title: An end-to-end approach to making self-folded 3D surface shapes by uniform heating

abstract: This paper presents an end-to-end approach for creating 3D shapes by self-folding planar sheets activated by uniform heating. These shapes can be used as the mechanical bodies of robots. The input to this process is a 3D geometry (e.g. an OBJ file). The output is a physical object with the specified geometry. We describe an algorithm pipeline that (1) identifies the overall geometry of the input, (2) computes a crease pattern that causes the sheet to self-fold into the desired 3D geometry when activated by uniform heating, (3) automatically generates the design of a 2D sheet with the desired pattern and (4) automatically generates the design files required to fabricate the 2D structure. We demonstrate these algorithms by applying them to complex 3D shapes. We demonstrate the fabrication of a self-folding object with over 50 faces from automatically generated design files. I. INTRODUCTION


title: A real-time continuous gesture recognition system for sign language

abstract: A large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a data glove. Sign language, which is usually known as a set of natural language with formal semantic definitions and syntactic rules, is a large set of hand gestures that are daily used to communicate with the hearing impaired. The most critical problem, end-point detection in a stream of gesture input is first solved and then statistical analysis is done according to four parameters in a gesture: posture, position, orientation, and motion. The authors have implemented a prototype system with a lexicon of 250 vocabularies and collected 196 training sentences in Taiwanese Sign Language (TWL). This system uses hidden Markov models (HMMs) for 51 fundamental postures, 6 orientations, and 8 motion primitives. In a signer-dependent way, a sentence of gestures based on these vocabularies can be continuously recognized in real-time and the average recognition rate is 80.4%,.


title: CABA: Continuous Authentication Based on BioAura

abstract: Most computer systems authenticate users only once at the time of initial login, which can lead to security concerns. Continuous authentication has been explored as an approach for alleviating such concerns. Previous methods for continuous authentication primarily use biometrics, e.g., fingerprint and face recognition, or behaviometrics, e.g., key stroke patterns. We describe CABA, a novel continuous authentication system that is inspired by and leverages the emergence of sensors for pervasive and continuous health monitoring. CABA authenticates users based on their BioAura, an ensemble of biomedical signal streams that can be collected continuously and non-invasively using wearable medical devices. While each such signal may not be highly discriminative by itself, we demonstrate that a collection of such signals, along with robust machine learning, can provide high accuracy levels. We demonstrate the feasibility of CABA through analysis of traces from the MIMIC-II dataset. We propose various applications of CABA, and describe how it can be extended to user identification and adaptive access control authorization. Finally, we discuss possible attacks on the proposed scheme and suggest corresponding countermeasures.


title: Light-load efficiency optimization for module integrated converters in photovoltaic systems

abstract: Efficient control strategies to optimize the light-load efficiency are necessary for module integrated converters (MICs) applied in photovoltaic (PV) systems. With no need for the additional current sensing, the MIC working in discontinuous conduction mode (DCM) automatically at light load can be achieved in this paper. Due to challenges in the efficiency optimization of the MIC and the trade-off between different losses in the MIC at light load, the maximum efficiency point tracking (MEPT) along with variable frequencies is proposed to further improve the light-load efficiency. With the proposed hybrid control strategies, the efficiency of a non-inverting Buck-Boost converter applied in PV systems can be greatly improved at light load. Furthermore, the proposed control strategies are analyzed in detail and verified with experimental results.


title: A Multi-Pass Approach to Large-Scale Connectomics

abstract: The field of connectomics faces unprecedented "big data" challenges. To reconstruct neuronal connectivity, automated pixel-level segmentation is required for petabytes of streaming electron microscopy data. Existing algorithms provide relatively good accuracy but are unacceptably slow, and would require years to extract connectivity graphs from even a single cubic millimeter of neural tissue. Here we present a viable real-time solution, a multi-pass pipeline optimized for shared-memory multicore systems, capable of processing data at near the terabyte-per-hour pace of multi-beam electron microscopes. The pipeline makes an initial fast-pass over the data, and then makes a second slow-pass to iteratively correct errors in the output of the fast-pass. We demonstrate the accuracy of a sparse slow-pass reconstruction algorithm and suggest new methods for detecting morphological errors. Our fast-pass approach provided many algorithmic challenges, including the design and implementation of novel shallow convolutional neural nets and the parallelization of watershed and object-merging techniques. We use it to reconstruct, from image stack to skeletons, the full dataset of Kasthuri et al. (463 GB capturing 120,000 cubic microns) in a matter of hours on a single multicore machine rather than the weeks it has taken in the past on much larger distributed systems.


title: RENC: Recursive Estimation of Node Characteristics using topological structure of complex networks

abstract: In this paper, we propose a recursive estimation method of node characteristics called RENC (recursive estimation of node characteristics) using the topological structure of a complex networks. RENC reduces the effect of noise by recursively estimating node characteristics using the topological structure of a network. In this paper, we also propose a network generation model called LRE (linkage with relative evaluation). The network generation model LRE is for simulating a social network, in which every node is likely to make decision based on relative evaluation, so that it can reproduce several characteristics of a social network. In this paper, we evaluate the effectiveness of our recursive estimation method RENC by applying RENC to several networks generated with LRE. Consequently, we show that the estimation accuracy of node characteristics can be improved by using our recursive estimation method RENC.


title: Agent-based modeling and simulation: ABMS examples

abstract: Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS "is a third way of doing science," in addition to traditional deductive and inductive reasoning (Axelrod 1997). Computational advances have made possible a growing number of agent-based models across a variety of application domains. Applications range from modeling agent behavior in the stock market, supply chains, and consumer markets, to predicting the spread of epidemics, the threat of bio-warfare, and the factors responsible for the fall of ancient civilizations. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing agent models, and illustrates the development of a simple agent-based model.


title: Irregularity detection on low tension electric installations by neural network ensembles

abstract: The volume of energy loss that Brazilian electric utilities have to deal with has been ever increasing. The electricity concessionaries are suffering significant and increasing loss in the last years, due to theft, measurement errors and many other kinds of irregularities. Therefore, there is a great concern from those companies to identify the profile of irregular customers, in order to reduce the volume of such losses. This paper presents the proposal of an intelligent system, composed of two neural networks ensembles, which intends to increase the level of accuracy in the identification of irregularities among low tension consumers. The data used to test the proposed system are from Light S.A. Company, the Rio de Janeiro concessionary. The results obtained presented a significant increase in the identification of irregular customers when compared to the current methodology employed by the company.


title: Semantic segmentation with millions of features: integrating multiple cues in a combined random forest approach

abstract: In this paper, we present a new combined approach for feature extraction, classification, and context modeling in an iterative framework based on random decision trees and a huge amount of features. A major focus of this paper is to integrate different kinds of feature types like color, geometric context, and auto context features in a joint, flexible and fast manner. Furthermore, we perform an in-depth analysis of multiple feature extraction methods and different feature types. Extensive experiments are performed on challenging facade recognition datasets, where we show that our approach significantly outperforms previous approaches with a performance gain of more than 15% on the most difficult dataset.


title: Effects of Excessive Internet Use on Undergraduate Students in Pakistan

abstract: This study was undertaken to investigate the positive and negative effects of excessive Internet use on undergraduate students. The Internet Effect Scale (IES), especially constructed by the authors to determine these effects, consisted of seven dimensions namely: behavioral problems, interpersonal problems, educational problems, psychological problems, physical problems, Internet abuse, and positive effects. The sample consisted of 200 undergraduate students studying at the GC University Lahore, Pakistan. A set of Pearson Product Moment correlations showed positive associations between time spent on the Internet and various dimensions of the IES indicating that excessive Internet use can lead to a host of problems of educational, physical, psychological and interpersonal nature. However, a greater number of students reported positive than negative effects of Internet use. Without negating the advantages of Internet, the current findings suggest that Internet use should be within reasonable limits focusin...


