title: Toward Massive Machine Type Cellular Communications

abstract: Cellular networks have been engineered and optimized to carrying ever-increasing amounts of mobile data, but over the last few years, a new class of applications based on machine-centric communications has begun to emerge. Automated devices such as sensors, tracking devices, and meters - often referred to as machine-to-machine (M2M) or machine-type communications (MTC) - introduce an attractive revenue stream for mobile network operators, if a massive number of them can be efficiently supported. The novel technical challenges posed by MTC applications include increased overhead and control signaling as well as diverse application-specific constraints such as ultra-low complexity, extreme energy efficiency, critical timing, and continuous data intensive uploading. This paper explains the new requirements and challenges that large-scale MTC applications introduce, and provides a survey on key techniques for overcoming them. We focus on the potential of 4.5G and 5G networks to serve both the high data rate needs of conventional human-type communications (HTC) subscribers and the forecasted billions of new MTC devices. We also opine on attractive economic models that will enable this new class of cellular subscribers to grow to its full potential.


title: The Beauty Match-Up Hypothesis: Congruence between Types of Beauty and Product Images in Advertising

abstract: Abstract This paper proposes the Beauty Match-Up Hypothesis, which has two main components: 1) That perceivers distinguish multiple types of physical attractiveness; and 2) That these specific types are seen as more or less suitable (i.e., better match-ups) for certain products when paired in advertising. A set of editors at major women's magazines sorted photos of professional fashion models. A multidimensional scaling analysis of these choices revealed six distinct types of good looks, and also identified prototypical fashion models who embodied these types. The beauty types were differentially associated with a set of perfumes and women's magazines representing diverse images. Implications of beauty types for product imagery in advertising are discussed.


title: Rejection-Cascade of Gaussians: Real-time adaptive background subtraction framework

abstract: Background-Foreground classification is a well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of Gaussians(CoG). We achieve a good improvement in speed without compromising the accuracy with respect to the baseline GMM model. We demonstrate a speed-up factor of 4-5x and 17 percent average improvement in accuracy over Wallflowers surveillance datasets. The CoG is then demonstrated to over the latent space representation of images of a convolutional variational autoencoder(VAE). We provide initial results over CDW-2014 dataset, which could speed up background subtraction for deep architectures.


A study of friction models and friction compensation


title: The ECO data set and the performance of non-intrusive load monitoring algorithms

abstract: Non-intrusive load monitoring (NILM) is a popular approach to estimate appliance-level electricity consumption from aggregate consumption data of households. Assessing the suitability of NILM algorithms to be used in real scenarios is however still cumbersome, mainly because there exists no standardized evaluation procedure for NILM algorithms and the availability of comprehensive electricity consumption data sets on which to run such a procedure is still limited. This paper contributes to the solution of this problem by: (1) outlining the key dimensions of the design space of NILM algorithms; (2) presenting a novel, comprehensive data set to evaluate the performance of NILM algorithms; (3) describing the design and implementation of a framework that significantly eases the evaluation of NILM algorithms using different data sets and parameter configurations; (4) demonstrating the use of the presented framework and data set through an extensive performance evaluation of four selected NILM algorithms. Both the presented data set and the evaluation framework are made publicly available.


title: Artificial intelligence in sports on the example of weight training

abstract: The overall goal of the present study was to illustrate the potential of artificial intelligence (AI) techniques in sports on the example of weight training. The research focused in particular on the implementation of pattern recognition methods for the evaluation of performed exercises on training machines. The data acquisition was carried out using way and cable force sensors attached to various weight machines, thereby enabling the measurement of essential displacement and force determinants during training. On the basis of the gathered data, it was consequently possible to deduce other significant characteristics like time periods or movement velocities. These parameters were applied for the development of intelligent methods adapted from conventional machine learning concepts, allowing an automatic assessment of the exercise technique and providing individuals with appropriate feedback. In practice, the implementation of such techniques could be crucial for the investigation of the quality of the execution, the assistance of athletes but also coaches, the training optimization and for prevention purposes. For the current study, the data was based on measurements from 15 rather inexperienced participants, performing 3-5 sets of 1012 repetitions on a leg press machine. The initially preprocessed data was used for the extraction of significant features, on which supervised modeling methods were applied. Professional trainers were involved in the assessment and classification processes by analyzing the video recorded executions. The so far obtained modeling results showed good performance and prediction outcomes, indicating the feasibility and potency of AI techniques in assessing performances on weight training equipment automatically and providing sportsmen with prompt advice.


title: An FPGA-Based Novel Digital PWM Control Scheme for BLDC Motor Drives

abstract: Development of advanced motor drives has yielded increases in efficiency and reliability. Residential and commercial appliances such as refrigerators and air conditioning systems use conventional motor drive technology. The machines found in these applications are characterized by low efficiency and high maintenance. A brushless dc (BLDC) motor drive is characterized by higher efficiency, lower maintenance, and higher cost. In a market driven by profit margins, the appliance industry is reluctant to replace the conventional motor drives with the advanced motor drives (BLDC) due to their higher cost. Therefore, it is necessary to have a low-cost but effective BLDC motor controller. This paper lays the groundwork for the development of a new low-cost IC for control of BLDC motors. A simple novel digital pulsewidth-modulation control has been implemented for a trapezoidal BLDC motor drive system. Due to the simplistic nature of this control, it has the potential to be implemented in a low-cost application-specific integrated circuit. The novel controller is modeled and verified using simulations. Experimental verification is carried out using field-programmable gate arrays to validate the claims presented.


title: Requirements Elicitation and Validation with Real World Scenes

abstract: A requirements specification defines the requirements for the future system at a conceptual level (i.e., class or type level). In contrast, a scenario represents a concrete example of current or future system usage. In early RE phases, scenarios are used to support the definition of high level requirements (goals) to be achieved by the new system. In many cases, those goals can to a large degree be elicited by observing, documenting and analyzing scenarios about current system usage. To support the elicitation and validation of the goals achieved by the existing system and to illustrate problems of the old system, we propose to capture current system usage using rich media (e.g., video, speech, pictures, etc.) and to interrelate those observations with the goal definitions. Thus, we aim at making the abstraction process which leads to the definition of the conceptual models more transparent and traceable. We relate the parts of the observations which have caused the definition of a goal or against which a goal was validated with the corresponding goal. These interrelations provide the basis for: 1) explaining and illustrating a goal model to, e.g., untrained stakeholders and/or new team members; 2) detecting, analyzing, and resolving a different interpretation of the observations; 3) comparing different observations using computed goal annotations; and 4) refining or detailing a goal model during later process phases. Using the PRIME implementation framework, we have implemented the PRIME-CREWS environment, which supports the interrelation of conceptual models and captured system usage observations. We report on our experiences with PRIME-CREWS gained in an experimental case study.


title: Adaptive Time-based Encoding for Energy-Efficient Large Cache Architectures

abstract: Demanding larger memory footprint and relying heavily on data locality has made last-level cache (LLC) a major contributor to overall energy consumption in modern computer systems. As a result, numerous techniques have been proposed to reduce power dissipation in LLCs via low power interconnects, energy-efficient signaling, and power-aware data encoding. One such technique that has proven successful at lowering dynamic power in cache interconnects is time-based data encoding that represents data with the time elapsed between subsequent pulses on a wire. Regrettably, a time-based data representation induces excessive transmission delay per every block transfer, thereby degrading the energy efficiency of memory intensive applications. This paper presents a novel adaptive mechanism that monitors characteristics of every application at runtime and intelligently uses time-based codes for LLC interconnects, thereby alleviating the diverse impact of longer transmission delay in time-based codes while still saving significant energy. Two adaptation approaches are realized for the proposed mechanism to monitor 1) application phases and 2) memory bursts. Experimental results on a set of 12 memory intensive parallel applications on a quad-core system indicate that the proposed encoding mechanism can improve system performance by an average of 9%, which results in improving the system energy-efficiency by 7% on average. Moreover, the proposed hardware controller consumes less than 1% area of a 4MB LLC.


