title: Automatic writer identification using connected-component contours and edge-based features of uppercase Western script

abstract: In this paper, a new technique for offline writer identification is presented, using connected-component contours (COCOCOs or CO/sup 3/s) in uppercase handwritten samples. In our model, the writer is considered to be characterized by a stochastic pattern generator, producing a family of connected components for the uppercase character set. Using a codebook of CO/sup 3/s from an independent training set of 100 writers, the probability-density function (PDF) of CC's was computed for an independent test set containing 150 unseen writers. Results revealed a high-sensitivity of the CO/sup 3/ PDF for identifying individual writers on the basis of a single sentence of uppercase characters. The proposed automatic approach bridges the gap between image-statistics approaches on one end and manually measured allograph features of individual characters on the other end. Combining the CO/sup 3/ PDF with an independent edge-based orientation and curvature PDF yielded very high correct identification rates.


title: Model-based Attack Detection Scheme for Smart Water Distribution Networks

abstract: In this manuscript, we present a detailed case study about model-based attack detection procedures for Cyber-Physical Systems (CPSs). In particular, using EPANET (a simulation tool for water distribution systems), we simulate a Water Distribution Network (WDN). Using this data and sub-space identification techniques, an input-output Linear Time Invariant (LTI) model for the network is obtained. This model is used to derive a Kalman filter to estimate the evolution of the system dynamics. Then, residual variables are constructed by subtracting data coming from EPANET and the estimates of the Kalman filter. We use these residuals and the Bad-Data and the dynamic Cumulative Sum (CUSUM) change detection procedures for attack detection. Simulation results are presented - considering false data injection and zero-alarm attacks on sensor readings, and attacks on control input - to evaluate the performance of our model-based attack detection schemes. Finally, we derive upper bounds on the estimator-state deviation that zero-alarm attacks can induce.


Symmetry-aware Sparse Voxel DAGs (SSVDAGs) for compression-domain tracing of high-resolution geometric scenes


title: Multirelational Organization of Large-scale Social Networks in an Online World

abstract: The capacity to collect fingerprints of individuals in online media has revolutionized the way researchers explore human society. Social systems can be seen as a nonlinear superposition of a multitude of complex social networks, where nodes represent individuals and links capture a variety of different social relations. Much emphasis has been put on the network topology of social interactions, however, the multidimensional nature of these interactions has largely been ignored, mostly because of lack of data. Here, for the first time, we analyze a complete, multirelational, large social network of a society consisting of the 300,000 odd players of a massive multiplayer online game. We extract networks of six different types of one-to-one interactions between the players. Three of them carry a positive connotation (friendship, communication, trade), three a negative (enmity, armed aggression, punishment). We first analyze these types of networks as separate entities and find that negative interactions differ from positive interactions by their lower reciprocity, weaker clustering, and fatter-tail degree distribution. We then explore how the interdependence of different network types determines the organization of the social system. In particular, we study correlations and overlap between different types of links and demonstrate the tendency of individuals to play different roles in different networks. As a demonstration of the power of the approach, we present the first empirical large-scale verification of the long-standing structural balance theory, by focusing on the specific multiplex network of friendship and enmity relations.


title: Making computing interesting to school students: teachers' perspectives

abstract: It is widely agreed that there is a need to excite more school students about computing. Considering teachers' views about student engagement is important to securing their support for any solution. We therefore present the results of a qualitative, questionnaire-based study on teachers' perceptions of the best ways to make the subject interesting. From 115 responses by UK computing teachers emerged a range of themes about the issues they felt were most important. We found that whilst their views reflected a range of approaches that are widely promoted in the literature and in national initiatives, there were also disconnects between teachers' views and wider discourses. Based on the results, we give specific recommendations for areas where more should be done to support teachers in making computing interesting to school students. Academics should do more to engage with teachers, especially if they wish to introduce deep computing principles in schools. Teachers expressed an interest in computing clubs in schools, but a strong support network for them is still needed. This may be an opportunity for businesses and universities to help support teachers.


title: Weakly supervised named entity classification

abstract: In this paper, we describe a new method for the problem of named entity classifica-tion for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple string match. Those positive examples can then be used to train a named entity classifier, by using the PU learning paradigm, which is learning from positive and unlabeled examples. We introduce a new convex formulation to solve this problem, and apply our technique in order to extract named entities from financial reports cor-responding to healthcare companies.


title: An efficient algorithm for a class of fused lasso problems

abstract: The fused Lasso penalty enforces sparsity in both the coefficients and their successive differences, which is desirable for applications with features ordered in some meaningful way. The resulting problem is, however, challenging to solve, as the fused Lasso penalty is both non-smooth and non-separable. Existing algorithms have high computational complexity and do not scale to large-size problems. In this paper, we propose an Efficient Fused Lasso Algorithm (EFLA) for optimizing this class of problems. One key building block in the proposed EFLA is the Fused Lasso Signal Approximator (FLSA). To efficiently solve FLSA, we propose to reformulate it as the problem of finding an "appropriate" subgradient of the fused penalty at the minimizer, and develop a Subgradient Finding Algorithm (SFA). We further design a restart technique to accelerate the convergence of SFA, by exploiting the special "structures" of both the original and the reformulated FLSA problems. Our empirical evaluations show that, both SFA and EFLA significantly outperform existing solvers. We also demonstrate several applications of the fused Lasso.


title: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights

abstract: The authors describe how a two-layer neural network can approximate any nonlinear function by forming a union of piecewise linear segments. A method is given for picking initial weights for the network to decrease training time. The authors have used the method to initialize adaptive weights over a large number of different training problems and have achieved major improvements in learning speed in every case. The improvement is best when a large number of hidden units is used with a complicated desired response. The authors have used the method to train the truck-backer-upper and were able to decrease the training time from about two days to four hours


title: Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways

abstract: Clinical pathways define the essential component of the complex care process, with the objective to optimize patient outcomes and resource allocation. Clinical pathway analysis has gained increased attention in order to augment the patient treatment process. In this demonstration paper, we propose Pathway-Finder, an interactive recommender system to visually explore and discover clinical pathways. The interactive web service efficiently collects and displays patient information in a meaningful way to support an effective personalized treatment plan. Pathway-Finder implements a Bayesian Network to discover causal relationships among different factors. To support real-time recommendation and visualization, a key-value structure has been implemented to traverse the Bayesian Network faster. Additionally, Pathway-Finder is a cloud based web service hosted on Microsoft Azure which enables the health providers to access the system without the need to deploy analytics infrastructure. We demonstrate Pathway-Finder to interactively recommend personalized interventions to minimize 30-day readmission risk for Heart Failure (HF).


title: An Intelligent Suspicious Activity Detection Framework (ISADF) for Video Surveillance Systems

abstract: Video Surveillance systems are playing vital role in ensuring the security at various public places like bus stops, railway stations, shopping malls, Airports etc. Suspicious activity recognition helps to prevent from threats and identify the causes after threat. Existing semi-automatic approaches depends on human intervention to detect the uncommon activities and suspicious behavior from video context. Due to these limitations they become non-intelligence, very slow and need more human observers. In this paper, to overcome these problems an Intelligent Suspicious Activity Detection Framework (ISADF) for Video data is proposed. This framework uses location dependent training data for intelligence and context (foreground) change information for suspicious activity detection. Experimental results show that ISADF is a high speed intelligent threat detection system than existing approaches.


title: A SiC JFET driver for a 5 kW, 150 kHz three-phase PWM converter

abstract: Silicon carbide (SiC) power semiconductor devices are capable of being operated at higher voltages, frequencies and temperatures than silicon power devices. These SiC device capabilities will provide the power electronics designer with new possibilities to produce compact designs. Presently the JFET is the only controlled turn off/on SiC device that is close to commercialization and available as restricted samples. However the JFET is a normally-on device that requires a negative gate voltage to turn off. In order to correctly design a gate driver one must understand the characteristics of the JFET. This paper presents a description of the JFET semiconductor structure, and the SiC JFET's static and dynamic characteristics from room temperature to 200 /spl deg/C. A SiC JFET gate driver circuit is presented and its performance described. The proposed gate driver improves the switching performance of the JFET by operating the gate in avalanche during the off time.