title: A major impediment to B2C success is...the concept 'B2C'

abstract: It's been over a decade now. We've forgotten how slow the adoption of consumer Internet commerce has been compared to other Internet growth metrics. And we're surprised when security scares like spyware and phishing result in lurches in consumer use.This paper re-visits an old theme, and finds that consumer marketing is still characterised by aggression and dominance, not sensitivity to customer needs. This conclusion is based on an examination of terms and privacy policy statements, which shows that businesses are confronting the people who buy from them with fixed, unyielding interfaces. Instead of generating trust, marketers prefer to wield power.These hard-headed approaches can work in a number of circumstances. Compelling content is one, but not everyone sells sex, gambling services, short-shelf-life news, and even shorter-shelf-life fashion goods. And, after decades of mass-media-conditioned consumer psychology research and experimentation, it's far from clear that advertising can convert everyone into salivating consumers who 'just have to have' products and services brand-linked to every new trend, especially if what you sell is groceries or handyman supplies.The thesis of this paper is that the one-dimensional, aggressive concept of B2C has long passed its use-by date. Trading is two-way -- consumers' attention, money and loyalty, in return for marketers' products and services, and vice versa.So B2C is conceptually wrong, and needs to be replaced by some buzzphrase that better conveys 'B-with-C' rather than 'to-C' and 'at-C'. Implementations of 'customised' services through 'portals' have to mature beyond data-mining-based manipulation to support two-sided relationships, and customer-managed profiles.It's all been said before, but now it's time to listen.


title: Comparison of Exact String Matching Algorithms for Biological Sequences

abstract: Exact matching of single patterns in DNA and amino acid sequences is studied. We performed an extensive experimental comparison of algorithms presented in the literature. In addition, we introduce new variations of earlier algorithms. The results of the comparison show that the new algorithms are efficient in practice.


title: Adaptive nonlocal filtering: a fast alternative to anisotropic diffusion for image enhancement

abstract: Nonlinear anisotropic diffusion algorithms provide significant improvement in image enhancement as compared to linear filters. However, the excessive computational cost of solving nonlinear PDEs precludes their use in real-time vision applications. We show that two orders of magnitude speed improvement is provided by a new image filtering paradigm in which an adaptively determined vector field specifies nonlocal application points for an image filter.


title: A framework integrating attribute-based policies into role-based access control

abstract: Integrated role-based access control (RBAC) and attribute-based access control (ABAC) is emerging as a promising paradigm. This paper proposes a framework that uses attribute-based policies to create a more traditional RBAC model. RBAC has been widely used, but has weaknesses: it is labor-intensive and time-consuming to build a model instance, and a pure RBAC system lacks flexibility to efficiently adapt to changing users, objects, and security policies. Particularly, it is impractical to manually make (and maintain) user to role assignments and role to permission assignments in industrial context characterized by a large number of users and/or security objects. ABAC has features complimentary to RBAC, and merging RBAC and ABAC has become an important research topic. This paper proposes a new approach to integrating ABAC with RBAC, by modeling RBAC in two levels. The aboveground level is a standard RBAC model extended with "environment". This level retains the simplicity of RBAC, supporting RBAC model verification/review. The "underground" level is used to represent security knowledge in terms of attribute-based policies, which automatically create the simple RBAC model in the aboveground level. These attribute-based policies bring to RBAC the advantages of ABAC: they are easy to build and easy to adapt to changes. Using this framework, we tackle the problem of permission assignment for large scale applications. This model is motivated by the characteristics and requirements of industrial control systems, and reflects in part certain approaches and practices common in the industry.


title: Grounding of Textual Phrases in Images by Reconstruction

abstract: Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual content is a challenging problem with many applications for human-computer interaction and image-text reference resolution. Few datasets provide the ground truth spatial localization of phrases, thus it is desirable to learn from data with no or little grounding supervision. We propose a novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly. During training our approach encodes the phrase using a recurrent network language model and then learns to attend to the relevant image region in order to reconstruct the input phrase. At test time, the correct attention, i.e., the grounding, is evaluated. If grounding supervision is available it can be directly applied via a loss over the attention mechanism. We demonstrate the effectiveness of our approach on the Flickr30k Entities and ReferItGame datasets with different levels of supervision, ranging from no supervision over partial supervision to full supervision. Our supervised variant improves by a large margin over the state-of-the-art on both datasets.


title: A fully-differential capacitive touch controller with input common-mode feedback for symmetric display noise cancellation

abstract: A fully-differential capacitive touch sensing method is proposed in which common-mode noise currents are symmetrically subtracted at the differential input of the first stage such that it doesn't contribute to dynamic range reduction in the later stages. And, for better sensitivity, the proposed method could accumulate signal charges in continuous time domain, and does not suffer from aliasing issues observed in many discrete-time charge integrating methods. Measurement results showed 42 dB SNR for a 1-mm diameter stylus on a 5-inch full-HD on-cell touch display panel.


title: A Scalable Data Access Layer to Manage Structured Heterogeneous Biomedical Data

abstract: This work presents a scalable data access layer, called PyEHR, designed to support the implementation of data management systems for secondary use of structured heterogeneous biomedical and clinical data. PyEHR adopts the openEHR's formalisms to guarantee the decoupling of data descriptions from implementation details and exploits structure indexing to accelerate searches. Data persistence is guaranteed by a driver layer with a common driver interface. Interfaces for two NoSQL Database Management Systems are already implemented: MongoDB and Elasticsearch. We evaluated the scalability of PyEHR experimentally through two types of tests, called "Constant Load" and "Constant Number of Records", with queries of increasing complexity on synthetic datasets of ten million records each, containing very complex openEHR archetype structures, distributed on up to ten computing nodes.


title: Monolithic multilevel GaN converter for envelope tracking in RF power amplifiers

abstract: This paper presents a monolithic multilevel converter realized in a depletion-mode GaN process and intended to operate as a drain supply modulator (DSM) for high efficiency radio-frequency (RF) power amplifiers (PAs). The custom prototype chip includes a four-level power stage with on-chip integrated gate drivers and damping networks designed to mitigate effects of parasitics during output voltage level transitions. An optimization algorithm is described to maximize the drain supply system efficiency using the level voltages and a minimum switching interval as optimization variables. The monolithic multilevel chip is used to construct a four-level converter prototype. Experimental results are presented for tracking 8 MHz sine-wave and 10 MHz LTE envelope signals. The converter output voltage exhibits fast and well damped level-to-level transients. For the 10 MHz LTE envelope signal, the converter achieves greater than 97.3% power stage efficiency at 3.5 W average output power level.


title: PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

abstract: The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.


title: Graph-based generation of action-adventure dungeon levels using answer set programming

abstract: The construction of dungeons in typical action-adventure computer games entails composing a complex arrangement of structural and temporal dependencies. It is not simple to generate dungeons with correct lock-and-key structures. In this paper we sketch a controllable approach to building graph-based models of acyclic dungeon levels via declarative constraint solving, that is capable of satisfying a range of hard gameplay and design constraints. We use a quantitative expressive range analysis to characterise the initial output of the system, present an example of the degree to which the output may be altered, and show a comparison with an alternate approach.


title: Experimental research in evolutionary computation

abstract: The Future of Experimental Research ::: We present a comprehensive, effective and very efficient methodology for the design and experimental analysis of search heuristics such as evolutionary algorithms, differential evolution, pattern search or even classical methods such as the Nelder-Mead simplex algorithm. ::: Our approach extends the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems. ::: The benefit of combining modern and classical statistical methods is demonstrated. Optimization practitioners receive valuable hints for choosing an adequate heuristic for their optimization problems -- theoreticians receive guidelines for testing results systematically on real problem instances. ::: We demonstrate how SPO improves the performance of many search heuristics significantly. However, this performance gain is not available for free. ::: Therefore, costs of this tuning process are discussed. ::: Several examples from theory and practice are used to illustrate typical pitfalls in experimentation. Software tools implementing procedures described in this tutorial are freely available.


title: Recent advances in transformation optics

abstract: Within the past a few years, transformation optics has emerged as a new research area, since it provides a general methodology and design tool for manipulating electromagnetic waves in a prescribed manner. Using transformation optics, researchers have demonstrated a host of striking phenomena and devices; many of which were only thought possible in science fiction. In this paper, we review the most recent advances in transformation optics. We focus on the theory, design, fabrication and characterization of transformation devices such as the carpet cloak, “Janus” lens and plasmonic cloak at optical frequencies, which allow routing light at the nanoscale. We also provide an outlook of the challenges and future directions in this fascinating area of transformation optics.


