions; iii) the method proposed is difficult to implement in commonly-used graphing software. Here we propose a simple alternative and sow how it can be implemented in SPSS.

negative: Decision Support Systems (DSS) in Construction Tendering Processes The successful execution of a construction project is heavily impacted by making the right decision during tendering processes. Managing tender procedures is very complex and uncertain involving coordination of many tasks and individuals with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is totally depends on intuition, subjective judgement or emotion. In making transparent decision and healthy competition tendering, there exists a need for flexible guidance tool for decision support. Aim of this paper is to give a review on current practices of Decision Support Systems (DSS) technology in construction tendering processes. Current practices of general tendering processes as applied to the most countries in different regions such as United States, Europe, Middle East and Asia are comprehensively discussed. Applications of Web-based tendering processes is also summarised in terms of its properties. Besides that, a summary of Decision Support System (DSS) components is included in the next section. Furthermore, prior researches on implementation of DSS approaches in tendering processes are discussed in details. Current issues arise from both of paper-based and Web-based tendering processes are outlined. Finally, conclusion is included at the end of this paper.

negative: An empirical resource for discovering cognitive principles of discourse organisation: the ANNODIS corpus This paper describes the ANNODIS resource, a discourse-level annotated corpus for French. The corpus combines two perspectives on discourse: a bottom-up approach and a top-down approach. The bottom-up view incrementally builds a structure from elementary discourse units, while the top-down view focuses on the selective annotation of multi-level discourse structures. The corpus is composed of texts that are diversified with respect to genre, length and type of discursive organisation. The methodology followed here involves an iterative design of annotation guidelines in order to reach satisfactory inter-annotator agreement levels. This allows us to raise a few issues relevant for the comparison of such complex objects as discourse structures. The corpus also serves as a source of empirical evidence for discourse theories. We present here two first analyses taking advantage of this new annotated corpus, one that tested hypotheses on constraints governing discourse structure, and another that studied the variations in composition and signalling of multi-level discourse structures.

negative: The slot-coupled hemispherical dielectric resonator antenna with a parasitic patch: applications to the circularly polarized antenna and wide-band antenna The aperture-coupled hemispherical dielectric resonator antenna (DRA) with a parasitic patch is studied rigorously. Using the Green's function approach, integral equations for the unknown patch and slot currents are formulated and solved using the method of moments. The theory is utilized to design a circularly polarized (CP) DRA and a wide-band linearly polarized (LP) DRA. In the former, the CP frequency and axial ratio (AR) can easily be controlled by the patch location and patch size, respectively, with the impedance matched by varying the slot length and microstrip stub length. It is important that the AR will not be affected when the input impedance is tuned, and the CP design is therefore greatly facilitated. For the wide-band LP antenna, a maximum bandwidth of 22% can be obtained, which is much wider than the previous bandwidth of 7.5% with no parasitic patches. Finally, the frequency-tuning characteristics of the proposed antenna are discussed. Since the parasitic patch can be applied to any DRAs, the method will find applications in practical DRA designs.

negative: Measuring Urban Social Diversity Using Interconnected Geo-Social Networks Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.

negative: SLING: A framework for frame semantic parsing We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.

negative: Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel Most previously proposed statistical models for the indoor multipath channel include only time of arrival characteristics. However, in order to use statistical models in simulating or analyzing the performance of systems employing spatial diversity combining, information about angle of arrival statistics is also required. Ideally, it would be desirable to characterize the full spare-time nature of the channel. In this paper, a system is described that was used to collect simultaneous time and angle of arrival data at 7 GHz. Data processing methods are outlined, and results obtained from data taken in two different buildings are presented. Based on the results, a model is proposed that employs the clustered "double Poisson" time-of-arrival model proposed by Saleh and Valenzuela (1987). The observed angular distribution is also clustered with uniformly distributed clusters and arrivals within clusters that have a Laplacian distribution.

negative: Context-Sensitive Flow Graph and Projective Single Assignment Form for Resolving Context-Dependency of Binary Code Program analysis on binary code is considered as difficult because one has to resolve destinations of indirect jumps. However, there is another difficulty of context-dependency that matters when one processes binary programs that are not compiler generated. In this paper, we propose a novel approach for tackling these difficulties and describe a way to reconstruct a control flow from a binary program with no extra assumptions than the operational meaning of machine instructions.