title: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems

abstract: Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.


title: From cognitivism to autopoiesis: towards a computational framework for the embodied mind

abstract: Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of ‘body-representations’, and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal ‘representations’ arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.


title: The Tricept robot: Inverse kinematics, manipulability analysis and closed-loop direct kinematics algorithm

abstract: This paper is aimed at presenting a study on the kinematics of the Tricept robot, which comprises a three-degree-of-freedom (dof) parallel structure having a radial link of variable length. The robot workspace is characterized and the inverse kinematics equation is obtained by using spherical coordinates. The inverse differential kinematics and statics are derived in terms of both an analytical and a geometric Jacobian, and a manipulability analysis along the various workspace directions is developed using the concept of force and velocity ellipsoids. A Jacobian-based Closed-Loop Direct Kinematics (CLDK) algorithm is presented to solve the direct kinematics problem along a given trajectory. Simulation results are illustrated for an industrial robot of the Tricept family.


title: A machine learning approach to TCP state monitoring from passive measurements

abstract: Many applications in the Internet use the reliable end-to-end Transmission Control Protocol (TCP) as a transport protocol due to practical considerations. There are many different TCP variants in use, and each variant uses a specific end-to-end congestion control algorithm to avoid congestion, while also attempting to share the underlying network capacity equally among the competing users. This paper shows how an intermediate node (e.g., a network operator) can identify the transmission state of the TCP client associated with a TCP flow by passively monitoring the TCP traffic. We demonstrate how the intermediate node can predict the Congestion Window (cwnd) size of the TCP client. The method can also be extended to predict other TCP transmission states of the client. We use a generic machine learning-based prediction approach for inferring cwnd within a flow from a passive traffic collected at an intermediate node. Our experimental results indicate the effectiveness of our prediction model with reasonably good accuracy across different scenarios and multiple TCP variants.


title: Human Action Recognition without Human

abstract: The objective of this paper is to evaluate “human action recognition without human”. Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101).


Teacher Turnover in High-Poverty Schools: What We Know and Can Do.


title: Recent Advances in Neural Program Synthesis

abstract: In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence. The successes in these problems has led researchers to consider the possibilities for intelligent systems to tackle a problem that humans have only recently themselves considered: program synthesis. This challenge is unlike others such as object recognition and speech translation, since its abstract nature and demand for rigor make it difficult even for human minds to attempt. While it is still far from being solved or even competitive with most existing methods, neural program synthesis is a rapidly growing discipline which holds great promise if completely realized. In this paper, we start with exploring the problem statement and challenges of program synthesis. Then, we examine the fascinating evolution of program induction models, along with how they have succeeded, failed and been reimagined since. Finally, we conclude with a contrastive look at program synthesis and future research recommendations for the field.


National systems of production, innovation and competence building


title: A Method of Defect Diagnosis for Integrated Grounding System in High-Speed Railway

abstract: An integrated grounding system (IGS) plays a significant role in reliable operations of a high-speed railway (HSR). The defect problems of grounding conductors have been frequently occurring due to the long-time service of an IGS and the large current from a traction system. Therefore, defect diagnosis should be conducted. Considering the topology of an IGS and the distribution feature of the surface potential, a method is proposed here to diagnose and locate the defect of the IGS. The grounding conductors can be classified into two main categories, i.e., the grounding grid in a traction substation and the integrated grounding line along a track. The surface potentials, which resulted from the IGS, can be investigated through the electromagnetic calculation of the grounding conductors. Thus, the relationship between the surface potentials and the levels of conductor corrosion can be quantified. As a result, the breakpoint location and the levels of the conductor corrosion of the grounding conductors can be determined by the surface potentials calculated. Finally, the IGS model is simulated in the current distribution, electromagnetic fields, grounding, and soil structure (CDEGS) analysis software, and the results verify the accuracy of the proposed model.


title: A 260 GHz fully integrated CMOS transceiver for wireless chip-to-chip communication

abstract: A fully integrated 260GHz OOK transceiver is demonstrated in 65nm CMOS. Communication at 10Gb/s has been verified over a range of 40 mm. The Tx/Rx dual on-chip antenna array is implemented with half-width leaky wave antennas. Each Tx consists of a quadrupler driven by a class-D−1 PA with a distributed OOK modulator, and outputs +5 dBm of EIRP. The Rx uses a double balanced mixer to down-convert to a V-band IF signal that is amplified with a wideband IF driver and demoduated on-chip.


title: Deep Classifiers from Image Tags in the Wild

abstract: This paper proposes direct learning of image classification from image tags in the wild, without filtering. Each wild tag is supplied by the user who shared the image online. Enormous numbers of these tags are freely available, and they give insight about the image categories important to users and to image classification. Our main contribution is an analysis of the Flickr 100 Million Image dataset, including several useful observations about the statistics of these tags. We introduce a large-scale robust classification algorithm, in order to handle the inherent noise in these tags, and a calibration procedure to better predict objective annotations. We show that freely available, wild tag can obtain similar or superior results to large databases of costly manual annotations.


title: Incremental linear discriminant analysis for classification of data streams

abstract: This paper presents a constructive method for deriving an updated discriminant eigenspace for classification when bursts of data that contains new classes is being added to an initial discriminant eigenspace in the form of random chunks. Basically, we propose an incremental linear discriminant analysis (ILDA) in its two forms: a sequential ILDA and a Chunk ILDA. In experiments, we have tested ILDA using datasets with a small number of classes and small-dimensional features, as well as datasets with a large number of classes and large-dimensional features. We have compared the proposed ILDA against the traditional batch LDA in terms of discriminability, execution time and memory usage with the increasing volume of data addition. The results show that the proposed ILDA can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.


title: Delta TFIDF : An Improved Feature Space for Sentiment Analysis

abstract: Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using three well known data sets.


title: Docker Cluster Management for the Cloud - Survey Results and Own Solution

abstract: Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud-aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS providers need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We close some of these gaps with our own integration components and tool enhancements, resulting in the currently most complete management suite.


title: Domino Logic with Variable Threshold Voltage Keeper

abstract: A variable threshold voltage keeper circuit technique is proposed for simultaneous power reduction and speed enhancement of domino logic circuits. The threshold voltage of a keeper transistor is dynamically modified during circuit operation to reduce contention current without sacrificing noise immunity. The variable threshold voltage keeper circuit technique enhances circuit evaluation speed by up to 60% while reducing power dissipation by 35% as compared to a standard domino (SD) logic circuit. The keeper size can be increased with the proposed technique while preserving the same delay or power characteristics as compared to a SD circuit. The proposed domino logic circuit technique offers 14% higher noise immunity as compared to a SD circuit with the same evaluation delay characteristics. Forward body biasing the keeper transistor is also proposed for improved noise immunity as compared to a SD circuit with the same keeper size. It is shown that by applying forward and reverse body biased keeper circuit techniques, the noise immunity and evaluation speed of domino logic circuits are simultaneously enhanced.


title: Investigation of virtual dimensionality and Broken Stick Rule for hyperspectral images

abstract: In an effort to assess effective linear dimensionality of hyperspectral images, a notion of virtual dimensionality (VD) has been developed, and it is being used in many papers. In this paper, we argue that VD is not a suitable method for that purpose. We then introduce an alternative method to VD that we call the Modified Broken-Stick Rule. We show a numerical example of implementing the new method on an AVIRIS image (used in previous studies) for which VD cannot be defined.


title: Discovery of topic flows of authors

abstract: With an increase in the number of Web documents, the number of proposed methods for knowledge discovery on Web documents have been increased as well. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web documents usually have time information such as publish years, so knowledge patterns over time can be captured by incorporating the time information. The temporal patterns of knowledge can be used to develop useful services such as a graph of research trends, finding similar authors (potential co-authors) to a particular author, or finding top researchers about a specific research domain. In this paper, we propose a new topic model, Author Topic-Flow (ATF) model, whose objective is to capture temporal patterns of research interests of authors over time, where each topic is associated with a research domain. The state-of-the-art model, namely Temporal Author Topic model, has the same objective as ours, where it computes the temporal patterns of authors by combining the patterns of topics. We believe that such ‘indirect’ temporal patterns will be poor than the ‘direct’ temporal patterns of our proposed model. The ATF model allows each author to have a separated variable which models the temporal patterns, so we denote it as ‘direct’ topic flow. The design of the ATF model is based on the hypothesis that ‘direct’ topic flows will be better than the ‘indirect’ topic flows. We prove the hypothesis is true by a structural comparison between the two models and show the effectiveness of the ATF model by empirical results.