title: Kinect Sensor Gesture and Activity Recognition: New Applications for Consumer Cognitive Systems

abstract: Cognitive consumer electronics (CE), the fastest-growing sector worldwide that is driven by machine intelligence and cognitive systems, is triggered and enabled by audio- and video-capturing devices, smart sensors, health- and fitness-monitoring devices, security and education electronics, and intelligent systems. Smart consumer sensors and cognitive systems are synergized through the Internet of Things (IoT) for optimal information sharing, communication, real-time updates, data analytics, and enhanced support for decision making. Biometric-based devices, originally intended for large-scale applications in airports, border controls, disaster zones, or refugee migration zones, are enabling a wide range of applications in commercial and consumer sectors as standalone systems or with interconnected sensor networks. This article introduces the applications of Microsoft Kinect in cognitive systems for smart CE, and, using Kinect sensors, a human-behavior cognition technology is presented for gesture and activity recognition. As a novel front end of pervasive cognitive systems, the challenges and applications of a Kinect sensor-based system will be explored in CE, such as smart automobiles, health care, surveillance, and activity recognition.


title: Deep Learning of Robotic Tasks using Strong and Weak Human Supervision

abstract: We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering. The scheme resembles natural teaching-learning processes used by humans to teach themselves and each other complex tasks, and consists of the following four stages. In the first stage the agent learns by itself an informative low-dimensional representations of raw input signals in an unsupervised learning manner. In the second stage the agent learns to mimic the human instructor using supervised learning so as to reach a basic performance level; the third stage is devoted to learning an instantaneous reward model. Here, the (human) instructor observes (possibly in real time) the agent performing the task and provides reward feedback. During this stage the agent monitors both itself and the instructor feedback and learns a reward model using supervised learning. This stage terminates when the reward model is sufficiently accurate. In the last stage a reinforcement learning algorithm is deployed to optimize the agent policy. The guidance reward signal in the reinforcement learning algorithm relies on the previously learned reward model. As a proof of concept for the proposed scheme, we designed a system consisting of deep convolutional neural networks, and applied it to successfully learn a computerized agent capable of autonomous highway steering over the well-known racing game Assetto Corsa.


title: Wearable textile microstrip patch antenna for multiple ISM band communications

abstract: A wearable probe-fed microstrip antenna manufactured from conductive textile fabric designed for multiple Industrial-Scientific-Medical (ISM) band communications is presented in this paper. The proposed antenna operating at 2.450 GHz, 4.725 Hz and 5.800 GHz consists of a patch and ground plane made of silver fabric mounted on a substrate of flexible low-permittivity foam. For verification, a reference prototype is manufactured from copper. The measurement of both antennas demonstrates the expected resonances, with some unexpected loss especially in the higher frequency range. Simulation results for the antenna in various bending condition indicate the robustness of the design with deviations of resonant frequencies in an acceptable range.


title: Uses & gratifications of a facebook media sharing group

abstract: This paper explores uses and gratifications of a content community on a social network service - a music video sharing group on Facebook. In a two-stage study, 20 users first generated words or phrases to describe how they used the group, and what they enjoyed about their use. These phrases were coded into 34 questionnaire items that were then completed by 57 new participants. Factor analysis on this data revealed four gratifications: contribution; discovery; social interaction and entertainment. These factors are interpreted and discussed, leading to design implications and guidelines aimed at informing the design of future online services that combine media sharing with social interaction to create online systems based on a rich and meaningful object-centered sociality.


title: A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing

abstract: Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties.


title: Visualization Rhetoric: Framing Effects in Narrative Visualization

abstract: Narrative visualizations combine conventions of communicative and exploratory information visualization to convey an intended story. We demonstrate visualization rhetoric as an analytical framework for understanding how design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation. We draw a parallel between narrative visualization interpretation and evidence from framing studies in political messaging, decision-making, and literary studies. Devices for understanding the rhetorical nature of narrative information visualizations are presented, informed by the rigorous application of concepts from critical theory, semiotics, journalism, and political theory. We draw attention to how design tactics represent additions or omissions of information at various levels-the data, visual representation, textual annotations, and interactivity-and how visualizations denote and connote phenomena with reference to unstated viewing conventions and codes. Classes of rhetorical techniques identified via a systematic analysis of recent narrative visualizations are presented, and characterized according to their rhetorical contribution to the visualization. We describe how designers and researchers can benefit from the potentially positive aspects of visualization rhetoric in designing engaging, layered narrative visualizations and how our framework can shed light on how a visualization design prioritizes specific interpretations. We identify areas where future inquiry into visualization rhetoric can improve understanding of visualization interpretation.


title: An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections

abstract: We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection. The circulant structure substantially reduces memory footprint and enables the use of the Fast Fourier Transform to speed up the computation. Considering a fully-connected neural network layer with d input nodes, and d output nodes, this method improves the time complexity from O(d2) to O(dlogd) and space complexity from O(d2) to O(d). The space savings are particularly important for modern deep convolutional neural network architectures, where fully-connected layers typically contain more than 90% of the network parameters. We further show that the gradient computation and optimization of the circulant projections can be performed very efficiently. Our experiments on three standard datasets show that the proposed approach achieves this significant gain in storage and efficiency with minimal increase in error rate compared to neural networks with unstructured projections.


title: Importance of Data Mining in Healthcare: A Survey

abstract: In this survey, we collect the related information that demonstrate the importance of data mining in healthcare. As the amount of collected health data is increasing significantly every day, it is believed that a strong analysis tool that is capable of handling and analyzing large health data is essential. Analyzing the health datasets gathered by electronic health record (EHR) systems, insurance claims, health surveys, and other sources, using data mining techniques is very complex and is faced with very specific challenges, including data quality and privacy issues. However, the applications of data mining in healthcare, advantages of data mining techniques over traditional methods, special characteristics of health data, and new health condition mysteries have made data mining very necessary for health data analysis.


title: Transparent Access to Grid-Based Compute Utilities

abstract: There is normally the need for different grid architectures depending on the constrains: from enterprise infrastructures, to partner or outsourced ones. We present the technology that enables the creation and combination of different grid architectures, offering the possibility to create federated grids that can be easily deployed as compute utilities on grid infrastructures based on the Globus Toolkit. Then, taking advantage of the uniform and standard interface of the proposed grid-based compute utilities, we present a way to enable transparent access to virtualized grids from local computing infrastructure managed by Sun Grid Engine, although similar approaches could be followed for other resource managers.


title: Jigsaw: indoor floor plan reconstruction via mobile crowdsensing

abstract: The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity is 100% correct.


Differential Testing for Software


title: Bijective spherical parametrization with low distortion

abstract: Abstract Computing a bijective spherical parametrization of a genus-0 surface with low distortion is a fundamental task in geometric modeling and processing. Current methods for spherical parametrization cannot, in general, control the worst case distortion of all triangles nor guarantee bijectivity. Given an initial bijective spherical parametrization, with high distortion, we develop a non-linear constrained optimization problem to refine it, with objective penalizing the presence of triangles degeneration and maximal distortion. By using a dynamic adjusting parameter and a constrained, iterative inexact block coordinate descent optimization method, we efficiently and robustly achieve a bijective and low distortion parametrization with an optimal sphere radius. Compared to the state-of-the-art methods, our method is robust to initial parametrization and not sensitive to parameter choice. We demonstrate that our method produces excellent results on numerous models undergoing simple to complex shapes, in comparison to several state-of-the-art methods.


title: CMOS Electrochemical Instrumentation for Biosensor Microsystems: A Review

abstract: Modern biosensors play a critical role in healthcare and have a quickly growing commercial market. Compared to traditional optical-based sensing, electrochemical biosensors are attractive due to superior performance in response time, cost, complexity and potential for miniaturization. To address the shortcomings of traditional benchtop electrochemical instruments, in recent years, many complementary metal oxide semiconductor (CMOS) instrumentation circuits have been reported for electrochemical biosensors. This paper provides a review and analysis of CMOS electrochemical instrumentation circuits. First, important concepts in electrochemical sensing are presented from an instrumentation point of view. Then, electrochemical instrumentation circuits are organized into functional classes, and reported CMOS circuits are reviewed and analyzed to illuminate design options and performance tradeoffs. Finally, recent trends and challenges toward on-CMOS sensor integration that could enable highly miniaturized electrochemical biosensor microsystems are discussed. The information in the paper can guide next generation electrochemical sensor design.


title: Full-resolution depth map estimation from an aliased plenoptic light field

abstract: In this paper we show how to obtain full-resolution depth maps from a single image obtained from a plenoptic camera. Previous work showed that the estimation of a low-resolution depth map with a plenoptic camera differs substantially from that of a camera array and, in particular, requires appropriate depth-varying antialiasing filtering. In this paper we show a quite striking result: One can instead recover a depth map at the same full-resolution of the input data. We propose a novel algorithm which exploits a photoconsistency constraint specific to light fields captured with plenoptic cameras. Key to our approach is handling missing data in the photoconsistency constraint and the introduction of novel boundary conditions that impose texture consistency in the reconstructed full-resolution images. These ideas are combined with an efficient regularization scheme to give depth maps at a higher resolution than in any previous method. We provide results on both synthetic and real data.