negative: Human-in-the-Loop Challenges for Entity Matching: A Midterm Report Entity matching (EM) has been a long-standing challenge in data management. In the past few years we have started two major projects on EM (Magellan and Corleone/Falcon). These projects have raised many human-in-the-loop (HIL) challenges. In this paper we discuss these challenges. In particular, we show how these challenges forced us to revise our solution architecture, from a typical RDBMS-style architecture to a very human-centric one, in which human users are first-class objects driving the EM process, using tools at pain-point places. We discuss how such solution architectures can be viewed as combining "tools in the loop" with "human in the loop". Finally, we discuss lessons learned which can potentially be applied to other problem settings. We also hope that more researchers will investigate EM, as it can be a rich "playground" for HIL research.

negative: ReticularSpaces: activity-based computing support for physically distributed and collaborative smart spaces Smart spaces research focuses on technology for multiple displays and devices for collocated participants. In most approaches, however, users have to cope with heterogeneous interfaces and information organization, as well as a lack of support for collaboration with mobile and remote users outside the smart space. In this paper, we present ReticularSpaces; a multi-display smart space system built on the principles of activity-based computing. The focus of ReticularSpaces is to support: (i) unified interaction with applications and documents through ReticularUI, a novel distributed user interfaces design; (ii) management of the complexity of tasks between users and displays; (iii) mobile users in a local, remote or 'nomadic' settings; and (iv) collaboration among local and remote users. We describe the motivation, design, and architecture of ReticularSpaces, and report from a preliminary feasibility study. The study shows that participants found ReticularSpaces useful and effective, but at the same time reveals new areas for research on smart environments.

negative: Advanced control strategies for balancing LED usage of AC LED driver This paper presents two enhanced methods to balance LEDs energy usage in the AC LED Driver. While conventional AC LED driver has energy consumption imbalance, the proposed control methods provide equally distributed energy usage. First method is to apply the appropriate firing voltage that is the threshold voltage for equalizing energy consumption of LEDs. This method improves balancing energy consumption within a rectified line cycle. The other method is to change the order of LEDs turning on sequence by every two rectified line cycle. These two approaches are compared with mathematical analysis in terms of the energy consumption of each LED. A prototype driver with three 12 W-LED strings is built. The proposed method 1 and method 2 achieve 0.0030 J and 0.0033 J standard deviation of energy consumption, respectively, compared to the 0.0070 J deviation for the conventional approach.

negative: Deep convolutional neural network based large-scale oil palm tree detection for high-resolution remote sensing images This paper proposed a deep convolutional neural network (DCNN) based framework for large-scale oil palm tree detection using high-resolution remote sensing images in Malaysia. Different from the previous palm tree or tree crown detection studies, the palm trees in our study area are very crowded and their crowns often overlap. Moreover, there are various land cover types in our study area, e.g. impervious, bare land, and other vegetation, etc. The main steps of our proposed method include large-scale and multi-class sample collection, AlexNet-based DCNN training and optimization, sliding window-based label prediction, and post-processing. Compared with the manually interpreted ground truth, our proposed method achieves detection accuracies of 92%–97% in our study area, which are greatly higher than the accuracies obtained from another two detection methods used in this paper.

negative: Magnet design based on transient behavior of an IPMSM in event of malfunction This paper deals with the approach of analyzing the magnet design based on transient behavior of an IPMSM during switching processes in the event of malfunction for automotive applications. Depending on the maximal current increase during the transient process the needed percentage of Dysprosium for the magnet design is conducted. Switching off strategy is introduced for both Voltage-Source- and Current-Source-Inverter for automotive application. Both inverters are compared concerning the transient current increase and respectively the Dy-content for the magnet design.

negative: Multiple asynchronous stimulus- and task-dependent hierarchies (STDH) within the visual brain's parallel processing systems. Results from a variety of sources, some many years old, lead ineluctably to a re-appraisal of the twin strategies of hierarchical and parallel processing used by the brain to construct an image of the visual world. Contrary to common supposition, there are at least three 'feed-forward' anatomical hierarchies that reach the primary visual cortex (V1) and the specialized visual areas outside it, in parallel. These anatomical hierarchies do not conform to the temporal order with which visual signals reach the specialized visual areas through V1. Furthermore, neither the anatomical hierarchies nor the temporal order of activation through V1 predict the perceptual hierarchies. The latter shows that we see (and become aware of) different visual attributes at different times, with colour leading form (orientation) and directional visual motion, even though signals from fast-moving, high-contrast stimuli are among the earliest to reach the visual cortex (of area V5). Parallel processing, on the other hand, is much more ubiquitous than commonly supposed but is subject to a barely noticed but fundamental aspect of brain operations, namely that different parallel systems operate asynchronously with respect to each other and reach perceptual endpoints at different times. This re-assessment leads to the conclusion that the visual brain is constituted of multiple, parallel and asynchronously operating task- and stimulus-dependent hierarchies (STDH); which of these parallel anatomical hierarchies have temporal and perceptual precedence at any given moment is stimulus and task related, and dependent on the visual brain's ability to undertake multiple operations asynchronously.