title: Live Speech Driven Head-and-Eye Motion Generators

abstract: This paper describes a fully automated framework to generate realistic head motion, eye gaze, and eyelid motion simultaneously based on live (or recorded) speech input. Its central idea is to learn separate yet interrelated statistical models for each component (head motion, gaze, or eyelid motion) from a prerecorded facial motion data set: 1) Gaussian Mixture Models and gradient descent optimization algorithm are employed to generate head motion from speech features; 2) Nonlinear Dynamic Canonical Correlation Analysis model is used to synthesize eye gaze from head motion and speech features, and 3) nonnegative linear regression is used to model voluntary eye lid motion and log-normal distribution is used to describe involuntary eye blinks. Several user studies are conducted to evaluate the effectiveness of the proposed speech-driven head and eye motion generator using the well-established paired comparison methodology. Our evaluation results clearly show that this approach can significantly outperform the state-of-the-art head and eye motion generation algorithms. In addition, a novel mocap+video hybrid data acquisition technique is introduced to record high-fidelity head movement, eye gaze, and eyelid motion simultaneously.


title: Singular spectrum-based matrix completion for time series recovery and prediction

abstract: Big data, characterized by huge volumes of continuously varying streams of information, present formidable challenges in terms of acquisition, processing, and transmission, especially when one considers novel technology platforms such as the Internet-of-Things and Wireless Sensor Networks. Either by design or by physical limitations, a large number of measurements never reach the central processing stations, making the task of data analytics even more problematic. In this work, we propose Singular Spectrum Matrix Completion (SS-MC), a novel approach for the simultaneous recovery of missing data and the prediction of future behavior in the absence of complete measurement sets. The goal is achieved via the solution of an efficient minimization problem which exploits the low rank representation of the associated trajectory matrices when expressed in terms of appropriately designed dictionaries obtained by leveraging the theory of Singular Spectrum Analysis. Experimental results in real datasets demonstrate that the proposed scheme is well suited for the recovery and prediction of multiple time series, achieving lower estimation error compared to state-of-the-art schemes.


Overview of ImageCLEFcaption 2017 - Image Caption Prediction and Concept Detection for Biomedical Images


title: A miniaturized, broadband power divider using loaded slow wave structures for wireless applications

abstract: In this paper, a miniaturized wideband two-way Wilkinson divider operating from 0.2 to 4.6-GHz is demonstrated. The proposed miniaturization concept depends on the use of artificial transmission lines (ATLs), to replace the conventional impedance transformers, in order to decrease the phase velocity of the propagating waves and accordingly achieving higher electric lengths using smaller physical dimensions. Broadband operation is obtained by employing a two section transformer. The implemented divider provides a fractional bandwidth of approximately 183% around a center frequency of 2.4 GHz while providing a good isolation between its outputs. A size reduction of 25%–35% approximately is achieved in comparison to the conventional divider.


title: Advances on Security Threats and Countermeasures for Cognitive Radio Networks: A Survey

abstract: Cognitive radio (CR) is regarded as an emerging technology, which equips wireless devices with the capability to adapt their operating parameters on the fly based on the radio environment, to utilize the scarce radio frequency spectrum in an efficient and opportunistic manner. However, due to the increasingly pervasive existence of smart wireless devices in cognitive radio networks (CRNs), CR systems are vulnerable to numerous security threats that affect the overall performance. There have been many significant advances on security threats and countermeasures in CRNs in the past few years. Our main goal in this paper is to present the state-of-the-art research results and approaches proposed for CRN security to protect both unlicensed secondary users and licensed primary users. Specifically, we present the recent advances on security threats/attacks and countermeasures in CRNs focusing more on the physical layer by categorizing them in terms of their types, their existence in the CR cycle, network protocol layers (exploited during their activities and defense strategies), and game theoretic approaches. The recent important attacks and countermeasures in CRNs are also summarized in the form of tables. We also present recommendations that can be followed while implementing countermeasures to enhance CRN security. With this paper, readers can have a more thorough understanding of CRN security attacks and countermeasures, as well as research trends in this area.


title: A Synthesis of the Effects of Occupational Therapy for Persons With Stroke, Part II: Remediation of Impairments

abstract: This article is the second of a two-part synthesis of research regarding the effects of occupational therapy to improve activity and participation and to reduce impairment for persons with stroke. Part I synthesized research findings for restoration of role participation and activity performance. Part II synthesizes research findings regarding the effects of occupational therapy to remediate psychosocial, cognitive-perceptual, and sensorimotor impairments. Only 29 studies involving 832 participants (mean age = 64.3 years) addressed these goals. No studies directly researched the effects of occupational therapy on depression after stroke. Eight studies addressed cognitive-perceptual abilities. The findings indicated that homemaking tasks resulted in greater improvement of cognitive ability than paper-and-pencil drills and that tasks that forced awareness of neglected space, including movement of the opposite limb into that space, improved unilateral neglect. Fifteen studies examined the effect of occupational therapy on various motor capacities after stroke. Coordinated movement improved under these conditions: (a) following written and illustrated guides for movement exercises, (b) using meaningful goal objects as targets, (c) practicing movements with specific goals, (d) moving both arms simultaneously but independently, and (e) imagining functional use of the affected limb. Research on inhibitory splinting was inconclusive. Based on these few studies and lack of replication, we could make only tentative recommendations for practice. Further definitive research is needed. Ma, H., & Trombly, C. A. (2002). A synthesis of the effects of occupational therapy for persons with stroke, part II:


Deep collective inference


title: StopWatcher: A Stop Sign Awareness Mobile Application for Driving Safety

abstract: Stop signs are a part of everyday traffic for drivers around the world. However, due to their smaller size, lack of lighting and the fact that they can become visually obscured from the road, stop signs have a tendency to be much less effective than traffic lights. To solve this problem, we developed an Android application: Stop Watcher, which is designed to alert a driver when they are approaching a stop sign using a Voice Notification System (VNS). We conducted a field test in a snowy environment. The test results demonstrate that the application can correctly detect all stop signs, even when some of them were obstructed by the snow.


title: Comparison of Control Strategies for Shunt Active Power Filters in Three-Phase Four-Wire Systems

abstract: Strategies for extracting the three-phase reference currents for shunt active power filters are compared, evaluating their performance under different source and load conditions with the new IEEE Standard 1459 power definitions. The study was applied to a three-phase four-wire system in order to include imbalance. Under balanced and sinusoidal voltages, harmonic cancellation and reactive power compensation can be attained in all the methods. However, when the voltages are distorted and/or unbalanced, the compensation capabilities are not equivalent, with some strategies unable to yield an adequate solution when the mains voltages are not ideal. Simulation and experimental results are included


title: A modified brain storm optimization

abstract: Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by human creative problem solving process. Human being is the most intelligent organism in the world and the brainstorming process popularly used by them has been demonstrated to be a significant and promising way to create great ideas for problem solving. BSO transplants the brainstorming process in human being into optimization algorithm design and gains successes. BSO generally uses the grouping, replacing, and creating operators to produce ideas as many as possible to approach the problem global optimum generation by generation. In this paper, we propose two novel designs to enhance the conventional BSO performance. The first design of the modified BSO (MBSO) is that it uses a simple grouping method (SGM) in the grouping operator instead of the clustering method to reduce the algorithm computational burden. The second design is that MBSO uses a novel idea difference strategy (IDS) in the creating operator instead of the Gaussian random strategy. The IDS not only contains open minded element to avoid the ideas being trapped by local optima, but also can match the search environment to create better new ideas for problem solving. Experiments have been conducted to illustrate the effectiveness and efficiency of the MBSO algorithm. Moreover, the contributions of SGM and IDS are investigated to show how and why MBSO can perform better than BSO.


title: Visualising convolutional neural network decisions in automated sleep scoring

abstract: Current sleep medicine relies on the supervised analysis of polysomnographic recordings, which comprise amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework for automated sleep classification, however, the lack of interpretability of its results has hampered CNN's further use in medicine. In this study, we train a CNN using as input Continuous Wavelet transformed EEG, EOG and EMG recordings from a publicly available dataset. The network achieved a 10-fold cross-validation Cohen's Kappa score of $\kappa = 0.71 \pm 0.01$. Further, we provide insights on how this network classifies individual epochs of sleep using Guided Gradient-weighted Class Activation Maps (Guided Grad-CAM). The proposed approach is able to produce fine-grained activation maps on time-frequency domain for each signal providing a useful tool for identifying relevant features in CNNs.


