title: Mahotas: Open source software for scriptable computer vision

abstract: Mahotas is a computer vision library for Python. It contains traditional image processing functionality such as filtering and morphological operations as well as more modern computer vision functions for feature computation, including interest point detection and local descriptors. The interface is in Python, a dynamic programming language, which is appropriate for fast development, but the algorithms are implemented in C++ and are tuned for speed. The library is designed to fit in with the scientific software ecosystem in this language and can leverage the existing infrastructure developed in that language. Mahotas is released under a liberal open source license (MIT License) and is available from http://github.com/luispedro/mahotas and from the Python Package Index (http://pypi.python.org/pypi/mahotas). Tutorials and full API documentation are available online at http://mahotas.readthedocs.org/.


title: A packaged hybrid Doherty PA for microwave links

abstract: The performance of a 6.5 GHz hybrid GaN Doherty power amplifier is reported. The amplifier is packaged in a plastic overmold QFN 9×9 package and includes discrete GaN transistors and integrated passive GaAs splitting, matching and combiner networks. Measured results, under continuous wave operation, at 6.5 GHz shows 18 dB gain, a P1dB of 42 dBm, and 21% PAE in 9 dB output power back off. These results are the first reported for a Doherty power amplifier that comes close to specifications for a typical point-to-point microwave link application. Furthermore, the presented hybrid approach is a step towards cost effective microwave Doherty power amplifiers.


title: PWP3D: Real-Time Segmentation and Tracking of 3D Objects

abstract: We formulate a probabilistic framework for simultaneous region-based 2D segmentation and 2D to 3D pose tracking, using a known 3D model. Given such a model, we aim to maximise the discrimination between statistical foreground and background appearance models, via direct optimisation of the 3D pose parameters. The foreground region is delineated by the zero-level-set of a signed distance embedding function, and we define an energy over this region and its immediate background surroundings based on pixel-wise posterior membership probabilities (as opposed to likelihoods). We derive the differentials of this energy with respect to the pose parameters of the 3D object, meaning we can conduct a search for the correct pose using standard gradient-based non-linear minimisation techniques. We propose novel enhancements at the pixel level based on temporal consistency and improved online appearance model adaptation. Furthermore, straightforward extensions of our method lead to multi-camera and multi-object tracking as part of the same framework. The parallel nature of much of the processing in our algorithm means it is amenable to GPU acceleration, and we give details of our real-time implementation, which we use to generate experimental results on both real and artificial video sequences, with a number of 3D models. These experiments demonstrate the benefit of using pixel-wise posteriors rather than likelihoods, and showcase the qualities, such as robustness to occlusions and motion blur (and also some failure modes), of our tracker.


title: Medical Image Segmentation using Marker Controlled Watershed Transformation 1

abstract: Segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is used to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (oversegmentation, sensitivity to noise).In this paper medical image segmentation using marker controlled watershed segmentation is presented. Objective of this paper is segmenting the medical image using marker controlled watershed segmentation, and comparing the results of directly applying watershed transformation and marker controlled watershed transformation.


title: Ranking document clusters using markov random fields

abstract: An important challenge in cluster-based document retrieval is ranking document clusters by their relevance to the query. We present a novel cluster ranking approach that utilizes Markov Random Fields (MRFs). MRFs enable the integration of various types of cluster-relevance evidence; e.g., the query-similarity values of the cluster's documents and query-independent measures of the cluster. We use our method to re-rank an initially retrieved document list by ranking clusters that are created from the documents most highly ranked in the list. The resultant retrieval effectiveness is substantially better than that of the initial list for several lists that are produced by effective retrieval methods. Furthermore, our cluster ranking approach significantly outperforms state-of- the-art cluster ranking methods. We also show that our method can be used to improve the performance of (state-of- the-art) results-diversification methods.


title: Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation

abstract: We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the subtask B. Our system comprises of multiple independent models such as neural networks, semantic-affective models and topic modeling that are combined in a probabilistic way. The novelty of the system is the employment of a topic modeling approach in order to adapt the semantic-affective space for each tweet. In addition, significant enhancements were made in the main system dealing with the data preprocessing and feature extraction including the employment of word embeddings. Each model is used to predict a tweet’s sentiment (positive, negative or neutral) and a late fusion scheme is adopted for the final decision.


title: Towards automatic personalised content creation for racing games

abstract: Evolutionary algorithms are commonly used to create high-performing strategies or agents for computer games. In this paper, we instead choose to evolve the racing tracks in a car racing game. An evolvable track representation is devised, and a multiobjective evolutionary algorithm maximises the entertainment value of the track relative to a particular human player. This requires a way to create accurate models of players' driving styles, as well as a tentative definition of when a racing track is fun, both of which are provided. We believe this approach opens up interesting new research questions and is potentially applicable to commercial racing games.


title: INTERSPEECH 2006- ICSLP Eigenvoice Conversion Based on Gaussian Mixture Model

abstract: This paper describes a novel framework of voice conversion (VC). We call it eigenvoice conversion (EVC). We apply EVC to the conversion from a source speaker’s voice to arbitrary target speakers’ voices. Using multiple parallel data sets consisting of utterancepairs of the source and multiple pre-stored target speakers, a canonical eigenvoice GMM (EV-GMM) is trained in advance. That conversion model enables us to flexibly control the speaker individuality of the converted speech by manually setting weight parameters. In addition, the optimum weight set for a specific target speaker is estimated using only speech data of the target speaker without any linguistic restrictions. We evaluate the performance of EVC by a spectral distortion measure. Experimental results demonstrate that EVC works very well even if we use only a few utterances of the target speaker for the weight estimation. Index Terms: speech synthesis, voice conversion, GMM, eigenvoice, unsupervised training.


title: Information-centric networking: seeing the forest for the trees

abstract: There have been many recent papers on data-oriented or content-centric network architectures. Despite the voluminous literature, surprisingly little clarity is emerging as most papers focus on what differentiates them from other proposals. We begin this paper by identifying the existing commonalities and important differences in these designs, and then discuss some remaining research issues. After our review, we emerge skeptical (but open-minded) about the value of this approach to networking.


title: GPU based parallel image processing library for embedded systems

abstract: Embedded image processing systems have many challenges, due to large computational requirements and other physical, power, and environmental constraints. However recent contemporary mobile devices include a graphical processing unit (GPU) in order to offer better use interface in terms of graphics. Some of these embedded GPUs also support OpenCL which allows the use of computation capacity of embedded GPUs for general purpose computing. Within this OpenCL support, challenges of image processing in embedded systems become easier to handle. In this paper, we present a new OpenCL-based image processing library, named TRABZ-10, which is specifically designed to run on an embedded platform. Our results show that the functions of TRABZ-10 show 7X speedup on embedded platform over the functions of OpenCV on average.


title: A computational framework for driver's visual attention using a fully convolutional architecture

abstract: It is a challenging and important task to perceive and interact with other traffic participants in a complex driving environment. The human vision system plays one of the crucial roles to achieve this task. Particularly, visual attention mechanisms allow a human driver to cleverly attend to the salient and relevant regions of the scene to further make necessary decisions for the safe driving. Thus, it is significant to investigate human vision systems with great potential to improve assistive, and even autonomous, vehicular technologies. In this paper, we investigate driver's gaze behavior to understand visual attention. We, first, present a Bayesian framework to model visual attention of a human driver. Further, based on the framework, we develop a fully convolutional neural network to estimate the salient region in a novel driving scene. We systematically evaluate the proposed method using on-road driving data and compare it with other state-of-the-art saliency estimation approaches. Our analyses show promising results.


title: Alzheimer's Disease and Parkinson's Disease

abstract: Although most cases of Alzheimer's and Parkinson's disease are sporadic, some cases are clearly familial. This review, part of the Genomic Medicine series, examines the genetics of these familial forms. Although the inherited forms are rare, the knowledge derived from investigating their pathobiology has improved our understanding of the pathobiology of the more common, sporadic forms of the diseases.