What does drawing reveal about thinking?


title: Tunable 1.25–2.1-GHz 4-Pole Bandpass Filter With Intrinsic Transmission Zero Tuning

abstract: A new concept of tuning transmission zeros (TZs) in 4-pole tunable bandpass filters is demonstrated. In this work, the tuning elements that were conventionally used for bandwidth control are also utilized for adjusting the TZs at the same time. As a result, the selectivity of the tunable filter can be controlled without increasing the filter order and complexity. A 4-pole stripline filter is used to demonstrate this concept. The filter can be continuously tuned from 1.25 to 2.1 GHz and two TZs are employed to improve the stopband suppression to > 60 dB at 200 MHz from the passband. These TZs can also be moved from the upper stopband to the lower stopband without any auxiliary tuning elements to significantly change the stopband suppression. The high rejection filter is useful in wireless systems and cognitive radios with large interferers.


title: Making prolog more expressive

abstract: Abstract This paper introduces extended programs and extended goals for logic programming. A clause in an extended program can have an arbitrary first-order formula as its body. Similarly, an extended goal can have an arbitrary first-order formula as its body. The main results of the paper are the soundness of the negation as failure rule and SLDNF-resolution for extended programs and goals. We show how the increased expressibility of extended programs and goals can be easily implemented in any PROLOG system which has a sound implementation of the negation as failure rule. We also show how these ideas can be used to implement first-order logic as a query language in a deductive database system. An application to integrity constraints in deductive database systems is also given.


title: Understanding compromised accounts on Twitter

abstract: Social media has become a valuable tool for hackers to disseminate misinformation through compromised accounts. A compromised account is an account accessed by a third party without the user's knowledge. Previous studies have found 13% of online adults experienced their social media accounts compromised. Since compromised accounts can have a significant adverse impact on the social media sites, this has led to the growing research on detecting compromised accounts. However, previous works are limited as they either focus on the detection of hacked accounts for spamming and phishing activities or utilize only twitter content information. In this paper, we performed a systematic study on compromised accounts in Twitter by identifying who compromise the accounts; what information they share, and what patterns their tweets present. Our findings suggest that the accounts can be compromised by two different types of hackers and the content they post tend to follow several common themes. We also showed that, in addition to the text content of the tweets, there are other meta-information that can be exploited to help improve the detection of compromised accounts.


title: Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

abstract: In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shift-reduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models in the CTB dataset.


title: Fully Autonomous First Order Mathematical Optimizer

abstract: this research paper describes a technique for performing mathematical optimization, which is free from any hyperparameters. The optimization requires only first order derivatives of the objective function with respect to the learning parameters in the parameter update equation. The step size, more commonly known as the learning rate, which is usually manually set for training is replaced by a heuristic which calculates an optimal step size relative to the instantaneous value of the objective function. This replacing heuristic doesn’t consume any statistical information and is based purely on relative geometric approximations; thereby, making the algorithm fully autonomous. Finally the results obtained on the MNIST dataset by using this technique are presented and discussed in brief while comparing to other optimization algorithms. More importantly, the algorithm proves its stability on typical machine learning problems and achieves performance comparable to Adam optimizer which is the current standard.


title: Seven Process Modeling Guidelines (7PMG)

abstract: When enterprises see their productivity, quality, or regulatory compliance challenged, they often set up process-aware information systems like ERP systems to bring about fundamental improvements. But without a proper understanding of the business processes that need to be supported, they are doomed to fail. Mapping business processes as graphical models is an important step in this kind of initiatives. It is estimated that organizations that had the best results spent more than 40 percent of the total project time on discovery and construction of their initial process model. Business process modeling software has greatly eased the standardization, storage, and sharing of diagrams. But despite existing tool support, there is a notable uncertainty among practitioners about how to create process models that analysts and business professionals can easily analyze and understand. Available modeling frameworks and guidelines provide insight into the major quality categories, but remain too abstract to be directly applicable in practice. Against this background, we propose a set of seven guidelines for process modeling, called 7PMG. Each of its elements is based on findings from sound quantitative research into the relationship between process modeling styles on the one hand and both model understanding and error-proneness on the other. In this way, 7PMG not only contrasts earlier work that has been criticized for its lack of empirical foundation. It also offers guidance that practitioners can apply in their business-process centered initiatives straightaway.


title: Autonomous Visual Navigation and Laser-Based Moving Obstacle Avoidance

abstract: Moving obstacle avoidance is a fundamental re- quirement for any robot operating in real environments, where pedestrians, bicycles and cars are present. In this paper, we propose and validate a framework for avoiding moving obstacles during visual navigation with a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such path, our robot is able to avoid static as well as moving obstacles, which were not present during teaching, and which are sensed by an on- board lidar. The proposed approach takes explicitly into account obstacle velocities, estimated using an appropriate Kalman-based observer. The velocities are then used to predict the obstacle positions within a tentacle-based approach. Finally, our approach is validated in a series of real outdoor experiments, showing that when the obstacle velocities are considered, the robot behaviour is safer, smoother, and faster than when it is not.


title: Contactless Respiration Monitoring Via Off-the-Shelf WiFi Devices

abstract: Non-invasive human sensing based on radio signals has attracted numerous research interests in recent years. Previous work mainly focused on detecting the presence of a person or identifying human gestures and activities. In this paper, we show that with off-the-shelf WiFi devices, fine-grained respiration information of a person under different sleeping positions can be extracted successfully. We do this by introducing a breath monitoring system based on WiFi signals. This system adopts off-the-shelf WiFi devices to continuously collect the fine-grained wireless channel state information (CSI) around a person. From the CSI, the rhythmic patterns associated with respiration and abrupt changes due to the body movement are identified. Compared to existing respiration monitoring systems that usually require special devices attached to human body, this system is completely contactless. In addition, different from many vision-based sleep monitoring systems, it is robust to low-light environments and does not raise privacy concerns. Preliminary testing results show that our system can reliably track a person's respiration reliably in different sleeping postures.


title: Efficient Scene text localization and recognition with local character refinement

abstract: An unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components.


title: Testbed for subjective evaluation of omnidirectional visual content

abstract: Omni-directional visual content is a form of representing graphical and cinematic media content which provides subjects with the ability to freely change their direction of view. Along with virtual reality, omnidirectional imaging is becoming a very important type of the modern media content. This brings new challenges to the omnidirectional visual content processing, especially in the field of compression and quality evaluation. More specifically, the ability to assess quality of omnidirectional images in reliable manner is a crucial step to provide a rich quality of immersive experience. In this paper we introduce a testbed suitable for subjective evaluations of omnidirectional visual contents. We also show the results of a conducted pilot experiment to illustrate the applicability of the proposed testbed.


title: Hierarchical Committee of Deep CNNs with Exponentially-Weighted Decision Fusion for Static Facial Expression Recognition

abstract: We present a pattern recognition framework to improve committee machines of deep convolutional neural networks (deep CNNs) and its application to static facial expression recognition in the wild (SFEW). In order to generate enough diversity of decisions, we trained multiple deep CNNs by varying network architectures, input normalization, and weight initialization as well as by adopting several learning strategies to use large external databases. Moreover, with these deep models, we formed hierarchical committees using the validation-accuracy-based exponentially-weighted average (VA-Expo-WA) rule. Through extensive experiments, the great strengths of our committee machines were demonstrated in both structural and decisional ways. On the SFEW2.0 dataset released for the 3rd Emotion Recognition in the Wild (EmotiW) sub-challenge, a test accuracy of 57.3% was obtained from the best single deep CNN, while the single-level committees yielded 58.3% and 60.5% with the simple average rule and with the VA-Expo-WA rule, respectively. Our final submission based on the 3-level hierarchy using the VA-Expo-WA achieved 61.6%, significantly higher than the SFEW baseline of 39.1%.


