title: Shooter localization and weapon classification with soldier-wearable networked sensors

abstract: The paper presents a wireless sensor network-based mobilecountersniper system. A sensor node consists of a helmetmountedmicrophone array, a COTS MICAz mote for internodecommunication and a custom sensorboard that implementsthe acoustic detection and Time of Arrival (ToA) estimationalgorithms on an FPGA. A 3-axis compass providesself orientation and Bluetooth is used for communicationwith the soldier's PDA running the data fusion and the userinterface. The heterogeneous sensor fusion algorithm canwork with data from a single sensor or it can fuse ToA orAngle of Arrival (AoA) observations of muzzle blasts andballistic shockwaves from multiple sensors. The system estimatesthe trajectory, the range, the caliber and the weapontype. The paper presents the system design and the resultsfrom an independent evaluation at the US Army AberdeenTest Center. The system performance is characterized by 1-degree trajectory precision and over 95% caliber estimationaccuracy for all shots, and close to 100% weapon estimationaccuracy for 4 out of 6 guns tested.


Ontology Learning


title: Multiagent Reinforcement Learning With Unshared Value Functions

abstract: One important approach of multiagent reinforcement learning (MARL) is equilibrium-based MARL, which is a combination of reinforcement learning and game theory. Most existing algorithms involve computationally expensive calculation of mixed strategy equilibria and require agents to replicate the other agents' value functions for equilibrium computing in each state. This is unrealistic since agents may not be willing to share such information due to privacy or safety concerns. This paper aims to develop novel and efficient MARL algorithms without the need for agents to share value functions. First, we adopt pure strategy equilibrium solution concepts instead of mixed strategy equilibria given that a mixed strategy equilibrium is often computationally expensive. In this paper, three types of pure strategy profiles are utilized as equilibrium solution concepts: pure strategy Nash equilibrium, equilibrium-dominating strategy profile, and nonstrict equilibrium-dominating strategy profile. The latter two solution concepts are strategy profiles from which agents can gain higher payoffs than one or more pure strategy Nash equilibria. Theoretical analysis shows that these strategy profiles are symmetric meta equilibria. Second, we propose a multistep negotiation process for finding pure strategy equilibria since value functions are not shared among agents. By putting these together, we propose a novel MARL algorithm called negotiation-based Q-learning (NegoQ). Experiments are first conducted in grid-world games, which are widely used to evaluate MARL algorithms. In these games, NegoQ learns equilibrium policies and runs significantly faster than existing MARL algorithms (correlated Q-learning and Nash Q-learning). Surprisingly, we find that NegoQ also performs well in team Markov games such as pursuit games, as compared with team-task-oriented MARL algorithms (such as friend Q-learning and distributed Q-learning).


title: Dismantling Composite Visualizations in the Scientific Literature

abstract: We are analyzing the visualizations in the scientific literature to enhance search services, detect plagiarism, ::: ::: and study bibliometrics. An immediate problem is the ubiquitous use of multi-part figures: single images with ::: ::: multiple embedded sub-visualizations. Such figures account for approximately 35% of the figures in the scientific ::: ::: literature. Conventional image segmentation techniques and other existing approaches have been shown ::: ::: to be ineffective for parsing visualizations. We propose an algorithm to automatically segment multi-chart visualizations ::: ::: into a set of single-chart visualizations, thereby enabling downstream analysis. Our approach first ::: ::: splits an image into fragments based on background color and layout patterns. An SVM-based binary classifier ::: ::: then distinguishes complete charts from auxiliary fragments such as labels, ticks, and legends, achieving ::: ::: an average 98.1% accuracy. Next, we recursively merge fragments to reconstruct complete visualizations, ::: ::: choosing between alternative merge trees using a novel scoring function. To evaluate our approach, we used ::: ::: 261 scientific multi-chart figures randomly selected from the Pubmed database. Our algorithm achieves 80% ::: ::: recall and 85% precision of perfect extractions for the common case of eight or fewer sub-figures per figure. ::: ::: Further, even imperfect extractions are shown to be sufficient for most chart classification and reasoning tasks ::: ::: associated with bibliometrics and academic search applications.


title: Do Cascades Recur?

abstract: Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media, and an active line of research has studied how such cascades, which form as content is reshared from person to person, develop and subside. In this paper, we perform a large-scale analysis of cascades on Facebook over significantly longer time scales, and find that a more complex picture emerges, in which many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between. We characterize recurrence by measuring the time elapsed between bursts, their overlap and proximity in the social network, and the diversity in the demographics of individuals participating in each peak. We discover that content virality, as revealed by its initial popularity, is a main driver of recurrence, with the availability of multiple copies of that content helping to spark new bursts. Still, beyond a certain popularity of content, the rate of recurrence drops as cascades start exhausting the population of interested individuals. We reproduce these observed patterns in a simple model of content recurrence simulated on a real social network. Using only characteristics of a cascade's initial burst, we demonstrate strong performance in predicting whether it will recur in the future.


title: The Enron Corpus : A New Dataset for Email Classification Research

abstract: Automated classification of email messages into user-specific folders and information extraction from chronologically ordered email streams have become interesting areas in text learning research. However, the lack of large benchmark collections has been an obstacle for studying the problems and evaluating the solutions. In this paper, we introduce the Enron corpus as a new test bed. We analyze its suitability with respect to email folder prediction, and provide the baseline results of a state-of-the-art classifier (Support Vector Machines) under various conditions, including the cases of using individual sections (From, To, Subject and body) alone as the input to the classifier, and using all the sections in combination with regression weights.


Exploring the Depths of Recurrent Neural Networks with Stochastic Residual Learning


title: Image Selection in Photo Albums

abstract: The selection of the best photos in personal albums is a task that is often faced by photographers. This task can become laborious when the photo collection is large and it contains multiple similar photos. Recent advances on image aesthetics and photo importance evaluation has led to the creation of different metrics for automatically assessing a given image. However, these metrics are intended for the independent assessment of an image, without considering the possible context implicitly present within photo albums. In this work, we perform a user study for assessing how users select photos when provided with a complete photo album---a task that better reflects how users may review their personal photos and collections. Using the data provided by our study, we evaluate how existing state-of-the-art photo assessment methods perform relative to user selection, focusing in particular on deep learning based approaches. Finally, we explore a recent framework for adapting independent image scores to collections and evaluate in which scenarios such an adaptation can prove beneficial.


title: Λολ: Functional Lattice Cryptography

abstract: This work describes the design, implementation, and evaluation of Λολ, a general-purpose software framework for lattice-based cryptography. The Λολ framework has several novel properties that distinguish it from prior implementations of lattice cryptosystems, including the following. Generality, modularity, concision: Λολ defines a collection of general, highly composable interfaces for mathematical operations used across lattice cryptography, allowing for a wide variety of schemes to be expressed very naturally and at a high level of abstraction. For example, we implement an advanced fully homomorphic encryption (FHE) scheme in as few as 2--5 lines of code per feature, via code that very closely matches the scheme's mathematical definition. Theory affinity: Λολ is designed from the ground-up around the specialized ring representations, fast algorithms, and worst-case hardness proofs that have been developed for the Ring-LWE problem and its cryptographic applications. In particular, it implements fast algorithms for sampling from theory-recommended error distributions over arbitrary cyclotomic rings, and provides tools for maintaining tight control of error growth in cryptographic schemes. Safety: Λολ has several facilities for reducing code complexity and programming errors, thereby aiding the correct implementation of lattice cryptosystems. In particular, it uses strong typing to statically enforce---i.e., at compile time---a wide variety of constraints among the various parameters. Advanced features: Λολ exposes the rich hierarchy of cyclotomic rings to cryptographic applications. We use this to give the first-ever implementation of a collection of FHE operations known as "ring switching," and also define and analyze a more efficient variant that we call "ring tunneling." Lastly, this work defines and analyzes a variety of mathematical objects and algorithms for the recommended usage of Ring-LWE in cyclotomic rings, which we believe will serve as a useful knowledge base for future implementations.


title: Musicians' and nonmusicians' short-term memory for verbal and musical sequences: Comparing phonological similarity and pitch proximity

abstract: Language-music comparative studies have highlighted the potential for shared resources or neural overlap in auditory short-term memory. However, there is a lack of behavioral methodologies for comparing verbal and musical serial recall. We developed a visual grid response that allowed both musicians and nonmusicians to perform serial recall of letter and tone sequences. The new method was used to compare the phonological similarity effect with the impact of an operationalized musical equivalent — pitch proximity. Over the course of three experiments, we found that short-term memory for tones had several similarities to verbal memory, including limited capacity and a significant effect of pitch proximity in nonmusicians. Despite being vulnerable to phonological similarity when recalling letters, however, musicians showed no effect of pitch proximity, a result that we suggest might reflect strategy differences. Overall, the findings support a limited degree of correspondence in the way that verbal and musical sounds are processed in auditory short-term memory.