MegaMIMO: Scaling Wireless Capacity with User Demands


title: Key Components of an Information Security Metrics Program Plan

abstract: An information security metrics program can provide organizations with a resource to manage, monitor, control, or improve aspects of an information security program. A set of five key components necessary to include when developing a plan for an information security metrics program is presented. Components are framed in relation to criteria from Chew et al. (2008), and include associated tasks designed to a) increase accountability, b) improve information security effectiveness and c) demonstrate compliance. Key Components of IT Security Metrics Program 4 This page intentionally left blank Key Components of IT Security Metrics Program 5 Table of


title: Strategies for Training Large Vocabulary Neural Language Models

abstract: Training neural network language models over large vocabularies is still computationally very costly compared to count-based models such as Kneser-Ney. At the same time, neural language models are gaining popularity for many applications such as speech recognition and machine translation whose success depends on scalability. We present a systematic comparison of strategies to represent and train large vocabularies, including softmax, hierarchical softmax, target sampling, noise contrastive estimation and self normalization. We further extend self normalization to be a proper estimator of likelihood and introduce an efficient variant of softmax. We evaluate each method on three popular benchmarks, examining performance on rare words, the speed/accuracy trade-off and complementarity to Kneser-Ney.


title: Automated regression testing of BPMN 2.0 processes: a capture and replay framework for continuous delivery

abstract: Regression testing is a form of software quality assurance (QA) that involves comparing the behavior of a newer version of a software artifact to its earlier correct behavior, and signaling the QA engineer when deviations are detected. Given the large potential in automated generation and execution of regression test cases for business process models in the context of running systems, powerful tools are required to make this practically feasible, more specifically to limit the potential impact on production systems, and to reduce the manual effort required from QA engineers. In this paper, we present a regression testing automation framework that implements the capture & replay paradigm in the context of BPMN 2.0, a domain-specific language for modeling and executing business processes. The framework employs parallelization techniques and efficient communication patterns to reduce the performance overhead of capturing. Based on inputs from the QA engineer, it manipulates the BPMN2 model before executing tests for isolating the latter from external dependencies (e.g. human actors or expensive web services) and for avoiding undesired side-effects. Finally, it performs a regression detection algorithm and reports the results to the QA engineer. We have implemented our framework on top of a BPMN2-compliant execution engine, namely jBPM, and performed functional validations and evaluations of its performance and fault-tolerance. The results, indicating 3.9% average capturing performance overhead, demonstrate that the implemented framework can be the foundation of a practical regression testing tool for BPMN 2.0, and a key enabler for continuous delivery of business process-driven applications and services.


title: Digital Comics Image Indexing Based on Deep Learning

abstract: The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.


title: Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series

abstract: Linear Dynamical System (LDS) is an elegant mathematical framework for modeling and learning multivariate time series. However, in general, it is difficult to set the dimension of its hidden state space. A small number of hidden states may not be able to model the complexities of a time series, while a large number of hidden states can lead to overfitting. In this paper, we study methods that impose an $\ell_1$ regularization on the transition matrix of an LDS model to alleviate the problem of choosing the optimal number of hidden states. We incorporate a generalized gradient descent method into the Maximum a Posteriori (MAP) framework and use Expectation Maximization (EM) to iteratively achieve sparsity on the transition matrix of an LDS model. We show that our Sparse Linear Dynamical System (SLDS) improves the predictive performance when compared to ordinary LDS on a multivariate clinical time series dataset.


title: The asymmetric traveling salesman problem: algorithms, instance generators and tests

abstract: The purpose of this paper is to provide a preliminary report on the first broad-based experimental comparison of modern heuristics for the asymmetric traveling salesmen problem (ATSP). There are currently three general classes of such heuristics: classical tour construction heuristics such as Nearest Neighbor and the Greedy algorithm, local search algorithms based on re-arranging segments of the tour, as exemplified by the Kanellakis-Papadimitriou algorithm [KP80], and algorithms based on patching together the cycles in a minimum cycle cover, the best of which are variants on an algorithm proposed by Zhang [Zha93]. We test implementations of the main contenders from each class on a variety of instance types, introducing a variety of new random instance generators modeled on real-world applications of the ATSP. Among the many tentative conclusions we reach is that no single algorithm is dominant over all instance classes, although for each class the best tours are found either by Zhang's algorithm or an iterated variant on Kanellakis-Papadimitriou.


title: Monitoring of Distributed Pipeline Systems by Wireless Sensor Networks

abstract: This paper describes a sensor network platform for pipeline system monitoring. Pipeline systems are widely used for distribution and transportation of petroleum, natural gas, water, and sewage. Leaks and ruptures due to an aging and fast decaying pipeline system infrastructure cost millions of dollars a year; they also make clear the necessity for continuous, automatic monitoring systems that can provide early detection and early warning of defects, such as corrosion and leaks, before they reach the magnitude of a major disaster. In this paper, we discuss how sensor networks can detect, localize, and quantify bursts, leaks and other anomalies in pipeline systems. Lamb waves are guided ultrasonic waves that can propagate for considerable distances in plates. Research has shown that it is possible to detect flaws over a large area with active sensing devices such as Lead Zirconate Titanate (PZT) for simultaneous actuation and sensing. PZT sensors can be mounted on the curve surface of the pipelines for generating and measuring guided waves that propagate along the pipes. A network of PZT actuators/sensors provide a real-time continuous and automatic monitoring of the health of the pipeline systems. Complications that encountered in Lamb wave propagation include the existence of multiple propagation modes and the dispersive nature of the modes, which makes the sensing, communication, and control a difficult task. In this paper, we will address the detection and localization problems for the proposed acoustic sensor networks using signal processing techniques. We will discuss strategies to deal with the Lamb wave dispersive propagation to achieve reasonable performance for real-time monitoring.


title: Memes as building blocks: a case study on evolutionary optimization + transfer learning for routing problems

abstract: A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking this cue, this paper presents a Memetic Computational Paradigm based on Evolutionary Optimization\(+\)Transfer Learning for search, one that models how human solves problems, and embarks on a study towards intelligent evolutionary optimization of problems through the transfers of structured knowledge in the form of memes as building blocks learned from previous problem-solving experiences, to enhance future evolutionary searches. The proposed approach is composed of four culture-inspired operators, namely, Learning, Selection, Variation and Imitation. The role of the learning operator is to mine for latent knowledge buried in past experiences of problem-solving. The learning task is modelled as a mapping between past problem instances solved and the respective optimized solution by maximizing their statistical dependence. The selection operator serves to identify the high quality knowledge that shall replicate and transmit to future search, while the variation operator injects new innovations into the learned knowledge. The imitation operator, on the other hand, models the assimilation of innovated knowledge into the search. Studies on two separate established NP-hard problem domains and a realistic package collection/deliver problem are conducted to assess and validate the benefits of the proposed new memetic computation paradigm.


title: Introduction to the special session on Topological Data Analysis, ICASSP 2016

abstract: Topological Data Analysis (TDA) is a topic which has recently seen many applications. The goal of this special session is to highlight the bridge between signal processing, machine learning and techniques in topological data analysis. In this way, we hope to encourage more engineers to start exploring TDA and its applications. This paper briefly introduces the standard techniques used in this area, delineates the common theme connecting the works presented in this session, and concludes with a brief summary of each of the papers presented.


title: What Makes Online Collectible Card Games Fun to Play?

abstract: Online Collectible Card Games is a relatively new genre of games that allow the players to collect cards, combine them into decks, and play the decks against opponents through the Internet. Players get engaged in all these three levels of the game, and we relate these levels to the theories of what makes a computer game fun to play. The Eye of Judgment (EoJ) is taken as an example of such a game, and we compare the theoretical study to the results of interviews with former EoJ players to validate the models.


title: The management of probabilistic data

abstract: It is often desirable to represent in a database, entities whose properties cannot be deterministically classified. The authors develop a data model that includes probabilities associated with the values of the attributes. The notion of missing probabilities is introduced for partially specified probability distributions. This model offers a richer descriptive language allowing the database to more accurately reflect the uncertain real world. Probabilistic analogs to the basic relational operators are defined and their correctness is studied. A set of operators that have no counterpart in conventional relational systems is presented. >


title: The empathic brain : how , when and why ?