title: A Graph-Based Security Framework for Securing Industrial IoT Networks From Vulnerability Exploitations

abstract: Industrial IoT (IIoT) refers to the application of IoT in industrial management to improve the overall operational efficiency. With IIoT that accelerates the industrial automation process by enrolling thousands of IoT devices, strong security foundations are to be deployed befitting the distributed connectivity and constrained functionalities of the IoT devices. Recent years witnessed severe attacks exploiting the vulnerabilities in the devices of IIoT networks. Moreover, attackers can use the relations among the vulnerabilities to penetrate deep into the network. This paper addresses the security issues in IIoT network because of the vulnerabilities existing in its devices. As graphs are efficient in representing relations among entities, we propose a graphical model representing the vulnerability relations in the IIoT network. This helps to formulate the security issues in the network as graph-theoretic problems. The proposed model acts as a security framework for the risk assessment of the network. Furthermore, we propose a set of risk mitigation strategies to improve the overall security of the network. The strategies include detection and removal of the attack paths with high risk and low hop-length. We also discuss a method to identify the strongly connected vulnerabilities referred as hot-spots. A use-case is discussed and various security parameters are evaluated. The simulation results with graphs of different sizes and structures are presented for the performance evaluation of the proposed techniques against the changing dynamics of the IIoT networks.


title: Aging, alzheimer's disease, and word recognition: A review of the recent literature*

abstract: Publisher Summary This chapter describes the concepts of normal healthy aging and also compares and contrasts this with dementia and Alzheimer's disease (AD). This chapter includes demographic information, operational definitions, incidence and prevalence rates of AD, and the latest, most up-to-date information pertaining to diagnosis of AD as it relates to normal, healthy aging. It discusses the recent developments pertaining to cognitive processing (word recognition in particular) in normal healthy older adults and describes the way by which this leads into the study of similar processing in AD individuals. It examines the current models of visual and auditory word recognition. This chapter also discusses the future of word recognition processing in these two populations. It is proposed in the chapter that this very simple task (word recognition) be included among other diagnostic tools for studying normal and deficit lexical information processing. The vast majority of the visual word recognition studies discussed previously has employed either the lexical decision task or the word-naming task. The particular cognitive process that is the focus of this chapter is word recognition (visual and auditory). Visual word recognition is central to several higher-level (i.e., comprehension) processes and has figured significantly in a variety of theories and models proposed to explain and account for cognitive functioning.


title: Massive MIMO, Non-Orthogonal Multiple Access and Interleave Division Multiple Access

abstract: This paper provides an overview on the rationales in incorporating massive multiple-input multiple-output (MIMO), non-orthogonal multiple access (NOMA), and interleave division multiple access (IDMA) in a unified framework. Our emphasis is on multi-user gain that refers to the advantage of allowing multi-user transmission in massive MIMO. Such a gain can potentially offer tens or even hundreds of times of rate increase. The main difficulty in achieving multi-user gain is the reliance on accurate channel state information (CSI) in the existing schemes. With accurate CSI, both OMA and NOMA can deliver performance not far away from capacity. Without accurate CSI, however, most of the existing schemes do not work well. We outline a solution to this difficulty based on IDMA and iterative data-aided channel estimation (DACE). This scheme can offer very high throughput and is robust against the pilot contamination problem. The receiver cost is low, since only maximum ratio combining (MRC) is involved and there is no matrix inversion or decomposition. Under time division duplex, accurate CSI acquired in the up-link can be used to support low-cost down-link solutions, such as zero forcing. These findings offer useful design considerations for future systems.


title: The Impact of the Exploring Computer Science Instructional Model in Chicago Public Schools

abstract: As part of the Taste of Computing project, the Exploring Computer Science (ECS) instructional model has been expanded to many high schools in the Chicago Public Schools system. The authors report on initial outcomes showing that students value the ECS course experience, resulting in increased awareness of and interest in the field of computer science. The authors also compare these results by race and gender. The data provide a good basis for exploring the impact of meaningful computer science instruction on students from groups underrepresented in computing; of several hundred students surveyed, nearly half were female, and over half were Hispanic or African American.


title: Unfolding the role of protein misfolding in neurodegenerative diseases

abstract: Recent evidence indicates that diverse neurodegenerative diseases might have a common cause and pathological mechanism — the misfolding, aggregation and accumulation of proteins in the brain, resulting in neuronal apoptosis. Studies from different disciplines strongly support this hypothesis and indicate that a common therapy for these devastating disorders might be possible. The aim of this article is to review the literature on the molecular mechanism of protein misfolding and aggregation, its role in neurodegeneration and the potential targets for therapeutic intervention in neurodegenerative diseases. Many questions still need to be answered and future research in this field will result in exciting new discoveries that might impact other areas of biology.


title: Natural language processing based features for sarcasm detection: An investigation using bilingual social media texts

abstract: The presence of sarcasm in text can hamper the performance of sentiment analysis. The challenge is to detect the existence of sarcasm in texts. This challenge is compounded when bilingual texts are considered, for example using Malay social media data. In this paper a feature extraction process is proposed to detect sarcasm using bilingual texts; more specifically public comments on economic related posts on Facebook. Four categories of feature that can be extracted using natural language processing are considered; lexical, pragmatic, prosodic and syntactic. We also investigated the use of idiosyncratic feature to capture the peculiar and odd comments found in a text. To determine the effectiveness of the proposed process, a non-linear Support Vector Machine was used to classify texts, in terms of the identified features, according to whether they included sarcastic content or not. The results obtained demonstrate that a combination of syntactic, pragmatic and prosodic features produced the best performance with an F-measure score of 0.852.


title: Exploring mobile government from the service quality perspective

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to identify service quality dimensions and their sub-dimensions for mobile government services. Despite studies conducted on mobile services, there is lack of a comprehensive framework of mobile government service quality. Researchers and practitioners must outline a taxonomy of mobile government service quality before they can begin to test their effects empirically. It cannot be assumed that e-government is the same as m-government. Therefore, it is important to understand the dimensions that affect mobile government service quality. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Mobile government service quality dimensions were extracted from the literature on m-government from its development and transition from e-government to service models being used. This helps understand what service quality dimensions are necessary when creating more efficient, reliable, and responsible forms of m-government. The dimensions are demonstrated within a holistic framework of m-government service quality, presented for both academic and practitioner appreciation. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: This paper identifies 20 mobile government service quality sub-dimensions classified within six dimensions. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The literature on mobile government service quality is scarce. With the expectation of mobile subscriptions worldwide reaching 8 billion by 2016, it is the most lucrative time to be researching how the design of mobile government affects service quality. This paper is the first to provide information on m-government service quality dimensions available for assessment.


title: Backward compatible HDR stereo matching: a hybrid tone-mapping-based framework

abstract: Stereo matching under complex circumstances, such as low-textured areas and high dynamic range (HDR) scenes, is an ill-posed problem. In this paper, we introduce a stereo matching approach for real-world HDR scenes which is backward compatible to conventional stereo matchers. For this purpose, (1) we compare and evaluate the tone-mapped disparity maps to find the most suitable tone-mapping approach for the stereo matching purpose. Thereof, (2) we introduce a combining graph-cut based framework for effectively fusing the tone-mapped disparity maps obtained from different tone-mapped input image pairs. And finally, (3) we generate reference ground truth disparity maps for our evaluation using the original HDR images and a customized stereo matching method for HDR inputs. Our experiments show that, combining the most effective features of tone-mapped disparity maps, an improved version of the disparity is achieved. Not only our results reduce the low dynamic range (LDR), conventional disparity errors by the factor of 3, but also outperform the other well-known tone-mapped disparities by providing the closest results to the original HDR disparity maps.


title: Algorithmic stability and hypothesis complexity

abstract: We introduce a notion of algorithmic stability of learning algorithms---that we term \emph{argument stability}---that captures stability of the hypothesis output by the learning algorithm in the normed space of functions from which hypotheses are selected. The main result of the paper bounds the generalization error of any learning algorithm in terms of its argument stability. The bounds are based on martingale inequalities in the Banach space to which the hypotheses belong. We apply the general bounds to bound the performance of some learning algorithms based on empirical risk minimization and stochastic gradient descent.