title: A Novel Sequential-Stage Optimization Strategy for an Interior Permanent Magnet Synchronous Generator Design

abstract: For an optimal design of an interior permanent magnet synchronous generator (IPMSG) for range the extended electric vehicle (REEV), many design variables and objective functions should be considered. Conventional optimization methods like the Taguchi method and multiobjective optimization algorithm have completeness or calculation time problems when solving the many design variables and objectives problems. To address these problems, a sequential-stage optimization strategy (SSOS) is proposed. In the first stage of the SSOS, the initial design result considering the various objective functions is derived by using the Taguchi method. In addition, the sensitive design variables are sorted through the calculation of synthetic signal to noise. In the second stage, the optimal solution for the sensitive design variables is derived using the surrogate assisted genetic algorithm (SAGA). The SAGA not only obtains an accurate and well-distributed Pareto front set but considerably reduces the number of function calls as well. In the last stage, the uncertainty consideration based on the worst-case scenario is applied to derive the robust optimal solution. By applying the proposed optimization strategy to the optimal design of IPMSG for REEV, an optimal solution is derived with fewer function calls, and the feasibility of the proposed optimization is verified by experimental results of manufactured generator.


title: Crowdsourcing: A Snapshot of Published Research

abstract: Crowdsourcing, originally defined as “taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in an open call,” is a distributed, collaborative, crossorganizational process seeing increased use among practitioners. As such, crowdsourcing presents great opportunities for information systems (IS) and business-related research. This paper presents preliminary findings from a foundational literature review of published crowdsourcing research from 2006 onward. We identify what crowdsourcing research is going on, where it is going on, and its foci. Our findings document increasing research interest in crowdsourcing and identify the primary publication outlets and home countries of authors involved in that research. Finally, we present a keyword analysis for identified articles, and relate those keywords to a preliminary framework describing crowdsourcing. These findings provide a good summary of current crowdsourcing research, and will help guide researchers interested in further crowdsourcing study.


title: Diatom autofocusing in brightfield microscopy: a comparative study

abstract: We present a number of autofocusing methods in light microscopy for use in diatom identification. Among these, the Tenengrad method has been considered one of the best. The basic requirements for a practical autofocusing system are speed, sharpness and robustness to noise. Recently other focus measures based on a modified Laplacian method are said to perform better than Tenengrad. We investigate two sound methods based on a modified Tenengrad and a modified Laplacian. Measurements show that they provide a reliable and suitable focus measure that outperform similar methods. We investigate the window size analysis dependency and perform an univariate analysis on the focus measures. The focusing techniques are implemented in an automatic slide scanning system for diatom detection and identification for its use in the ADIAC project.


title: SDLB: A Scalable and Dynamic Software Load Balancer for Fog and Mobile Edge Computing

abstract: Mobile Edge Computing (MEC) provides computing/storage offloading and resource virtualization to mobile devices at the network edge. A load balancer is a necessary network function to determine the destination MEC host of each packet from a mobile device, for such virtualization. Due to the new characteristics of MEC, such as resource limitation and high dynamics, existing solutions of cloud load balancer cannot be directly applied to MEC. This paper presents a new design of a Scalable and Dynamic Load Balancer, called SDLB, that satisfies the requirements of MEC. The core algorithm of SDLB is minimal perfect hashing, which provides two perfect features as a load balancer. Evaluation results show that SDLB is faster by 4x to 10x and uses much less (


title: Parasitic-Aware Common-Centroid Binary-Weighted Capacitor Layout Generation Integrating Placement, Routing, and Unit Capacitor Sizing

abstract: Capacitor sizing is a crucial step when designing charge-scaling digital-to-analog converters (DACs). Larger capacitor size can achieve better circuit accuracy and performance due to less impact from process gradient, parasitic mismatch, and local variation. However, it also results in larger chip area and higher power consumption. The size of binary-weighted capacitors in charge-scaling DACs is highly sensitive to the routing parasitics. Unmatched routing parasitics among binary-weighted capacitors will lead to large capacitor size for satisfying circuit accuracy and performance. Previous work focuses on the study of generating high-quality common-centroid placement of unit capacitor arrays while ignoring routing parasitics. None of them address the sizing of binary-weighed capacitors. This paper presents the first problem formulation in the literature which simultaneously considers capacitor sizing and parasitic matching during common-centroid capacitor layout generation such that the power consumption is minimized while the circuit accuracy/performance is also satisfied. Experimental results show that the proposed approach can achieve very significant chip area and power reductions compared with the state-of-the-art approaches.


title: Spanish Multicenter Normative Studies (NEURONORMA Project): Norms for the Rey–Osterrieth Complex Figure (Copy and Memory), and Free and Cued Selective Reminding Test

abstract: The Rey-Osterrieth complex figure (ROCF) and the free and cued selective reminding test (FCSRT) are frequently used in clinical practice. The ROCF assesses visual perception, constructional praxis, and visuospatial memory, and the FCSRT assesses verbal learning and memory. As part of the Spanish Normative Studies (NEURONORMA), we provide age- and education-adjusted norms for the ROCF (copy and memory) and for the FCSRT. The sample consists of 332 and 340 participants, respectively, who are cognitively normal, community dwelling, and ranging in age from 50 to 94 years. Tables are provided to convert raw scores to age-adjusted scaled scores. These were further converted into education-adjusted scaled scores by applying regression-based adjustments. Although age and education affected the score of the ROCF and FCSRT, sex was found to be unrelated in this normal sample. The normative data presented here were obtained from the same study sample as all other NEURONORMA norms and the same statistical procedures were applied. These co-normed data will allow clinicians to compare scores from one test with all the tests included in the project.


Recent Advances in Radiology


title: A Practical Attack to De-anonymize Social Network Users

abstract: Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates and have millions of registered users. In this paper, we introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, we show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is sufficient to uniquely identify this person, or, at least, to significantly reduce the set of possible candidates. That is, rather than tracking a user's browser as with cookies, it is possible to track a person. To determine the group membership of a user, we leverage well-known web browser history stealing attacks. Thus, whenever a social network user visits a malicious website, this website can launch our de-anonymization attack and learn the identity of its visitors. The implications of our attack are manifold, since it requires a low effort and has the potential to affect millions of social networking users. We perform both a theoretical analysis and empirical measurements to demonstrate the feasibility of our attack against Xing, a medium-sized social network with more than eight million members that is mainly used for business relationships. Furthermore, we explored other, larger social networks and performed experiments that suggest that users of Facebook and LinkedIn are equally vulnerable.


title: Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position

abstract: Abstract Suggested by the structure of the visual nervous system, a new algorithm is proposed for pattern recognition. This algorithm can be realized with a multilayered network consisting of neuron-like cells. The network, “neocognitron”, is self-organized by unsupervised learning, and acquires the ability to recognize stimulus patterns according to the differences in their shapes: Any patterns which we human beings judge to be alike are also judged to be of the same category by the neocognitron. The neocognitron recognizes stimulus patterns correctly without being affected by shifts in position or even by considerable distortions in shape of the stimulus patterns.