abstract: Recent imaging results suggest that individuals automatically share the emotions of others when exposed to their emotions. We question the assumption of the automaticity and propose a contextual approach, suggesting several modulatory factors that might influence empathic brain responses. Contextual appraisal could occur early in emotional cue evaluation, which then might or might not lead to an empathic brain response, or not until after an empathic brain response is automatically elicited. We propose two major roles for empathy; its epistemological role is to provide information about the future actions of other people, and important environmental properties. Its social role is to serve as the origin of the motivation for cooperative and prosocial behavior, as well as help for effective social communication.


title: Chirp spread spectrum as a modulation technique for long range communication

abstract: Long range low power is a family of technologies promising to connect thousands of sensors to the future internet of things. Within this family of possible technology choices, two different branches have emerged: the standards based on spreaded wideband communication and the standards based on narrowband communication, both promising to reach long range connectivity at very low power. More specifically, this paper focuses on chirp spread spectrum (CSS). This paper presents a detailed model of CSS, showing that the symbols are not perfectly orthogonal. The results show the potential communication ranges are close to narrowband networks and the robustness against interfering signals is 22 dB better for spreading factor 10 than BPSK. Although wideband communication is robust to interference, in long range communication, this robustness is insufficient due to the long range and the larger footprint. The large propagation losses due to the long range and the larger footprint make CSS prone to collisions with other noise sources, possibly larger than the coding gain.


title: Review of non-isolated bi-directional DC-DC converters for plug-in hybrid electric vehicle charge station application at municipal parking decks

abstract: There is a growing interest on plug-in hybrid electric vehicles (PHEV's) due to energy security and green house gas emission issues, as well as the low electricity fuel cost. As battery capacity and all-electric range of PHEV's are improved, and potentially some PHEV's or EV's need fast charging, there is increased demand to build high power off-board charging infrastructures. A charge station architecture for municipal parking decks has been proposed, which has a DC microgrid to interface with multiple DC-DC chargers, distributed renewable power generations and energy storage, and provides functionalities of normal and rapid charging, grid support such as reactive and real power injection (including V2G), current harmonic filtering and load balance. Several non-isolated bidirectional DC-DC converters suited for charge station applications have been reviewed and compared, as the major focus of this paper. Half bridge converter is a good candidate but it is difficult to maintain high efficiency in wide battery pack voltage range. A variable frequency pulse width modulation (VFPWM) scheme is proposed to mitigate this issue. Finally three-level bi-directional DC-DC converter is suggested to be employed in this application. A 10kW prototype verifies that 95.1–97.9% full load efficiency can be achieved in charging mode with 180–360V battery pack voltage. In addition, the inductor size is only one third of the half bridge counterpart, which is a great advantage for high power converters.


title: Comment on "Wandering Minds: The Default Network and Stimulus-Independent Thought"

abstract: Mason et al. (Reports, 19 January 2007, p. 393) attributed activity in certain regions of the “resting” brain to the occurrence of mind-wandering. However, previous research has demonstrated the difficulty of distinguishing this type of stimulus-independent thought from stimulus-oriented thought (e.g., watchfulness). Consideration of both possibilities is required to resolve this ambiguity.


title: Cognition-based and affect-based trust as mediators of leader behavior influences on team performance.

abstract: We develop a model in which cognitive and affective trust in the leader mediate the relationship between leader behavior and team psychological states that, in turn, drive team performance. The model is tested on a sample of 191 financial services teams in Hong Kong and the U.S. Servant leadership influenced team performance through affect-based trust and team psychological safety. Transformational leadership influenced team performance indirectly through cognition-based trust. Cognition-based trust directly influenced team potency and indirectly (through affect-based trust) influenced team psychological safety. The effects of leader behavior on team performance were fully mediated through the trust in leader variables and the team psychological states. Servant leadership explained an additional 10% of the variance in team performance beyond the effect of transformational leadership. We discuss implications of these results for research on the relationship between leader behavior and team performance, and for efforts to enhance leader development by combining knowledge from different leadership theories.


title: Medusa: a programming framework for crowd-sensing applications

abstract: The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.


title: Superjunction Power Devices, History, Development, and Future Prospects

abstract: Superjunction has arguably been the most creative and important concept in the power device field since the introduction of the insulated gate bipolar transistor (IGBT) in the 1980s. It is the only concept known today that has challenged and ultimately proved wrong the well-known theoretical study on the limit of silicon in high-voltage devices. This paper deals with the history, device and process development, and the future prospects of Superjunction technologies. It covers fundamental physics, technological challenges as well as aspects of design and modeling of unipolar devices, such as CoolMOS. The superjunction concept is compared to other methods of enhancing the conductivity of power devices (from bipolar to employment of wide-bandgap materials) to derive its set of benefits and limitations. This paper closes with the application of the superjunction concept to other structures or materials, such as terminations, superjunction IGBTs, or silicon carbide Field Effect Transistors (FETs).


title: An innovative ontology-driven system supporting personnel selection: the OntoHR case

abstract: This paper describes the initial development of an HRM system that aims to decrease the gap between higher vocational education and the labour market for a specific job in the ICT sector. This paper focuses specifically on the delineation of a process model and the selection of a suitable job role (information system analyst) that is valid across organisational and cultural boundaries. The process model implies various applied uses of this ontology-based system, including mapping qualifications in vocational education to current and valid job roles, testing and evaluating the student applicant on the basis of labour market driven competencies, and providing ad-hoc support to educational institutions by elucidating the weaknesses of particular VET curricula.


title: Automatic node selection for Deep Neural Networks using Group Lasso regularization

abstract: We examine the effect of the Group Lasso (gLasso) regularizer in selecting the salient nodes of Deep Neural Network (DNN) hidden layers by applying a DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of gLasso regularization, one for outgoing weight vectors and another for incoming weight vectors, as well as two sizes of DNNs: 2048 hidden layer nodes and 4096 nodes. Furthermore, we compare gLasso and L2 regularizers. Our experiment results demonstrate that our DNN training, in which the gLasso regularizer was embedded, successfully selected the hidden layer nodes that are necessary and sufficient for achieving high classification power.


title: Using Sentence Compression to Develop Visual Analytics for Student Responses to Short Answer Questions

abstract: In this paper, we report on early research to visualize and summarize student responses to short answer questions. Recently published, graph-based multi-sentence compression algorithms have been successfully applied to summarize opinions – a domain area with many similarities to short answer responses. Initial investigations reveal that visual analytics for short answer questions can be derived from the output of graph-based multisentence compression algorithms. A proposed open source short answer analytics tool is also briefly discussed, along with an evaluation plan. The proposed analytics tool will allow lecturers and tutors to have a high level overview of how students have responded to questions, identify knowledge gaps and provide feedback to students.


title: Identifying quantum phase transitions with adversarial neural networks

abstract: The identification of phases of matter is a challenging task, especially in quantum mechanics, where the complexity of the ground state appears to grow exponentially with the size of the system. Traditionally, physicists have to identify the relevant order parameters for the classification of the different phases. We here follow a radically different approach: we address this problem with a state-of-the-art deep learning technique, adversarial domain adaptation. We derive the phase diagram of the whole parameter space starting from a fixed and known subspace using unsupervised learning. This method has the advantage that the input of the algorithm can be directly the ground state without any ad hoc feature engineering. Furthermore, the dimension of the parameter space is unrestricted. More specifically, the input data set contains both labeled and unlabeled data instances. The first kind is a system that admits an accurate analytical or numerical solution, and one can recover its phase diagram. The second type is the physical system with an unknown phase diagram. Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture. Once these layers are trained, we can attach an unsupervised learner to the network to find phase transitions. We show the success of this technique by applying it on several paradigmatic models: the Ising model with different temperatures, the Bose-Hubbard model, and the Su-Schrieffer-Heeger model with disorder. The method finds unknown transitions successfully and predicts transition points in close agreement with standard methods. This study opens the door to the classification of physical systems where the phase boundaries are complex such as the many-body localization problem or the Bose glass phase.