title: Propagating multi-class pixel labels throughout video frames

abstract: The effective propagation of pixel labels through the spatial and temporal domains is vital to many computer vision and multimedia problems, yet little attention have been paid to the temporal/video domain propagation in the past. Previous video label propagation algorithms largely avoided the use of dense optical flow estimation due to their computational costs and inaccuracies, and relied heavily on complex (and slower) appearance models. We show in this paper the limitations of pure motion and appearance based propagation methods alone, especially the fact that their performances vary on different type of videos. We propose a probabilistic framework that estimates the reliability of the sources and automatically adjusts the weights between them. Our experiments show that the “dragging effect” of pure optical-flow-based methods are effectively avoided, while the problems of pure appearance-based methods such the large intra-class variance is also effectively handled.


title: A space alignment method for cold-start tv show recommendations

abstract: In recent years, recommendation algorithms have become one of the most active research areas driven by the enormous industrial demands. Most of the existing recommender systems focus on topics such as movie, music, e-commerce etc., which essentially differ from the TV show recommendations due to the cold-start and temporal dynamics. Both effectiveness (effectively handling the cold-start TV shows) and efficiency (efficiently updating the model to reflect the temporal data changes) concerns have to be addressed to design real-world TV show recommendation algorithms. In this paper, we introduce a novel hybrid recommendation algorithm incorporating both collaborative user-item relationship as well as item content features. The cold-start TV shows can be correctly recommended to desired users via a so called space alignment technique. On the other hand, an online updating scheme is developed to utilize new user watching behaviors. We present experimental results on a real TV watch behavior data set to demonstrate the significant performance improvement over other state-of-the-art algorithms.


title: Delegation is inheritance

abstract: Inheritance and delegation are alternate methods for incremental definition and sharing. It has commonly been believed that delegation provides a more powerful model. This paper demonstrates that there is a “natural” model of inheritance which captures all of the properties of delegation. Independently, certain constraints on the ability of delegation to capture inheritance are demonstrated. Finally, a new framework which fully captures both delegation and inheritance is outlined, and some of the ramifications of this hybrid model are explored.


title: Compact Metamaterial-Based UHF RFID Antennas: Deformed Omega and Split-Ring Resonator Structures

abstract: Over the past decade, researchers have shown significant advances in the area of radio frequency identification (RFID) and metamaterials. RFID is being applied to a wide spectrum of industries and metamaterial-based antennas are beginning to perform just as well as existing larger printed antennas. This paper presents two novel metamaterial-based antennas for passive ultra-high frequency (UHF) RFID tags. It is shown that by implementing omega-like elements and split-ring resonators into the design of an antenna for an UHF RFID tag, the overall size of the antenna can be significantly reduced to dimensions of less than 0.15λ0, while preserving the performance of the antenna.


title: A type-and-effect system for asynchronous, typed events

abstract: Implicit concurrency between handlers is important for event driven systems because it helps simultaneously promote modularity and scalability. Knowing the side-effect of the handlers is critical in these systems to avoid concurrency hazards such as data races. As event systems are dynamic because statically known and unknown handlers can register at almost any time during program execution, static effect analyses must reconcile over competing goals such as precision, soundness and modularity. We recently developed asynchronous, typed events, a system that can analyze the effects of the handlers at runtime. This mechanism utilizes runtime information to enable precise effect computation and greatly improves concurrency between handlers. In this paper, we present the formal underpinnings of asynchronous, typed events, and examine the concurrency safety properties it provides. The technical innovations of our system include a novel effect system to soundly approximate the dynamism introduced by runtime handlers registration, a static analysis to precompute the effects and a dynamic analysis that uses the precomputed effects to improve concurrency. Our design simplifies modular concurrency reasoning and avoids concurrency hazards.


title: An experimental comparison of classification algorithms for imbalanced credit scoring data sets

abstract: In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regression, neural networks and decision trees, this paper will also explore the suitability of gradient boosting, least square support vector machines and random forests for loan default prediction.Five real-world credit scoring data sets are used to build classifiers and test their performance. In our experiments, we progressively increase class imbalance in each of these data sets by randomly under-sampling the minority class of defaulters, so as to identify to what extent the predictive power of the respective techniques is adversely affected. The performance criterion chosen to measure this effect is the area under the receiver operating characteristic curve (AUC); Friedman's statistic and Nemenyi post hoc tests are used to test for significance of AUC differences between techniques.The results from this empirical study indicate that the random forest and gradient boosting classifiers perform very well in a credit scoring context and are able to cope comparatively well with pronounced class imbalances in these data sets. We also found that, when faced with a large class imbalance, the C4.5 decision tree algorithm, quadratic discriminant analysis and k-nearest neighbours perform significantly worse than the best performing classifiers.


title: Behaviorial Finance: Past Battles and Future Engagements

abstract: Market efficiency is at the center of the battle of standard finance versus behavioral finance versus investment professionals. But the battle is not joined because the term “market efficiency” has two meanings. One meaning is that investors cannot systematically beat the market. The other is that security prices are rational. Rational prices reflect only utilitarian characteristics, such as risk, not value-expressive characteristics, such as sentiment. Behavioral finance has shown, however, that value-expressive characteristics matter in both investor choices and asset prices. Therefore, the discipline of finance would do well to accept the first meaning of market efficiency and reject the notion that security prices are rational. We could then stop fighting the market efficiency battle and focus on exploring (1) asset-pricing models that reflect both value-expressive and utilitarian characteristics and (2) the benefits, both utilitarian and value expressive, that investment professionals provide to inve...


title: Programming Morphogenesis through Systems and Synthetic Biology.

abstract: Mammalian tissue development is an intricate, spatiotemporal process of self-organization that emerges from gene regulatory networks of differentiating stem cells. A major goal in stem cell biology is to gain a sufficient understanding of gene regulatory networks and cell-cell interactions to enable the reliable and robust engineering of morphogenesis. Here, we review advances in synthetic biology, single cell genomics, and multiscale modeling, which, when synthesized, provide a framework to achieve the ambitious goal of programming morphogenesis in complex tissues and organoids.


title: Cooperative Control of Distributed Energy Storage Systems in a Microgrid

abstract: Energy storage systems (ESSs) are often proposed to support the frequency control in microgrid systems. Due to the intermittency of the renewable generation and constantly changing load demand, the charging/discharging of various ESSs in an autonomous microgrid needs to be properly coordinated to ensure the supply-demand balance. Recent research has discovered that the charging/discharging efficiency of ESSs has remarkable dependence on the charging/discharging rate and state-of-charge of the ESS. This paper proposes a distributed cooperative control strategy for coordinating the ESSs to maintain the supply-demand balance and minimize the total power loss associated with charging/discharging inefficiency. The effectiveness of the proposed approach is validated by simulation results.


title: NoSQL Systems for Big Data Management

abstract: The advent of Big Data created a need for out-of-the-box horizontal scalability for data management systems. This ushered in an array of choices for Big Data management under the umbrella term NoSQL. In this paper, we provide a taxonomy and unified perspective on NoSQL systems. Using this perspective, we compare and contrast various NoSQL systems using multiple facets including system architecture, data model, query language, client API, scalability, and availability. We group current NoSQL systems into seven broad categories: Key-Value, Table-type/Column, Document, Graph, Native XML, Native Object, and Hybrid databases. We also describe application scenarios for each category to help the reader in choosing an appropriate NoSQL system for a given application. We conclude the paper by indicating future research directions.


title: A review of the use of virtual reality head-mounted displays in education and training

abstract: In the light of substantial improvements to the quality and availability of virtual reality (VR) hardware seen since 2013, this review seeks to update our knowledge about the use of head-mounted displays (HMDs) in education and training. Following a comprehensive search 21 documents reporting on experimental studies were identified, quality assessed, and analysed. The quality assessment shows that the study quality was below average according to the Medical Education Research Study Quality Instrument, especially for the studies that were designed as user evaluations of educational VR products. The review identified a number of situations where HMDs are useful for skills acquisition. These include cognitive skills related to remembering and understanding spatial and visual information and knowledge; psychomotor skills related to head-movement, such as visual scanning or observational skills; and affective skills related to controlling your emotional response to stressful or difficult situations. Outside of these situations the HMDs had no advantage when compared to less immersive technologies or traditional instruction and in some cases even proved counterproductive because of widespread cybersickness, technological challenges, or because the immersive experience distracted from the learning task.


