title: Mapping learning and game mechanics for serious games analysis

abstract: Although there is a consensus on the instructional potential of Serious Games (SGs), there is still a lack of methodologies and tools not only for design but also to support analysis and assessment. Filling this gap is one of the main aims of the Games and Learning Alliance (http://www.galanoe.eu) European Network of Excellence on Serious Games, which has a focus upon pedagogy-driven SGs. This paper relies on the assumption that the fundamental aspect of SG design consists in the translation of learning goals/practices into mechanical element of gameplay, serving to an instructional purpose beside that of play and fun. This paper proposes the Learning Mechanics–Game Mechanics (LM-GM) model, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in an SG. The LM-GM model includes a set of pre-defined game mechanics and pedagogical elements that we have abstracted from literature on game studies and learning theories. ::: ::: Designers and analysts can exploit these mechanics to draw the LM-GM map for a game, so as to identify and highlight its main pedagogical and entertainment features, and their interrelations. The tool may also be useful for teachers to evaluate the effectiveness of a given game and better understand how to implement it in educational settings. A case study is reported to illustrate the framework's support in determining how gameplay and pedagogy intertwine in an SG. Finally, the paper presents the results of two comparative user tests demonstrating the advantages of the proposed model with respect to a similar state-of-the-art framework.


title: On Natural Language Generation of Formal Argumentation

abstract: In this paper we provide a first analysis of the research questions that arise when dealing with the problem of communicating pieces of formal argumentation through natural language interfaces. It is a generally held opinion that formal models of argumentation naturally capture human argument, and some preliminary studies have focused on justifying this view. Unfortunately, the results are not only inconclusive, but seem to suggest that explaining formal argumentation to humans is a rather articulated task. Graphical models for expressing argumentation-based reasoning are appealing, but often humans require significant training to use these tools effectively. We claim that natural language interfaces to formal argumentation systems offer a real alternative, and may be the way forward for systems that capture human argument.


title: Scaffolding feedback to maximize long-term error correction

abstract: Scaffolded feedback was tested against three other feedback presentation methods (standard corrective feedback, minimal feedback, and answer-until-correct multiple-choice feedback) over both short- and long-term retention intervals in order to assess which method would produce the most robust gains in error correction. Scaffolded feedback was a method designed to take advantage of the benefits of retrieval practice by providing incremental hints until the correct answer could be self-generated. In Experiments 1 and 3, on an immediate test, final memory for the correct answer was lowest for questions given minimal feedback, moderate for the answer-until-correct condition, and equally high in the scaffolded feedback condition and the standard feedback condition. However, tests of the maintenance of the corrections over a 30-min delay (Experiment 2) and over a 1-day delay (Experiment 3) demonstrated that scaffolded feedback gave rise to the best memory for the correct answers at a delay.


A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets


title: A survey of autonomic computing—degrees, models, and applications

abstract: Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ‘hype’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.


title: On prediction of resource consumption of service requests in cloud environments

abstract: The functional efficiency of cloud solutions depends on their responsive allocation of processing resources to individual tasks according to their predefined completion times. An optimum usage of resources requires an accurate prediction of resource consumption. In this paper we introduce a novel method for predicting the resource consumption of processing requests in diverse consumption classes in a system consisting of a plentitude of processing units. We further enhance the prediction algorithm with different mechanisms to determine the resource consumption of a variety of request classes in order to increase the precision of the prediction. We also show that the prediction algorithm is robust against abrupt and large variations of consumption and recovers rapidly.


title: Using CVSS in Attack Graphs

abstract: Derived from attack models, attack graphs are providing an efficient way to model attack scenarios intended against computer networks. Such graphs are using CVE database in which all known vulnerabilities are gathered. The CVSS framework is aiming to give numeric scores to each vulnerability recorded in the CVE database, which represent its characteristics and quantify its security impacts. In this paper we adapt attack graphs definition in order to be able to use them in conjunction with CVSS framework. The aim of our work is to provide a way to give an assessment of the impact of attacks on the hosts of the target network. This assessment is made using a host damage score and a network damage score, which take into account the characteristics and consequences of each atomic attack constituting an attack scenario.