negative: From Theory to Experimental Evaluation: Resource Management in Software-Defined Vehicular Networks Managing resources in dynamic vehicular environments is a tough task, which is becoming more challenging with the increased number of access technologies today available in connected cars (e.g., IEEE 802.11, LTE), in the variety of applications provided on the road (e.g., safety, traffic efficiency, and infotainment), in the amount of driving awareness/coordination required (e.g., local, context, and cooperative awareness), and in the level of automation toward zero-accident driving (e.g., platooning and autonomous driving). The open programmability and logically centralized control features of the software–defined networking (SDN) paradigm offer an attractive means to manage communication and networking resources in the vehicular environment and promise improved performance. In this paper, we enumerate the potentials of software-defined vehicular networks, analyze the need to rethink the traditional SDN approach from theoretical and practical standpoints when applied in this application context, and present an emulation approach based on the proposed node car architecture in Mininet-WiFi to showcase the applicability and some expected benefits of SDN in a selected use case scenario.

negative: Violent and nonviolent video games differentially affect physical aggression for individuals high vs. low in dispositional anger. Although numerous experiments have shown that exposure to violent video games (VVG) causes increases in aggression, relatively few studies have investigated the extent to which this effect differs as a function of theoretically relevant individual difference factors. This study investigated whether video game content differentially influences aggression as a function of individual differences in trait anger. Participants were randomly assigned to play a violent or nonviolent video game before completing a task in which they could behave aggressively. Results showed that participants high in trait anger were the most aggressive, but only if they first played a VVG. This relationship held while statistically controlling for dimensions other than violent content on which game conditions differed (e.g. frustration, arousal). Implications of these findings for models explaining the effects of video games on behavior are discussed.

negative: Avoiding, finding and fixing spreadsheet errors - A survey of automated approaches for spreadsheet QA Spreadsheet programs can be found everywhere in organizations and they are used for a variety of purposes, including financial calculations, planning, data aggregation and decision making tasks. A number of research surveys have however shown that such programs are particularly prone to errors. Some reasons for the error-proneness of spreadsheets are that spreadsheets are developed by end users and that standard software quality assurance processes are mostly not applied. Correspondingly, during the last two decades, researchers have proposed a number of techniques and automated tools aimed at supporting the end user in the development of error-free spreadsheets. In this paper, we provide a review of the research literature and develop a classification of automated spreadsheet quality assurance (QA) approaches, which range from spreadsheet visualization, static analysis and quality reports, over testing and support to model-based spreadsheet development. Based on this review, we outline possible opportunities for future work in the area of automated spreadsheet QA.

negative: Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach During summer, melt ponds have a significant influence on Arctic sea-ice albedo. The melt pond fraction (MPF) also has the ability to forecast the Arctic sea-ice in a certain period. It is important to retrieve accurate melt pond fraction (MPF) from satellite data for Arctic research. This paper proposes a satellite MPF retrieval model based on the multi-layer neural network, named MPF-NN. Our model uses multi-spectral satellite data as model input and MPF information from multi-site and multiperiod visible imagery as prior knowledge for modeling. It can effectively model melt ponds evolution of different regions and periods over the Arctic. Evaluation results show that the MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and a correlation coefficient of 0.73. The seasonal distribution of MPF is also consistent with previous results.