title: How to critically appraise an article

abstract: Critical appraisal is a systematic process used to identify the strengths and weaknesses of a research article in order to assess the usefulness and validity of research findings. The most important components of a critical appraisal are an evaluation of the appropriateness of the study design for the research question and a careful assessment of the key methodological features of this design. Other factors that also should be considered include the suitability of the statistical methods used and their subsequent interpretation, potential conflicts of interest and the relevance of the research to one’s own practice. This Review presents a 10-step guide to critical appraisal that aims to assist clinicians to identify the most relevant high-quality studies available to guide their clinical practice.


title: Personalized Advertisement Recommendation: A Ranking Approach to Address the Ubiquitous Click Sparsity Problem

abstract: We study the problem of personalized advertisement recommendation (PAR), which consist of a user visiting a system (website) and the system displaying one of $K$ ads to the user. The system uses an internal ad recommendation policy to map the user's profile (context) to one of the ads. The user either clicks or ignores the ad and correspondingly, the system updates its recommendation policy. PAR problem is usually tackled by scalable \emph{contextual bandit} algorithms, where the policies are generally based on classifiers. A practical problem in PAR is extreme click sparsity, due to very few users actually clicking on ads. We systematically study the drawback of using contextual bandit algorithms based on classifier-based policies, in face of extreme click sparsity. We then suggest an alternate policy, based on rankers, learnt by optimizing the Area Under the Curve (AUC) ranking loss, which can significantly alleviate the problem of click sparsity. We conduct extensive experiments on public datasets, as well as three industry proprietary datasets, to illustrate the improvement in click-through-rate (CTR) obtained by using the ranker-based policy over classifier-based policies.


title: Review: A survey of active and passive indoor localisation systems

abstract: In recent years the need for indoor localisation has increased. Earlier systems have been deployed in order to demonstrate that indoor localisation can be done. Many researchers are referring to location estimation as a crucial component in numerous applications. There is no standard in indoor localisation thus the selection of an existing system needs to be done based on the environment being tracked, the accuracy and the precision required. Modern localisation systems use various techniques such as Received Signal Strength Indicator (RSSI), Time of Arrival (TOA), Time Difference of Arrival (TDOA) and Angle of Arrival (AOA). This paper is a survey of various active and passive localisation techniques developed over the years. The majority of the localisation techniques are part of the active systems class due to the necessity of tags/electronic devices carried by the person being tracked or mounted on objects in order to estimate their position. The second class called passive localisation represents the estimation of a person's position without the need for a physical device i.e. tags or sensors. The assessment of the localisation systems is based on the wireless technology used, positioning algorithm, accuracy and precision, complexity, scalability and costs. In this paper we are comparing various systems presenting their advantages and disadvantages.


title: Learning Multiple Tasks with Kernel Methods

abstract: We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis shows that the problem of estimating many task functions with regularization can be cast as a single task learning problem if a family of multi-task kernel functions we define is used. These kernels model relations among the tasks and are derived from a novel form of regularizers. Specific kernels that can be used for multi-task learning are provided and experimentally tested on two real data sets. In agreement with past empirical work on multi-task learning, the experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single-task learning particularly when there are many related tasks but few data per task.


title: A Decision Support System for Planning Promotion Time Slots

abstract: We report on the development of a Decision Support System (DSS) to plan the best assignment for the weekly promotion space of a TV station. Each product to promote has a given target audience that is best reached at specific time periods during the week. The DSS aims to maximize the total viewing for each product within its target audience while fulfilling a set of constraints defined by the user. The purpose of this paper is to describe the development and successful implementation of a heuristic-based scheduling software system that has been developed for a major Portuguese TV station.


title: COSMOS: computation offloading as a service for mobile devices

abstract: There is great potential for boosting the performance of mobile devices by offloading computation-intensive parts of mobile applications to the cloud. The full realization of this potential is hindered by a mismatch between how individual mobile devices demand computing resources and how cloud providers offer them: offloading requests from a mobile device usually require quick response, may be infrequent, and are subject to variable network connectivity, whereas cloud resources incur relatively long setup times, are leased for long time quanta, and are indifferent to network connectivity. In this paper, we present the design and implementation of the COSMOS system, which bridges this gap by providing computation offloading as a service to mobile devices. COSMOS efficiently manages cloud resources for offloading requests to both improve offloading performance seen by mobile devices and reduce the monetary cost per request to the provider. COSMOS also effectively allocates and schedules offloading requests to resolve the contention for cloud resources. Moreover, COSMOS makes offloading decisions in a risk-controlled manner to overcome the uncertainties caused by variable network connectivity and program execution. We have implemented COSMOS for Android and explored its design space through computation offloading experiments to Amazon EC2 across different applications and in various settings. We find that COSMOS, configured with the right design choices, has significant potential in reducing the cost of providing cloud resources to mobile devices while at the same time enabling mobile computation speedup.


title: Learning Multimodal Dictionaries

abstract: Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.


title: Artificial Intelligence in Medicine

abstract: This book constitutes the refereed proceedings of the 15th Conference on Artificial Intelligence in Medicine, AIME 2015, held in Pavia, Italy, in June 2015. The 19 revised full and 24 short papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: process mining and phenotyping; data mining and machine learning; temporal data mining; uncertainty and Bayesian networks; text mining; prediction in clinical practice; and knowledge representation and guidelines.


title: alpha-synuclein from platelets is not phosphorylated at serine 129 in Parkinson's disease and multiple system atrophy.

abstract: Parkinson's disease (PD) and multiple system atrophy (MSA) are characterized pathologically by inclusions in the brain containing alpha-synuclein, which is phosphorylated at serine 129. alpha-Synuclein is present not only in the brain but also in platelets; platelets have previously been used to study mitochondrial function in PD. We undertook to determine whether alpha-synuclein extracted from platelets of patients with PD and MSA is phosphorylated at serine 129 and could be used as a peripheral marker of these disorders. Immunoblots indicated that platelet alpha-synuclein is not phosphorylated at serine 129 in PD and MSA.


title: ModChecker: Kernel Module Integrity Checking in the Cloud Environment

abstract: Kernel modules are an integral part of most operating systems (OS) as they provide flexible ways of adding new functionalities (such as file system or hardware support) to the kernel without the need to recompile or reload the entire kernel. Aside from providing an interface between the user and the hardware, these modules maintain system security and reliability. Malicious kernel level exploits (e.g. code injections) provide a gateway to a system's privileged level where the attacker has access to an entire system. Such attacks may be detected by performing code integrity checks. Several commodity operating systems (such as Linux variants and MS Windows) maintain signatures of different pieces of kernel code in a database for code integrity checking purposes. However, it quickly becomes cumbersome and time consuming to maintain a database of legitimate dynamic changes in the code, such as regular module updates. In this paper we present Mod Checker, which checks in-memory kernel modules' code integrity in real time without maintaining a database of hashes. Our solution applies to virtual environments that have multiple virtual machines (VMs) running the same version of the operating system, an environment commonly found in large cloud servers. Mod Checker compares kernel module among a pool of VMs within a cloud. We thoroughly evaluate the effectiveness and runtime performance of Mod Checker and conclude that Mod Checker is able to detect any change in a kernel module's headers and executable content with minimal or no impact on the guest operating systems' performance.