title: Enhancing Supervised Learning with Unlabeled Data

abstract: In a wide variety of supervised learning scenarios, there is a small set of labeled data, along with a large pool of unlabeled data. In this thesis, we present a new semi-supervised learning method called co-learning that is designed to use unlabeled data to enhance standard supervised learning algorithms. The idea is that two or more standard supervised learning algorithms can leverage off the fact that they have different representations of the hypotheses and they are likely to detect different patterns in labeled data. We also design an active co-learning strategy to bootstrap our co-leaning procedure when the originally labeled data set is too small to provide accurate confidence estimate for the learned hypotheses. We provide a priority sampling technique as the selection component in our active co-learning method. We evaluate our co-learning algorithms on several datasets from a commonly used data repository in the machine learning community. We also test our co-learning method on text categorization. The contribution of this research is to put forward a new semi-supervised learning approach for learning with a small number of labeled examples, and explore the applicability of our co-learning strategy in real world applications.


title: Compact Elongated Mushroom (EM)-EBG Structure for Enhancement of Patch Antenna Array Performances

abstract: A compact elongated mushroom electromagnetic band-gap (EM-EBG) structure, exploiting the thickness of the substrate to achieve higher isolation compared to the case of the conventional mushroom EBG (CM-EBG), is proposed for the enhancement of the performances of patch antenna arrays. Guidelines, based on fairly accurate formulas, are provided for the design of the stop-band of the structure. The compactness of the EM-EBG is investigated and shown to be superior to that of the CM-EBG in practical cases. The superior reduction of mutual coupling of the EM-EBG is demonstrated by full-wave and experimental results both for a pair of patches and for a 4-element patch array. Finally, the benefits of the EM-EBG in terms of array processing, specifically side-lobe level (SLL) control and direction of arrival (DOA) estimation, are presented.


title: Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth.

abstract: Although the Internet has transformed the way our world operates, it has also served as a venue for cyberbullying, a serious form of misbehavior among youth. With many of today's youth experiencing acts of cyberbullying, a growing body of literature has begun to document the prevalence, predictors, and outcomes of this behavior, but the literature is highly fragmented and lacks theoretical focus. Therefore, our purpose in the present article is to provide a critical review of the existing cyberbullying research. The general aggression model is proposed as a useful theoretical framework from which to understand this phenomenon. Additionally, results from a meta-analytic review are presented to highlight the size of the relationships between cyberbullying and traditional bullying, as well as relationships between cyberbullying and other meaningful behavioral and psychological variables. Mixed effects meta-analysis results indicate that among the strongest associations with cyberbullying perpetration were normative beliefs about aggression and moral disengagement, and the strongest associations with cyberbullying victimization were stress and suicidal ideation. Several methodological and sample characteristics served as moderators of these relationships. Limitations of the meta-analysis include issues dealing with causality or directionality of these associations as well as generalizability for those meta-analytic estimates that are based on smaller sets of studies (k Language: en


title: FML-based Prediction Agent and Its Application to Game of Go

abstract: In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a robot engine for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in NUTN and OPU, for example, the number of MCTS simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO, produced by Fujisoft incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.


title: A Framework for Large-Scale Detection of Web Site Defacements

abstract: Web site defacement, the process of introducing unauthorized modifications to a Web site, is a very common form of attack. In this paper we describe and evaluate experimentally a framework that may constitute the basis for a defacement detection service capable of monitoring thousands of remote Web sites systematically and automatically. In our framework an organization may join the service by simply providing the URLs of the resources to be monitored along with the contact point of an administrator. The monitored organization may thus take advantage of the service with just a few mouse clicks, without installing any software locally or changing its own daily operational processes. Our approach is based on anomaly detection and allows monitoring the integrity of many remote Web resources automatically while remaining fully decoupled from them, in particular, without requiring any prior knowledge about those resources. We evaluated our approach over a selection of dynamic resources and a set of publicly available defacements. The results are very satisfactory: all attacks are detected while keeping false positives to a minimum. We also assessed performance and scalability of our proposal and we found that it may indeed constitute the basis for actually deploying the proposed service on a large scale.


title: Automated Online Exam Proctoring

abstract: Massive open online courses and other forms of remote education continue to increase in popularity and reach. The ability to efficiently proctor remote online examinations is an important limiting factor to the scalability of this next stage in education. Presently, human proctoring is the most common approach of evaluation, by either requiring the test taker to visit an examination center, or by monitoring them visually and acoustically during exams via a webcam. However, such methods are labor intensive and costly. In this paper, we present a multimedia analytics system that performs automatic online exam proctoring. The system hardware includes one webcam, one wearcam, and a microphone for the purpose of monitoring the visual and acoustic environment of the testing location. The system includes six basic components that continuously estimate the key behavior cues: user verification, text detection, voice detection, active window detection, gaze estimation, and phone detection. By combining the continuous estimation components, and applying a temporal sliding window, we design higher level features to classify whether the test taker is cheating at any moment during the exam. To evaluate our proposed system, we collect multimedia (audio and visual) data from $\text{24}$ subjects performing various types of cheating while taking online exams. Extensive experimental results demonstrate the accuracy, robustness, and efficiency of our online exam proctoring system.


title: The Gudhi library: Simplicial complexes and persistent homology

abstract: We present the main algorithmic and design choices that have been made to represent complexes and compute persistent homology in the Gudhi library. The Gudhi library (Geometric Understanding in Higher Dimensions) is a generic C++ library for computational topology. Its goal is to provide robust, efficient, flexible and easy to use implementations of state-of-the-art algorithms and data structures for computational topology. We present the different components of the software, their interaction and the user interface. We justify the algorithmic and design decisions made in Gudhi and provide benchmarks for the code. The software, which has been developped by the first author, will be available soon at project.inria.fr/gudhi/software/ .


Fraglets - a Metabolistic Execution Model for Communication Protocols


title: Patau syndrome with a long survival. A case report.

abstract: Trisomy 13 is a clinically severe entity; 85% of the patients do not survive beyond one year, and most children die before completing six months of age. We report a female child, 28 months old, white, the fourth child of a non-consanguineous couple, who presented trisomy 13. The child was born at term, from a vaginal delivery, weighing 2600 g. At birth, she was cyanotic, icteric, spastic, and cried weakly. The initial clinical examination detected polydactyly in the left hand, congenital clubfoot and convex soles, ocular hypertelorism, a low nasal bridge, numerous hemangiomas distributed throughout the body, cardiomegaly, and perimembranous inter-ventricular communication. There was no cleft lip or palate. On physical examination at 18 months old, the child weighed 6,900 g, had a cephalic perimeter of 41 cm, a thoracic perimeter of 43 cm and was 76 cm tall. At 28 months, she weighed 10,760 g and was 88.5 cm tall. Neuropsychomotor development retardation was evident from birth and, according to the psychologist and the social assistant of APAE (Handicapped Parents and Friends Association) in Canguçu, Rio Grande do Sul, there was a noticeable improvement after physiotherapy and recreational sessions.


title: A Fast-Converging MPPT Technique for Photovoltaic System Under Fast-Varying Solar Irradiation and Load Resistance

abstract: Under fast varying solar irradiation and load resistance, a fast-converging maximum power point tracking system is required to ensure the photovoltaic system response rapidly with minimum power losses. Traditionally, maximum power point locus was used to provide such a fast response. However, the algorithm requires extra control loop or intermittent disconnection of the PV module. Hence, this paper proposes a simpler fast-converging maximum power point tracking technique, which excludes the extra control loop and intermittent disconnection. In the proposed algorithm, the relationship between the load line and the I-V curve is used with trigonometry rule to obtain the fast response. Results of the simulation and experiment using single-ended primary-inductor converter showed that the response of the proposed algorithm is four times faster than the conventional incremental conductance algorithm during the load and solar irradiation variation. Consequently, the proposed algorithm has higher efficiency.