title: Constraints on the Calibration of Performance

abstract: Abstract We examined the effect of experimenter-controlled incentives and feedback on the calibration of performance. Subjects answered 36 reading comprehension and 8 mathematical multiple-choice questions and rated the accuracy of their responses. Perfect calibration was possible only when true and estimated test performance were approximately equal. Incentives for improved performance (i.e., doubling the credit people received for correct answers) adversely affected performance and calibration compared to the same incentives for improved calibration (i.e., doubling credit for minimizing the error between true and estimated performance). Feedback had no effect on performance or accuracy nor did it interact with the incentive variable. An examination of coefficient α suggested a strong response bias by individuals when calibrating their performance; individuals tended to rate their performance accuracy consistently regardless of item difficulty or whether they answered the item correctly. Educational implications were discussed.


title: Ethical Choice in Unforeseen Circumstances

abstract: For autonomous systems to be allowed to share environments with people, their manufacturers need to guarantee that the system behaves within acceptable legal, but also ethical, limits. Formal verification has been used to test if a system behaves within specified legal limits. This paper proposes an ethical extension to a rational agent controlling an Unmanned Aircraft(UA). The resulting agent is able to distinguish among possible plans and execute the most ethical choice it has. We implement a prototype and verify that when an agent does behave unethically, it does so because no more-ethical possibility is available.


title: Mining Quality Phrases from Massive Text Corpora

abstract: Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method.


title: Improving access to school health services as perceived by school professionals

abstract: The organisation of health assessments by preventive health services focusing on children’s health and educational performance needs to be improved due to evolving health priorities such as mental health problems, reduced budgets and shortages of physicians and nurses. We studied the impact on the school professionals’ perception of access to school health services (SHS) when a triage approach was used for population-based health assessments in primary schools. The triage approach involves pre-assessments by SHS assistants, with only those children in need of follow-up being assessed by a physician or nurse. The triage approach was compared with the usual approach in which all children are assessed by physicians and nurses. We conducted a cross-sectional study, comparing school professionals’ perceptions of the triage and the usual approach to SHS. The randomly selected school professionals completed digital questionnaires about contact frequency, the approachability of SHS and the appropriateness of support from SHS. School care coordinators and teachers were invited to participate in the study, resulting in a response of 444 (35.7%) professionals from schools working with the triage approach and 320 (44.6%) professionals working with the usual approach. Respondents from schools using the triage approach had more contacts with SHS and were more satisfied with the appropriateness of support from SHS than respondents in the approach-as-usual group. No significant differences were found between the two groups in terms of the perceived approachability of SHS. School professionals were more positive about access to SHS when a triage approach to routine assessments was in place than when the usual approach was used. Countries with similar population-based SHS systems could benefit from a triage approach which gives physicians and nurses more opportunities to attend schools for consultations and assessments of children on demand.


title: High-Efficiency Doherty Power Amplifiers: Historical Aspect and Modern Trends

abstract: In modern wireless communication systems, it is very important to realize simultaneously high-efficiency and linear operation of power amplifiers. This paper-overview describes the historical aspect of the Doherty approach to the power-amplifier design introduced in 1936 and modern trends in Doherty amplifier (DA) design techniques using multistage and asymmetric multiway architectures. To increase efficiency over the power backoff range, the switchmode class-E, conventional class-F, or inverse class-F operation mode by controlling the second and third harmonics can be used in the load network. The DA with a series-connected load, and inverted, push–pull, balanced, and parallel Doherty architectures are also described and discussed. Finally, examples of the lumped DA implemented in monolithic microwave integrated circuits, digitally driven Doherty technique, and broadband capability of the two-stage DA are given.


title: An Accurate Pseudoempirical Model of Winding Loss Calculation in HF Foil and Round Conductors in Switchmode Magnetics

abstract: When operating higher up in frequency, the copper losses in transformer windings will significantly rise due to enhanced skin and proximity effect. This leads to a high need to develop new methods to accurately evaluate winding losses at higher frequencies. This paper investigates the effect of different geometrical parameters at a wide range of frequencies in order to propose a pseudoempirical formula for winding loss calculation in high-frequency transformers. A comprehensive analysis of the edge effect and ac resistance is done by performing more than 12 300 2-D finite element simulations on foil and round conductors. Unlike previous studies which mostly focused on specific case studies with limited applications, this model provides very high accuracy, especially where the most common analytical models drastically underestimate the winding losses, with a wide-range applicability which could be of interest for designers to avoid time consuming FEM simulation without compromising with the accuracy. Several transformers are built and the model is experimentally verified with a good agreement.


title: An LDA-SVM Active Learning Framework for Web Service Classification

abstract: Classifying web services and labeling them based on their functional features have played a major role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. Existing approaches leverage text mining techniques and follow a supervised learning process, which involves building a classifier from a training set of services and applying the classifier to other services. This process requires intensive human effort on labeling services in the training set. In this paper, we propose to leverage the idea of pool-based active learning to realize a scalable service classification approach. Instead of manually labeling a large number of services to construct a complete training set, the approach starts with a base classifier with a small set of training set and iteratively asks for the labels of the most informative services outside of the initial training set. By doing this, the classifier can achieve comparable accuracy compared to traditional classification method with much smaller size of training set. We use SVM as the base classifier due to its effectiveness in text classification. We also incorporate probabilistic topic models to address the issues caused by sparse term vectors generated from service descriptions and reduce the dimensions to improve the efficiency. We conducted a comprehensive experimental study on real-world service data to demonstrate the effectiveness of the proposed approach.


title: Gait Planning and Stability Control of a Quadruped Robot

abstract: In order to realize smooth gait planning and stability control of a quadruped robot, a new controller algorithm based on CPG-ZMP central pattern generator-zero moment point is put forward in this paper. To generate smooth gait and shorten the adjusting time of the model oscillation system, a new CPG model controller and its gait switching strategy based on Wilson-Cowan model are presented in the paper. The control signals of knee-hip joints are obtained by the improved multi-DOF reduced order control theory. To realize stability control, the adaptive speed adjustment and gait switch are completed by the real-time computing of ZMP. Experiment results show that the quadruped robot’s gaits are efficiently generated and the gait switch is smooth in the CPG control algorithm. Meanwhile, the stability of robot’s movement is improved greatly with the CPG-ZMP algorithm. The algorithm in this paper has good practicability, which lays a foundation for the production of the robot prototype.


title: Mobile technology and the digitization of healthcare

abstract: The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.


title: Balanced pulse generator for UWB radar application

abstract: A balanced pulse generator delivering high repetitive pulses of high voltage amplitude and pulse widths in the picosecond region is described. The output pulses of a npn silicon power transistor operating in an avalanche mode, drive a step recovery diode (SRD) waveform edge sharpener. As commercially available ultra-fast SRDs have commonly low breakdown voltage, the powerful avalanche pulses with a high voltage of 65V are first split into balanced pulses using a balun transformer. Then the balanced pulses with half amplitude are fed to ultra-fast SRD pulse shaping circuits exhibiting nearly balanced pulses with peak values of 25V (26V) and pulse widths of 140 ps (135 ps). The pulses are intended for application in near-field UWB radar sensors.