title: A smart parking system to minimize searching time, fuel consumption and CO2 emission

abstract: This paper proposes a Smart Parking System (SPS) based on Internet of Thing (IoT) and Wireless Sensors Network (WSN). It is assumed that Global Positioning System (GPS) and wireless communication system is placed in the vehicle. GPS gets the position of vehicle and wireless communication send it to server. A smartphone uses for user to remotely view the parking, status of spots and book a slot manually. The proposed system decreases the searching time of free slot by giving route to free slot. As the fuel consumption and carbon dioxide (CO2) emission decreases by decreasing the distance and wise versa because there is direct relationship between them. It is shown in simulations result at the end of the paper that the objectives of the paper to minimize searching time, fuel consumption and CO2 emission achieved.


title: Automatic group sparse coding

abstract: Sparse Coding (SC), which models the data vectors as sparse linear combinations over basis vectors (i.e., dictionary), has been widely applied in machine learning, signal processing and neuroscience. Recently, one specific SC technique, Group Sparse Coding (GSC), has been proposed to learn a common dictionary over multiple different groups of data, where the data groups are assumed to be pre-defined. In practice, this may not always be the case. In this paper, we propose Automatic Group Sparse Coding (AutoGSC), which can (1) discover the hidden data groups; (2) learn a common dictionary over different data groups; and (3) learn an individual dictionary for each data group. Finally, we conduct experiments on both synthetic and real world data sets to demonstrate the effectiveness of AutoGSC, and compare it with traditional sparse coding and Non-negative Matrix Factorization (NMF) methods.


title: Collective Intelligence Education, Enhancing the Collaborative Learning

abstract: Education continually maintains challenges evidenced by an evolution since its origins. The process of learning in education must be conceived in a decentralized and collaboration context of day-to-day, in addition, the rapid and deep technological transformation carried out at the end of the 20th century and the beginning of the 21st century, especially in the Information and Communications Technologies (ICT), confronts universities with greater pressure to demonstrate the effectiveness of their educational efforts and improvement of the teachinglearning environment. Collective intelligence (CI) is an emerging field that already has a significant impact in many areas and it will have great implications for education, not only from the side of new methodologies but also as a challenge for education, which is currently more focused on the individual that in the collective. This article proposes a model of Collective Intelligence Education with ICT, combining two strategies: the management of ideas and assessment in real time, in class. A collaborative platform called FABRICIUS has been created; it supports these two elements to encourage collaboration, empowerment and student engagement in the learning process. This research proposes a list of metrics to measure individual and collective performance of a course. Findings of 11 trials in Europe and Latin America show the efficiency of the model. Finally, the necessity of connecting university management and innovation in this field is discussed.


title: How Social Media Is Changing Conflict

abstract: Social media increasingly plays a role in conflict and contentious politics. Politicians, leaders, insurgents, and protestors all have used it as a tool for communication. At the same time, scholars have turned to social media as a source of new data on conflict. I provide a framework for understanding social mediaâ€™s influence on conflict through four interrelated points: (1) social media reduces the costs of communication, (2) it increases the speed and dissemination of information, (3) scholars should focus on the strategic interaction and competitive adaption of actors in response to communication technology changes, and (4) the new data that social media provides are not only an important resource, but also fundamentally change the information available to conflict actors, thereby shaping the conflict itself. In sum, social mediaâ€™s influence on conflict defies simplistic explanations that argue that it privileges incumbents or challengers.


title: 3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation from Single Depth Images

abstract: We propose a simple, yet effective approach for real-time hand pose estimation from single depth images using three-dimensional Convolutional Neural Networks (3D CNNs). Image based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to the lack of 3D spatial information. Our proposed 3D CNN taking a 3D volumetric representation of the hand depth image as input can capture the 3D spatial structure of the input and accurately regress full 3D hand pose in a single pass. In order to make the 3D CNN robust to variations in hand sizes and global orientations, we perform 3D data augmentation on the training data. Experiments show that our proposed 3D CNN based approach outperforms state-of-the-art methods on two challenging hand pose datasets, and is very efficient as our implementation runs at over 215 fps on a standard computer with a single GPU.


title: Assessing a class of software tools

abstract: The roles and capabilities of LDRA software Testbeds and their appropriate environments have been described in a number of papers [1,2]. The way in which management uses the tools as elements of a controlled software development environment is described in [3]. The principal benefits of such use are that management has the assurance that software development standards are enforced, and has reliable information concerning project status. The explicit standards enforced by the use of these tools are described in detail in [2]. One class of these standards is that of test effectiveness, which is measured primarily through three test effectiveness metrics reinforced by a code auditing capability. This paper attempts to quantify the benefits of using such a software Testbed in providing assurance of the absence of program errors. The attempt is made from two viewpoints, the theoretical and the experimental. The theoretical aspect is important because the practical use of a tool may fail to demonstrate that the tool can be a powerful detector of a class of errors simply because no errors of that type were present in the software sample validated. Finally the paper attempts to summarise some of the experiences gained through the use of the tools over a twelve year period.


title: Thinking may be more than computing

abstract: Abstract The uncomputable parts of thinking (if there are any) can be studied in much the same spirit that Turing (1950) suggested for the study of its computable parts. We can develop precise accounts of cognitive processes that, although they involve more than computing, can still be modelled on the machines we call ‘computers’. In this paper, I want to suggest some ways that this might be done, using ideas from the mathematical theory of uncomputability (or Recursion Theory). And I want to suggest some uses to which the resulting models might be put. (The reader more interested in the models and their uses than the mathematics and its theorems, might want to skim or skip the mathematical parts.)


title: Publishing data from electronic health records while preserving privacy: A survey of algorithms

abstract: The dissemination of Electronic Health Records (EHRs) can be highly beneficial for a range of medical studies, spanning from clinical trials to epidemic control studies, but it must be performed in a way that preserves patients’ privacy. This is not straightforward, because the disseminated data need to be protected against several privacy threats, while remaining useful for subsequent analysis tasks. In this work, we present a survey of algorithms that have been proposed for publishing structured patient data, in a privacy-preserving way. We review more than 45 algorithms, derive insights on their operation, and highlight their advantages and disadvantages. We also provide a discussion of some promising directions for future research in this area.


title: Visual Question Answering with Question Representation Update (QRU)

abstract: Our method aims at reasoning over natural language questions and visual images. Given a natural language question about an image, our model updates the question representation iteratively by selecting image regions relevant to the query and learns to give the correct answer. Our model contains several reasoning layers, exploiting complex visual relations in the visual question answering (VQA) task. The proposed network is end-to-end trainable through back-propagation, where its weights are initialized using pre-trained convolutional neural network (CNN) and gated recurrent unit (GRU). Our method is evaluated on challenging datasets of COCO-QA and VQA and yields state-of-the-art performance.


title: An intuitive daylighting performance analysis and optimization approach

abstract: The effective integration of daylighting considerations into the design process requires many issues to be considered simultaneously, such as daily and seasonal variations, illumination, and thermal comfort. To address the need for early integration into the design process, a new approach called Lightsolve has been developed. Its key objectives are to support the design process using a goal-oriented approach based on iterative design improvement suggestions; to provide climate-based annual metrics in a visual and synthesized format; and to relate quantitative and qualitative performance criteria using daylighting analysis data in various forms. This methodology includes the development of a time-segmentation process to represent weather and time in a condensed form, the adaptation of daylight metrics that encompass temporal and spatial considerations, and the creation of an interactive analysis interface to explore design options and design iterations. This system relies on optimization techniques to generate these suggestions. Lightsolve allows the designer to explore other design alternatives that may better fulfil the objectives and to learn about appropriate strategies to resolve daylight or sunlight penetration issues. It offers architects and building engineers support for daylighting design that can be employed interactively within the existing design process.


title: Online product review as an indicator of users’ degree of innovativeness and product adoption time: a longitudinal analysis of text reviews

abstract: Online reviews have become extremely valuable sources of information about products and their customers as electronic commerce continues to proliferate rapidly. Previous research has shown that reviews of a product change and evolve over its life. Identifying and understanding patterns of change in reviews and the forces that shape them is an underexplored topic with substantial potential for predicting and improving the market performance of products. In this study, we analyze review text of nearly 50 products over the course of their lives. Our longitudinal analysis of reviews reveals changes in certain personality-related characteristics of buyers in ways that are consistent with the predictions of product adoption and diffusion theories. The main findings and conclusions still hold when we replicate the same procedure on reviews of a different product category. Accordingly, based on online user-generated content in the form of online reviews, this research introduces a novel empirical method for identifying the product adoption and diffusion stage. Implications of the study for theory, methodology, and practice are discussed.