A Situated Ontology for Practical NLP


title: A form-based dialogue manager for spoken language applications

abstract: A popular approach to dialogue management is based on a finite state model, where user utterances trigger transitions between the dialogue states, and these states, in turn, determine the system's response. The paper describes an alternative dialogue planning algorithm based on the notion of filling in an electronic form, or "E-form". Each slot has associated prompts that guide the user through the dialogue, and a priority that determines the order in which the system tries to acquire information. These slots can be optional or mandatory. However, the user is not restricted to follow the system's lead, and is free to ignore the prompts and take the initiative in the dialogue. The E-form based dialogue planner has been used in an application to search a database of used car advertisements. The goal is to assist the user in selecting, from this database, a small list of cars which match their constraints. For a large number of dialogues collected from over 600 naive users, we found over 70% compliance in answering specific system prompts.


title: Design pattern recovery in object-oriented software

abstract: An approach to recover object oriented design patterns from design and code is presented. The pattern recovery process is based on a multi-stage filtering strategy to avoid combinatorial explosion on large software systems. To maintain independence from the language and the case tools adopted in developing software, both design and code are mapped into an intermediate representation. The multi-stage searching strategy allows to safely determine pattern candidates. To assess the effectiveness of the pattern recovery process a portable environment written in Java has been developed. Based on this environment, experimental results on public domain and industrial software were obtained and are discussed in the paper. Evidence is shown that, by exploiting information about method calls as a further constraint beyond the structural ones, the number of false positives is reduced.


title: A Spatiotemporal Ensemble Approach to Rainfall Forecasting

abstract: This paper proposes a new ensemble method built upon a deep neural network architecture. We use a set of meteorological models for rain forecast as base predictors. Each meteorological model is provided to a channel of the network and, through a convolution operator, the prediction models are weighted and combined. As a result, the predicted value produced by the ensemble depends on both the spatial neighborhood and the temporal pattern. We conduct some computational experiments in order to compare our approach to other ensemble methods widely used for daily rainfall prediction. The results show that our architecture based on ConvLSTM networks is a strong candidate to solve the problem of combining predictions in a spatiotemporal context.


title: Hidden Path: Dynamic Software Watermarking Based on Control Flow Obfuscation

abstract: Software watermarking is a general tool usedto combat software piracy by embedding identifying informationinto a program. Most existing schemes of softwarewatermarking have the weak point that the security onlydepends on the stealth of the watermark structure. Besides,the watermarking is independent of program in semantic leveland can be destroyed via fairly straightforward semanticspreservingcode transformations. To mitigate such problems,this paper introduces control flow obfuscation to build anovel dynamic software watermarking framework. By usingcontrol flow obfuscation, we skilfully connect several obfuscatedbranches to construct a hidden execution path. The watermarkingis divided to fragments and each fragment is “stored”in each obfuscated branch. As a part of obfuscated branches,watermarking is closely associated with program internal logic.The security can be reduced to the difficulty of reversing theseobfuscated branches, rather than the stealth of watermarkstructure. We choose neural network as obfuscator; and deployour watermarking in SPEC-2006int benchmarks. We providethe evaluation in overhead, data-rate, resiliency. Experimentalresults indicate that the proposed method has enough resilienceagainst common attacks, yet practical in terms of performance


title: A Smartphone-Based Tool for Assessing Parkinsonian Hand Tremor

abstract: The aim of this study is to propose a practical smartphone-based tool to accurately assess upper limb tremor in Parkinson's disease (PD) patients. The tool uses signals from the phone's accelerometer and gyroscope (as the phone is held or mounted on a subject's hand) to compute a set of metrics which can be used to quantify a patient's tremor symptoms. In a small-scale clinical study with 25 PD patients and 20 age-matched healthy volunteers, we combined our metrics with machine learning techniques to correctly classify 82% of the patients and 90% of the healthy volunteers, which is high compared to similar studies. The proposed method could be effective in assisting physicians in the clinic, or to remotely evaluate the patient's condition and communicate the results to the physician. Our tool is low cost, platform independent, noninvasive, and requires no expertise to use. It is also well matched to the standard clinical examination for PD and can keep the patient “connected” to his physician on a daily basis. Finally, it can facilitate the creation of anonymous profiles for PD patients, aiding further research on the effectiveness of medication or other overlooked aspects of patients’ lives.