title: Fundamentals of Business Intelligence

abstract: This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.


title: A taxonomy of tasks for guiding the evaluation of multidimensional visualizations

abstract: The design of multidimensional visualization techniques is based on the assumption that a graphical representation of a large dataset can give more insight to a user, by providing him/her a more intuitive support in the process of exploiting data. When developing a visualization technique, the analytic and exploratory tasks that a user might need or want to perform on the data should guide the choice of the visual and interaction metaphors implemented by the technique. Usability testing of visualization techniques also needs the definition of users' tasks. The identification and understanding of the nature of the users' tasks in the process of acquiring knowledge from visual representations of data is a recent branch in information visualization research. Some works have proposed taxonomies to organize tasks that a visualization technique should support. This paper proposes a taxonomy of visualization tasks, based on existing taxonomies as well as on the observation of users performing exploratory tasks in a multidimensional data set using two different visualization techniques, Parallel Coordinates and RadViz. Different scenarios involving low-level tasks were estimated for the completion of some high-level tasks, and they were compared to the scenarios observed during the users' experiments.


title: Hardware-software partitioning and pipelined scheduling of transformative applications

abstract: Transformative applications are computation intensive applications characterized by iterative dataflow behavior. Typical examples are image processing applications like JPEG, MPEG, etc. The performance of embedded hardware-software systems that implement transformative applications can be maximized by obtaining a pipelined design. We present a tool for hardware-software partitioning and pipelined scheduling of transformative applications. The tool uses iterative partitioning and pipelined scheduling to obtain optimal partitions that satisfy the timing and area constraints. The partitioner uses a branch and bound approach with a unique objective function that minimizes the initiation interval of the final design. We present techniques for generation of good initial solution and search-space limitation for the branch and bound algorithm. A candidate partition is evaluated by generating its pipelined schedule. The scheduler uses a novel retiming heuristic that optimizes the initiation interval, number of pipeline stages, and memory requirements of the particular design alternative. We evaluate the performance of the retiming heuristic by comparing it with an existing technique. The effectiveness of the entire tool is demonstrated by a case study of the JPEG image compression algorithm. We also evaluate the run time and design quality of the tool by experimentation with synthetic graphs.


title: Windows Azure Storage: a highly available cloud storage service with strong consistency

abstract: Windows Azure Storage (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.


title: EFFECT OF NITRATE LEVELS AS A FERTILIZER OR AS A FUNGAL NUTRITION ON THE AGGRESSIVENESS OF RHIZOCTONIA SOLANI ON FABA BEAN

abstract: The effect of sodium nitrate level as a fertilizer on aggressiveness of the fungus Rhizoctonia solani on faba bean plants was studied either in pots experiment or in vitro. Plants grown in infested soil amended with different levels of sodium nitrate showed that under zero amended nitrate or under high level of nitrate disease severity was very severe which reflected on decreasing the number of emerging plants , weight of foliage system, root weight , plant height and number of plant leaves . The levels of nitrate in between showed decreasing of disease severity. In vitro studies was carried out by putting peeled germinated seeds on R. solani fungal growth growing on Czapek’s Dox agar medium supplemented with different levels of sodium nitrate. The fungus becomes more virulent when grown on media with increasingly higher concentration of nitrate. This was evident when disease was determined either visually or by determination of Polyphenol oxidase (PPO) activity of infected germinated seeds.. Transverse cross sections of faba bean radicals subjected to the fungus showed that increasing nitrate level raised the number and intensity of the developed infection cushions. The obtained results clearly monitor that higher level of nitrate in growth medium clearly enhanced the aggressiveness of the fungus which resulted in severe damage of the outer cortical layers of faba bean radical just after 16 h. from subjecting the host to the pathogen.


title: Multivariate Time Series Classification with WEASEL+MUSE

abstract: Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. Dealing with this high-dimensional data is challenging for every classifier for at least two aspects: First, an MTS is not only characterized by individual feature values, but also by the interplay of features in different dimensions. Second, this typically adds large amounts of irrelevant data and noise. We present our novel MTS classifier WEASEL+MUSE which addresses both challenges. WEASEL+MUSE builds a multivariate feature vector, first using a sliding-window approach applied to each dimension of the MTS, then extracts discrete features per window and dimension. The feature vector is subsequently fed through feature selection, removing non-discriminative features, and analysed by a machine learning classifier. The novelty of WEASEL+MUSE lies in its specific way of extracting and filtering multivariate features from MTS by encoding context information into each feature. Still the resulting feature set is small, yet very discriminative and useful for MTS classification. Based on a popular benchmark of 20 MTS datasets, we found that WEASEL+MUSE is among the most accurate classifiers, when compared to the state of the art. The outstanding robustness of WEASEL+MUSE is further confirmed based on motion gesture recognition data, where it out-of-the-box achieved similar accuracies as domain-specific methods.


A Declarative Domain Model Can Serve as Design Document


title: A nonlinear adaptive observer approach for state of charge estimation of lithium-ion batteries

abstract: A state of charge estimation method for lithium-ion batteries is presented. First, the problem is formulated, and existing literature is reviewed. Then an equivalent circuit is used to model the battery, and an indirect nonlinear adaptive observer approach is developed to estimate the state of charge. Parameter identification and state estimation scheme are discussed. Simulation result based on real-world battery test data is shown to support the validity of the proposed method. Issues related to production code implementation are also addressed.


title: Comparison of Ensemble Approaches: Mixture of Experts and AdaBoost for a Regression Problem

abstract: Two machine learning approaches: mixture of experts and AdaBoost.R2 were adjusted to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The computationally intensive experiments were conducted aimed to compare empirically the prediction accuracy of ensemble models generated by the methods. The analysis of the results was performed using statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple n×n comparisons. No statistically significant differences were observed among the best ensembles: two generated by mixture of experts and two by AdaBoost.R2 employing multilayer perceptrons and general linear models as base learning algorithms.


title: Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions

abstract: One of the main challenges in Zero-Shot Learning of visual categories is gathering semantic attributes to accompany images. Recent work has shown that learning from textual descriptions, such as Wikipedia articles, avoids the problem of having to explicitly define these attributes. We present a new model that can classify unseen categories from their textual description. Specifically, we use text features to predict the output weights of both the convolutional and the fully connected layers in a deep convolutional neural network (CNN). We take advantage of the architecture of CNNs and learn features at different layers, rather than just learning an embedding space for both modalities, as is common with existing approaches. The proposed model also allows us to automatically generate a list of pseudo- attributes for each visual category consisting of words from Wikipedia articles. We train our models end-to-end us- ing the Caltech-UCSD bird and flower datasets and evaluate both ROC and Precision-Recall curves. Our empirical results show that the proposed model significantly outperforms previous methods.


title: MATHEMATICS ANXIETY AND ACHIEVEMENT AMONG SECONDARY SCHOOL STUDENTS

abstract: Research has shown that mathematics achievement in students is influenced by psychological factors such as mathematics anxiety. Weaknesses among students in learning mathematics in particular will affect the efforts of various sectors in making Malaysia a fully developed nation by 2020. The purpose of this study was to determine mathematics anxiety and mathematics achievement among secondary school students in Selangor, Malaysia. The research examined the differences in mathematics anxiety according to gender as well as the differences in mathematics achievement of students based on the level of mathematics anxiety. The study involved195 Form Four students (86 male and 109 female). The instrument used to measure differences was adapted from the Fennema-Sherman Mathematics Attitudes Scale. The data was analyzed using Statistical Package for the Social Sciences (SPSS) to determine the mean, frequency, t-test and one-way ANOVA. The findings of the study indicated that there is mathematics anxiety among secondary school students. The t-test showed that the mean difference between mathematics anxiety and gender is not significant. The ANOVA test showed that there were significant differences in achievement based on the level of mathematics anxiety. Thus, math anxiety is one factor that affects student achievement. Therefore, teachers should strive to understand mathematics anxiety and implement teaching and learning strategies so that students can overcome their anxiety.