title: Using fish lateral line sensing to improve seismic acquisition and processing

abstract: Bioengineering, which studies the principles and design of biological systems, is a field that has inspired the development of several technologies that are currently in use. In this work, we use concepts from the fish lateral line sensing mechanism and apply them to seismic imaging processing. The lateral line is a sensory system composed of an integrated array of mechanical sensors spanning along the fish body. We compare the array of sensors along body fish with the seismic acquisition, which employs an array of equally spaced identical mechanical sensors to image the Earth's subsurface. In both situations, the mechanical sensors capture and process mechanical vibrations from the environment to produce useful information. We explore the strategy of using the low-pass and high-pass sensors schema of fish lateral line to improve the seismic technique. We use the full-wave inversion method to compare the conventional acquisition procedure of identical sensors with alternative sets of different sensors, which mimics the fish lateral line. Our results show that the alternate sensors arrangement surpasses the performance of the conventional acquisition method, using just half of the input information. The results point at an image processing technique that is computationally more efficient and economical than the usual seismic processing method.


Articulation of English vowels in running speech: A real-time MRI study


title: Chronic immune sensory polyradiculopathy with cranial and peripheral nerve involvement

abstract: Chronic immune sensory polyradiculopathy (CISP) is a recently described restricted form of chronic inflammatory demyelinating polyradiculopathy (CIDP) [1]. It preferentially affects large myelinated fibres of the posterior roots, is associated with normal peripheral nerve conduction studies, and may respond to immunomodulatory treatment. We describe a patient with clinical features of CISP and additional cranial nerve involvement. A 61 year old woman presented with a 10 year relapsing-remitting neurological disorder. She initially presented with nausea, headache and photophobia followed by diplopia and unilateral ptosis consistent with an oculomotor nerve palsy. Intracranial CT, MRI, MRA, catheter angiography and CSF were all normal. Symptoms resolved over 18 months but an inflammatory cause was suspected after a relapse and treatment with 2 g/kg intravenous immunoglobulin (IVIg) was effective. Further relapses with additional poor balance, facial numbness and distal sensory symptoms responded promptly to IVIg. Intravenous methylprednisolone worsened her symptoms. Neurological examination revealed a partial right oculomotor nerve palsy. Pin prick sensation was reduced in a right trigeminal nerve distribution. Limb power was normal but reflexes were reduced in the upper limbs and absent in the lower limbs with flexor plantars. Romberg’s test was positive. Pin prick was reduced on the ulnar aspect of the right forearm and up to the mid-shins bilaterally. Vibration sense was reduced up to both ankles and proprioception impaired up to the right ankle. Examination after IVIg revealed that ophthalmoplegia, ptosis and a timed Romberg’s test had all improved. Systemic inflammatory/autoimmune blood tests were unremarkable and these included full blood count, ESR, CRP, ANA, ANCA, complement, rheumatoid factor, ENA, protein electrophoresis, immunofixation, acetylcholine receptor antibodies, immunoglobulins, ganglioside serology, lyme serology, treponemal serology and anti-neuronal antibodies. CSF was acellular with normal glucose and a raised protein level of 0.71 g/L (normal 0.15–0.65 g/L). Oligoclonal bands were absent. Neurophysiological data are presented in the appendix. In summary, the absolute values of all neurophysiological investigations listed were normal. However, the ulnar nerve SNAPs were significantly asymmetrical and the motor F-waves prolonged. Thermal thresholds were abnormal in the feet and right hand. Somatosensory evoked potentials were poorly formed in the right lower limb. Asymmetry of motor evoked potential latencies following cervical spine stimulation was consistent with a lesion distal to the intervertebral foramina. Electronic supplementary material The online version of this article (doi:10.1007/s00415-011-6326-0) contains supplementary material, which is available to authorized users.


A Hermeneutic Approach for Conducting Literature Reviews and Literature Searches


title: A single-ended chopper-stabilized ISFET amplifier for continuous pH measurement applications

abstract: This paper introduces a novel low-power Ion-Sensitive Field Effect Transistor (ISFET) readout configuration compatible with standard CMOS technology that utilizes chopping technique to reduce the 1/f noise and the offset of the readout circuitry. In addition, the proposed scheme tackles the well-known non-idealities of the ISFET such as the trapped charge and the long-term drift by using a resetting switch, while suppressing the imperfections of the resetting switch with the proposed chopping amplifier. The readout circuitry can be applied to both continuous time or difference pH measurements. Simulation results using standard 0.35µm CMOS technology shows 20 dB reduction in the output noise while the ISFET readout circuit including the integrator consumes 950nW.


title: Control of Rotary Series Elastic Actuator for Ideal Force-Mode Actuation in Human–Robot Interaction Applications

abstract: To realize ideal force control of robots that interact with a human, a very precise actuating system with zero impedance is desired. For such applications, a rotary series elastic actuator (RSEA) has been introduced recently. This paper presents the design of RSEA and the associated control algorithms. To generate joint torque as desired, a torsional spring is installed between a motor and a human joint, and the motor is controlled to produce a proper spring deflection for torque generation. When the desired torque is zero, the motor must follow the human joint motion, which requires that the friction and the inertia of the motor be compensated. The human joint and the body part impose the load on the RSEA. They interact with uncertain environments and their physical properties vary with time. In this paper, the disturbance observer (DOB) method is applied to make the RSEA precisely generate the desired torque under such time-varying conditions. Based on the nominal model preserved by the DOB, feedback and feedforward controllers are optimally designed for the desired performance, i.e., the RSEA: (1) exhibits very low impedance and (2) generates the desired torque precisely while interacting with a human. The effectiveness of the proposed design is verified by experiments.


title: Dynamic World Modeling Using Vertical Line Stereo

abstract: This paper describes a real time 3-D vision system which uses stereo matching of vertical edge segments. The system is designed to permit a mobile robot to avoid obstacles and to position itself within an indoor environment. The system uses real time edge tracking to lock onto stereo matches. Stereo matching is performed using a global version of dynamic programming for matching stereo segments.


title: Private traits and attributes are predictable from digital records of human behavior

abstract: We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.


title: Complete coverage path planning of mobile robots for humanitarian demining

abstract: Purpose – The purpose of this paper is to present a path planning algorithm for a non‐circular shaped mobile robot to autonomously navigate in an unknown area for humanitarian demining. For that purpose the path planning problem comes down to planning a path from some starting location to a final location in an area so that the robot covers all the reachable positions in the area while following the planned path.Design/methodology/approach – The proposed algorithm uses occupancy grid map representation of the area. Every free cell in the grid map represents a node in the graph being searched to find the complete coverage path. The complete coverage path is followed by the dynamic window algorithm, which includes robot's kinematic and dynamic constraints.Findings – The proposed algorithm finds the complete coverage path in the graph accounting for the dimensions of the mobile robot, where non‐circular shaped robots can be easily included. The algorithms are implemented under the ROS (robot operating system...


title: HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments

abstract: The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the science of happiness into users' daily lives by steering them towards behaviors that increase happiness. With the goal of building technology that can understand how people express their happy moments in text, we crowd-sourced HappyDB, a corpus of 100,000 happy moments that we make publicly available. This paper describes HappyDB and its properties, and outlines several important NLP problems that can be studied with the help of the corpus. We also apply several state-of-the-art analysis techniques to analyze HappyDB. Our results demonstrate the need for deeper NLP techniques to be developed which makes HappyDB an exciting resource for follow-on research.


title: Learning Multilingual Word Representations using a Bag-of-Words Autoencoder

abstract: Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations.