Model-Based Development of Self-Adaptive Autonomous Vehicles using the SMARDT Methodology


Pyrroline-5-carboxylate reductase 1 promotes proliferation and inhibits apoptosis in non-small cell lung cancer


Real-time 3-D visual SLAM with A hand-held camera


title: Securing Cloud Servers Against Flooding Based DDOS Attacks

abstract: Cloud computing is still a juvenile and most dynamic field characterized by a buzzing IT industry. Virtually every industry and even some parts of the public sector are taking on cloud computing today, either as a provider or as a consumer. It has now become essentially an inseparable part of everyone's life. The cloud thus has become a part of the critical global infrastructure but is unique in that it has no customary borders to safeguard it from attacks. Once weakened these web servers can serve as a launching point for conducting further attacks against users in the cloud. One such attack is the DoS or its version DDOS attack. Distributed Denial of Service (DdoS) Attacks have recently emerged as one of the most newsworthy, if not the greatest weaknesses of the Internet. DDoS attacks cause economic losses due to the unavailability of services and potentially serious security problems due to incapacitation of critical infrastructures. This paper presents a simple distance estimation based technique to detect and prevent the cloud from flooding based DDoS attack and thereby protect other servers and users from its adverse effects.


title: The Children’s Empathy Quotient and Systemizing Quotient: Sex Differences in Typical Development and in Autism Spectrum Conditions

abstract: Children’s versions of the Empathy Quotient (EQ-C) and Systemizing Quotient (SQ-C) were developed and administered to n = 1,256 parents of typically developing children, aged 4–11 years. Both measures showed good test–retest reliability and high internal consistency. As predicted, girls scored significantly higher on the EQ-C, and boys scored significantly higher on the SQ-C. A further sample of n = 265 children with Autism Spectrum Conditions (ASC) scored significantly lower on the EQ-C, and significantly higher on the SQ-C, compared to typical boys. Empathy and systemizing in children show similar patterns of sex differences to those observed in adults. Children with ASC tend towards a ‘hyper-masculinized’ profile, irrespective of sex.


title: Research Productivity of Sports Medicine Fellowship Faculty

abstract: BACKGROUND ::: Research productivity is considered an important factor in academic advancement in sports medicine. No study to date has evaluated academic productivity and correlates of academic rank for sports medicine fellowship faculty. ::: ::: ::: PURPOSE ::: To describe the academic productivity of American Orthopaedic Society for Sports Medicine (AOSSM) fellowship program faculty and to determine the association between academic productivity, fellowship characteristics, and academic rank. ::: ::: ::: STUDY DESIGN ::: Descriptive epidemiology study. ::: ::: ::: METHODS ::: Characteristics of orthopaedic sports medicine fellowship programs were obtained from the AOSSM and program websites. Metrics of academic productivity (Hirsch index [h index], I-10 index, publications, citations, and number of publications in several journals) were obtained from Scopus. Statistical analyses were conducted to determine whether academic productivity differs with fellowship attributes and academic rank. ::: ::: ::: RESULTS ::: A total of 90 AOSSM sports medicine fellowship programs with 610 associated faculty members were identified. Faculty were predominantly male (94%), at academic medical centers (74%), members of AOSSM (71%), and sports medicine-fellowship trained (84%). Faculty had a median of 18 (range, 0-684) publications overall, including a median of 3 (range, 0-161) publications since 2012. All measures of academic productivity were significantly higher among faculty employed at academic medical centers compared with those not employed at academic centers (P < .05 in all cases). On multivariate ordinal regression analysis, the best correlates of higher academic rank were higher cumulative h index (1.22; P < .001) and longer time in practice since fellowship (1.14; P < .001), which predicted 63.8% of the variance in academic rank. Fellowships with a larger number of fellows had more publications and citations per faculty member, higher faculty cumulative h index, and more publications in the American Journal of Sports Medicine and Arthroscopy per faculty member (P < .017). Regional differences were present, with the Northeast fellowship faculty the most productive, followed by the Midwest. ::: ::: ::: CONCLUSION ::: Higher cumulative h index and more years in practice were the best correlates of higher academic rank among AOSSM sports medicine fellowship faculty. Research productivity was higher among faculty employed at academic centers in the Northeast and Midwest regions and at programs with a larger number of fellows.