title: A Perceptual Measure for Deep Single Image Camera Calibration

abstract: Most current single image camera calibration methods rely on specific image features or user input, and cannot be applied to natural images captured in uncontrolled settings. We propose inferring directly camera calibration parameters from a single image using a deep convolutional neural network. This network is trained using automatically generated samples from a large-scale panorama dataset, and considerably outperforms other methods, including recent deep learning-based approaches, in terms of standard L2 error. However, we argue that in many cases it is more important to consider how humans perceive errors in camera estimation. To this end, we conduct a large-scale human perception study where we ask users to judge the realism of 3D objects composited with and without ground truth camera calibration. Based on this study, we develop a new perceptual measure for camera calibration, and demonstrate that our deep calibration network outperforms other methods on this measure. Finally, we demonstrate the use of our calibration network for a number of applications including virtual object insertion, image retrieval and compositing.


title: DEVA: sensing emotions in the valence arousal space in software engineering text

abstract: Existing tools for automated sentiment analysis in software engineering text suffer from either or both of two limitations. First, they are developed for non-technical domain and perform poorly when operated on software engineering text. Second, those tools attempt to detect valence only, and cannot capture arousal or individual emotional states such as excitement, stress, depression, and relaxation. In this paper, we present the first sentiment analysis tool, DEVA, which is especially designed for software engineering text and also capable of capturing the aforementioned emotional states through the detection of both arousal and valence. We also create a ground-truth dataset containing 1,795 JIRA issue comments. From a quantitative evaluation using this dataset, DEVA is found to have more than 82% precision and more than 78% recall.


title: Popular song summarization using chorus section detection from audio signal

abstract: Music signal is a one-dimensional temporal sequence. It thus incurs difficulty for the listeners to quickly capturing the mostly attracting parts in popular songs, unless the listeners play the song until the ending. In order to improve the listening experience, music summarization, a tool to summarize the song using the most attractive sections, is needed. In the paper, a system and method is presented to summarize the popular songs by detecting the chorus sections from the input audio signal. The proposed summarization system uses the unique audio feature representation method, i.e. the octave-dependent probabilistic latent semantic analysis, and the chorus detection algorithm that combines the repeated segment extraction and chorus identification. The performance of music summarization is evaluated on the song database with the ground truth of chorus sections, i.e. the start and ending time-stamp of each chorus section. As we know, it is the first systematically evaluation of music summary performance. In terms of multiple metrics such as the boundary accuracy, precision, recall and F1, we show that the proposed system is much superior to the widely accepted methods.


title: Designing a cluster-based covert channel to evade disk investigation and forensics

abstract: Data confidentiality on a computer can be achieved using encryption. However, encryption is ineffective under a forensic investigation mainly because the presence of encrypted data on a disk can be easily detected and disk owners can subsequently be forced (by law or other means) to release decryption keys. To evade forensic investigation, intelligent information hiding techniques that support plausible deniability have been proposed as an alternative to encryption; plausible deniability allows an evader to hide data in a manner such that he/she can deny the very existence of the data. In this paper, we present a new, plausible deniability approach to store sensitive information on a cluster-based filesystem. Under the proposed approach, a covert channel is used to encode the sensitive information by modifying the fragmentation patterns in the cluster distribution of an existing file. As opposed to existing schemes, the proposed covert channel does not require storage of any additional information on the filesystem. Moreover, the channel provides two-fold plausible deniability so that an investigator without the key cannot prove the presence of hidden information. We derive the theoretical capacity of the covert channel and show that a capacity of up to 24 bits/cluster can be achieved on a half-empty disk. The proposed data hiding and recovery algorithms are implemented on FAT32 based disk drives and we show that the disk (read/write) access time of the algorithms is quite low as compared to the contemporary approaches. We also present statistics about the incidence of file fragmentation on actual file systems from 52 disk drives belonging to a diverse set of users. Based on these statistics, we present guidelines for selecting good cover files. Finally, we show that even if an investigator gets suspicious, he/she will incur an unreasonably high O(m^2) complexity to reveal an m bit hidden message.


title: Techniques of EMG signal analysis: detection, processing, classification and applications

abstract: Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.


title: Efficient Big Data Processing in Hadoop MapReduce

abstract: This tutorial is motivated by the clear need of many organizations, companies, and researchers to deal with big data volumes efficiently. Examples include web analytics applications, scientific applications, and social networks. A popular data processing engine for big data is Hadoop MapReduce. Early versions of Hadoop MapReduce suffered from severe performance problems. Today, this is becoming history. There are many techniques that can be used with Hadoop MapReduce jobs to boost performance by orders of magnitude. In this tutorial we teach such techniques. First, we will briefly familiarize the audience with Hadoop MapReduce and motivate its use for big data processing. Then, we will focus on different data management techniques, going from job optimization to physical data organization like data layouts and indexes. Throughout this tutorial, we will highlight the similarities and differences between Hadoop MapReduce and Parallel DBMS. Furthermore, we will point out unresolved research problems and open issues.


title: Covfefe: A Computer Vision Approach For Estimating Force Exertion

abstract: Cumulative exposure to repetitive and forceful activities may lead to musculoskeletal injuries which not only reduce workers' efficiency and productivity, but also affect their quality of life. Thus, widely accessible techniques for reliable detection of unsafe muscle force exertion levels for human activity is necessary for their well-being. However, measurement of force exertion levels is challenging and the existing techniques pose a great challenge as they are either intrusive, interfere with human-machine interface, and/or subjective in the nature, thus are not scalable for all workers. In this work, we use face videos and the photoplethysmography (PPG) signals to classify force exertion levels of 0\%, 50\%, and 100\% (representing rest, moderate effort, and high effort), thus providing a non-intrusive and scalable approach. Efficient feature extraction approaches have been investigated, including standard deviation of the movement of different landmarks of the face, distances between peaks and troughs in the PPG signals. We note that the PPG signals can be obtained from the face videos, thus giving an efficient classification algorithm for the force exertion levels using face videos. Based on the data collected from 20 subjects, features extracted from the face videos give 90\% accuracy in classification among the 100\% and the combination of 0\% and 50\% datasets. Further combining the PPG signals provide 81.7\% accuracy. The approach is also shown to be robust to the correctly identify force level when the person is talking, even though such datasets are not included in the training.


title: Adversarial Text Generation Without Reinforcement Learning

abstract: Generative Adversarial Networks (GANs) have experienced a recent surge in popularity, performing competitively in a variety of tasks, especially in computer vision. However, GAN training has shown limited success in natural language processing. This is largely because sequences of text are discrete, and thus gradients cannot propagate from the discriminator to the generator. Recent solutions use reinforcement learning to propagate approximate gradients to the generator, but this is inefficient to train. We propose to utilize an autoencoder to learn a low-dimensional representation of sentences. A GAN is then trained to generate its own vectors in this space, which decode to realistic utterances. We report both random and interpolated samples from the generator. Visualization of sentence vectors indicate our model correctly learns the latent space of the autoencoder. Both human ratings and BLEU scores show that our model generates realistic text against competitive baselines.


title: Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification

abstract: Acoustic emission (AE)-signal-based techniques have recently been attracting researchers' attention to rotational machine health monitoring and diagnostics due to the advantages of the AE signals over the extensively used vibration signals. Unlike vibration-based methods, the AE-based techniques are in their infant stage of development. From the perspective of machine health monitoring and fault detection, developing an AE-based methodology is important. In this paper, a methodology for rotational machine health monitoring and fault detection using empirical mode decomposition (EMD)-based AE feature quantification is presented. The methodology incorporates a threshold-based denoising technique into EMD to increase the signal-to-noise ratio of the AE bursts. Multiple features are extracted from the denoised signals and then fused into a single compressed AE feature. The compressed AE features are then used for fault detection based on a statistical method. A gear fault detection case study is conducted on a notional split-torque gearbox using AE signals to demonstrate the effectiveness of the methodology. A fault detection performance comparison using the compressed AE features with the existing EMD-based AE features reported in the literature is also conducted.