title: The Current State

abstract: As organizations adopt component-based software engineering, it becomes essential to clearly define its characteristics, advantages and organizational implications. This report presents key discussion points from a workshop on CBSE and provides a useful synthesis of participants' diverse perspectives and experiences.


title: Smart Home Mobile RFID-Based Internet-of-Things Systems and Services

abstract: Smart homes can apply new Internet-Of-Things concepts along with RFID technologies for creating ubiquitous services. This paper introduces a novel read-out method for a hierarchical wireless master-slave RFID reader architecture of multi standard NFC (Near Field Communication) and UHF (Ultra High Frequency) technologies to build a smart home service system that benefits in terms of cost, energy consumption and complexity. Various smart home service use cases such as washing programs, cooking, shopping and elderly health care are described as examples that make use of this system.


title: Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites

abstract: The present work describes the first example of a wearable salivary metabolite biosensor based on the integration of a printable enzymatic electrode on a mouthguard. The new mouthguard enzymatic biosensor, based on an immobilized lactate oxidase and a low potential detection of the peroxide product, exhibits high sensitivity, selectivity and stability using whole human saliva samples. Such non-invasive mouthguard metabolite biosensors could tender useful real-time information regarding a wearer's health, performance and stress level, and thus hold considerable promise for diverse biomedical and fitness applications.


title: People Detection and Tracking with TOF Sensor

abstract: This paper presents a method to detect, track and count people which pass a confined space using the latest 3D Time-of-Flight (ToF) sensor technology. A slicing algorithm is employed and a novel method is proposed which derives the slicing level based on the probability density function and histogram respectively. It was found that the algorithm, exploiting the data of one sensor only, works very well for normal situations. However, additional information needs to be made available to the system to solve specials cases, such as wheel chairs, push chairs or very closely spaced people.


title: CTracker : a Distributed BitTorrent Tracker Based on Chimera

abstract: There are three major open issues in the BitTorrent peer discovery system, which are not solved by any of the currently deployed solutions. These issues seriously threaten BitTorrent's scalability, ...


Energy Efficiency in Internet of Things : An Overview


title: Divided Representation of Concurrent Goals in the Human Frontal Lobes

abstract: The anterior prefrontal cortex (APC) confers on humans the ability to simultaneously pursue several goals. How does the brain’s motivational system, including the medial frontal cortex (MFC), drive the pursuit of concurrent goals? Using brain imaging, we observed that the left and right MFC, which jointly drive single-task performance according to expected rewards, divide under dual-task conditions: While the left MFC encodes the rewards driving one task, the right MFC concurrently encodes those driving the other task. The same dichotomy was observed in the lateral frontal cortex, whereas the APC combined the rewards driving both tasks. The two frontal lobes thus divide for representing simultaneously two concurrent goals coordinated by the APC. The human frontal function seems limited to driving the pursuit of two concurrent goals simultaneously.


title: High-performance symmetric-gate and CMOS-compatible V/sub t/ asymmetric-gate FinFET devices

abstract: Double-gate FinFET devices with asymmetric and symmetric polysilicon gates have been fabricated. Symmetric gate devices show drain currents competitive with fully optimized bulk silicon technologies. Asymmetric-gate devices show |V/sub t/|/spl sim/0.1 V, with off-currents less than 100 nA/um at V/sub gs/=0.


title: Survey of Domain-Specific Performance Measures in Assistive Robotic Technology ABSTRACT

abstract: Assistive robotics have been developed for several domains, including autism, eldercare, intelligent wheelchairs, assistive robotic arms, external limb prostheses, and stroke rehabilitation. Work in assistive robotics can be divided into two larger research areas: technology development, where new devices, software, and interfaces are created; and clinical application, where assistive technology is applied to a given end-user population. Moving from technology development towards clinical applications is a significant challenge. Developing performance metrics for assistive robots can unveil a larger set of challenges. For example, what well established performance measures should be used for evaluation to lend credence to a particular assistive robotic technology from a clinician's perspective? In this paper, we survey several areas of assistive robotic technology in order to demonstrate domain-specific means for evaluating the performance of an assistive robot system.


title: Quantum resistant public key cryptography: a survey

abstract: Public key cryptography is widely used to secure transactions over the Internet. However, advances in quantum computers threaten to undermine the security assumptions upon which currently used public key cryptographic algorithms are based. In this paper, we provide a survey of some of the public key cryptographic algorithms that have been developed that, while not currently in widespread use, are believed to be resistant to quantum computing based attacks and discuss some of the issues that protocol designers may need to consider if there is a need to deploy these algorithms at some point in the future.


title: QCRI live speech translation system

abstract: We present QCRI’s Arabic-to-English speech translation system. ::: It features modern web technologies to capture live audio, and broadcasts Arabic transcriptions and English translations simultaneously. Our Kaldi-based ASR system uses the Time Delay Neural Network architecture, while our Machine Translation (MT) ::: system uses both phrase-based and neural frameworks. Although our neural MT system is slower than the phrase-based system, it produces significantly better translations and is memory efficient.


title: Personalized mathematical word problem generation

abstract: Word problems are an established technique for teaching mathematical modeling skills in K-12 education. However, many students find word problems unconnected to their lives, artificial, and uninteresting. Most students find them much more difficult than the corresponding symbolic representations. To account for this phenomenon, an ideal pedagogy might involve an individually crafted progression of unique word problems that form a personalized plot. ::: ::: We propose a novel technique for automatic generation of personalized word problems. In our system, word problems are generated from general specifications using answer-set programming (ASP). The specifications include tutor requirements (properties of a mathematical model), and student requirements (personalization, characters, setting). Our system takes a logical encoding of the specification, synthesizes a word problem narrative and its mathematical model as a labeled logical plot graph, and realizes the problem in natural language. Human judges found our problems as solvable as the textbook problems, with a slightly more artificial language.


title: Tools for understanding and optimizing robotic gait training

abstract: This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.


title: Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

abstract: A successful, efficacious and safe drug must have a balance of properties, including potency against its intended target, appropriate absorption, distribution, metabolism, and elimination (ADME) properties and an acceptable safety profile. Achieving this balance of, often conflicting, requirements is a major challenge in drug discovery. Approaches to simultaneously optimizing many factors in a design are broadly described under the term 'multi-parameter optimization' (MPO). In this review, we will describe how MPO can be applied to efficiently design and select high quality compounds and describe the range of methods that have been employed in drug discovery, including; simple 'rules of thumb' such as Lipinski's rule; desirability functions; Pareto optimization; and probabilistic approaches that take into consideration the uncertainty in all drug discovery data due to predictive error and experimental variability. We will explore how these methods have been applied to predicted and experimental data to reduce attrition and improve the productivity of the drug discovery process.


title: A Quantum Spatial Graph Convolutional Neural Network using Quantum Passing Information

abstract: In this paper, we develop a new Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. Unlike state-of-the-art Graph Convolutional Neural Network (GCN) models, the proposed QSGCNN model incorporates the process of identifying transitive aligned vertices between graphs, and transforms arbitrary sized graphs into fixed-sized aligned vertex grid structures. To further learn representative graph characteristics, a new quantum spatial graph convolution is proposed and employed to extract multi-scale vertex features, in terms of quantum passing information between grid vertices of each graph. Since the quantum spatial convolution preserves the property of the input grid structures, the proposed QSGCNN model allows to directly employ the traditional convolutional neural network to further learn from the global graph topology, providing an end-to-end deep learning architecture that integrates the graph representation and learning in the quantum spatial graph convolution layer and the traditional convolutional layer for graph classifications. We demonstrate the effectiveness of the proposed QSGCNN model in terms of the theoretical connections to state-of-the-art methods. The proposed QSGCNN model addresses the shortcomings of information loss and imprecise information representation arising in existing GCN models associated with SortPooling or SumPooling layers. Experimental results on benchmark graph classification datasets demonstrate the effectiveness of the proposed QSGCNN model.