Domain Attention with an Ensemble of Experts


title: A 16 Gb/s 3.7 mW/Gb/s 8-Tap DFE Receiver and Baud-Rate CDR With 31 kppm Tracking Bandwidth

abstract: A 16 Gb/s I/O link receiver fabricated in 22 nm CMOS SOI technology is presented. Attenuation and ISI of transmitted NRZ data across PCB channels are equalized with a CTLE feeding an 8-tap DFE. The first tap uses digital speculation and the following seven taps are realized by means of the switched-capacitor technique. Timing recovery and control are performed with a Mueller-Muller type-A baud-rate CDR. The architecture is half-rate and requires one phase rotator. In total, each slice has six comparators to recover data and timing information. The secondorder digital CDR operates at quarter-rate and features a low-latency implementation of the proportional path. At 16 Gb/s, 1 Vppd PRBS31 data transmitted without FFE equalization is recovered across a PCB channel with 34 dB attenuation at 8 GHz. The measured tracking bandwidth is 31 kppm (16 GHz ± 496 MHz), and an amplitude of 3 UIPP is tolerated at 1 MHz sinusoidal jitter. The sinusoidal jitter amplitude tolerance measured at 10 Gb/s is 0.4 UIPP at 10 MHz and remains above 0.2 UIPP up to 1 GHz with PRBS31 data recovered (BER <; 10 ::: -12 ::: ) across a PCB channel with 27 dB attenuation at 5 GHz. The power efficiency is 3.7 mW/Gb/s, including the full-rate clock receiver.


title: Matricide: A Critique of the Literature

abstract: Matricide, the killing of mothers by their biological children, is a very rare event, comprising less that 2% of all U.S. homicides in which the victim-offender relationship is known. This manuscript examines more than 20 years of U.S. homicides to determine the age and gender characteristics of matricide offenders. These data reveal that most mothers are killed by their adult sons. Daughters younger than 18 years are the most infrequent killers of mothers. This article examines the incidence of parricide, the involvement of sons and daughters in matricidal incidents, and synthesizes the literature in terms of offender gender. Special attention is given to female matricide offenders, given the lack of research currently available with respect to this population. Implications for practice, policy, and research are discussed.


title: Leaf Classification Using Completed Local Binary Pattern of Textures

abstract: This paper, introduces utilizing the magnitude component of Local Binary Pattern (LBP) apart from sign component (which is considered as conventional method). We applied this Completed Local Binary Pattern (CLBP) on plant leaf classification by randomly taken divergent blocks of each texture data set. This approach is also useful for the identification of quality leaves for the automation of grading process in commercial crops like Tobacco etc. By combining Center pixel CLBP (CCLBP), Signed component of CLBP (SCLBP) and magnitude part of CLBP (MCLBP) there is a considerable development can be achieved for rotationally invariant texture classification.


title: Artificial intelligence in production management: A review of the current state of affairs and research trends in academia

abstract: Smart systems and artificial intelligence are playing an ever-increasing role in our daily lives. This trend does not spare industry and production, entailing the potential to gradually replace traditional management tasks. Although the numbers for AI applications in production are continuously growing, the respective publications do not appear to spend much thought on long-term implications. Based on 74 publications in 5 databases, this paper presents the results of a systematic literature review on artificial intelligence in production management over the last twenty years. The review finds that applications that have been of high interest in this regard are process planning and control as well as scheduling. Both tasks are situated in the realms of standard managerial functions and are thus prone to be accomplished by autonomous systems in the future. We suggest that there are currently no management models available to represent the growing dependence on cyber-technical systems, and researchers need to address this shortcoming to pave the way for the production management of tomorrow.


title: IoT based indoor location detection system for smart home environment

abstract: Smart home environment is expected to meet the requirements, essentially for the aging population, to support the concept of “aging in place”, to provide reliable care and to ensure safety and proper diagnosis by keeping track of daily living, medical condition of the resident and providing feedback to the caregiver. In order to meet these requirements, smart home of today should support a number of functionalities. One such functionality of a smart home environment is the location detection. In this work, we propose a voice based location detection system which can be integrated in a smart home environment. Our location detection system uses Amazon Echo as the voice interface and HC-SR04 ultrasonic sensor to detect location of specific patients. The proposed location detection will be suitable for large scale application where we may need to keep track of multiple patients. Moreover, the inclusion of voice enabled feature to this system will reduce the burden of learning curve of new technologies on family and caregivers thereby improving the quality of life.


title: Bayesian Interpolation

abstract: Although Bayesian analysis has been in use since Laplace, the Bayesian method of model-comparison has only recently been developed in depth. In this paper, the Bayesian approach to regularization and model-comparison is demonstrated by studying the inference problem of interpolating noisy data. The concepts and methods described are quite general and can be applied to many other data modeling problems. Regularizing constants are set by examining their posterior probability distribution. Alternative regularizers (priors) and alternative basis sets are objectively compared by evaluating the evidence for them. Occam's razor is automatically embodied by this process. The way in which Bayes infers the values of regularizing constants and noise levels has an elegant interpretation in terms of the effective number of parameters determined by the data set. This framework is due to Gull and Skilling.


title: Gender-Role Orientation as Determinant of Entrepreneurial Self-Efficacy

abstract: Entrepreneurial self-efficacy (ESE) is often included in entrepreneurial intentions models to explain why some individuals are more likely than others to become entrepreneurs. An unsettled question among researchers is whether ESE differs between men and women. While early studies seem to suggest that men have higher ESE than women, more recent studies are inconclusive. Lacking empirical support for gender differences in ESE compels researchers to look for other factors to explain variation in entrepreneurial self-efficacy. The present study confirms two recent studies finding no significant difference in ESE between men and women in a representative sample of MBA students. This finding leads to an investigation of gender-role orientation as a possible determinant of differences in entrepreneurial self-efficacy. Results indicate that the relationship between gender-role orientation and entrepreneurial self-efficacy is complex and multifaceted. Early in the venture creation process, the searching and planning tasks demand creativity and innovation where a strong mix of masculine and feminine traits (androgyny) improves performance. Later in the venture creation process, an individual (male or female) with a strong masculine orientation seems better suited for undertaking entrepreneurial tasks associated with persuading and leading


title: Robust Sentiment Detection on Twitter from Biased and Noisy Data

abstract: In this paper, we propose an approach to automatically detect sentiments on Twitter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words that compose these messages. Moreover, we leverage sources of noisy labels as our training data. These noisy labels were provided by a few sentiment detection websites over twitter data. In our experiments, we show that since our features are able to capture a more abstract representation of tweets, our solution is more effective than previous ones and also more robust regarding biased and noisy data, which is the kind of data provided by these sources.


title: An IoT-based elderly behavioral difference warning system

abstract: Today, we are forward to meeting an older people society in the world. The elderly people have become a high risk of dementia or depression. In recent years, with the rapid development of internet of things (IoT) techniques, it has become a feasible solution to build a system that combines IoT and cloud techniques for detecting and preventing the elderly dementia or depression. This paper proposes an IoT-based elderly behavioral difference warning system for early depression and dementia warning. The proposed system is composed of wearable smart glasses, a BLE-based indoor trilateration position, and a cloud-based service platform. As a result, the proposed system can not only reduce human and medical costs, but also improve the cure rate of depression or delay the deterioration of dementia.


title: Scalable and modularized RTL compilation of Convolutional Neural Networks onto FPGA

abstract: Despite its popularity, deploying Convolutional Neural Networks (CNNs) on a portable system is still challenging due to large data volume, intensive computation and frequent memory access. Although previous FPGA acceleration schemes generated by high-level synthesis tools (i.e., HLS, OpenCL) have allowed for fast design optimization, hardware inefficiency still exists when allocating FPGA resources to maximize parallelism and throughput. A direct hardware-level design (i.e., RTL) can improve the efficiency and achieve greater acceleration. However, this requires an in-depth understanding of both the algorithm structure and the FPGA system architecture. In this work, we present a scalable solution that integrates the flexibility of high-level synthesis and the finer level optimization of an RTL implementation. The cornerstone is a compiler that analyzes the CNN structure and parameters, and automatically generates a set of modular and scalable computing primitives that can accelerate various deep learning algorithms. Integrating these modules together for end-to-end CNN implementations, this work quantitatively analyzes the complier's design strategy to optimize the throughput of a given CNN model with the FPGA resource constraints. The proposed methodology is demonstrated on Altera Stratix-V GXA7 FPGA for AlexNet and NIN CNN models, achieving 114.5 GOPS and 117.3 GOPS, respectively. This represents a 1.9× improvement in throughput when compared to the OpenCL-based design. The results illustrate the promise of the automatic compiler solution for modularized and scalable hardware acceleration of deep learning.