title: Dynamic Bernoulli Embeddings for Language Evolution

abstract: Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of words change over time. We use dynamic embeddings to analyze three large collections of historical texts: the U.S. Senate speeches from 1858 to 2009, the history of computer science ACM abstracts from 1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We find dynamic embeddings provide better fits than classical embeddings and capture interesting patterns about how language changes.


title: Multivariate Density-Based 3D Shape Descriptors

abstract: We address the 3D object retrieval problem using multivariate density-based shape descriptors. Considering the fusion of first and second order local surface information, we construct multivariate features up to five dimensions and process them by the kernel density estimation methodology to obtain descriptor vectors. We can compute these descriptors very efficiently using the fast Gauss transform algorithm. We also make use of descriptor level information fusion by concatenating descriptor vectors to increase their discrimination power further. To render the resulting descriptors storage-wise efficient, we develop two analytical tools, marginalization and probability density suppression, for descriptor dimensionality reduction. The experiments on two different databases, Princeton Shape Benchmark and Sculpteur, show that, boosted with both feature level and descriptor level information fusion, and powered with fast computational schemes, the density-based shape description framework enables effective and efficient 3D object retrieval.


title: Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids

abstract: An energy management system (EMS) determines the dispatching of generation units based on an optimizer that requires the forecasting of both renewable resources and loads. The forecasting system discussed in this paper includes a representation of the uncertainties associated with renewable resources and loads. The proposed modeling generates fuzzy prediction interval models that incorporate an uncertainty representation of future predictions. The model is demonstrated using solar and wind generation and local load data from a real microgrid in Huatacondo, Chile, for one-day ahead forecasts to obtain the expected values together with fuzzy prediction intervals to represent future measurement bounds with a certain coverage probability. The proposed prediction interval models would help to enable the development of robust microgrid EMS.


title: BPMN Security Extensions for Healthcare Process

abstract: The modelling of healthcare process is inherently complicated due to its multi-disciplinary character. Business Process Model and Notation (BPMN) has been considered and applied to model and demonstrate the flexibility and variability of the activities that involved in healthcare process. However, with the growing usage of digital information and IoT technology in the healthcare system, the issue of information security and privacy becomes the main concern in term of both store and management of electronic health record (EHR). Therefore, it is very important to capture the security requirements at conceptual level in order to identify the security needs in the first place. BPMN is lacking of the ability to model and present security concepts such as confidentiality, integrity, and availability in a suitable way. This will increase the vulnerability of the system and make the future development of security for the system more difficult. In this paper we provide a solution to model the security concepts in BPMN by extending it with new designed security elements, which can be integrated with the BPMN diagram smoothly.


title: A Mechanical Proof of the Unsolvability of the Halting Problem

abstract: Abstract : The authors describe a proof by a computer program of the unsolvability of the halting problem. The halting problem is posed in a constructive, formal language. The computational paradigm formalized is Pure LISP, not Turing machines. They believe this is the first instance of a machine proving that a given problem is not solvable by machine.


title: A 0.5-V 85-nW rail-to-rail operational amplifier with a cross-coupled output stage

abstract: A 0.5-V rail-to-rail operational amplifier (op-amp) with a large common-mode input range is proposed. It has a two-stage structure which consists of a complementary input stage and a novel cross-coupled output stage. The cross-coupled output stage increases the transconductances of MOSFETs of the output stage without increasing the chip area. And hence, it increases the drivability for a capacitive load. Using HSPICE simulations, it is verified that the proposed circuit has a DC gain of more than 80 dB for common-mode input voltages from 40 mV to 400 mV with a supply voltage of 0.5 V. Unity-gain frequency is 8.2 kHz with a capacitive load of 40 pF and the power consumption is 85nW including all bias circuits.


title: PTSD Psychotherapy Outcome Predicted by Brain Activation During Emotional Reactivity and Regulation

abstract: Objective:Exposure therapy is an effective treatment for posttraumatic stress disorder (PTSD), but many patients do not respond. Brain functions governing treatment outcome are not well characterized. The authors examined brain systems relevant to emotional reactivity and regulation, constructs that are thought to be central to PTSD and exposure therapy effects, to identify the functional traits of individuals most likely to benefit from treatment.Method:Individuals with PTSD underwent functional MRI (fMRI) while completing three tasks assessing emotional reactivity and regulation. Participants were then randomly assigned to immediate prolonged exposure treatment (N=36) or a waiting list condition (N=30). A random subset of the prolonged exposure group (N=17) underwent single-pulse transcranial magnetic stimulation (TMS) concurrent with fMRI to examine whether predictive activation patterns reflect causal influence within circuits. Linear mixed-effects modeling in line with the intent-to-treat principle w...


title: Open data, open review and open dialogue in making social sciences plausible

abstract: Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis's (2012) statement that, "the scientific process demands the highest standards of quality, ethics and rigour," they are worth implementing. More importantly, they are feasible to work on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source.


title: How Alluring Are Dark Personalities? The Dark Triad and Attractiveness in Speed Dating

abstract: Dark Triad traits (narcissism, psychopathy, and Machiavellianism) are linked to the pursuit of short-term mating strategies, but they may have differential effects on actual mating success in naturalistic scenarios: Narcissism may be a facilitator for men's short-term mating success, while Machiavellianism and psychopathy may be detrimental. To date, little is known about the attractiveness of Dark Triad traits in women. In a speed-dating study, we assessed participants' Dark Triad traits, Big Five personality traits, and physical attractiveness in N = 90 heterosexual individuals (46 women and 44 men). Each participant rated each partner's mate appeal for short- and long-term relationships. Across both sexes, narcissism was positively associated with mate appeal for short- and long-term relationships. Further analyses indicated that these associations were due to the shared variance among narcissism and extraversion in men and narcissism and physical attractiveness in women, respectively. In women, psychopathy was also positively associated with mate appeal for short-term relationships. Regarding mating preferences, narcissism was found to involve greater choosiness in the rating of others' mate appeal (but not actual choices) in men, while psychopathy was associated with greater openness towards short-term relationships in women. Copyright © 2016 European Association of Personality Psychology


title: Integrating importance, non-redundancy and coherence in graph-based extractive summarization

abstract: We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure nonredundancy and local coherence of the summary by means of an optimization step. Our graph based method is applied to scientific articles from the journal PLOS Medicine. We use human judgements to evaluate the coherence of our summaries. We compare ROUGE scores and human judgements for coherence of different systems on scientific articles. Our method performs considerably better than other systems on this data. Also, our graph-based summarization technique achieves state-of-the-art results on DUC 2002 data. Incorporating our local coherence measure always achieves the best results.


title: The Role of Habit in Post-Adoption Switching of Personal Information Technologies: An Empirical Investigation

abstract: Unlike technology users in business organizations, users of personal information technologies are usually not bound to specific products and have the freedom to switch from one product to a substitute. As a unique and widespread product level post-adoption behavior, IT user switching has not garnered sufficient attention in the current literature. Prior research has suggested that a consumer‘s decision to switch follows careful reasoning on three distinct groups of factors: push, pull, and mooring. Given the highly routinized nature of post-adoption IT use, we draw from research on habit in social psychology and post-adoption user behavior literatures, and argue that users‘ habit plays a critical role in post-adoption IT switching. Specifically, we posit that the habit of using the incumbent product both contributes to the mooring effects during the formation of intention to switch, and moderates the relationship between intention and switching. We tested our hypotheses on a sample of 414 users presented with a choice of switching their Web browsers. Our findings confirm the direct influence of potential switchers‘ habit on switching intention, and the interaction between habit and switching intention on switching. Our overall model explains 55 percent of total variance in users‘ intention to switch and 23 percent of total variance in user switching. This study advances the theoretical and empirical understanding of post-adoption technology switching, valuable to both researchers and practitioners.


title: Computational methods for the image segmentation of pigmented skin lesions

abstract: The clinical requirement for the early diagnosis of malignant skin lesions from images is introduced and justified.An up-to-date review about the proposed techniques for the image segmentation of pigmented skin lesions is presented.Additionally, the tasks related to image acquisition and pre-processing are also taken into account.The techniques are introduced, classified, and some examples of their results, are illustrated and discussed.This review is of interest both for researchers and for health professionals. Background and objectivesBecause skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation. MethodsTechniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle. ResultsThe techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results. ConclusionsThe image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency.