title: Development and Validation of the Smartphone Addiction Inventory (SPAI)

abstract: Objective ::: The aim of this study was to develop a self-administered scale based on the special features of smartphone. The reliability and validity of the Smartphone Addiction Inventory (SPAI) was demonstrated. ::: ::: ::: Methods ::: A total of 283 participants were recruited from Dec. 2012 to Jul. 2013 to complete a set of questionnaires, including a 26-item SPAI modified from the Chinese Internet Addiction Scale and phantom vibration and ringing syndrome questionnaire. There were 260 males and 23 females, with ages 22.9±2.0 years. Exploratory factor analysis, internal-consistency test, test-retest, and correlation analysis were conducted to verify the reliability and validity of the SPAI. Correlations between each subscale and phantom vibration and ringing were also explored. ::: ::: ::: Results ::: Exploratory factor analysis yielded four factors: compulsive behavior, functional impairment, withdrawal and tolerance. Test–retest reliabilities (intraclass correlations = 0.74–0.91) and internal consistency (Cronbach's α = 0.94) were all satisfactory. The four subscales had moderate to high correlations (0.56–0.78), but had no or very low correlation to phantom vibration/ringing syndrome. ::: ::: ::: Conclusion ::: This study provides evidence that the SPAI is a valid and reliable, self-administered screening tool to investigate smartphone addiction. Phantom vibration and ringing might be independent entities of smartphone addiction.


title: Computer-Vision-Based Fabric Defect Detection: A Survey

abstract: The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.


title: The strategy of batch using dynamic cache for streaming media

abstract: The batch is an important technique for delivering video over Internet or VoD. It is a key method to improve effect for video multicast. In this paper, we research the batch strategy of proxy cache for streaming media using dynamic cache, proposed the three kind of cache algorithm for proxy cache: window batch, size batch, efficient batch. These methods increased the length of batch, solved the problem for latency time of batch in video muticast, improved the byte-hit ratio of proxy cache for streaming media, and economized the resources of network backbone. Event-driven simulations are conducted to evaluate these kinds of strategy are better than prefix cache and segment cache.


title: On Strategyproof Conference Peer Review

abstract: We consider peer review in a conference setting where there is typically an overlap between the set of reviewers and the set of authors. This overlap can incentivize strategic reviews to influence the final ranking of one's own papers. In this work, we address this problem through the lens of social choice, and present a theoretical framework for strategyproof and efficient peer review. We first present and analyze an algorithm for reviewer-assignment and aggregation that guarantees strategyproofness and a natural efficiency property called unanimity, when the authorship graph satisfies a simple property. Our algorithm is based on the so-called partitioning method, and can be thought as a generalization of this method to conference peer review settings. We then empirically show that the requisite property on the authorship graph is indeed satisfied in the submission data from the ICLR conference, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review. Finally, we complement our positive results with negative theoretical results where we prove that under various ways of strengthening the requirements, it is impossible for any algorithm to be strategyproof and efficient.


title: An energy-harvesting sensor architecture and toolkit for building monitoring and event detection

abstract: Understanding building usage patterns and resource consumption, particularly for existing buildings, requires a sensing infrastructure for the building. Often, deploying these sensors and obtaining real-time information is hindered by installation and maintenance difficulties resulting from scaling down and powering these devices. Devices that rely on batteries are limited by the scale of the batteries and the maintenance cost of replacing them while AC mains powered sensors incur high upfront installation costs. To mitigate these burdens, we present a new architecture for designing building-monitoring focused energy-harvesting sensors. The key to this architecture is masking the inevitable inter-mittency provided by energy-harvesting with a trigger abstraction that activates the device only when there is useful work to be done. In this paper, we describe our architecture and demonstrate how it supports existing energy-harvesting sensor designs. Further, we realize three additional design points within the architecture and demonstrate how the sensors are effective at building monitoring and event detection. The sensors, however, are classically disruptive: they improve ease of installation and maintenance, but to do so, they sacrifice some fidelity and reliability. Whether this trade-off is acceptable remains to be explored, but the technology needed to do so is now here.


title: Developing a Virtual Reality Game User Experience Test Method Based on EEG Signals

abstract: User Experience (UX) of virtual reality games is usually measured by subjective and objective methods: subjective methods are most convenient way to rating usability, comfort, satisfactory, etc., however, subjective methods are more easily to be misguided. Eye tracking is one of the most popular objective methods to measure usability. However, when wearing a VR helmet, eye tracker is hard to use to record eye movement data. In our study, we developed a new method and indicator to test virtual reality game UX: Electroencephalography(EEG) signals and the brain functional connectivity (FC). We tested two kinds of VR games by using EEG signals, the results showed the FC has a significant difference for two VR games of different usability, and a significant difference were observed in Gamma band, which indicated EEG can be a good tool to analyze UX of virtual reality game.


Hybrid Human-Machine Scientific Information Extraction


title: Optimization of sparse matrix-vector multiplication on emerging multicore platforms

abstract: We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientific study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.


title: Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision.

abstract: In the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered by a combination of the availability of significantly larger data sets, thanks in part to a corresponding growth in big data, and the arrival of new graphics-processing-unit (GPU)-based hardware that enables these large data sets to be processed in reasonable timescales. Suddenly, a wide variety of long-standing problems in machine learning, artificial intelligence, and computer vision have seen significant improvements, often sufficient to break through long-standing performance barriers. Across multiple fields, these achievements have inspired the development of improved tools and methodologies leading to even broader applicability of deep learning. The new generation of smart assistants, such as Alexa, Hello Google, and others, have their roots and learning algorithms tied to deep learning. In this article, we review the current state of deep learning, explain what it is, why it has managed to improve on the long-standing techniques of conventional neural networks, and, most importantly, how you can get started with adopting deep learning into your own research activities to solve both new and old problems and build better, smarter consumer devices and services.


title: Toward semantic interoperability in oneM2M architecture

abstract: The oneM2M standard is a global initiative led jointly by major standards organizations around the world in order to develop a unique architecture for M2M communications. Prior standards, and also oneM2M, while focusing on achieving interoperability at the communication level, do not achieve interoperability at the semantic level. An expressive ontology for IoT called IoT-O is proposed, making best use of already defined ontologies in specific domains such as sensor, observation, service, quantity kind, units, or time. IoT-O also defines some missing concepts relevant for IoT such as thing, node, actuator, and actuation. The extension of the oneM2M standard to support semantic data interoperability based on IoT-O is discussed. Finally, through comprehensive use cases, benefits of the extended standard are demonstrated, ranging from heterogeneous device interoperability to autonomic behavior achieved by automated reasoning.


title: Crista egregia: a geometrical model of the crista ampullaris, a sensory surface that detects head rotations

abstract: The crista ampullaris is the epithelium at the end of the semicircular canals in the inner ear of vertebrates, which contains the sensory cells involved in the transduction of the rotational head movements into neuronal activity. The crista surface has the form of a saddle, or a pair of saddles separated by a crux, depending on the species and the canal considered. In birds, it was described as a catenoid by Landolt et al. (J Comp Neurol 159(2):257-287, doi: 10.1002/cne.901590207 , 1972). In the present work, we establish that this particular form results from principles of invariance maximization and energy minimization. The formulation of the invariance principle was inspired by Takumida (Biol Sci Space 15(4):356-358, 2001). More precisely, we suppose that in functional conditions, the equations of linear elasticity are valid, and we assume that in a certain domain of the cupula, in proximity of the crista surface, (1) the stress tensor of the deformed cupula is invariant under the gradient of the pressure, (2) the dissipation of energy is minimum. Then, we deduce that in this domain the crista surface is a minimal surface and that it must be either a planar, or helicoidal Scherk surface, or a piece of catenoid, which is the unique minimal surface of revolution. If we add the hypothesis that the direction of invariance of the stress tensor is unique and that a bilateral symmetry of the crista exists, only the catenoid subsists. This finding has important consequences for further functional modeling of the role of the vestibular system in head motion detection and spatial orientation.