Charm: Facilitating Dynamic Analysis of Device Drivers of Mobile Systems


title: Search-Based Procedural Content Generation: A Taxonomy and Survey

abstract: The focus of this survey is on research in applying evolutionary and other metaheuristic search algorithms to automatically generating content for games, both digital and nondigital (such as board games). The term search-based procedural content generation is proposed as the name for this emerging field, which at present is growing quickly. A taxonomy for procedural content generation is devised, centering on what kind of content is generated, how the content is represented and how the quality/fitness of the content is evaluated; search-based procedural content generation in particular is situated within this taxonomy. This article also contains a survey of all published papers known to the authors in which game content is generated through search or optimisation, and ends with an overview of important open research problems.


title: ActiveClean: Interactive Data Cleaning For Statistical Modeling

abstract: Analysts often clean dirty data iteratively--cleaning some data, executing the analysis, and then cleaning more data based on the results. We explore the iterative cleaning process in the context of statistical model training, which is an increasingly popular form of data analytics. We propose ActiveClean, which allows for progressive and iterative cleaning in statistical modeling problems while preserving convergence guarantees. ActiveClean supports an important class of models called convex loss models (e.g., linear regression and SVMs), and prioritizes cleaning those records likely to affect the results. We evaluate ActiveClean on five real-world datasets UCI Adult, UCI EEG, MNIST, IMDB, and Dollars For Docs with both real and synthetic errors. The results show that our proposed optimizations can improve model accuracy by up-to 2.5x for the same amount of data cleaned. Furthermore for a fixed cleaning budget and on all real dirty datasets, ActiveClean returns more accurate models than uniform sampling and Active Learning.


title: Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data?

abstract: This paper combines three exploratory data analysis methods, principal component methods, hierarchical clustering and partitioning, to enrich the description of the data. Principal component methods are used as preprocessing step for the clustering in order to denoise the data, transform categorical data in continuous ones or balanced groups of variables. The principal component representation is also used to visualize the hierarchical tree and/or the partition in a 3D-map which allows to better understand the data. The proposed methodology is available in the HCPC (Hierarchical Clustering on Principal Components) function of the FactoMineR package.


title: A REVIEW ON ABSTRACTIVE SUMMARIZATION METHODS

abstract: Text summarization is the process of extracting sal ient information from the source text and to presen t that information to the user in the form of summary. It is very difficult for human beings to manually summarize large documents of text . Automatic abstractive summarization provides the r equired solution but it is a challenging task because it requires de eper analysis of text. In this paper, a survey on a bstractive text summarization methods has been presented. Abstractive summarization methods are classified into t wo categories i.e. structured based approach and seman tic based approach. The main idea behind these methods has been discussed. Besides the main idea, the strengths and weaknesses of each method have al so been highlighted. Some open research issues in abst ractive summarization have been identified and will address for future research. Finally, it is conclud ed from the literature studies that most of the a bstractive summarization methods produces highly coherent, cohesive, information rich and less redundant summary.


Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations


title: A single-trim CMOS bandgap reference with a 3σ inaccuracy of ±0.15% from −40°C to 125°C

abstract: A CMOS bandgap reference with an inaccuracy of ±0.15% (3 σ) from -40°C to 125°C is presented. In contrast to prior art, it requires only a single trim to achieve this level of precision. A detailed analysis of the various error sources is provided, and techniques to reduce them are discussed. The prototype bandgap reference draws 55 μA from a 1.8 V supply, and occupies 0.12 mm2 in a 0.16 μm CMOS process. Experimental results from two runs show that, with the use of chopping and higher-order curvature correction to remove non-PTAT errors, the residual error of a bandgap reference is mainly PTAT, and can be removed by a single room temperature trim.


title: ModeleR: An enviromental model repository as knowledge base for experts

abstract: In this paper, we present the development of ModeleR, a repository of models accessible from the web, which enables the user to design, document, manage, and execute environmental models. The technique and features offered can be applied to any scientific context. Based on the development of its ontology, a metadata system has been established to document the modeling process. The set of models managed from ModeleR reflects the knowledge base of the experts of the system, allowing other experts to reuse, replicate, and delve deeper into the existing models in the repository. Different levels of integration have been included, from the conceptual description of the model to the process needed to execute a model from a remote server, acting as an execution engine through the use of workflow managers. In this paper, we present the problems encountered as well as the solutions reached on developing a prototype of ModeleR set up for ecosystem research and an environmental monitoring lab.


title: Selecting the right mobile app paradigms

abstract: The explosion of mobile applications both in number and variety raises the need of shedding light on their architecture, composition and quality. Indeed, it is crucial to understand which mobile application paradigm fits better to what type of application and usage. Such understanding has direct consequences on the user experience, the development cost and sale revenues of mobile apps. In this paper, we identified four main mobile application paradigms and evaluated them from the developer, user and service provider viewpoints. To ensure objectivity and soundness we start by defining high level criteria and then breaking down in finer-grained criteria. After a theoretical evaluation an implementation was carried out as a practical verification. The selected application is object recognition app, which is both exciting and challenging to develop.


title: Cache coherence for GPU architectures

abstract: While scalable coherence has been extensively studied in the context of general purpose chip multiprocessors (CMPs), GPU architectures present a new set of challenges. Introducing conventional directory protocols adds unnecessary coherence traffic overhead to existing GPU applications. Moreover, these protocols increase the verification complexity of the GPU memory system. Recent research, Library Cache Coherence (LCC) [34, 54], explored the use of time-based approaches in CMP coherence protocols. This paper describes a time-based coherence framework for GPUs, called Temporal Coherence (TC), that exploits globally synchronized counters in single-chip systems to develop a streamlined GPU coherence protocol. Synchronized counters enable all coherence transitions, such as invalidation of cache blocks, to happen synchronously, eliminating all coherence traffic and protocol races. We present an implementation of TC, called TC-Weak, which eliminates LCC's trade-off between stalling stores and increasing L1 miss rates to improve performance and reduce interconnect traffic. By providing coherent L1 caches, TC-Weak improves the performance of GPU applications with inter-workgroup communication by 85% over disabling the non-coherent L1 caches in the baseline GPU. We also find that write-through protocols outperform a writeback protocol on a GPU as the latter suffers from increased traffic due to unnecessary refills of write-once data.


title: Sound event detection in real life audio using perceptual linear predictive feature with neural network

abstract: Over the last few years, sound event detection problem has received increasing interest due to its applications in home security, listening through walls and video retrieval etc. [1, 2, 3]. Event detection from sound is a challenging task due to the change in channel behavior and the number of sound sources over time. In this paper, a multi-label classifier is proposed for sound event detection in a polyphonic environment. The proposed classifier uses spectral coefficients of perceptual linear predictive (PLP) feature with convolution neural network. Results show that the proposed classifier using PLP reduces error rate for individual classes as compared to classifiers using other features such as loudness and Mel-Frequency Cepstral coefficients.


title: Eye-Tracking of Men’s Preferences for Waist-to-Hip Ratio and Breast Size of Women

abstract: Studies of human physical traits and mate preferences often use questionnaires asking participants to rate the attractiveness of images. Female waist-to-hip ratio (WHR), breast size, and facial appearance have all been implicated in assessments by men of female attractiveness. However, very little is known about how men make fine-grained visual assessments of such images. We used eye-tracking techniques to measure the numbers of visual fixations, dwell times, and initial fixations made by men who viewed front-posed photographs of the same woman, computer-morphed so as to differ in her WHR (0.7 or 0.9) and breast size (small, medium, or large). Men also rated these images for attractiveness. Results showed that the initial visual fixation (occurring within 200 ms from the start of each 5 s test) involved either the breasts or the waist. Both these body areas received more first fixations than the face or the lower body (pubic area and legs). Men looked more often and for longer at the breasts, irrespective of the WHR of the images. However, men rated images with an hourglass shape and a slim waist (0.7 WHR) as most attractive, irrespective of breast size. These results provide quantitative data on eye movements that occur during male judgments of the attractiveness of female images, and indicate that assessments of the female hourglass figure probably occur very rapidly.


title: Long-term monitoring of live cell proliferation in presence of PVP-Hypericin: a new strategy using ms pulses of LED and the fluorescent dye CFSE.

abstract: During fluorescent live cell imaging it is critical to keep excitation light dose as low as possible, especially in the presence of photosensitizer drugs, which generate free radicals upon photobleaching. During fluorescent imaging, stress by excitation and free radicals induces serious cell damages that may arrest the cell cycle. This limits the usefulness of the technique for drug discovery, when prolonged live cell imaging is necessary. This paper presents a strategy to provide gentle experimental conditions for dynamic monitoring of the proliferation of human lung epithelial carcinoma cells (A549) in the presence of the photosensitizer Polyvinylpyrrolidone-Hypericin. The distinctive strategy of this paper is based on the stringent environmental control and optimizing the excitation light dose by (i) using a low-power pulsed blue light-emitting diode with short pulse duration of 1.29 ms and (ii) adding a nontoxic fluorescent dye called carboxyfluorescein-diacetate-succinimidyl-ester (CFSE) to improve the fluorescence signals. To demonstrate the usefulness of the strategy, fluorescence signals and proliferation of dual-marked cells, during 5-h fluorescence imaging under pulsed excitation, were compared with those kept under continuous excitation and nonmarked reference cells. The results demonstrated 3% cell division and 2% apoptosis due to pulsed excitation compared to no division and 85% apoptosis under the continuous irradiation. Therefore, our strategy allows live cell imaging to be performed over longer time scales than with conventional continuous excitation.


title: A Survey on Smart Grid Potential Applications and Communication Requirements

abstract: Information and communication technologies (ICT) represent a fundamental element in the growth and performance of smart grids. A sophisticated, reliable and fast communication infrastructure is, in fact, necessary for the connection among the huge amount of distributed elements, such as generators, substations, energy storage systems and users, enabling a real time exchange of data and information necessary for the management of the system and for ensuring improvements in terms of efficiency, reliability, flexibility and investment return for all those involved in a smart grid: producers, operators and customers. This paper overviews the issues related to the smart grid architecture from the perspective of potential applications and the communications requirements needed for ensuring performance, flexible operation, reliability and economics.