title: Impact of packetization and functional split on C-RAN fronthaul performance

abstract: Cloud-RAN (CRAN) is considered as one key en-abler for beyond 4G networks, offering multiplexing gains, and advanced cooperation and coordinated signal processing. However, a key obstacle in the adoption of the CRAN architecture is that it requires very high capacity and low latency fronthaul (FH) links to carry raw I/Q samples between remote radio heads (RRH) and the baseband units (BBUs). These capacity requirements could be reduced by a more flexible split of baseband processing between BBUs and RRHs. Nevertheless, while moving some of the processing back into the RRH is expected to reduce FH rates, the amount of reduction mainly depends on the split, cell load, scenario and it might also introduce some delays. To this end, this paper studies the impact of different functional splits on the FH capacity for representative scenarios. Furthermore, we propose the use of a packet-based fronthaul network and study the joint impact of different packetization methods and RRH-BBU functional splits on the FH rate and latency. Based on this study, we provide some insights on the feasibility and optimality of different combinations, and the potential multiplexing benefits in terms of numbers of RRHs one could support over a single Ethernet-based FH network.


title: The Financial Impact of IT Governance Mechanisms' Adoption: An Empirical Analysis with Brazilian Firms

abstract: Recently, it appears on the agenda of many organizations the concept of IT governance in order to justify and mainly optimize IT investments. Some studies have shown that companies which have good IT governance models generate superior returns on their IT investments than their competitors. However, there is a lack of scientific research confirming that effective IT governance results in better financial performance. In this paper, we verified if companies which have adopted IT governance mechanisms improve their financial performance, examining changes on performance pre and post adoption controlling for industry mean changes. We found that companies which have adopted IT governance practices improved their performance when compared to the control group, especially regarding about profitability measures. Furthermore, we found that effects of IT governance mechanisms' adoption on financial performance are stronger in the year following adoption than in the year which IT governance was adopted.


title: Code-Switch Language Model with Inversion Constraints for Mixed Language Speech Recognition

abstract: We propose a first ever code-switch language model for mixed language speech recognition that incorporates syntactic constraints by a code-switch boundary prediction model, a code-switch translation model, and a reconstruction model. A WFST-based decoder then recognizes speech by combining an acoustic model, a pronunciation model and the code-switch language model in an integrated approach. Our proposed approach avoids making early decisions on code-switch boundaries and is therefore more robust than previous approaches. Our proposed system using the code-switch language model outperforms a baseline of interpolated language models by a statistically significant 0.91% on a mixed language lecture speech corpus, and 1.25% on a mixed language lunch conversation corpus. Our method also outperforms a language model that permits code-switch at all word boundaries by a statistically significant 1.35% on the lecture speech corpus and 1.69% on the lunch conversation corpus.


AT WHAT RATE DO LEARNERS LEARN AND RETAIN NEW VOCABULARY FROM READING A GRADED READER


title: M.: A simple approach to ordinal classification

abstract: Machine learning methods for classification problems commonly assume that the class values are unordered. However, in many practical applications the class values do exhibit a natural order--for example, when learning how to grade. The standard approach to ordinal classification converts the class value into a numeric quantity and applies a regression learner to the transformed data, translating the output back into a discrete class value in a post-processing step. A disadvantage of this method is that it can only be applied in conjunction with a regression scheme. ::: ::: In this paper we present a simple method that enables standard classification algorithms to make use of ordering information in class attributes. By applying it in conjunction with a decision tree learner we show that it outperforms the naive approach, which treats the class values as an unordered set. Compared to special-purpose algorithms for ordinal classification our method has the advantage that it can be applied without any modification to the underlying learning scheme.


title: Texture analysis of medical images.

abstract: The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.


title: Enemy at the gate: threats to information security

abstract: A firm can build more effective security strategies by identifying and ranking the severity of potential threats to its IS efforts.


title: Optical Flow Estimation for Flame Detection in Videos

abstract: Computational vision-based flame detection has drawn significant attention in the past decade with camera surveillance systems becoming ubiquitous. Whereas many discriminating features, such as color, shape, texture, etc., have been employed in the literature, this paper proposes a set of motion features based on motion estimators. The key idea consists of exploiting the difference between the turbulent, fast, fire motion, and the structured, rigid motion of other objects. Since classical optical flow methods do not model the characteristics of fire motion (e.g., non-smoothness of motion, non-constancy of intensity), two optical flow methods are specifically designed for the fire detection task: optimal mass transport models fire with dynamic texture, while a data-driven optical flow scheme models saturated flames. Then, characteristic features related to the flow magnitudes and directions are computed from the flow fields to discriminate between fire and non-fire motion. The proposed features are tested on a large video database to demonstrate their practical usefulness. Moreover, a novel evaluation method is proposed by fire simulations that allow for a controlled environment to analyze parameter influences, such as flame saturation, spatial resolution, frame rate, and random noise.


title: 49 GHz 6-bit programmable divider in SiGe BiCMOS

abstract: In this paper, a 6-bit true modular programmable frequency divider with division ratios ranging from 64 to 127 is reported. It is composed of a divider chain of 6 divide-by-2/3 cells, and ECL stages that are introduced as synchronization circuits for programming inputs. The synchronization circuits have CMOS input for compatibility with programming circuits. The stand-alone divider chain is functional up to an input clock frequency of 49 GHz. The combination of the divider chain with synchronization circuits is functional up to 44 GHz. The 6 stage divider draws 34 mA current from a 2.7 V supply. The synchronization circuits draw 30 mA from a 3 V supply. The circuit is fabricated in a 0.13 μm SiGe BiCMOS technology, and is well suited for millimeter-wave phase-locked loop (PLL) circuits which require fine frequency resolution.


title: Understanding the Metropolis-Hastings algorithm

abstract: Abstract We provide a detailed, introductory exposition of the Metropolis-Hastings algorithm, a powerful Markov chain method to simulate multivariate distributions. A simple, intuitive derivation of this method is given along with guidance on implementation. Also discussed are two applications of the algorithm, one for implementing acceptance-rejection sampling when a blanketing function is not available and the other for implementing the algorithm with block-at-a-time scans. In the latter situation, many different algorithms, including the Gibbs sampler, are shown to be special cases of the Metropolis-Hastings algorithm. The methods are illustrated with examples.


title: Here's My Point: Joint Pointer Architecture for Argument Mining

abstract: One of the major goals in automated argumentation mining is to uncover the argument structure present in argumentative text. In order to determine this structure, one must understand how different individual components of the overall argument are linked. General consensus in this field dictates that the argument components form a hierarchy of persuasion, which manifests itself in a tree structure. This work provides the first neural network-based approach to argumentation mining, focusing on the two tasks of extracting links between argument components, and classifying types of argument components. In order to solve this problem, we propose to use a joint model that is based on a Pointer Network architecture. A Pointer Network is appealing for this task for the following reasons: 1) It takes into account the sequential nature of argument components; 2) By construction, it enforces certain properties of the tree structure present in argument relations; 3) The hidden representations can be applied to auxiliary tasks. In order to extend the contribution of the original Pointer Network model, we construct a joint model that simultaneously attempts to learn the type of argument component, as well as continuing to predict links between argument components. The proposed joint model achieves state-of-the-art results on two separate evaluation corpora, achieving far superior performance than a regular Pointer Network model. Our results show that optimizing for both tasks, and adding a fully-connected layer prior to recurrent neural network input, is crucial for high performance.


title: Spectra of infant EEG within the first year of life: A pilot study

abstract: Rhythmic activities in electroencephalography (EEG) have been extensively studied in adults and classic rhythms are found to correlate with specific human brain functions. However, less has been investigated in infant EEG, and EEG rhythms in infants at early ages have not been well characterized in terms of their frequency ranges. In the present pilot study, we investigated rhythmic activities in infant EEG recorded weekly from 4-8 months using high-density EEG sensor nets. The developmental changes of EEG rhythms in different frequency bands along maturation were evaluated through spectral analysis. Their longitudinal scalp maps were also studied to understand their plausible functional correlates. The present study aims to enrich the sparse knowledge about the developing patterns of EEG rhythms within the first year of life from EEG recordings of high temporal and spatial resolutions.


title: Wireless valve position monitoring: A MEMS approach

abstract: This paper presents a Microelectromechanical systems (MEMS) solution to rotary valve position monitoring. With a device holding commercial off-the-shelf inertial sensors, we develop a sensing algorithm encompassing self-calibration, and noise and drift removal. Our repeatable results show an accuracy of ±5° for part-turn valves and ±10% of a turn for multi-turn models. Our small energy-efficient wireless solution is a cheap and reliable replacement for products on the market.