title: Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks

abstract: Energy disaggregation is the process of extracting the power consumptions of multiple appliances from the total consumption signal of a building. Artificial Neural Networks (ANN) have been very popular for this task in the last decade. In this paper we propose two recurrent network architectures that use sliding window for real-time energy disaggregation. We compare this approach to existing techniques using six metrics and find that it scores better for multi-state devices. Finally, we compare ANNs that use Gated Recurrent Unit neurons against those using Long Short-Term Memory neurons and find that they perform equally.


title: Latent Alignment and Variational Attention

abstract: Neural attention has become central to many state-of-the-art models in natural language processing and related domains. Attention networks are an easy-to-train and effective method for softly simulating alignment; however, the approach does not marginalize over latent alignments in a probabilistic sense. This property makes it difficult to compare attention to other alignment approaches, to compose it with probabilistic models, and to perform posterior inference conditioned on observed data. A related latent approach, hard attention, fixes these issues, but is generally harder to train and less accurate. This work considers variational attention networks, alternatives to soft and hard attention for learning latent variable alignment models, with tighter approximation bounds based on amortized variational inference. We further propose methods for reducing the variance of gradients to make these approaches computationally feasible. Experiments show that for machine translation and visual question answering, inefficient exact latent variable models outperform standard neural attention, but these gains go away when using hard attention based training. On the other hand, variational attention retains most of the performance gain but with training speed comparable to neural attention.


Learning Ensembles of Convolutional Neural Networks


title: METU dataset: A big dataset for benchmarking trademark retrieval

abstract: Trademark retrieval (TR) is the problem of retrieving similar trademarks (logos) for a query, and the main aim is to detect copyright infringements in trademarks. Since there are millions of companies worldwide, automatically retrieving similar trademarks has become an important problem, and currently, checking trademark infringements is mostly performed manually by humans. However, although there have been many attempts for automated TR, as also acknowledged in the community, the problem is largely unsolved. One of the main reasons for that is the unavailability of a publicly available comprehensive dataset that includes the various challenges of the TR problem. In this article, we propose and introduce a large dataset composed of more than 930,000 trademarks, and evaluate the existing approaches in the literature on this dataset. We show that the existing methods are far from being useful in such a challenging dataset, and we hope that the dataset can facilitate the development of better methods to make progress in the performance of trademark retrieval systems.


title: Automatic generation of system test cases from use case specifications

abstract: In safety critical domains, system test cases are often derived from functional requirements in natural language (NL) and traceability between requirements and their corresponding test cases is usually mandatory. The definition of test cases is therefore time-consuming and error prone, especially so given the quickly rising complexity of embedded systems in many critical domains. Though considerable research has been devoted to automatic generation of system test cases from NL requirements, most of the proposed approaches re- quire significant manual intervention or additional, complex behavioral modelling. This significantly hinders their applicability in practice. In this paper, we propose Use Case Modelling for System Tests Generation (UMTG), an approach that automatically generates executable system test cases from use case spec- ifications and a domain model, the latter including a class diagram and constraints. Our rationale and motivation are that, in many environments, including that of our industry partner in the reported case study, both use case specifica- tions and domain modelling are common and accepted prac- tice, whereas behavioural modelling is considered a difficult and expensive exercise if it is to be complete and precise. In order to extract behavioral information from use cases and enable test automation, UMTG employs Natural Language Processing (NLP), a restricted form of use case specifica- tions, and constraint solving.


title: Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews

abstract: Enthymemes, that are arguments with missing premises, are common in natural language text. They pose a challenge for the field of argument mining, which aims to extract arguments from such text. If we can detect whether a premise is missing in an argument, then we can either fill the missing premise from similar/related arguments, or discard such enthymemes altogether and focus on complete arguments. In this paper, we draw a connection between explicit vs. implicit opinion classification in reviews, and detecting arguments from enthymemes. For this purpose, we train a binary classifier to detect explicit vs. implicit opinions using a manually labelled dataset. Experimental results show that the proposed method can discriminate explicit opinions from implicit ones, thereby providing encouraging first step towards enthymeme detection in natural language texts.


title: Mechatronic design and development of a non-holonomic omnidirectional mobile robot for automation of primary production

abstract: AbstractThis paper presents the mechatronic system design and development of a four-wheel drive/steer (4WD4S) mobile robot as a non-holonomic omnidirectional robot, MARIO—Mobile Autonomous Robot for Intelligent Operations. An innovative integrated application of CAD/CAM/CAE and RP has been employed for rapid development of the robot chassis and other mechanical parts by using different software tools. Most of the parts were designed by 3D CAD software which allows further CAE analysis including structural and motion analysis. To reduce the manufacturing time and cost, CAM and RP have been used to manufacture the main parts. These master parts are manufactured by workshop machining and 3D printing. Robot Operating System (ROS) alongside Gazebo simulator have been utilized for modelling, simulation, software development and visualization which enabled to develop the optimum embedded system to control the robot. With this integrated approach, a prototype of MARIO has been developed with a reasonably low manu...


title: Malicious Firmware Detection with Hardware Performance Counters

abstract: Critical infrastructure components nowadays use microprocessor-based embedded control systems. It is often infeasible, however, to employ the same level of security measures used in general purpose computing systems, due to the stringent performance and resource constraints of embedded control systems. Furthermore, as software sits atop and relies on the firmware for proper operation, software-level techniques cannot detect malicious behavior of the firmware. In this work, we propose ConFirm, a low-cost technique to detect malicious modifications in the firmware of embedded control systems by measuring the number of low-level hardware events that occur during the execution of the firmware. In order to count these events, ConFirm leverages the Hardware Performance Counters (HPCs), which readily exist in many embedded processors. We propose a comparison-based technique to detect malicious modifications in firmwares with simple control-flows. For firmwares with more complex control-flows, we use machine learning techniques to automatically extract the relations among different hardware events. This method significantly reduces the number of pre-stored valid HPC signatures without compromising the detection accuracy. Finally, we reduce the consumption of local resources by implementing a remote-based detection mechanism. We evaluate the detection capability and performance overhead of the proposed technique on various types of firmware running on ARM- and PowerPC-based embedded processors. Experimental results demonstrate its practicality and effectiveness.


title: Regret of Queueing Bandits

abstract: We consider a variant of the multiarmed bandit problem where jobs queue for service, and service rates of different servers may be unknown. We study algorithms that minimize queueregret: the (expected) difference between the queue-lengths obtained by the algorithm, and those obtained by a “genie”-aided matching algorithm that knows exact service rates. A naive view of this problem would suggest that queue-regret should grow logarithmically: since queue-regret cannot be larger than classical regret, results for the standard MAB problem give algorithms that ensure queue-regret increases no more than logarithmically in time. Our paper shows surprisingly more complex behavior. In particular, the naive intuition is correct as long as the bandit algorithm’s queues have relatively long regenerative cycles: in this case queue-regret is similar to cumulative regret, and scales (essentially) logarithmically. However, we show that this “early stage” of the queueing bandit eventually gives way to a “late stage”, where the optimal queue-regret scaling is O(1/t). We demonstrate an algorithm that (order-wise) achieves this asymptotic queue-regret, and also exhibits close to optimal switching time from the early stage to the late stage.


title: TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks

abstract: Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.


title: Multi-Cell MIMO Cooperative Networks: A New Look at Interference

abstract: This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.