title: Transformation of a Virtual Action Plan into a Motor Plan in the Premotor Cortex

abstract: Before preparing to initiate a forthcoming motion, we often acquire information about the future action without specifying actual motor parameters. The information for planning an action at this conceptual level can be provided with verbal commands or nonverbal signals even before the associated motor targets are visible. Under these conditions, the information signifying a virtual action plan must be transformed to information that can be used for constructing a motor plan to initiate specific movements. To determine whether the premotor cortex is involved in this process, we examined neuronal activity in the dorsal premotor cortex (PMd) of monkeys performing a behavioral task designed to isolate the behavioral stages of the acquisition of information for a future action and the construction of a motor plan. We trained the animals to receive a symbolic instruction (color and shape of an instruction cue) to determine whether to select the right or left of targets to reach, despite the physical absence of targets. Subsequently, two targets appeared on a screen at different locations. The animals then determined the correct target (left or right) based on the previous instruction and prepared to initiate a reaching movement to an actual target. The experimental design dissociated the selection of the right/left at an abstract level (action plan) from the physical motor plan. Here, we show that activity of individual PMd neurons initially reflects a virtual action plan transcending motor specifics, before these neurons contribute to a transformation process that leads to activity encoding a motor plan.


title: Do Explanations make VQA Models more Predictable to a Human?

abstract: A rich line of research attempts to make deep neural networks more transparent by generating human-interpretable 'explanations' of their decision process, especially for interactive tasks like Visual Question Answering (VQA). In this work, we analyze if existing explanations indeed make a VQA model -- its responses as well as failures -- more predictable to a human. Surprisingly, we find that they do not. On the other hand, we find that human-in-the-loop approaches that treat the model as a black-box do.


title: Efficiently measuring a quantum device using machine learning

abstract: Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices.


title: A survey of graph edit distance

abstract: Inexact graph matching has been one of the significant research foci in the area of pattern analysis. As an important way to measure the similarity between pairwise graphs error-tolerantly, graph edit distance (GED) is the base of inexact graph matching. The research advance of GED is surveyed in order to provide a review of the existing literatures and offer some insights into the studies of GED. Since graphs may be attributed or non-attributed and the definition of costs for edit operations is various, the existing GED algorithms are categorized according to these two factors and described in detail. After these algorithms are analyzed and their limitations are identified, several promising directions for further research are proposed.


title: Video Metrology without the Image-to-Ground Homography

abstract: The ability to measure metric information from videos is an essential functionality in security software packages that populate databases with human morphometric and gait descriptors. The configuration of such security systems often involves calibrating the cameras in the surveillance network. For this purpose, markers are placed on the ground in the vicinity of each camera location in order to compute a critical image-to-ground homography. This paper shows how the homography computation can be avoided by describing the key components of a system designed for view-independent video metrology. Experiments on measuring the height of subjects and ground distances from different camera views demonstrate the viability of the approach. In addition, it is shown that the calibration model of the proposed method yields more accurate measurements than the frequently used square pixel camera model.


title: Math Anxiety: Personal, Educational, and Cognitive Consequences

abstract: Highly math-anxious individuals are characterized by a strong tendency to avoid math, which ultimately undercuts their math competence and forecloses important career paths. But timed, on-line tests reveal math-anxiety effects on whole-number arithmetic problems (e.g., 46 + 27), whereas achievement tests show no competence differences. Math anxiety disrupts cognitive processing by compromising ongoing activity in working memory. Although the causes of math anxiety are undetermined, some teaching styles are implicated as risk factors. We need research on the origins of math anxiety and on its “signature” in brain activity, to examine both its emotional and its cognitive components.


title: CRISPR-Cas systems for editing, regulating and targeting genomes

abstract: Targeted genome editing using engineered nucleases has rapidly gone from being a niche technology to a mainstream method used by many biological researchers. This widespread adoption has been largely fueled by the emergence of the clustered, regularly interspaced, short palindromic repeat (CRISPR) technology, an important new approach for generating RNA-guided nucleases, such as Cas9, with customizable specificities. Genome editing mediated by these nucleases has been used to rapidly, easily and efficiently modify endogenous genes in a wide variety of biomedically important cell types and in organisms that have traditionally been challenging to manipulate genetically. Furthermore, a modified version of the CRISPR-Cas9 system has been developed to recruit heterologous domains that can regulate endogenous gene expression or label specific genomic loci in living cells. Although the genome-wide specificities of CRISPR-Cas9 systems remain to be fully defined, the power of these systems to perform targeted, highly efficient alterations of genome sequence and gene expression will undoubtedly transform biological research and spur the development of novel molecular therapeutics for human disease.