title: Imaging of Insufficiency Fractures

abstract: This review article focuses on occurrence, imaging, and differential diagnosis of insufficiency fractures. Prevalence and the most common sites of insufficiency fractures and their clinical implications are discussed. Insufficiency fractures are due to normal stress exerted on weakened bone. Most commonly, postmenopausal osteoporosis is the cause for insufficiency fractures. Additional conditions affecting bone turnover include osteomalacia, chronic renal failure, and high dose corticosteroid therapy. It is a challenge for the radiologist to detect and diagnose insufficiency fractures as well as to differentiate them from malignant fractures. Radiographs are the basic modality used for screening of insufficiency fractures, yet depending on the location of the fractures sensitivity is limited. Magnetic resonance imaging (MRI) is a very sensitive tool to visualize bone marrow abnormalities associated with insufficiency fractures and has allowed differentiation of benign versus malignant fractures. Thin section Multidetector CT depicts subtle fracture lines allowing direct visualization of cortical and trabecular bone. Dedicated Mikro-CTs (Xtreme-CT) can detect subtle fractures reaching an in-plane resolution of 80 μm. Bone scintigraphy still plays a role in detecting fractures, with good sensitivity but unsatisfactory specificity. PET-CT with hybrid scanners has been the upcoming modality for the differentiation of benign from malignant fractures. Bone densitometry and clinical fracture history may determine the future risk of possible insufficiency fractures.


title: Plastic Changes in the Central Auditory System After Hearing Loss, Restoration of Function, and During Learning

abstract: Traditionally the auditory system was considered a hard-wired sensory system; this view has been challenged in recent years in light of the plasticity of other sensory systems, particularly the visual and somatosensory systems. Practical experience in clinical audiology together with the use of prosthetic devices, such as cochlear implants, contributed significantly to the present view on the plasticity of the central auditory system, which was originally based on data obtained in animal experiments. The loss of auditory receptors, the hair cells, results in profound changes in the structure and function of the central auditory system, typically demonstrated by a reorganization of the projection maps in the auditory cortex. These plastic changes occur not only as a consequence of mechanical lesions of the cochlea or biochemical lesions of the hair cells by ototoxic drugs, but also as a consequence of the loss of hair cells in connection with aging or noise exposure. In light of the aging world population and the increasing amount of noise in the modern world, understanding the plasticity of the central auditory system has its practical consequences and urgency. In most of these situations, a common denominator of central plastic changes is a deterioration of inhibition in the subcortical auditory nuclei and the auditory cortex. In addition to the processes that are elicited by decreased or lost receptor function, the function of nerve cells in the adult central auditory system may dynamically change in the process of learning. A better understanding of the plastic changes in the central auditory system after sensory deafferentation, sensory stimulation, and learning may contribute significantly to improvement in the rehabilitation of damaged or lost auditory function and consequently to improved speech processing and production.


title: Real-Time Optical Wireless Communication: Field-Trial in an Industrial Production Environment

abstract: We present the first field-trial of a real-time optical wireless communication (OWC) system in an industrial manufacturing environment. Using spatial diversity schemes, we demonstrate how OWC can provide reliable low-latency communication despite the rapid movements of a production robot.


title: A brain-inspired spiking neural network model with temporal encoding and learning

abstract: Abstract Neural coding and learning are important components in cognitive memory system, by processing the sensory inputs and distinguishing different patterns to allow for higher level brain functions such as memory storage and retrieval. Benefitting from biological relevance, this paper presents a spiking neural network of leaky integrate-and-fire (LIF) neurons for pattern recognition. A biologically plausible supervised synaptic learning rule is used so that neurons can efficiently make a decision. The whole system contains encoding, learning and readout. Utilizing the temporal coding and learning, networks of spiking neurons can effectively and efficiently perform various classification tasks. It can classify complex patterns of activities stored in a vector, as well as the real-world stimuli. Our approach is also benchmarked on the nonlinearly separable Iris dataset. The proposed approach achieves a good generalization, with a classification accuracy of 99.63% for training and 92.55% for testing. In addition, the trained networks demonstrate that the temporal coding is a viable means for fast neural information processing.


title: Latent Skill Embedding for Personalized Lesson Sequence Recommendation

abstract: Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specic assessments. Akin to collaborative ltering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.


title: Positive Organizational Behavior in the Workplace The Impact of Hope, Optimism, and Resilience

abstract: Drawing from the foundation of positive psychology and the recently emerging positive organizational behavior, two studies (N = 1,032 and N = 232) test hypotheses on the impact that the selected positive psychological resource capacities of hope, optimism, and resilience have on desired work-related employee outcomes. These outcomes include performance (self-reported in Study 1 and organizational performance appraisals in Study 2), job satisfaction, work happiness, and organizational commitment. The findings generally support that employees’ positive psychological re source capacities relate to, and contribute unique variance to, the outcomes. However, hope, and, to a lesser extent, optimism and resilience, do differentially contribute to the various outcomes. Utility analysis supports the practical implications of the study results.


title: A new ranking method for principal components analysis and its application to face image analysis

abstract: In this work, we investigate a new ranking method for principal component analysis (PCA). Instead of sorting the principal components in decreasing order of the corresponding eigenvalues, we propose the idea of using the discriminant weights given by separating hyperplanes to select among the principal components the most discriminant ones. The method is not restricted to any particular probability density function of the sample groups because it can be based on either a parametric or non-parametric separating hyperplane approach. In addition, the number of meaningful discriminant directions is not limited to the number of groups, providing additional information to understand group differences extracted from high-dimensional problems. To evaluate the discriminant principal components, separation tasks have been performed using face images and three different databases. Our experimental results have shown that the principal components selected by the separating hyperplanes allow robust reconstruction and interpretation of the data, as well as higher recognition rates using less linear features in situations where the differences between the sample groups are subtle and consequently most difficult for the standard and state-of-the-art PCA selection methods.


title: A Proposed Scale for Measuring E-service Quality

abstract: With rapid growth of the Internet and the globalization of market, companies accepted and adopted the new information and communication technology to offer e-services to their customers. This paper addresses e-service quality issue in the electronic marketplace. The purpose of this paper is to extend what are the main service quality dimensions in the realm of e-service. The paper explores e-service quality dimensions based on a review of the development of e-service quality scales and the SERVQUAL scale. It proposes an 8-dimension scale for measuring e-service quality with rewording and modifying of the SERVQUAL instrument: Website design, reliability, fulfillment, security, responsiveness, personalization, information and empathy. This paper concludes by discussing the findings of this study and highlighting areas for the future research in the realm of e-service quality. With rapid growth of the Internet and the globalization of market, companies accepted and adopted the new information and communication technology in the performance of their activities, not only to support traditional activities, but also to support those arising from new opportunities, mainly from the Internet. Electronic commerce and online business stand out among these opportunities. Most companies are establishing Websites, which are regarded as a new channel to conduct business transaction, and customers can make purchase through companies’ Websites. It enables companies to access a global market with low operating cost, to offer information in depth, and to provide customers electronic service (e-service) with superior quality by means of the interactivity of the Internet, which increases the competition among companies. Both concepts of e-service and e-service quality have become increasing important issues in research. E-service is different from traditional service, which is based on interactive information flow between customers and service providers. E-service quality has been regarded as having the potential not only to deliver strategic benefits, but also to enhance operational efficiency and profitability [12, 26]. E-service is becoming even more critical for companies to retain and attract customers. What brings online customers back to company’s websites is a sense of loyalty that comes from good services offered by companies. Oliveria et al. (2002) suggest that companies can achieve competitive capabilities by offering good eservices to customers [24]. Service quality has strong impacts on customer satisfaction on the performance of companies. Improving e-service quality to satisfy and retain customers is becoming a challenging issue. There are a range of studies on the dimensions, measures and attributes of e-service quality. The purpose of this paper is to uncover and interpret the current researches in the dimensions of e-service quality, and propose a theoretical scale to measure e-service quality, which provides fresh insight into the dimensions of e-service quality.