Recording and controlling the 4D light field in a microscope


title: Design of compact millimeter wave massive MIMO dual-band (28/38 GHz) antenna array for future 5G communication systems

abstract: In this article, a compact millimeter wave massive MIMO dual-band (28/38 GHz) antenna array for future 5G communication systems is proposed. A compact high gain dual-band (28/38) series fed antenna array with size of (13× 20 mm2) is nominated to design the massive MIMO antenna system. The simulated results shows that the impedance bandwidth (S11< −10 dB) is achieved around 28 GHz and 38 GHz with a high gain of 12.07 and 13.46 dB, respectively. The proposed six-sector base station will have 6 sub-sectors (array antennas) each covering a range of 40° and 30° at 28 and 38 GHz, respectively, in azimuth plane (θ).


title: Generating 3D Adversarial Point Clouds

abstract: Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds. Given many safety-critical 3D applications such as autonomous driving, it is important to study how adversarial point clouds could affect current deep 3D models. In this work, we propose several novel algorithms to craft adversarial point clouds against PointNet, a widely used deep neural network for point cloud processing. Our algorithms work in two ways: adversarial point perturbation and adversarial point generation. For point perturbation, we shift existing points negligibly. For point generation, we generate either a set of independent and scattered points or a small number (1-3) of point clusters with meaningful shapes such as balls and airplanes which could be hidden in the human psyche. In addition, we formulate six perturbation measurement metrics tailored to the attacks in point clouds and conduct extensive experiments to evaluate the proposed algorithms on the ModelNet40 3D shape classification dataset. Overall, our attack algorithms achieve a success rate higher than 99% for all targeted attacks.


title: OxIOD: The Dataset for Deep Inertial Odometry

abstract: Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and drones. Exploiting inertial data for accurate and reliable navigation and localization has attracted significant research and industrial interest, as IMU measurements are completely ego-centric and generally environment agnostic. Recent studies have shown that the notorious issue of drift can be significantly alleviated by using deep neural networks (DNNs), e.g. IONet. However, the lack of sufficient labelled data for training and testing various architectures limits the proliferation of adopting DNNs in IMU-based tasks. In this paper, we propose and release the Oxford Inertial Odometry Dataset (OxIOD), a first-of-its-kind data collection for inertial-odometry research, with all sequences having ground-truth labels. Our dataset contains 158 sequences totalling more than 42 km in total distance, much larger than previous inertial datasets. Another notable feature of this dataset lies in its diversity, which can reflect the complex motions of phone-based IMUs in various everyday usage. The measurements were collected with four different attachments (handheld, in the pocket, in the handbag and on the trolley), four motion modes (halting, walking slowly, walking normally, and running), five different users, four types of off-the-shelf consumer phones, and large-scale localization from office buildings. Deep inertial tracking experiments were conducted to show the effectiveness of our dataset in training deep neural network models and evaluate learning-based and model-based algorithms. The OxIOD Dataset is available at: this http URL


title: See the Glass Half Full: Reasoning About Liquid Containers, Their Volume and Content

abstract: Humans have rich understanding of liquid containers and their contents; for example, we can effortlessly pour water from a pitcher to a cup. Doing so requires estimating the volume of the cup, approximating the amount of water in the pitcher, and predicting the behavior of water when we tilt the pitcher. Very little attention in computer vision has been made to liquids and their containers. In this paper, we study liquid containers and their contents, and propose methods to estimate the volume of containers, approximate the amount of liquid in them, and perform comparative volume estimations all from a single RGB image. Furthermore, we show the results of the proposed model for predicting the behavior of liquids inside containers when one tilts the containers. We also introduce a new dataset of Containers Of liQuid contEnt (COQE) that contains more than 5,000 images of 10,000 liquid containers in context labelled with volume, amount of content, bounding box annotation, and corresponding similar 3D CAD models.


title: The state of ransomware. Trends and mitigation techniques

abstract: This paper contains an analysis of the payload of the popular ransomware for Windows, Android, Linux, and MacOSX platforms. Namely, VaultCrypt (CrypVault), TeslaCrypt, NanoLocker, Trojan-Ransom.Linux.Cryptor, Android Simplelocker, OSX/KeRanger-A, WannaCry, Petya, NotPetya, Cerber, Spora, Serpent ransomware were put under the microscope. A set of characteristics was proposed to be used for the analysis. The purpose of the analysis is generalization of the collected data that describes behavior and design trends of modern ransomware. The objective is to suggest ransomware threat mitigation techniques based on the obtained information. The novelty of the paper is the analysis methodology based on the chosen set of 13 key characteristics that helps to determine similarities and differences thorough the list of ransomware put under analysis. Most of the ransomware samples presented were manually analyzed by the authors eliminating contradictions in descriptions of ransomware behavior published by different malware research laboratories through verification of the payload of the latest versions of ransomware.


title: Additive manufacturing for in situ repair of osteochondral defects

abstract: Tissue engineering holds great promise for injury repair and replacement of defective body parts. While a number of techniques exist for creating living biological constructs in vitro, none have been demonstrated for in situ repair. Using novel geometric feedback-based approaches and through development of appropriate printing-material combinations, we demonstrate the in situ repair of both chondral and osteochondral defects that mimic naturally occurring pathologies. A calf femur was mounted in a custom jig and held within a robocasting-based additive manufacturing (AM) system. Two defects were induced: one a cartilage-only representation of a grade IV chondral lesion and the other a two-material bone and cartilage fracture of the femoral condyle. Alginate hydrogel was used for the repair of cartilage; a novel formulation of demineralized bone matrix was used for bone repair. Repair prints for both defects had mean surface errors less than 0.1 mm. For the chondral defect, 42.8 ? 2.6% of the surface points had errors that were within a clinically acceptable error range; however, with 1 mm path planning shift, an estimated ~75% of surface points could likely fall within the benchmark envelope. For the osteochondral defect, 83.6 ? 2.7% of surface points had errors that were within clinically acceptable limits. In addition to implications for minimally invasive AM-based clinical treatments, these proof-of-concept prints are some of the only in situ demonstrations to-date, wherein the substrate geometry was unknown a priori. The work presented herein demonstrates in situ AM, suggests potential biomedical applications and also explores in situ-specific issues, including geometric feedback, material selection and novel path planning techniques.


title: Collusion-resistant mechanisms for single-parameter agents

abstract: We consider the problem of designing mechanisms with the incentive property that no coalition of agents can engage in a collusive strategy that results in an increase in the combined utility of the coalition. For single parameter agents, we give a characterization that essentially restricts such mechanisms to those that post a "take it or leave it" price to for each agent in advance. We then consider relaxing the incentive property to only hold with high probability. In this relaxed model, we are able to design approximate profit maximizing auctions and approximately efficient auctions. We generalized these results to give a methodology for designing collusion resistant mechanisms for single parameter agents. In addition, we give several results for a weaker incentive property from the literature known as group strategyproofness.


title: Three-Dimensional Magnetic Resonance Imaging Based on Time-of-Flight Magnetic Resonance Angiography for Superficial Cerebral Arteriovenous Malformation

abstract: Direct surgery remains important for the treatment of superficial cerebral arteriovenous malformation (AVM). Surgical planning on the basis of careful analysis from various neuroimaging modalities can aid in resection of superficial AVM with favorable outcome. Three-dimensional (3D) magnetic resonance (MR) imaging reconstructed from time-of-flight (TOF) MR angiography was developed as an adjunctive tool for surgical planning of superficial AVM. 3-T TOF MR imaging without contrast medium was performed preoperatively in patients with superficial AVM. The images were imported into OsiriX imaging software and the 3D reconstructed MR image was produced using the volume rendering method. This 3D MR image could clearly visualize the surface angioarchitecture of the AVM with the surrounding brain on a single image, and clarified feeding arteries including draining veins and the relationship with sulci or fissures surrounding the nidus. 3D MR image of the whole AVM angioarchitecture was also displayed by skeletonization of the surrounding brain. Preoperative 3D MR image corresponded to the intraoperative view. Feeders on the brain surface were easily confirmed and obliterated during surgery, with the aid of the 3D MR images. 3D MR imaging for surgical planning of superficial AVM is simple and noninvasive to perform, enhances intraoperative orientation, and is helpful for successful resection.


title: Improving optical character recognition through efficient multiple system alignment

abstract: Individual optical character recognition (OCR) engines vary in the types of errors they commit in recognizing text, particularly poor quality text. By aligning the output of multiple OCR engines and taking advantage of the differences between them, the error rate based on the aligned lattice of recognized words is significantly lower than the individual OCR word error rates. This lattice error rate constitutes a lower bound among aligned alternatives from the OCR output. Results from a collection of poor quality mid-twentieth century typewritten documents demonstrate an average reduction of 55.0% in the error rate of the lattice of alternatives and a realized word error rate (WER) reduction of 35.8% in a dictionary-based selection process. As an important precursor, an innovative admissible heuristic for the A* algorithm is developed, which results in a significant reduction in state space exploration to identify all optimal alignments of the OCR text output, a necessary step toward the construction of the word hypothesis lattice. On average 0.0079% of the state space is explored to identify all optimal alignments of the documents.