negative: Multimaterial 3D Printing of Graphene-Based Electrodes for Electrochemical Energy Storage Using Thermoresponsive Inks. The current lifestyles, increasing population, and limited resources result in energy research being at the forefront of worldwide grand challenges, increasing the demand for sustainable and more efficient energy devices. In this context, additive manufacturing brings the possibility of making electrodes and electrical energy storage devices in any desired three-dimensional (3D) shape and dimensions, while preserving the multifunctional properties of the active materials in terms of surface area and conductivity. This paves the way to optimized and more efficient designs for energy devices. Here, we describe how three-dimensional (3D) printing will allow the fabrication of bespoke devices, with complex geometries, tailored to fit specific requirements and applications, by designing water-based thermoresponsive inks to 3D-print different materials in one step, for example, printing the active material precursor (reduced chemically modified graphene (rCMG)) and the current collector (copper) for supercapacitors or anodes for lithium-ion batteries. The formulation of thermoresponsive inks using Pluronic F127 provides an aqueous-based, robust, flexible, and easily upscalable approach. The devices are designed to provide low resistance interface, enhanced electrical properties, mechanical performance, packing of rCMG, and low active material density while facilitating the postprocessing of the multicomponent 3D-printed structures. The electrode materials are selected to match postprocessing conditions. The reduction of the active material (rCMG) and sintering of the current collector (Cu) take place simultaneously. The electrochemical performance of the rCMG-based self-standing binder-free electrode and the two materials coupled rCMG/Cu printed electrode prove the potential of multimaterial printing in energy applications.

negative: Knowledge management and organizational culture: a theoretical integrative framework Purpose – Organizational culture is a critical factor in building and reinforcing knowledge management in organizations. However, there is no theoretical framework that comprehensively explains the effect of organizational culture on knowledge management in organizations. This paper endeavors to develop a theoretical integrative framework for organizational knowledge management and organizational culture. Design/methodology/approach – This is a conceptual paper. It modifies the ‘‘competing value framework’’ by adding a new dimension representing ethical and trusting culture, and then integrates it with the SECI model of knowledge creation and conversion by identifying the conceptual parallels between the two frameworks and then analyzing the interaction effects among the dimensions. Findings – Based on the congruity between the modified competing values framework and the knowledge creation and conversion framework, the paper formulates six propositions about the propensity of organizations of different dominant cultural styles to engage in the four processes of knowledge creation and conversion. Research limitations/implications – The dynamic nature of the framework presented in the paper points to the importance of longitudinal and comparative research in understanding the effects of organizational culture on organizational knowledge management systems in organizations. Practical implications – The proposed integrative framework would facilitate organizational learning and lead to the improvement of knowledge management practices in organizations as it helps managers to understand the linkages between culture and knowledge management. Originality/value – This paper presents a new framework linking organizational culture to knowledge management. It moves away from analyzing culture only in terms of its positive and negative influences on knowledge management. Instead, it suggests a typology of the kind of knowledge management processes that organizations are likely to focus on depending on the culture that prevails in an organization.

negative: Improving lip-reading performance for robust audiovisual speech recognition using DNNs This paper presents preliminary experiments using the Kaldi toolkit [1] to investigate audiovisual speech recognition (AVSR) in noisy environments using deep neural networks (DNNs). In particular we use a single-speaker large vocabulary, continuous audiovisual speech corpus to compare the performance of visual-only, audio-only and audiovisual speech recognition. The models trained using the Kaldi toolkit are compared with the performance of models trained using conventional hidden Markov models (HMMs). In addition, we compare the performance of a speech recognizer both with and without visual features over nine different SNR levels of babble noise ranging from 20dB down to -20dB. The results show that the DNN outperforms conventional HMMs in all experimental conditions, especially for the lip-reading only system, which achieves a gain of 37.19% accuracy (84.67% absolute word accuracy). Moreover, the DNN provides an effective improvement of 10 and 12dB SNR respectively for both the single modal and bimodal speech recognition systems. However, integrating the visual features using simple feature fusion is only effective in SNRs at 5dB and above. Below this the degradion in accuracy of an audiovisual system is similar to the audio only recognizer.

negative: Towards value-based pricing — An integrative framework for decision making Despite a recent surge of interest, the subject of pricing in general and value-based pricing in particular has received little academic investigation. Yet, pricing has a huge impact on financial results, both in absolute terms and relative to other instruments of the marketing mix. The objective of this paper is to present a comprehensive framework for pricing decisions which considers all relevant dimensions and elements for profitable and sustainable pricing decisions. The theoretical framework is useful for guiding new product pricing decisions as well as for implementing price-repositioning strategies for existing products. The practical application of this framework is illustrated by a case study involving the pricing decision for a major product launch at a global chemical company. D 2003 Elsevier Inc. All rights reserved.