A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications


“It Don’t Mean a Thing if It Ain’t Got that Swing”– an Alternative Concept for Understanding the Evolution of Dance and Music in Human Beings


title: The Effects of Faking on the Construct Validity of Personality Questionnaires: A Direct Faking Measure Approach

abstract: Some authors clearly showed that faking reduces the construct validity of personality questionnaires, whilst many others found no such effect. A possible explanation for mixed results could be searched for in a variety of methodological strategies in forming comparison groups supposed to differ in the level of faking: candidates vs. non-candidates; groups of individuals with "high" vs. "low" social desirability score; and groups given instructions to respond honestly vs. instructions to "fake good". All three strategies may be criticized for addressing the faking problem indirectly – assuming that comparison groups really differ in the level of response distortion, which might not be true. Therefore, in a within-subject design study we examined how faking affects the construct validity of personality inventories using a direct measure of faking. The results suggest that faking reduces the construct validity of personality questionnaires gradually – the effect was stronger in the subsample of participants who distorted their responses to a greater extent.


CS229 Final Report Reinforcement Learning to Play Mario


title: Social Identity, Self-Categorization, and the Communication of Group Norms

abstract: We articulate the role of norms within the social identity perspective as a basis for theorizing a number of manifestly communicative phenomena. We describe how group norms are cognitively represented as context-dependent prototypes that capture the distinctive properties of groups. The same process that governs the psychological salience of different prototypes, and thus generates group normative behavior, can be used to understand the formation, perception, and diffusion of norms, and also how some group members, for example, leaders, have more normative influence than others. We illustrate this process across a number of phenomena and make suggestions for future interfaces between the social identity perspective and communication research. We believe that the social identity approach represents a truly integrative force for the communication discipline.


title: Extraction and Classification of Road Markings Using Mobile Laser Scanning Point Clouds

abstract: This study aims at building a robust method for semiautomated information extraction of pavement markings detected from mobile laser scanning (MLS) point clouds. The proposed workflow consists of three components: 1) preprocessing, 2) extraction, and 3) classification. In preprocessing, the three-dimensional (3-D) MLS point clouds are converted into radiometrically corrected and enhanced two-dimensional (2-D) intensity imagery of the road surface. Then, the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu's thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters by using a manually defined decision tree. A study was conducted by using the MLS dataset acquired in Xiamen, Fujian, China. The results demonstrated that the proposed workflow and method can achieve 92% in completeness, 95% in correctness, and 94% in F -score.


title: Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

abstract: Diversity among the members of a team of classifiers is deemed to be a key issue in classifier combination. However, measuring diversity is not straightforward because there is no generally accepted formal definition. We have found and studied ten statistics which can measure diversity among binary classifier outputs (correct or incorrect vote for the class label): four averaged pairwise measures (the Q statistic, the correlation, the disagreement and the double fault) and six non-pairwise measures (the entropy of the votes, the difficulty index, the Kohavi-Wolpert variance, the interrater agreement, the generalized diversity, and the coincident failure diversity). Four experiments have been designed to examine the relationship between the accuracy of the team and the measures of diversity, and among the measures themselves. Although there are proven connections between diversity and accuracy in some special cases, our results raise some doubts about the usefulness of diversity measures in building classifier ensembles in real-life pattern recognition problems.


title: A Survey of Wearable Biometric Recognition Systems

abstract: The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.


title: Simple broadband Gysel combiner with a single coupled line

abstract: A coupled-Gysel broadband combiner/divider is proposed and demonstrated. The new concept relies on using a single coupled line segment in the design. Significant improvement in bandwidth are realized while maintaining low-loss, ease of design, and flexibility. The coupled-Gysel is demonstrated with a 2.5 – 8 GHz (105% fractional bandwidth) divider with 0.1 dB divider loss, and a 3.4 – 10.2 GHz (100% fractional bandwidth) with 0.2 dB divider loss.


title: Successful embryo recovery from swine by a minimal invasive technique.

abstract: An endoscopic embryo flushing technique was elaborated to make possible the minimal invasive collection of ova from swine. After experience with pig laparoscopy, the method of endoscopic embryo recovery in small ruminants was adapted to the abdominal anatomy of swine. Twelve oestrus-synchronized gilts were operated 5 days after artificial insemination. In 10 cases both uterine horns were flushed by using a Rüsch-Gold balloon catheter, flushing cannula and atraumatic grasping forceps beside the 10 mm diameter optic. The whole procedure took approximately 35-45 min. The average collection rate was 40.4%. According to the results, endoscopic embryo recovery can be a useful method in swine reproductive research and in top breeding in the future.


title: Intelligent Financial Fraud Detection Practices: An Investigation

abstract: Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.


title: A regression-based analytic model for capacity planning of multi-tier applications

abstract: The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making model adaptivity to the observed workload changes a critical requirement for model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then, we use this approximation in an analytic model of a simple network of queues, each queue representing a tier, and show the approximation's effectiveness for modeling diverse workloads with a changing transaction mix over time. Using two case studies, we investigate factors that impact the efficiency and accuracy of the proposed performance prediction models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.


title: Large-Scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation

abstract: Abstract : The linear programming (LP) is one of the most popular necessary optimization tool used for data analytics as well as in various scientific fields. However, the current state-of-art algorithms suffer from scalability issues when processing Big Data. For example, the commercial optimization software IBM CPLEX cannot handle an LP with more than hundreds of thousands variables or constraints. Existing algorithms are fundamentally hard to scale because they are inevitably too complex to parallelize. To address the issue, we study the possibility of using the Belief Propagation (BP) algorithm as an LP solver. BP has shown remarkable performances on various machine learning tasks and it naturally lends itself to fast parallel implementations. Despite this, very little work has been done in this area. In particular, while it is generally believed that BP implicitly solves an optimization problem, it is not well understood under what conditions the solution to a BP converges to that of a corresponding LP formulation. Our efforts consist of two main parts. First, we perform a theoretic study and establish the conditions in which BP can solve LP [1,2]. Although there has been several works studying the relation between BP and LP for certain instances, our work provides a generic condition unifying all prior works for generic LP. Second, utilizing our theoretical results, we develop a practical BP-based parallel algorithms for solving generic LPs, and it shows 71x speed up while sacrificing only 0.1 accuracy compared to the state-of-art exact algorithm. As a result of the study, the PIs have published two conference papers and two follow-up journal papers are under submission. We refer the readers to our published work for details.


title: A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models

abstract: In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNN-LMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular RNN-LMs.


title: Redefining Context Windows for Word Embedding Models: An Experimental Study

abstract: Distributional semantic models learn vector representations of words through the contexts they occur in. Although the choice of context (which often takes the form of a sliding window) has a direct influence on the resulting embeddings, the exact role of this model component is still not fully understood. This paper presents a systematic analysis of context windows based on a set of four distinct hyper-parameters. We train continuous Skip-Gram models on two English-language corpora for various combinations of these hyper-parameters, and evaluate them on both lexical similarity and analogy tasks. Notable experimental results are the positive impact of cross-sentential contexts and the surprisingly good performance of right-context windows.