title: Social influence based clustering of heterogeneous information networks

abstract: Social networks continue to grow in size and the type of information hosted. We witness a growing interest in clustering a social network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence based clustering framework for analyzing heterogeneous information networks with three unique features. First, we introduce a novel social influence based vertex similarity metric in terms of both self-influence similarity and co-influence similarity. We compute self-influence and co-influence based similarity based on social graph and its associated activity graphs and influence graphs respectively. Second, we compute the combined social influence based similarity between each pair of vertices by unifying the self-similarity and multiple co-influence similarity scores through a weight function with an iterative update method. Third, we design an iterative learning algorithm, SI-Cluster, to dynamically refine the K clusters by continuously quantifying and adjusting the weights on self-influence similarity and on multiple co-influence similarity scores towards the clustering convergence. To make SI-Cluster converge fast, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear parametric programming problem of single variable. Our experiment results show that SI-Cluster not only achieves a better balance between self-influence and co-influence similarities but also scales extremely well for large graph clustering.


Literature Review on Extractive Text Summarization Approaches


title: On the Economics of Offline Password Cracking

abstract: We develop an economic model of an offline password cracker which allows us to make quantitative predictions about the fraction of accounts that a rational password attacker would crack in the event of an authentication server breach. We apply our economic model to analyze recent massive password breaches at Yahoo!, Dropbox, LastPass and AshleyMadison. All four organizations were using key-stretching to protect user passwords. In fact, LastPass' use of PBKDF2-SHA256 with 10^5 hash iterations exceeds 2017 NIST minimum recommendation by an order of magnitude. Nevertheless, our analysis paints a bleak picture: the adopted key-stretching levels provide insufficient protection for user passwords. In particular, we present strong evidence that most user passwords follow a Zipf's law distribution, and characterize the behavior of a rational attacker when user passwords are selected from a Zipf's law distribution. We show that there is a finite threshold which depends on the Zipf's law parameters that characterizes the behavior of a rational attacker — if the value of a cracked password (normalized by the cost of computing the password hash function) exceeds this threshold then the adversary's optimal strategy is always to continue attacking until each user password has been cracked. In all cases (Yahoo!, Dropbox, LastPass and AshleyMadison) we find that the value of a cracked password almost certainly exceeds this threshold meaning that a rational attacker would crack all passwords that are selected from the Zipf's law distribution (i.e., most user passwords). This prediction holds even if we incorporate an aggressive model of diminishing returns for the attacker (e.g., the total value of 500 million cracked passwords is less than 100 times the total value of 5 million passwords). On a positive note our analysis demonstrates that memory hard functions (MHFs) such as SCRYPT or Argon2i can significantly reduce the damage of an offline attack. In particular, we find that because MHFs substantially increase guessing costs a rational attacker will give up well before he cracks most user passwords and this prediction holds even if the attacker does not encounter diminishing returns for additional cracked passwords. Based on our analysis we advocate that password hashing standards should be updated to require the use of memory hard functions for password hashing and disallow the use of non-memory hard functions such as BCRYPT or PBKDF2.


title: Study & Analysis of Web Content Mining Tools to Improve Techniques of Web Data Mining

abstract:  Abstract—Web content mining in normal parlance is to download information available on the websites. Such a process involves tremendous stress and time-taking. To augment such a process the software related to web content mining can be used so that a computer can use this software or tools to download the essential information that one would require. It collects the appropriate and perfectly fitting information from websites that one requires. In this paper several tools for web content mining are discussed and their relative merits and demerits are mentioned.


title: An Operations Research Model For The Evaluation Of An Airport Terminal: SLAM (Simple Landside Aggregate Model

abstract: The Simple Landside Aggregate Model (SLAM) is a model for estimating capacity and delays in airport passenger terminals. SLAM is designed to answer â€œwhat ifâ€ questions about alternative configurations of the various processing and holding facilities in a terminal. It consists of a network of modules, one for each facility of the terminal. These modules are based on a set of quite simple mathematical formulas to be used for the estimation of the capacity of each facility (in terms of passengers per hour) and the level of service (LOS) associated with it. LOS is quantified both in terms of â€œspace available per facility occupantâ€ and waiting time for being processed.