negative: Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications This paper summarizes results on a recently introduced family of Datalog-based languages, called Datalog+/-, which is a new framework for tractable ontology querying, and for a variety of other applications. Datalog+/- extends plain Datalog by features such as existentially quantified rule heads and, at the same time, restricts the rule syntax so as to achieve decidability and tractability. In particular, we discuss three paradigms ensuring decidability: chase termination, guardedness, and stickiness.

negative: A Framework for Highly-Available Composed Real-Time Internet Services Bhaskaran Raman Qualifying Examination Applicationservicesfor theend-userareall importantin today’scommunicationnetworks,andcoulddictatethesuccessor failureof technologyor serviceproviders[39]. It is importantto develop and deploy applicationfunctionality quickly [18]. The ability to compose servicesfrom independentprovidersprovidesa flexible way to quickly build new end-to-endfunctionality. Suchcompositionof services acrossthe network and acrossdifferent serviceproviders becomesespeciallyimportant in the context of growing popularityandheterogeneityin 3G+ accessnetworks and devices[25]. In this work, we provide a framework for constructing such composed services on the Internet. Robustnessandhigh-availability arecrucial for Internet services.While cluster-basedmodelsfor resilienceto failureshave beenbuilt for web-servers[27] aswell asproxy services[11, 4], theseareinadequatein thecontext of composedservices.This is especiallyso whenthe application sessionis long-lived, andfailureshaveto behandledduring asession. In the context of composed services, we address the important and challenging issues of resilience to failures, and adapting to changes in overall performance during longlived sessions. Our framework is basedon a connection-orientedoverlay network of compute-clusterson theInternet.Theoverlay network provides the context for composingservices over the wide-area,and monitoring for livenessand performanceof a session. We have performedinitial analysesof the feasibility of network failure detectionover the wide-areaInternet. And we have a preliminaryevaluation of the overheadassociatedwith suchan overlay network. We presentour plansfor furtherevaluationandrefinement of thearchitecture;andfor examiningissuesrelatedto the creationof theoverlaytopology. 1 Intr oduction Application functionality for the end-useris thedriving force behinddevelopmentof technologyandits adoption. It is importantto be ableto developanddeploy new functionality quickly [18]. Compositionof existing servicesto achieve new functionalityenablessuchquick development throughreuseof alreadydeployed components.Consider for example,thescenarioshown in Figure1. Video-on-demand server Replicated instances Service Provider A

negative: Blockchain-based sharing services : What blockchain technology can contribute to smart cities Background: The notion of smart city has grown popular over the past few years. It embraces several dimensions depending on the meaning of the word “smart” and benefits from innovative applications of new kinds of information and communications technology to support communal sharing. Methods: By relying on prior literature, this paper proposes a conceptual framework with three dimensions: (1) human, (2) technology, and (3) organization, and explores a set of fundamental factors that make a city smart from a sharing economy perspective. Results: Using this triangle framework, we discuss what emerging blockchain technology may contribute to these factors and how its elements can help smart cities develop sharing services. Conclusions: This study discusses how blockchain-based sharing services can contribute to smart cities based on a conceptual framework. We hope it can stimulate interest in theory and practice to foster discussions in this area.

negative: Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands. The term 'synergy' - from the Greek synergia - means 'working together'. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project "The Hand Embodied" (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

negative: A Framework for Clustering Massive Text and Categorical Data Streams Many applications such as news group filtering, text crawling, and document organization require real time clustering and segmentation of text data records. The categorical data stream clustering problem also has a number of applications to the problems of customer segmentation and real time trend analysis. We will present an online approach for clustering massive text and categorical data streams with the use of a statistical summarization methodology. We present results illustrating the effectiveness of the technique.

negative: Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework In recent years, there has been a considerable interest in the area of mobile robotics and educational technologies [1-7]. For control engineers and researchers, there is a wealth of literature dealing with wheeled mobile robots (WMR) control and their applications. However, while the subject of kinematic modeling of WMR is well documented and easily understood by students, the subject of dynamic modeling of WMR has not been addressed adequately in the literature. The dynamics of WMR are highly nonlinear and involve non-holonomic constraints which makes difficult their modeling and analysis especially for new engineering students starting their research in this field. Therefore, a detailed and accurate dynamic model describing the WMR motion need to be developed to offer students a general framework for simulation analysis and model based control system design.