Answer-Aware Attention on Grounded Question Answering in Images


title: Comparison of comb and pulse train signal designs for active sonar

abstract: Doppler-sensitive waveforms are often used in shallow water to mitigate high reverberation levels. This paper compares three Doppler sensitive sonar waveforms including coherently-processed pulse trains, uniform frequency combs, and nonuniform co-prime frequency combs in terms of their delay-Doppler responses. It is shown that the use of comb waveforms can reduce Doppler sidelobes without trading Doppler resolution, unlike windowing. The pulse train and uniform comb are evaluated in terms of their ambiguity functions, while the co-prime comb was processed using a technique that exploits the co-prime structure to reduce the number of tones necessary to achieve a delay-Doppler response similar to that of a uniform comb. The cost of using a co-prime comb rather than a uniform comb is increased bandwidth extent. Brief discussion of the peak-to-average power ratio (PAPR) of these signals is also included.


title: A 3D human motion refinement method based on sparse motion bases selection

abstract: Motion capture (MOCAP) is an important technique that is widely used in many areas such as computer animation, film industry, physical training and so on. Even with professional MOCAP system, the missing marker problems always occur. Motion refinement is an essential preprocessing step for MOCAP data based applications. Although many existing approaches for motion refinement have been developed, it is still a challenging task due to the complexity and diversity of human motion. A data driven based motion refinement method is proposed in this paper, which modifies the traditional sparse coding process for special task of motion recovery from missing parts. Meanwhile, the objective function is derived by taking both statistical and kinematical property of motion data into account. Poselet model and moving window grouping are applied in the proposed method to achieve a fine-grained feature representation, which preserves the embedded spatial-temporal kinematic information. 5 motion dictionaries are learnt for each kind of poselet from training data in parallel. The motion refine problem is finally solved as an e1-minimization problem. Compared with several state-of-art motion refine methods, the experimental result shows that our approach outperforms the competitors.


title: A High Step-Down Transformerless Single-Stage Single-Switch AC/DC Converter

abstract: This paper presents a high step-down tranformerless single-stage single-switch ac/dc converter suitable for universal line applications (90-270 Vrms) . The topology integrates a buck-type power-factor correction (PFC) cell with a buck-boost dc/dc cell and part of the input power is coupled to the output directly after the first power processing. With this direct power transfer feature and sharing capacitor voltages, the converter is able to achieve efficient power conversion, high power factor, low voltage stress on intermediate bus (less than 130 V) and low output voltage without a high step-down transformer. The absence of transformer reduces the component counts and cost of the converter. Unlike most of the boost-type PFC cell, the main switch of the proposed converter only handles the peak inductor current of dc/dc cell rather than the superposition of both inductor currents. Detailed analysis and design procedures of the proposed circuit are given and verified by experimental results.


title: Learning deep representations for graph clustering

abstract: Recently deep learning has been successfully adopted in many applications such as speech recognition and image classification. In this work, we explore the possibility of employing deep learning in graph clustering. We propose a simple method, which first learns a nonlinear embedding of the original graph by stacked autoencoder, and then runs k-means algorithm on the embedding to obtain clustering result. We show that this simple method has solid theoretical foundation, due to the similarity between autoencoder and spectral clustering in terms of what they actually optimize. Then, we demonstrate that the proposed method is more efficient and flexible than spectral clustering. First, the computational complexity of autoencoder is much lower than spectral clustering: the former can be linear to the number of nodes in a sparse graph while the latter is super quadratic due to eigenvalue decomposition. Second, when additional sparsity constraint is imposed, we can simply employ the sparse autoencoder developed in the literature of deep learning; however, it is nonstraightforward to implement a sparse spectral method. The experimental results on various graph datasets show that the proposed method significantly outperforms conventional spectral clustering, which clearly indicates the effectiveness of deep learning in graph clustering.


title: Developing generic dynamic models for the 2030 Eastern Interconnection grid

abstract: The Eastern Interconnection Planning Collaborative (EIPC) has built three major power flow cases for the 2030 Eastern Interconnection (EI) based on various levels of energy/environmental policy conditions, technology advances, and load growth. Using the power flow cases, this report documents the process of developing the generic 2030 dynamic models using typical dynamic parameters. The constructed model was validated indirectly using the synchronized phasor measurements by removing the wind generation temporarily.


title: Interpretable Deep Neural Networks for Single-Trial EEG Classification

abstract: Background: In cognitive neuroscience the potential of Deep Neural Networks (DNNs) for solving complex classification tasks is yet to be fully exploited. The most limiting factor is that DNNs as notorious 'black boxes' do not provide insight into neurophysiological phenomena underlying a decision. Layer-wise Relevance Propagation (LRP) has been introduced as a novel method to explain individual network decisions. New Method: We propose the application of DNNs with LRP for the first time for EEG data analysis. Through LRP the single-trial DNN decisions are transformed into heatmaps indicating each data point's relevance for the outcome of the decision. Results: DNN achieves classification accuracies comparable to those of CSP-LDA. In subjects with low performance subject-to-subject transfer of trained DNNs can improve the results. The single-trial LRP heatmaps reveal neurophysiologically plausible patterns, resembling CSP-derived scalp maps. Critically, while CSP patterns represent class-wise aggregated information, LRP heatmaps pinpoint neural patterns to single time points in single trials. Comparison with Existing Method(s): We compare the classification performance of DNNs to that of linear CSP-LDA on two data sets related to motor-imaginery BCI. Conclusion: We have demonstrated that DNN is a powerful non-linear tool for EEG analysis. With LRP a new quality of high-resolution assessment of neural activity can be reached. LRP is a potential remedy for the lack of interpretability of DNNs that has limited their utility in neuroscientific applications. The extreme specificity of the LRP-derived heatmaps opens up new avenues for investigating neural activity underlying complex perception or decision-related processes.


title: Attacking OpenSSL Implementation of ECDSA with a Few Signatures

abstract: In this work, we give a lattice attack on the ECDSA implementation in the latest version of OpenSSL, which implement the scalar multiplication by windowed Non-Adjacent Form method. We propose a totally different but more efficient method of extracting and utilizing information from the side-channel results, remarkably improving the previous attacks. First, we develop a new efficient method, which can extract almost all information from the side-channel results, obtaining 105.8 bits of information per signature on average for 256-bit ECDSA. Then in order to make the utmost of our extracted information, we translate the problem of recovering secret key to the Extended Hidden Number Problem, which can be solved by lattice reduction algorithms. Finally, we introduce the methods of elimination, merging, most significant digit recovering and enumeration to improve the attack. Our attack is mounted to the {series secp256k1} curve, and the result shows that only 4 signatures would be enough to recover the secret key if the Flush+Reload attack is implemented perfectly without any error,which is much better than the best known result needing at least 13 signatures.


title: Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain – computer interface

abstract: Abstract There has been an increase in research interest for brain–computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.


title: The Most Favorable Impulsive Interference for Ternary CDMA

abstract: Ternary Code Division Multiple Access (CDMA) with a variable number of non-zero chips per symbol allows to control the transmission impulsiveness, ranging from non-impulsive signaling (with all chips being non-zero) to a fully impulsive signal (with only one chip per symbol being non-zero). The former corresponds to conventional CDMA while the latter to Impulse-Radio (IR). Recent work showed that the fully impulsive signal maximizes the Average Signal to Interference Ratio (ASIR) in asynchronous multiple user environment. We extend this result to information measures, and show that by adapting the impulsiveness figure (i.e., the number of non zero chips) to the channel conditions, the system can create a favorable interference environment for the other users. In particular, fully impulsive signaling maximizes the Shannon capacity of each user (treating interference from other users as noise). On the other hand, at transmission rates strictly below capacity, less impulsive signaling is better in terms of decoding error probability.


title: Social Marketing: Its Definition and Domain:

abstract: The author argues that social marketing has been defined improperly in much of the literature. A revised definition is proposed and the domain of social marketing defined. He concludes with suggest...


title: Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

abstract: Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model's simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.


title: A Text Line Detection Method for Mathematical Formula Recognition

abstract: Text line detection is a prerequisite procedure of mathematical formula recognition, however, many incorrectly segmented text lines are often produced due to the two-dimensional structures of mathematics when using existing segmentation methods such as Projection Profiles Cutting or white space analysis. In consequence, mathematical formula recognition is adversely affected by these incorrectly detected text lines, with errors propagating through further processes. Aimed at mathematical formula recognition, we propose a text line detection method to produce reliable line segmentation. Based on the results produced by PPC, a learning based merging strategy is presented to combine incorrectly split text lines. In the merging strategy, the features of layout and text for a text line and those between successive lines are utilised to detect the incorrectly split text lines. Experimental results show that the proposed approach obtains good performance in detecting text lines from mathematical documents. Furthermore, the error rate in mathematical formula identification is reduced significantly through adopting the proposed text line detection method.