Multi – Armed Bandit for Pricing


title: IMS Tunable X-Band Oscillators Using Planar Resonators

abstract: Signal sources are essential devices for radar and communication systems. The demand for oscillators has existed for very long time. In 1888, Heinrich Hertz introduced the first known oscillator to prove Maxwell's equations experimentally [1]. He used a dipole as the frequency determining structure and a spark-gap generator as the signal source. His invention signaled the birth of wireless communications because Hertz confirmed the existence of electromagnetic radiation which was postulated by Michael Faraday and James Clerk Maxwell.


title: Lyapunov based model reference adaptive control for aerial manipulation

abstract: This paper presents a control scheme to achieve dynamic stability in an aerial vehicle with dual multi-degree of freedom manipulators using a lyapunov based model reference adaptive control. Our test flight results indicate that we can accurately model and control our aerial vehicle when both moving the manipulators and interacting with target objects. Using the Lyapunov stability theory, the controller is proven to be stable. The simulation results showed how the MRAC is capable of stabilizing the oscillations produced from the unstable PI-D attitude control loop. Finally a high level control system based on a switching automaton is proposed in order to ensure the safety of the aerial manipulation missions.


title: Antenna-in-Package Design for Wirebond Interconnection to Highly Integrated 60-GHz Radios

abstract: This paper first presents a quasi-cavity-backed, guard-ring-directed, substrate-material-modulated slot antenna. The antenna, intended for use in highly integrated 60-GHz radios, is deliberately designed to exhibit capacitive input impedance to suit low-cost wire-bonding packaging and assembly technique. The antenna implemented in a thin cavity-down ceramic ball grid array (CBGA) package in low-temperature cofired ceramic (LTCC) technology has achieved an acceptable impedance bandwidth from 59 to 65 GHz with an estimated efficiency of 94%. At millimeter-wave (mm-wave) frequency 60 GHz, one of key challenges is how to realize low-loss interconnection between a radio chip and an antenna using wire-bonding technique. This paper then addresses this issue in the framework of antenna-in-package (AiP) design at 60 GHz and proposes a new solution to the challenge. Detailed wirebond design method and results are given. A major concern with AiP is the risk of the antenna coupling to the radio chip. This paper also evaluates this unwanted coupling and shows that the coupling from the in-package antenna to the on-chip inductor is lower than 30 dB for the worst case. These results clearly demonstrate the feasibility and promise of the elegant AiP technology for emerging high-speed short-range 60-GHz wireless communications.


title: Redundancy-Reduced MobileNet Acceleration on Reconfigurable Logic for ImageNet Classification

abstract: Modern Convolutional Neural Networks (CNNs) excel in image classification and recognition applications on large-scale datasets such as ImageNet, compared to many conventional feature-based computer vision algorithms. However, the high computational complexity of CNN models can lead to low system performance in power-efficient applications. In this work, we firstly highlight two levels of model redundancy which widely exist in modern CNNs. Additionally, we use MobileNet as a design example and propose an efficient system design for a Redundancy-Reduced MobileNet (RR-MobileNet) in which off-chip memory traffic is only used for inputs/outputs transfer while parameters and intermediate values are saved in on-chip BRAM blocks. Compared to AlexNet, our RR-mobileNet has 25\(\times \) less parameters, 3.2\(\times \) less operations per image inference but 9%/5.2% higher Top1/Top5 classification accuracy on ImageNet classification task. The latency of a single image inference is only 7.85 ms.


title: Object Instance Search in Videos via Spatio-Temporal Trajectory Discovery

abstract: Given a specific object as query, object instance search aims to not only retrieve the images or frames that contain the query, but also locate all its occurrences. In this work, we explore the use of spatio-temporal cues to improve the quality of object instance search from videos. To this end, we formulate this problem as the spatio-temporal trajectory search problem, where a trajectory is a sequence of bounding boxes that locate the object instance in each frame. The goal is to find the top- $K$ trajectories that are likely to contain the target object. Despite the large number of trajectory candidates, we build on a recent spatio- temporal search algorithm for event detection to efficiently find the optimal spatio- temporal trajectories in large video volumes , with complexity linear to the video volume size. We solve the key bottleneck in applying this approach to object instance search by leveraging a randomized approach to enable fast scoring of any bounding boxes in the video volume. In addition , we present a new dataset for video object instance search. Experimental results on a 73-hour video dataset demonstrate that our approach improves the performance of video object instance search and localization over the state-of-the-art search and tracking methods.


title: Big Data Analytics for Dynamic Energy Management in Smart Grids

abstract: The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of large volumes of data generated by the vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.