title: Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

abstract: In recent years, new platforms and sensors in photogrammetry, remote sensing and computer vision areas have become available, such as Unmanned Aircraft Vehicles (UAV), oblique camera systems, common digital cameras and even mobile phone cameras. Images collected by all these kinds of sensors could be used as remote sensing data sources. These sensors can obtain large-scale remote sensing data which consist of a great number of images. Bundle block adjustment of large-scale data with conventional algorithm is very time and space (memory) consuming due to the super large normal matrix arising from large-scale data. In this paper, an efficient Block-based Sparse Matrix Compression (BSMC) method combined with the Preconditioned Conjugate Gradient (PCG) algorithm is chosen to develop a stable and efficient bundle block adjustment system in order to deal with the large-scale remote sensing data. The main contribution of this work is the BSMC-based PCG algorithm which is more efficient in time and memory than the traditional algorithm without compromising the accuracy. Totally 8 datasets of real data are used to test our proposed method. Preliminary results have shown that the BSMC method can efficiently decrease the time and memory requirement of large-scale data. A stable and efficient bundle block adjustment system to deal with large-scale remote sensing data is built.The BSMC method is introduced to combine with PCG aiming to decrease the memory requirement.PCG algorithm is applied to solve the large normal equation.


title: Facial Soft-Tissue Spaces and Retaining Ligaments of the Midcheek

abstract: Background:This anatomical study was undertaken to define the soft-tissue spaces, retaining ligaments, and their relations in the midcheek.Methods:Sixty fresh hemifaces were dissected. The retaining ligaments and facial spaces were defined and their dimensions recorded. The course of the key vessels


title: Discrete-Time Rewards Model-Checked

abstract: This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For this purpose, the temporal logic probabilistic CTL is extended with reward constraints. This allows to formulate complex measures – involving expected as well as accumulated rewards – in a precise and succinct way. Algorithms to efficiently analyze such formulae are introduced. The approach is illustrated by model-checking a probabilistic cost model of the IPv4 zeroconf protocol for distributed address assignment in ad-hoc networks.


title: Cultural differences in sensitivity to social context: Detecting affective incongruity using the N400

abstract: East Asians and Asian-Americans tend to allocate relatively greater attention to background context compared to European Americans across a variety of cognitive and neural measures. We sought to extend these findings of cultural differences to affective stimuli using the N400, which has been shown to be sensitive to deep processing of affective information. The degree to which Asian-Americans and European Americans responded to semantic incongruity between emotionally expressive faces (i.e., smiling or frowning) and background affective scenes was measured. As predicted, Asian-Americans showed a greater N400 to incongruent trials than to congruent trials. In contrast, European Americans showed no difference in amplitude across the two conditions. Furthermore, greater affective N400 incongruity was associated with higher interdependent self-construals. These data suggest that Asian-Americans and those with interdependent self-construals process the relationship between perceived facial emotion and affectiv...


title: How speedy is SPDY

abstract: SPDY is increasingly being used as an enhancement to HTTP/1.1. To understand its impact on performance, we conduct a systematic study of Web page load time (PLT) under SPDY and compare it to HTTP. To identify the factors that affect PLT, we proceed from simple, synthetic pages to complete page loads based on the top 200 Alexa sites. We find that SPDY provides a significant improvement over HTTP when we ignore dependencies in the page load process and the effects of browser computation. Most SPDY benefits stem from the use of a single TCP connection, but the same feature is also detrimental under high packet loss. Unfortunately, the benefits can be easily overwhelmed by dependencies and computation, reducing the improvements with SPDY to 7% for our lower bandwidth and higher RTT scenarios. We also find that request prioritization is of little help, while server push has good potential; we present a push policy based on dependencies that gives comparable performance to mod_spdy while sending much less data.


title: Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

abstract: Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.


title: SOFT ERROR ISSUE AND IMPORTANCE OF LOW ALPHA SOLDERS FOR MICROELECTRONICS PACKAGING

abstract: To satisfy the ever-increasing demand for higher density (functionality) and lower power (portability), the dimensions and operating voltages of the modern electronic devices are being reduced frequently. This has brought new challenges both from the technology and materials point of view. One such issue is soft error, the temporary malfunction of device caused by the effect of radiation on the Si ICs. One such radiation is high energy alpha particle whose main source is the solders used in the packaging. The continued scaling of complementary metal oxide semiconductor device technologies has led to continued device shrinkage and decreases in the operating voltage of the device transistors. Scaling has meant denser circuitry overall, thinner silicon (e.g., silicon on insulator) in logic applications, and less charge on capacitors for volatile memory. These trends have resulted in devices being more sensitive to soft errors since now low energy alpha particles can flip a memory bit or alter timing in a logic circuit. Due to the use of flip-chip joints and developments towards 3D packaging, the solder bumps have moved very closer to the active Si devices, where even the low energy alpha ray having short range is able to induce soft error. One of the major sources of alpha particle radiation is the solders used for joining components in the packaging and they contain alpha emitters and there is increasing demand of Low Alpha activity Pb-free solders. The present paper reviews the issue of soft error in depth covering its historical background, causes and effects on electronic devices along with mitigation efforts. The importance of low alpha solders in microelectronics packaging applications is discussed in the light of soft-error issue.


title: Towards Publish/Subscribe Functionality on Graphs

abstract: In this work, we introduce the publish/subscribe paradigm to support continuous query processing over evolving graphs and motivate it for a number of applications and a variety of possible continuous queries. To the best of our knowledge, this is the rst work in the literature that considers supporting publish/subscribe in graphs; we focus specically on massive and dynamically evolving graphs due to the nature of the problem and the type of targeted applications. To this end, we design a proof-of-concept ltering algorithm for supporting structural matching of continuous graph queries against updates in the evolving graph and demonstrate the need for ecient ltering by experimentally comparing our algorithm against a baseline approach.


title: Gyri-precise head model of transcranial direct current stimulation: Improved spatial focality using a ring electrode versus conventional rectangular pad

abstract: The spatial resolution of conventional transcranial direct current stimulation (tDCS) is considered to be relatively diffuse owing to skull dispersion. However, we show that electric fields may be clustered at distinct gyri/sulci sites because of details in tissue architecture/conductivity, notably cerebrospinal fluid (CSF). We calculated the cortical electric field/current density magnitude induced during tDCS using a high spatial resolution (1mm 3 ) magnetic resonance imaging (MRI)-derived finite element human head model; cortical gyri/sulci were resolved. The spatial focality of conventional rectangular-pad (7×5cm 2 ) and the ring (4×1) electrode configurations were compared. The rectangular-pad configuration resulted in diffuse (unfocal) modulation, with discrete clusters of electric field magnitude maxima. Peak-induced electric field magnitude was not observed directly underneath the pads, but at an intermediate lobe. The 4×1 ring resulted in enhanced spatial focality, with peak-induced electric field magnitude at the sulcus and adjacent gyri directly underneath the active electrode. Cortical structures may be focally targeted by using ring configurations. Anatomically accurate high-resolution MRI-based forward-models may guide the "rational" clinical design and optimization of tDCS.


title: Deep Multi-Sensor Lane Detection

abstract: Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. To address this issue, we propose a novel deep neural network that takes advantage of both LiDAR and camera sensors and produces very accurate estimates directly in 3D space. We demonstrate the performance of our approach on both highways and in cities, and show very accurate estimates in complex scenarios such as heavy traffic (which produces occlusion), fork, merges and intersections.


title: Maslow and the motivation hierarchy: measuring satisfaction of the needs.

abstract: For each of the 5 needs in Maslow's motivational hierarchy (physiological, safety-security, belongingness, esteem, and self-actualization), operational definitions were developed from Maslow's theory of motivation. New measures were created based on the operational definitions (1) to assess the satisfaction of each need, (2) to assess their expected correlations (a) with each of the other needs and (b) with four social and personality measures (i.e., family support, traditional values, anxiety/worry, and life satisfaction), and (3) to test the ability of the satisfaction level of each need to statistically predict the satisfaction level of the next higher-level need. Psychometric tests of the scales conducted on questionnaire results from 386 adult respondents from the general population lent strong support for the validity and reliability of all 5 needs measures. Significant positive correlations among the scales were also found; that is, the more each lower-level need was satisfied, the more the next higher-level need was satisfied. Additionally, as predicted, family support, traditional values, and life satisfaction had significant positive correlations with the satisfaction of all 5 needs, and the anxiety/worry facet of neuroticism had significant negative correlations with the satisfaction of all the needs. Multiple regression analyses revealed that the satisfaction of each higher-level need was statistically predicted by the satisfaction of the need immediately below it in the hierarchy, as expected from Maslow's theory.