title: TouchIn: Sightless two-factor authentication on multi-touch mobile devices

abstract: Mobile authentication is indispensable for preventing unauthorized access to multi-touch mobile devices. Existing mobile authentication techniques are often cumbersome to use and also vulnerable to shoulder-surfing and smudge attacks. This paper focuses on designing, implementing, and evaluating TouchIn, a two-factor authentication system on multi-touch mobile devices. TouchIn works by letting a user draw on the touchscreen with one or multiple fingers to unlock his mobile device, and the user is authenticated based on the geometric properties of his drawn curves as well as his behavioral and physiological characteristics. TouchIn allows the user to draw on arbitrary regions on the touchscreen without looking at it. This nice sightless feature makes TouchIn very easy to use and also robust to shoulder-surfing and smudge attacks. Comprehensive experiments on Android devices confirm the high security and usability of TouchIn.


title: Does the Standardization Process Matter? A Study of Cost Effectiveness in Hospital Drug Formularies

abstract: While research on the cost effectiveness of standardization to date has focused on the impact of different degrees of standardization, it has paid insufficient attention to the process an organization uses to formulate and implement standardized procedures. Drawing on the organizational decision-making literature and procedural fairness literature, this study identifies a number of key process features in standardization and argues that variations in these process features across organizations help account for the varying success of standardization in achieving cost effectiveness. Using hospital drug standardization for coronary artery and pneumonia cases to ground much of the argument, I analyzed inpatient discharge data from Florida, Illinois, New York, and Texas, combined with an original survey of 243 hospital pharmacy directors. The results indicated that an increasing degree of standardization was associated with cost effectiveness when the level of formal objectivity in creating the standardized procedure was high and when there was due process in resolving disputes about the standardized procedure. This finding broadly supports the argument that the cost effectiveness of standardization depends not just on the degree of standardization but also on the process by which the standardized procedures are created and implemented.


title: On Iris Spoofing Using Print Attack

abstract: Human iris contains rich textural information which serves as the key information for biometric identifications. It is very unique and one of the most accurate biometric modalities. However, spoofing techniques can be used to obfuscate or impersonate identities and increase the risk of false acceptance or false rejection. This paper revisits iris recognition with spoofing attacks and analyzes their effect on the recognition performance. Specifically, print attack with contact lens variations is used as the spoofing mechanism. It is observed that print attack and contact lens, individually and in conjunction, can significantly change the inter-personal and intra-personal distributions and thereby increase the possibility to deceive the iris recognition systems. The paper also presents the IIITD iris spoofing database, which contains over 4800 iris images pertaining to over 100 individuals with variations due to contact lens, sensor, and print attack. Finally, the paper also shows that cost effective descriptor approaches may help in counter-measuring spooking attacks.


title: Mechanism design in large games: incentives and privacy

abstract: We study the problem of implementing equilibria of complete information games in settings of incomplete information, and address this problem using"recommender mechanisms."A recommender mechanism is one that does not have the power to enforce outcomes or to force participation, rather it only has the power to suggestion outcomes on the basis of voluntary participation. We show that despite these restrictions, recommender mechanisms can implement equilibria of complete information games in settings of incomplete information under the condition that the game is large---i.e. that there are a large number of players, and any player's action affects any other's payoff by at most a small amount. Our result follows from a novel application of differential privacy. We show that any algorithm that computes a correlated equilibrium of a complete information game while satisfying a variant of differential privacy---which we call joint differential privacy---can be used as a recommender mechanism while satisfying our desired incentive properties. Our main technical result is an algorithm for computing a correlated equilibrium of a large game while satisfying joint differential privacy. Although our recommender mechanisms are designed to satisfy game-theoretic properties, our solution ends up satisfying a strong privacy property as well. No group of players can learn"much"about the type of any player outside the group from the recommendations of the mechanism, even if these players collude in an arbitrary way. As such, our algorithm is able to implement equilibria of complete information games, without revealing information about the realized types.


title: Impact of IEEE 1609.4 channel switching on the IEEE 802.11p beaconing performance

abstract: The IEEE 802.11p Wireless Access in Vehicular Environment (WAVE) protocol can only transmit packets on one channel. To support multi-channel operations, channel switching procedures have been proposed in IEEE 1609.4. This paper provides an analysis of the beaconing performance of IEEE 802.11p when these channel switching procedures are used. To analyse the performance of beaconing, simulation experiments are conducted using the simulation tool OMNeT++. An overview is given of existing solutions which can be applied to minimise the impact of channel switching on IEEE 802.11p performance. An interesting observation is that present solutions focus on optimising for use of the Service Channels, and some even deteriorate the performance of the Control Channel.


title: Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes

abstract: We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.


title: RumourEval 2019: Determining Rumour Veracity and Support for Rumours

abstract: This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year's SemEval event. Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the dangers of "fake news" have become a mainstream concern. Yet automated support for rumour checking remains in its infancy. For this reason, it is important that a shared task in this area continues to provide a focus for effort, which is likely to increase. We therefore propose a continuation in which the veracity of further rumours is determined, and as previously, supportive of this goal, tweets discussing them are classified according to the stance they take regarding the rumour. Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also included.


title: Utilization of Smartphone Applications by Anesthesia Providers

abstract: Health care-related apps provide valuable facts and have added a new dimension to knowledge sharing. The purpose of this study is to understand the pattern of utilization of mobile apps specifically created for anesthesia providers. Smartphone app stores were searched, and a survey was sent to 416 anesthesia providers at 136 anesthesiology residency programs querying specific facets of application use. Among respondents, 11.4% never used, 12.4% used less than once per month, 6.0% used once per month, 12.1% used 2-3 times per month, 13.6% used once per week, 21% used 2-3 times per week, and 23.5% used daily. Dosage/pharmaceutical apps were rated the highest as most useful. 24.6% of the participants would pay less than $2.00, 25.1% would pay $5.00, 30.3% would pay $5-$10.00, 9.6% would pay $10-$25.00, 5.1% would pay $25-$50.00, and 5.1% would pay more than $50.00 if an app saves 5-10 minutes per day or 30 minutes/week. The use of mobile phone apps is not limited to reiterating information from textbooks but provides opportunities to further the ever-changing field of anesthesiology. Our survey illustrates the convenience of apps for health care professionals. Providers must exercise caution when selecting apps to ensure best evidence-based medicine.


title: Specification and design of a prototype filter for filter bank based multicarrier transmission

abstract: The specifications of filter banks for multicarrier transmission systems with a large number of subchannels are discussed, with application to xDSL and power line communication in mind. The near perfect reconstruction (PR) modulated approach is considered and the importance, for the system, of the prototype filter delay is stressed. An existing design technique known to be particularly relevant to the context is revisited from a frequency sampling perspective. The performance results in terms of subchannel noise floor and delay are given for several filter lengths and an experimental validation is provided. Finally, an improvement to the design technique is proposed, which brings a gain of 3.3 dB in subchannel interference power level.


title: Bilayer Segmentation of Webcam Videos Using Tree-Based Classifiers

abstract: This paper presents an automatic segmentation algorithm for video frames captured by a (monocular) webcam that closely approximates depth segmentation from a stereo camera. The frames are segmented into foreground and background layers that comprise a subject (participant) and other objects and individuals. The algorithm produces correct segmentations even in the presence of large background motion with a nearly stationary foreground. This research makes three key contributions: First, we introduce a novel motion representation, referred to as “motons,” inspired by research in object recognition. Second, we propose estimating the segmentation likelihood from the spatial context of motion. The estimation is efficiently learned by random forests. Third, we introduce a general taxonomy of tree-based classifiers that facilitates both theoretical and experimental comparisons of several known classification algorithms and generates new ones. In our bilayer segmentation algorithm, diverse visual cues such as motion, motion context, color, contrast, and spatial priors are fused by means of a conditional random field (CRF) model. Segmentation is then achieved by binary min-cut. Experiments on many sequences of our videochat application demonstrate that our algorithm, which requires no initialization, is effective in a variety of scenes, and the segmentation results are comparable to those obtained by stereo systems.


title: Graphical Overlays: Using Layered Elements to Aid Chart Reading

abstract: Reading a visualization can involve a number of tasks such as extracting, comparing or aggregating numerical values. Yet, most of the charts that are published in newspapers, reports, books, and on the Web only support a subset of these tasks. In this paper we introduce graphical overlays-visual elements that are layered onto charts to facilitate a larger set of chart reading tasks. These overlays directly support the lower-level perceptual and cognitive processes that viewers must perform to read a chart. We identify five main types of overlays that support these processes; the overlays can provide (1) reference structures such as gridlines, (2) highlights such as outlines around important marks, (3) redundant encodings such as numerical data labels, (4) summary statistics such as the mean or max and (5) annotations such as descriptive text for context. We then present an automated system that applies user-chosen graphical overlays to existing chart bitmaps. Our approach is based on the insight that generating most of these graphical overlays only requires knowing the properties of the visual marks and axes that encode the data, but does not require access to the underlying data values. Thus, our system analyzes the chart bitmap to extract only the properties necessary to generate the desired overlay. We also discuss techniques for generating interactive overlays that provide additional controls to viewers. We demonstrate several examples of each overlay type for bar, pie and line charts.