title: Decomposing Motion and Content for Natural Video Sequence Prediction

abstract: We propose a deep neural network for the prediction of future frames in natural video sequences. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. Our model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. Our model is end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human activity videos using KTH, Weizmann action, and UCF-101 datasets. We show state-of-the-art performance in comparison to recent approaches. To the best of our knowledge, this is the first end-to-end trainable network architecture with motion and content separation to model the spatiotemporal dynamics for pixel-level future prediction in natural videos.


title: Similarities and differences between human-human and human-automation trust: An integrative review.

abstract: The trust placed in diagnostic aids by the human operator is a critical psychological factor that influences operator reliance on automation. Studies examining the nature of human interaction with automation have revealed that users have a propensity to apply norms of human–human inter-personal interaction to their interaction with ‘intelligent machines’. Nevertheless, there exist subtle differences in the manner in which humans perceive and react to automated aids compared to human team-mates. In the present paper, the concept of trust in human–automation dyads is compared and contrasted with that of human–human dyads. A theoretical framework that synthesizes and describes the process of trust development in humans vs automated aids is proposed and implications for the design of decision aids are provided. Potential implications of this research include the improved design of decision support systems by incorporating features into automated aids that elicit operator responses mirroring responses in human...


title: A personalized courseware recommendation system based on fuzzy item response theory

abstract: With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning field currently. In past years, many researchers made efforts in developing e-learning systems with personalized learning mechanism to assist on-line learning. However, most of them focused on using learner's behaviors, interests, or habits to provide personalized e-learning services. These systems usually neglected to concern if learner's ability and the difficulty of courseware are matched each other. Generally, recommending an inappropriate courseware might result in learner's cognitive overhead or disorientation during a learning process. To promote learning efficiency and effectiveness, we present a personalized courseware recommendation system (PCRS) based on the proposed fuzzy item response theory (FIRT), which can recommend courseware with appropriate difficult level to learner through learner gives a fuzzy response of understanding percentage for the learned courseware. Experiment results show that applying the proposed fuzzy item response theory to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.


title: Goal Theory, Motivation, and School Achievement: An Integrative Review

abstract: The purpose of this review is to document the directions and recent progress in our understanding of the motivational dynamics of school achievement. Based on the accumulating research it is concluded that the quality of student learning as well as the will to continue learning depends closely on an interaction between the kinds of social and academic goals students bring to the classroom, the motivating properties of these goals and prevailing classroom reward structures. Implications for school reform that follow uniquely from a motivational and goal-theory perspective are also explored.


title: PICO, PICOS and SPIDER: a comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews

abstract: BackgroundQualitative systematic reviews are increasing in popularity in evidence based health care. Difficulties have been reported in conducting literature searches of qualitative research using the PICO search tool. An alternative search tool, entitled SPIDER, was recently developed for more effective searching of qualitative research, but remained untested beyond its development team.MethodsIn this article we tested the ‘SPIDER’ search tool in a systematic narrative review of qualitative literature investigating the health care experiences of people with Multiple Sclerosis. Identical search terms were combined into the PICO or SPIDER search tool and compared across Ovid MEDLINE, Ovid EMBASE and EBSCO CINAHL Plus databases. In addition, we added to this method by comparing initial SPIDER and PICO tools to a modified version of PICO with added qualitative search terms (PICOS).ResultsResults showed a greater number of hits from the PICO searches, in comparison to the SPIDER searches, with greater sensitivity. SPIDER searches showed greatest specificity for every database. The modified PICO demonstrated equal or higher sensitivity than SPIDER searches, and equal or lower specificity than SPIDER searches. The modified PICO demonstrated lower sensitivity and greater specificity than PICO searches.ConclusionsThe recommendations for practice are therefore to use the PICO tool for a fully comprehensive search but the PICOS tool where time and resources are limited. Based on these limited findings the SPIDER tool would not be recommended due to the risk of not identifying relevant papers, but has potential due to its greater specificity.


title: Permanent Magnets for MEMS

abstract: This paper reviews the state of the art for the microfabrication of permanent magnets applicable to microelectromechanical systems (MEMS). Permanent magnets are a key building block for the realization of magnetically based MEMS sensors, actuators, and energy converters. In this paper, the basic theories and operational concepts for permanent magnets are first described. Then, different classes of permanent-magnet materials and associated performance tradeoffs are introduced. Challenges relating to the integration of permanent magnets into MEMS applications are then discussed. Last, a summary and review of previously reported fabrication strategies and material properties is provided.


title: Intrinsic Subspace Evaluation of Word Embedding Representations

abstract: We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that directly show whether or not representations contain the subspaces necessary to satisfy these criteria. Current intrinsic evaluations are mostly based on the overall similarity or full-space similarity of words and thus view vector representations as points. We show the limits of these point-based intrinsic evaluations. We apply our evaluation methodology to the comparison of a count vector model and several neural network models and demonstrate important properties of these models.


title: Reliability test and IMC investigation of lead and lead free solder joints on different surface finish processes

abstract: In this study, lead free (Sn-4Ag-0.5Cu (SAC)) and lead (62Sn-36Pb-2Ag (SP)) solder joint on various surface finished of ball grid array (BGA) pad, such as Immersion Tin (ImSn), Organic solderability preservatives (OSPs), Ni-P/Pd/Au (ENEPIG), and Ni-P/Au (ENIG) was investigated by its interface morphology and its physical properties. To evaluate the mechanical and physical properties of BGA pad with lead and lead free solder joint, it was tested by bonding strength measuring, shear force test and interfacial microstructure. The IMC (Inter metallic compound) of SAC and SP solder with these finishes were also studied by using Focused Ion Beam (FIB) microscope to observe the crystal grain, morphology and size. It was found that the IMC crystal lattice is dependent on solder material and the solder joint reliability is dependent on both the solder material as well as the surface finish. Moreover, the shear test results show that the shear strength of solder joints could not be significantly influenced by the thickness and morphology of the interfacial IMC.


title: Web Service Classification Based on Automatic Semantic Annotation and Ensemble Learning

abstract: With the development of Web Service Technology, the quantity of the web services published on the Internet is increasing rapidly. Recognizing each web service intelligently becomes the key of efficiently using Internet. And the first step of recognization is to classify the web services accurately. To classify a huge amount of web services becomes a difficulty job. Therefore, in order to support applications of web services more effectively, an automatic web service classification method is needed. In this paper, the common WSDL files are regarded as the study object. Since web service is described by WSDL, the traditional document classification method cannot be applied directly. In the paper, a new method is proposed which applies automatic web service semantic annotation and uses three classification method: NaA¯ve Bayes, SVM and REP Tree, furthermore ensemble learning is applied. According to the experiment done on 951 WSDL files and 19 categories, the accuracy was 87.39%.


title: YASS: Yet another suffix stripper

abstract: Stemmers attempt to reduce a word to its stem or root form and are used widely in information retrieval tasks to increase the recall rate. Most popular stemmers encode a large number of language-specific rules built over a length of time. Such stemmers with comprehensive rules are available only for a few languages. In the absence of extensive linguistic resources for certain languages, statistical language processing tools have been successfully used to improve the performance of IR systems. In this article, we describe a clustering-based approach to discover equivalence classes of root words and their morphological variants. A set of string distance measures are defined, and the lexicon for a given text collection is clustered using the distance measures to identify these equivalence classes. The proposed approach is compared with Porter's and Lovin's stemmers on the AP and WSJ subcollections of the Tipster dataset using 200 queries. Its performance is comparable to that of Porter's and Lovin's stemmers, both in terms of average precision and the total number of relevant documents retrieved. The proposed stemming algorithm also provides consistent improvements in retrieval performance for French and Bengali, which are currently resource-poor.


title: Citance-based retrieval and summarization using IR and machine learning

abstract: We consider the three interesting problems posed by the CL-SciSumm series of shared tasks. Given a reference document D and a set \(C_D\) of citances for D: (1) find the span of reference text that corresponds to each citance \(c \in C_D\), (2) identify the facet corresponding to each span of reference text from a predefined list of five facets, and (3) construct a summary of at most 250 words for D based on the reference spans. The shared task provided annotated training and test sets for these problems. This paper describes our efforts and the results achieved for each problem, and also a discussion of some interesting parameters of the datasets, which may spur further improvements and innovations.


title: Temporal Annotation in the Clinical Domain

abstract: This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.