title: Automatic Assessment of Japanese Text Readability Based on a Textbook Corpus

abstract: Department of Electrical Engineering and Computer Science Graduate School of Engineering Nagoya University Chikusa-ku, Nagoya, 464-8603, JAPAN ssato@nuee.nagoya-u.ac.jp, {matuyosi,kondoh}@sslab.nuee.nagoya-u.ac.jp Abstract This paper describes a method of readability measurement of Japanese texts based on a newly compiled textbook corpus. The textbook corpus consists of 1,478 sample passages extracted from 127 textbooks of elementary school, junior high school, high school, and university; it is divided into thirteen grade levels and the total size is about a million characters. For a given text passage, the readability measurement method determines the grade level to which the passage is the most similar by using character-unigram models, which are constructed from the textbook corpus. Because this method does not require sentence-boundary analysis and word-boundary analysis, it is applicable to texts that include incomplete sentences and non-regular text fragments. The performance of this method, which is measured by the correlation coefficient, is considerably high (R > 0.9); in case that the length of a text passage is limited in 25 characters, the correlation coefficient is still high (R = 0.83).


title: Adaptive Replacement Strategies for MOEA/D

abstract: Multiobjective evolutionary algorithms based on decomposition (MOEA/D) decompose a multiobjective optimization problem into a set of simple optimization subproblems and solve them in a collaborative manner. A replacement scheme, which assigns a new solution to a subproblem, plays a key role in balancing diversity and convergence in MOEA/D. This paper proposes a global replacement scheme which assigns a new solution to its most suitable subproblems. We demonstrate that the replacement neighborhood size is critical for population diversity and convergence, and develop an approach for adjusting this size dynamically. A steady-state algorithm and a generational one with this approach have been designed and experimentally studied. The experimental results on a number of test problems have shown that the proposed algorithms have some advantages.


title: An Efficient Algorithm for Non-Negative Matrix Factorization with Random Projections

abstract: Non-negative matrix factorization (NMF) is one of the most popular decomposition techniques for multivariate data. NMF is a core method for many machine-learning related computational problems, such as data compression, feature extraction, word embedding, recommender systems etc. In practice, however, its application is challenging for large datasets. The efficiency of NMF is constrained by long data loading times, by large memory requirements and by limited parallelization capabilities. Here we present a novel and efficient compressed NMF algorithm. Our algorithm applies a random compression scheme to drastically reduce the dimensionality of the problem, preserving well the pairwise distances between data points and inherently limiting the memory and communication load. Our algorithm supersedes existing methods in speed. Nonetheless, it matches the best non-compressed algorithms in reconstruction precision.


title: Associative Algorithms for Computational Creativity

abstract: Computational creativity, the generation of new, unimagined ideas or artifacts by a machine that are deemed creative by people, can be applied in the culinary domain to create novel and flavorful dishes. In fact, we have done so successfully using a combinatorial algorithm for recipe generation combined with statistical models for recipe ranking and selection. However, the algorithm of creation in our prior work may be difficult for people to interpret, understand, and ultimately adopt because the process differs from the process of human creativity theorized in the psychology and neuroscience literatures. In this paper, to address this issue, we discuss how human creativity, including in the food arena, may be built on associations and how an algorithm also built on associations can be more relatable to people so they can interact with the tool more easily. We propose a computational creativity approach that extends the data mining technique of association rule mining to generate new food recipes. We illustrate this associative algorithm on real-world culinary data.


The role of the diencephalon in regulating ocular tension.


title: Organ Segmentation in Poultry Viscera Using RGB-D

abstract: We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11 % is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28 % using only basic 2D image features.


title: Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

abstract: Although latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers the problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we exploit textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user’s preferences and targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.


title: Globally Optimized Linear Windowed Tone Mapping

abstract: This paper introduces a new tone mapping operator that performs local linear adjustments on small overlapping windows over the entire input image. While each window applies a local linear adjustment that preserves the monotonicity of the radiance values, the problem is implicitly cast as one of global optimization that satisfies the local constraints defined on each of the overlapping windows. Local constraints take the form of a guidance map that can be used to effectively suppress local high contrast while preserving details. Using this method, image structures can be preserved even in challenging high dynamic range (HDR) images that contain either abrupt radiance change, or relatively smooth but salient transitions. Another benefit of our formulation is that it can be used to synthesize HDR images from low dynamic range (LDR) images.


title: Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination

abstract: To partly address people's concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user's profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.


title: Skeptical Environmentalism: The Limits of Philosophy and Science

abstract: Preliminary Table of Contents: Acknowledgements Introduction Part 1. Knowledge 1. The Nature of Nature 2. Organism and Mechanism Part 2. Obligation 3. A Place on Earth 4. The Moral Compass Part 3. Hope 5. Environmentalism without Illusions Notes Bibliography Index


title: N: Meta-analysis in clinical trials

abstract: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.


title: A 3-dimensional sift descriptor and its application to action recognition

abstract: In this paper we introduce a 3-dimensional (3D) SIFT descriptor for video or 3D imagery such as MRI data. We also show how this new descriptor is able to better represent the 3D nature of video data in the application of action recognition. This paper will show how 3D SIFT is able to outperform previously used description methods in an elegant and efficient manner. We use a bag of words approach to represent videos, and present a method to discover relationships between spatio-temporal words in order to better describe the video data.


title: On Realistically Attacking Tor with Website Fingerprinting

abstract: Abstract Website fingerprinting allows a local, passive observer monitoring a web-browsing client’s encrypted channel to determine her web activity. Previous attacks have shown that website fingerprinting could be a threat to anonymity networks such as Tor under laboratory conditions. However, there are significant differences between laboratory conditions and realistic conditions. First, in laboratory tests we collect the training data set together with the testing data set, so the training data set is fresh, but an attacker may not be able to maintain a fresh data set. Second, laboratory packet sequences correspond to a single page each, but for realistic packet sequences the split between pages is not obvious. Third, packet sequences may include background noise from other types of web traffic. These differences adversely affect website fingerprinting under realistic conditions. In this paper, we tackle these three problems to bridge the gap between laboratory and realistic conditions for website fingerprinting. We show that we can maintain a fresh training set with minimal resources. We demonstrate several classification-based techniques that allow us to split full packet sequences effectively into sequences corresponding to a single page each. We describe several new algorithms for tackling background noise. With our techniques, we are able to build the first website fingerprinting system that can operate directly on packet sequences collected in the wild.


title: Finding Groups in Data: An Introduction to Cluster Analysis

abstract: 1. Introduction. 2. Partitioning Around Medoids (Program PAM). 3. Clustering large Applications (Program CLARA). 4. Fuzzy Analysis. 5. Agglomerative Nesting (Program AGNES). 6. Divisive Analysis (Program DIANA). 7. Monothetic Analysis (Program MONA). Appendix 1. Implementation and Structure of the Programs. Appendix 2. Running the Programs. Appendix 3. Adapting the Programs to Your Needs. Appendix 4. The Program CLUSPLOT. References. Author Index. Subject Index.


title: Mining source code repositories at massive scale using language modeling

abstract: The tens of thousands of high-quality open source software projects on the Internet raise the exciting possibility of studying software development by finding patterns across truly large source code repositories. This could enable new tools for developing code, encouraging reuse, and navigating large projects. In this paper, we build the first giga-token probabilistic language model of source code, based on 352 million lines of Java. This is 100 times the scale of the pioneering work by Hindle et al. The giga-token model is significantly better at the code suggestion task than previous models. More broadly, our approach provides a new “lens” for analyzing software projects, enabling new complexity metrics based on statistical analysis of large corpora. We call these metrics data-driven complexity metrics. We propose new metrics that measure the complexity of a code module and the topical centrality of a module to a software project. In particular, it is possible to distinguish reusable utility classes from classes that are part of a program's core logic based solely on general information theoretic criteria.


title: Fully Bayesian spatio-temporal modeling of FMRI data

abstract: We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).


title: Character Level based Detection of DGA Domain Names

abstract: Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification. Few studies are available that compare the effectiveness of these approaches for character based text classification with each other. In this paper we perform such an empirical comparison for the important cybersecurity problem of DGA detection: classifying domain names as either benign vs. produced by malware (i.e., by a Domain Generation Algorithm). Training and evaluating on a dataset with 2M domain names shows that there is surprisingly little difference between various convolutional neural network (CNN) and recurrent neural network (RNN) based architectures in terms of accuracy, prompting a preference for the simpler architectures, since they are faster to train and to score, and less prone to overfitting.