title: A Bayesian network-based framework for semantic image understanding

abstract: Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian networks (BN), have proven to be an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships. In this paper, we present a general-purpose knowledge integration framework that employs BN in integrating both low-level and semantic features. The efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images. The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or outdoor scenes. With these diverse examples, we demonstrate that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data to solve inherently uncertain vision problems.


title: Electromigration of NiSi poly gated electrical fuse and its resistance behaviors induced by high temperature

abstract: Insight is given on improved behaviors of the programmed NiSi polygated electrical fuse (eFuse) during the high temperature storage (HTS) test. By using a noble transmission electron microscopy (TEM) that includes scanning transmission electron microscopy (STEM), energy dispersive x-ray spectrometry (EDS), electron energy loss spectrometry (EELS) and nano-beam electron diffraction (NBED), microstructural behavior and phase transition of NiSi in the fuse link are painstakingly investigated before and after HTS test. It is found that improved post-resistance of eFuse is attributed to the low temperature growth of Ni 3 Si 2 induced by HTS test at 250°C, which is microscopically proven by both ex-situ and in-situ TEM. In fact, Ni agglomeration, in which Ni resides around void formed in the fuse link, plays an important role of this cystallization. As results, the root causes of improved post-resistance of eFuse are qualitatively substantiated with respect to dynamic phase transformation and microstructural change in the fuse link.


title: Probabilistic neural-network structure determination for pattern classification

abstract: Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.


title: Removing Shadows from Images

abstract: Illumination conditions cause problems for many computer vision algorithms. In particular, shadows in an image can cause segmentation, tracking, or recognition algorithms to fail. In this paper we propose a method to process a 3-band colour image to locate, and subsequently remove shadows. The result is a 3-band colour image which contains all the original salient information in the image, except that the shadows are gone.We use the method set out in [1] to derive a 1-d illumination invariant shadow-free image. We then use this invariant image together with the original image to locate shadow edges. By setting these shadow edges to zero in an edge representation of the original image, and by subsequently re-integrating this edge representation by a method paralleling lightness recovery, we are able to arrive at our sought after full colour, shadow free image. Preliminary results reported in the paper show that the method is effective.A caveat for the application of the method is that we must have a calibrated camera. We show in this paper that a good calibration can be achieved simply by recording a sequence of images of a fixed outdoor scene over the course of a day. After calibration, only a single image is required for shadow removal. It is shown that the resulting calibration is close to that achievable using measurements of the camera's sensitivity functions.


title: Breast reconstruction after mastectomy for breast cancer.

abstract: Just as there have been improvements in the early detection and treatment of breast cancer, there have also been improvements in the techniques used for breast reconstruction after mastectomy. There are many reconstructive methods available, using either autologous tissue or implants,[1][1] each


title: Induction of decision trees

abstract: The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.


title: Applicability of big data analytics to massive MIMO systems

abstract: The evolving 5G standards promise green communications with enhanced data services and significant link reliability. Massive multi input multi output (MIMO) techniques are the driving force behind green communications, since they provide better energy efficiency with reduced transmit power. The massive data generated from such mobile communication systems, is a rich data source of great value. Procuring useful analytics from this precious resource, a big data aware 5G mobile communication system can be developed. A particular choice of big analytics brings in the concept of large random matrix models and single ring law. In this paper, first, big data analytics is performed in the context of a mobile user communicating to, either a massive MIMO or a massive MIMO orthogonal frequency division multiplexing (OFDM) system. Constructive insights such as transmitted (source) signal correlation analysis (attributed to certain network events), channel correlation analysis (attributed to user mobility) have been extracted. Ring law also has its roots in signal detection, which suggests that few other signal detection algorithms may be suitable candidates for signal/channel correlation analysis. Therefore, second, a proposed extension of an information theoretic criterion (ITC) based signal detection algorithm, for correlation analysis, is compared with ring law. Using massive MIMO and MIMO-OFDM system simulations, the said correlation analyses have confirmed the prevalence of ring law. Third, it is deduced that integrating big data analytics with massive MIMO system improves spectral efficiency.


title: Challenges for 5G transport networks

abstract: 5G mobile communications is seen as the enabler for the networked society where connectivity will be available anywhere and anytime to anyone and anything. The details of 5G are the subject to ongoing research and debate, mostly focused on understanding radio technologies that can enable the 5G vision. So far, less work has been dedicated to the challenges that 5G will pose to the transport network. This paper provides a first analysis of the key challenges to 5G transport in terms of capacity, flexibility and costs, for example. Different use cases are discussed as well as technology options and control plane concepts.


title: Near-Space Vehicle-Borne SAR With Reflector Antenna for High-Resolution and Wide-Swath Remote Sensing

abstract: Near-space is recognized as the atmospheric region from 20 to 100 km above the Earth's surface. Near-space vehicles offer several advantages compared to low earth orbit satellites and airplanes because near-space vehicles are not constrained by orbital mechanics and fuel consumption. These advantages provide potential for future remote sensing applications, but little related work has been published. This paper explains what near-space is and how it should be exploited for remote sensing applications. A near-space vehicle-borne synthetic aperture radar (SAR) with reflector antenna and digital beamforming on receive is proposed for high-resolution and wide-swath (HRWS) remote sensing. The system configuration, signal model, imaging scheme, system performance, and nadir echo suppression are investigated. An example system is conceptually designed, along with its system performance analysis. It is shown that the near-space vehicle-borne SAR with reflector antenna can operate with high flexibility and reconfigurability, thus enabling a satisfactory HRWS remote sensing performance.


title: A Criterion for Uniqueness of Lagrangian Trajectories for Weak Solutions of the 3D Navier-Stokes Equations

abstract: Foias, Guillope, & Temam showed in 1985 that for a given weak solution \({u\in L^\infty(0,T;L^2)\cap L^2(0,T;H^1)}\) of the three-dimensional Navier-Stokes equations on a domain Ω, one can define a ‘trajectory mapping’ \({\Phi:\Omega\times[0,T]\rightarrow\Omega}\) that gives a consistent choice of trajectory through each initial condition \({a\in\Omega,\,\xi_a(t)=\Phi(a,t)}\) , and that respects the volume-preserving property one would expect for smooth flows. The uniqueness of this mapping is guaranteed by the theory of renormalised solutions of non-smooth ODEs due to DiPerna & Lions.


title: Investigating the role of alexithymia on the empathic deficits found in schizotypy and autism spectrum traits

abstract: Alexithymia, the inability to identify and describe one's emotional experience, is elevated in many clinical populations, and related to poor interpersonal functioning. Alexithymia is also associated with empathic deficits in individuals with autism spectrum disorders. Accordingly, a better understanding of alexithymia could elucidate the nature of social-cognitive deficits transdiagnostically. We investigated alexithymia and components of empathy in relation to schizotypal and autism spectrum traits in healthy college students. Specifically, we examined higher-order components of empathic processing that involve perspective taking and other-oriented concern, which are reduced in alexithymia. Higher-order empathic processing was inversely correlated with both schizotypal and autism spectrum traits. Bootstrapping techniques revealed that alexithymia had a significant indirect effect on the relationship between higher-order empathy and these personality traits; thus, alexithymia contributes uniquely to their relationship. These findings suggest alexithymia represents one possible mechanism for the development of empathic deficits in these populations. These results are consistent with the perspective that awareness of one's own emotional state may predicate a successful empathic response to another's. This work highlights the importance of a consideration of alexithymia in elucidating the nature of empathic deficits in various clinical populations, and points to a potential point of social intervention. 2015 Published by Elsevier Ltd.


title: Unsupervised Learning from URL Corpora

abstract: This paper illustrates the utility of URL information in unsupervised learning. We outline the motivation behind the usage of URL information upfront, and present two techniques for unsupervised learning from URL corpora. First, we devise a similarity measure for URL pairs putting down the intuitions behind the same and verify its goodness by using it for clustering. Further, we outline a method for keyword identification using the similarity measure. Then, we explore the usage of character N-grams for flat clustering of URL corpora. The motivation to keep URLs compact forces the usage of a lot of variations of the same word, which is a very unique kind of forcibly inserted noise. N-gram based models are very tolerant to such noise and are very compact too. We then compare the two similarity measures using a rank correlation measure, which reveals that their similarity is very subjective on the corpus used and depends on the distinctiveness of the clusters in the corpus. Given that URLs are small entities, our techniques are magnitudes faster than unsupervised techniques on full-text corpora and require far less information than the latter. To the best of our knowledge, this is the first attempt on unsupervised learning from URL information.