title: Highway Networks

abstract: There is plenty of theoretical and empirical evidence that depth of neural networks is a crucial ingredient for their success. However, network training becomes more difficult with increasing depth and training of very deep networks remains an open problem. In this extended abstract, we introduce a new architecture designed to ease gradient-based training of very deep networks. We refer to networks with this architecture as highway networks, since they allow unimpeded information flow across several layers on"information highways". The architecture is characterized by the use of gating units which learn to regulate the flow of information through a network. Highway networks with hundreds of layers can be trained directly using stochastic gradient descent and with a variety of activation functions, opening up the possibility of studying extremely deep and efficient architectures.


title: GS1 Connected Car Using EPCIS-ONS System

abstract: Recently, a number of car companies focus on collecting and analyzing data from connected car to provide accurate and variety of integrated information services to users. To provide integrated connected car services, however, there are still two main problems in existing systems. First, there are not well-defined standard car-related data models for data sharing which is basic prerequisite of integrated service. Second, each company stores the data to the closed server and blocks the access so that the data is available only inside the respective data silos. Because of these reasons, it is hard to combine various data to provide integrated information service. To solve the problems, we propose two solutions. 1) Define events of the car life cycle, and data models for data sharing. 2) Establish open system for data sharing and service registration based on GS1 standard. Upon proposed models and system, we have implemented an example service, traffic light control, as a third party service to show the feasibility of our system. Therefore, this research enables to provide open system to car companies and third party service providers to make services associated with the connected car.


title: Cognitive Monitoring via Eye Tracking in Virtual Reality Pedestrian Environments

abstract: Recent advances in virtual and augmented reality have led to the development of a number of simulations for different applications. In particular, virtual reality simulations for monitoring, evaluation, training, and education have started to emerge. In this work, we introduce a virtual reality environment that provides an immersive traffic simulation designed to observe behavior and monitor relevant skills and abilities of pedestrians who may be at risk, such as elderly persons with cognitive impairments. Methods for interaction using hand and arm gestures are complemented by passive, integrated eye tracking to allow for monitoring of cognition, reaction times, and behavior.


title: Review and Evaluation of Mindfulness-Based iPhone Apps

abstract: Background ::: ::: There is growing evidence for the positive impact of mindfulness on wellbeing. Mindfulness-based mobile apps may have potential as an alternative delivery medium for training. While there are hundreds of such apps, there is little information on their quality. ::: ::: Objective ::: ::: This study aimed to conduct a systematic review of mindfulness-based iPhone mobile apps and to evaluate their quality using a recently-developed expert rating scale, the Mobile Application Rating Scale (MARS). It also aimed to describe features of selected high-quality mindfulness apps. ::: ::: Methods ::: ::: A search for “mindfulness” was conducted in iTunes and Google Apps Marketplace. Apps that provided mindfulness training and education were included. Those containing only reminders, timers or guided meditation tracks were excluded. An expert rater reviewed and rated app quality using the MARS engagement, functionality, visual aesthetics, information quality and subjective quality subscales. A second rater provided MARS ratings on 30% of the apps for inter-rater reliability purposes. ::: ::: Results ::: ::: The “mindfulness” search identified 700 apps. However, 94 were duplicates, 6 were not accessible and 40 were not in English. Of the remaining 560, 23 apps met inclusion criteria and were reviewed. The median MARS score was 3.2 (out of 5.0), which exceeded the minimum acceptable score (3.0). The Headspace app had the highest average score (4.0), followed by Smiling Mind (3.7), iMindfulness (3.5) and Mindfulness Daily (3.5). There was a high level of inter-rater reliability between the two MARS raters. ::: ::: Conclusions ::: ::: Though many apps claim to be mindfulness-related, most were guided meditation apps, timers, or reminders. Very few had high ratings on the MARS subscales of visual aesthetics, engagement, functionality or information quality. Little evidence is available on the efficacy of the apps in developing mindfulness.


title: Stakeholder Management and Sport Facilities: A Case Study of the Emirates Stadium

abstract: This case study presents an analysis of how Arsenal Football Club, a leading football club in England, manages different stakeholders in the context of the Emirates Stadium. The objectives of this case study are threefold. First, it identifies different stakeholders of the Emirates Stadium and, using the Mendelow power/interest matrix, considers the different levels of stakeholder power and interest. The second aim of this case study is to illustrate various stakeholder management strategies that Arsenal Football Club has implemented in relation to four stakeholder groups – the London Borough of Islington; local community residents; supporters; and disabled supporters. The third objective is to present a number of best practice recommendations in relation to stakeholder management and sport facility management. While best practice in sport facility management has tended to focus on internal-facing benchmarking this case highlights the importance of successful stakeholder management to achieving success in facility operation. However it notes that the tendency to focus on the qualitative aspects of stadium management through stakeholder consultation cannot be measured by traditional quantitative benchmarks.


title: Orbital Angular Momentum (OAM) Antennas via Mode Combining and Canceling in Near-field

abstract: Orbital angular momentum (OAM) mode combining and canceling in the near-field was investigated using a Cassegrain dual-reflectarray antenna composed of multiple microstrip patches on the main and sub-reflectarrays. Microstrip patches on dielectric substrates were designed to radiate the particular OAM modes for arithmetic mode combining, where two OAM wave-generating reflectarrays are very closely placed in the near-field. We conducted near-field antenna measurements at 18 [GHz] by manually replacing the sub-reflectarray substrates with different OAM mode numbers of 0, ±1, when the OAM mode number of the main reflectarray was fixed to +1. We subsequently checked the azimuthal phase distributions of the reflected total electromagnetic waves in the near-field, and verified that the OAM waves mutually reflected from the main and sub-reflectarrays are added or subtracted to each other according to their OAM mode numbers. Based on our proposal, an OAM mode-canceling reflectarray antenna was designed, and the following measurements indicate that the antenna has a better reflection bandwidth and antenna gain than a conventional reflectarray antenna. The concept of OAM mode canceling in the near-field can contribute widely to a new type of low-profile, broad-reflection bandwidth, and high-gain antenna.


title: Anatomical study of the paraspinal approach to the lumbar spine

abstract: The original description of the paraspinal posterior approach to the lumbar spine was for spinal fusion, especially regarding lumbosacral spondylolisthesis treatment. In spite of the technical details described by Wiltse, exact location of the area where the sacrospinalis muscle has to be split remains somewhat unclear. The goal of this study was to provide topographic landmarks to facilitate this surgical approach. Thirty cadavers were dissected in order to precisely describe the anatomy of the trans-muscular paraspinal approach. The level of the natural cleavage plane between the multifidus and the longissimus part of the sacrospinalis muscle was noted and measurements were done between this level and the midline at the level of the spinous process of L4. A natural cleavage plane between the multifidus and the longissimus part of the sacrospinalis muscle was present in all cases. There was a fibrous separation between the two muscular parts in 55 out of 60 cases. The mean distance between the level of the cleavage plane and the midline was 4 cm (2.4-5.5 cm). In all cases, small arteries and veins were present, precisely at the level of the cleavage plane. We found it possible to easily localize the anatomical cleavage plane between the multifidus part and the longissimus part of the sacrospinalis muscle. First the superficial muscular fascia is opened near the midline, exposing the posterior aspect of the sacrospinalis muscle. Then, the location of the muscular cleft can be found by identifying the perforating vessels leaving the anatomical inter-muscular space.


title: Groupware research and technology issues with application to software process management

abstract: Technology for actively supporting groups of collaborating users is being applied to many kinds of cooperative work activities. The following topics are addressed: (1) the authors' definition of groupware, (2) a conceptual framework in which to examine research issues, and (3) high-level groupware research issues within this framework. One potentially fertile area of application for such technologies is the problem of software process management. Software process means one of the teamwork, cooperation, coordination, and communication activities that occur within and across groups and organizations of persons throughout the life of software projects, including processes that occur under the broad categories of proposal writing, software engineering, development, and maintenance. This application domain contains a number of research issues and technology problems in such areas as communication, distribution, concurrence control, and human-computer interface design. The research areas needed for groupware to facilitate software processes are described. >


title: Detecting stealth software with Strider GhostBuster

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