title: A High Voltage Gain DC–DC Converter Integrating Coupled-Inductor and Diode–Capacitor Techniques

abstract: The high-voltage gain converter is widely employed in many industry applications, such as photovoltaic systems, fuel cell systems, electric vehicles, and high-intensity discharge lamps. This paper presents a novel single-switch high step-up nonisolated dc-dc converter integrating coupled inductor with extended voltage doubler cell and diode-capacitor techniques. The proposed converter achieves extremely large voltage conversion ratio with appropriate duty cycle and reduction of voltage stress on the power devices. Moreover, the energy stored in leakage inductance of coupled inductor is efficiently recycled to the output, and the voltage doubler cell also operates as a regenerative clamping circuit, alleviating the problem of potential resonance between the leakage inductance and the junction capacitor of output diode. These characteristics make it possible to design a compact circuit with high static gain and high efficiency for industry applications. In addition, the unexpected high-pulsed input current in the converter with coupled inductor is decreased. The operating principles and the steady-state analyses of the proposed converter are discussed in detail. Finally, a prototype circuit is implemented in the laboratory to verify the performance of the proposed converter.


title: Mechanical comparison in cadaver specimens of three different 90-degree double-plate osteosyntheses for simulated C2-type distal humerus fractures with varying bone densities.

abstract: Objectives: To investigate the bone-implant-anchorage of 90-degree double-plate osteosynthesis in simulated complete intra-articular distal humerus fractures using conventional reconstruction plates (CRP), locking compression plates (LCP), and distal humerus plates (DHP), depending on the bone mineral density (BMD) of the cadaver specimens. Methods: Groups (CRP, LCP, DHP, n = 8; LCP, DHP, n = 13) in distal humerus cadaver bones were created based on BMD. The fracture model was an unstable intraarticular distal humerus fracture with a transverse osteotomy gap representing metaphyseal comminution (AO type 13-C2.3). Flexion and extension stiffness as well as cycles until failure due to screw pullout under cyclic loading were evaluated. Estimates of BMD values, below which failure was likely to occur, were determined. Results: Stiffness values were not significantly different between groups (extension: P = 0.881, flexion: P = 0.547). Under cyclic loading, consistent screw pullout failure occurred at BMD values below about 400 mg/cm 3 for CRP and below about 300 mg/cm 3 for LCP constructs. Comparing BMD-matched groups of 8 and 13 specimens respectively, the failure rate was significantly lower for the DHP (0/8) than for the CRP (5/8; P = 0.026) and tended to be lower for the DHP (0/13) as compared to the LCP (4/13; P = 0.096). Conclusion: Bone-implant anchorage was different between locking and nonlocking plate constructs and depended on BMD. While in good bone quality implant choice was not critical, both locking plates provided superior resistance against screw loosening as compared to the CRP at low BMD values (<420 mg/cm 3 ). Based on our laboratory results, we conclude that locking plates such as the LCP and DHP are constructs designed to keep anatomical reduction in the presence of comminution and poor bone quality in a low intra-articular fracture of the distal humerus.


title: Nonlinear noise reduction : a case study on experimental data

abstract: We apply a recently proposed nonlinear noise-reduction method to time sequences from two different experiments. We demonstrate that it is not difficult to choose the parameters of this algorithm, even though we use no other information about the underlying dynamics than the data themselves. The noise reduction is very robust with respect to changes in the choice of parameters. The reliability of the result is tested by an analysis of the corrections. We discuss the effect of noise reduction on estimates of dimensions, entropies, and Liapunov exponents. For comparison we process one of the sets, densely sampled Taylor-Couette flow data, with a global filter based on singular value decomposition


title: Biomedical signal processing (in four parts)

abstract: This is the third in a series of four tutorial papers on biomedical signal processing and concerns the estimation of the power spectrum (PS) and coherence function (CF) od biomedical data. The PS is introduced and its estimation by means of the discrete Fourier transform is considered in terms of the problem of resolution in the frequency domain. The periodogram is introduced and its variance, bias and the effects of windowing and smoothing are considered. The use of the autocovariance function as a stage in power spectral estimation is described and the effects of windows in the autocorrelation domain are compared with the related effects of windows in the original time domain. The concept of coherence is introduced and the many ways in which coherence functions might be estimated are considered.


title: Big Data for Big Business? A Taxonomy of Data-Driven Business Models Used by Start-Up Firms

abstract: The purpose of this paper is to propose a taxonomy of business models used by start-up firms that rely on data as a resource of major importance for their business – aka data-driven business models (DDBMs). Furthermore, it proposes a DDBM framework to systematically analyse and compare DDBMS in the start-up world. To develop the taxonomy of DDBM, business model descriptions of a random sample of 100 start-up firms were coded using a DDBM framework, comprising of six dimensions with in total 35 features, derived from literature. By a subsequent application of clustering algorithms to the coded descriptions different types of business model were identified. Case studies of four companies from the sample were conducted to validate the findings. The taxonomy derived from our research consists of six different types of start-up DDBM. These types are characterised by a subset of six of nine clustering variables from the DDBM framework. The paper helps not only future researchers to structure their work around data-driven business models but also companies to build new DDBMs. The proposed taxonomy will help companies to position their activities in the current landscape. This paper explores how start-up business models capture value from big data. A proposed taxonomy consisting of six types of start-up business model is developed. The paper also introduces the DDBM framework with which the business models can be studied and analysed.


title: A structuration analysis of Small and Medium Enterprise (SME) adoption of E-Commerce: The case of Tanzania

abstract: This study highlights the importance of the context in E-Commerce adoption.And contributes towards analytical understanding of ICT adoption and use in SMEs.By exposing three structures that inform SMEs interaction and use of e-commerce. There have been several studies on E-Commerce adoption in developing countries. However, few have investigated least developed countries (LDCs). Of those that have, their findings tend to be descriptive in nature falling short of a theoretical contribution grounded in the sociocultural context. The purpose of this paper is to provide a theoretical analysis of E-Commerce adoption in an LDC, Tanzania. Specifically, the study seeks to identify structural practices associated with E-Commerce in Tanzanian SMEs. Using structuration theory and following an interpretivist stance the findings show that Tanzanian SMEs enact three major E-Commerce structural practices: (1) Marketing and image-building through use of websites, (2) Transacting through extensive use of mobile technology (3) Technical problem-solving through establishing partnerships. These practices are informed by organisational and environmental factors that define the sociocultural, technological and economic contextual setting. Considerable evidence is found that SMEs use websites in a limited way, as they draw upon their understanding of websites as being incompatible with the Tanzanian cultural bargaining system, which is characterised by cash transactions and face to face bargaining. Websites are viewed primarily as a platform from which SMEs can portray a sophisticated image and advertise/market their products. SMEs use mobile technology extensively, as they draw on the technologys ability to offer transactive capability, mobility and communication. Findings also point to technological challenges which SMEs face from the environment, mainly from a lack of supporting industry and institutional support. SMEs hence establish partnerships with international organisations that can support them in overcoming technological challenges. Partnerships with large ICT organisations require stringent conditions, such as the requirement for SMEs to have certification in order to be affiliated. The study gives practitioners a better understanding of how SMEs perceive E-Commerce in Tanzania amidst organisational and environmental opportunities and constraints, and in so doing practitioners can better design appropriate E-Commerce context-specific policies and interventions that address SME problems. This will ensure that available resources become utilised in a more effective manner without negative consequences.


title: Full-text citation analysis: A new method to enhance scholarly networks

abstract: In this article, we use innovative full‐text citation analysis along with supervised topic modeling and network‐analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full‐text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author‐contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be “important publications” for the target topic (keyword), and we use these cited publications to validate our topic‐ranking result and to compare different publication‐ranking lists. Evaluation results show that full‐text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency–inverted document frequency (tf–idf), language model, BM25, PageRank, and PageRank + language model (p


Abstract Syntax from Concrete Syntax


title: ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences

abstract: In this paper, we propose a new algorithm, called ClaSP for mining frequent closed sequential patterns in temporal transaction data. Our algorithm uses several efficient search space pruning methods together with a vertical database layout. Experiments on both synthetic and real datasets show that ClaSP outperforms currently well known state of the art methods, such as CloSpan.