title: Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

abstract: We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network to learn complex global trends from the large number of patients, and Gaussian Processes (GP) to probabilistically model individual time-series given relatively small number of visits per patient. We evaluate our model on diverse and heterogeneous tasks from EHR datasets and show practical advantages over standard time-series deep models such as pure Recurrent Neural Network (RNN).


title: Milling pretreatment of sugarcane bagasse and straw for enzymatic hydrolysis and ethanol fermentation

abstract: The effectiveness of ball milling (BM) and wet disk milling (WDM) on treating sugarcane bagasse and straw were compared. Pretreated materials were characterized by wide angle X-ray diffraction analysis, particle-size distribution and scanning electron microscopy and the effectiveness of pretreatments was evaluated by enzymatic hydrolysis and fermentation. Glucose and xylose hydrolysis yields at optimum conditions for BM-treated bagasse and straw were 78.7% and 72.1% and 77.6% and 56.8%, respectively. Maximum glucose and xylose yields for bagasse and straw using WDM were 49.3% and 36.7% and 68.0% and 44.9%, respectively. BM improved the enzymatic hydrolysis by decreasing the crystallinity, while the defibrillation effect observed for WDM samples seems to have favored enzymatic conversion. Bagasse and straw BM hydrolysates were fermented by Saccharomyces cerevisiae strains. Ethanol yields from total fermentable sugars using a C6-fermenting strain reached 89.8% and 91.8% for bagasse and straw hydrolysates, respectively, and 82% and 78% when using a C6/C5 fermenting strain.


The Role of Social Agile Practices for Direct and Indirect Communication in Information Systems Development Teams


title: Analysis of a Three-Level LLC Series Resonant Converter for High- and Wide-Input-Voltage Applications

abstract: In this paper, the analysis of a three-level LLC series resonant converter (TL LLC SRC) for high- and wide input-voltage applications is presented. It consists of two half-bridge LLC SRCs in series, sharing a resonant inductor and a transformer. Its main advantages are that the voltage across each switch is clamped at half of the input voltage and that voltage balance is achieved and simple driving signals . Thus it is suitable for high-inputvoltage applications. Based on the results of these analyses, a design example is provided and its validity is confirmed by an experiment involving a prototype converter with an input of 600V and an output of 48 V/20 A.


Research on attention memory networks as a model for learning natural language inference


title: A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning Agents

abstract: This paper proposes a low-cost, easily realizable strategy to equip a reinforcement learning (RL) agent the capability of behaving ethically. Our model allows the designers of RL agents to solely focus on the task to achieve, without having to worry about the implementation of multiple trivial ethical patterns to follow. Based on the assumption that the majority of human behavior, regardless which goals they are achieving, is ethical, our design integrates human policy with the RL policy to achieve the target objective with less chance of violating the ethical code that human beings normally obey.


title: Real-Time Vehicle Signal Lights Recognition with HDR Camera

abstract: Light signal recognition is one of the key issues in autonomous vehicle. Unlike normal object detection and recognition, which can be done by using different sensors, light signal recognition is naturally a computer vision problem. Although commercialized ADAS (Advanced Driving Assistance System) products, such as mobileye, could be used for rear-end collision warning, a cost-effective approach is still needed. In this paper, we have developed a novel two-stage approach to detect vehicles and recognize signal lights from a single image in real-time. Distinct from current state-of-the-art light recognition algorithms, we adopted a HDR (High Dynamic Range) camera instead of a color camera. Taking advantage of the different dynamic range of a HDR camera, the detection of a vehicle and the recognition of the signal light have been done in bright and dark channels, respectively. Furthermore, unlike previous approaches where pair taillight has to be extracted explicitly, we use detected vehicle region instead. On a large database, "Lights Patterns" (LPs) are learned by a multi-layer perception neural network. The robustness and efficiency of the proposed approach has been verified by the experimental results conducted on some real on-road videos.


title: Deriving a Large Scale Taxonomy from Wikipedia

abstract: We take the category system in Wikipedia as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexicosyntactic matching. As a result we are able to derive a large scale taxonomy containing a large amount of subsumption, i.e. isa, relations. We evaluate the quality of the created resource by comparing it with ResearchCyc, one of the largest manually annotated ontologies, as well as computing semantic similarity between words in benchmarking datasets.


Mining Frequent Co-occurrence Patterns across Multiple Data Streams.


title: A biometric-secure e-voting system for election processes

abstract: In this paper we propose a multifaceted online e-voting system. The proposed system is capable of handling electronic ballots with multiple scopes at the same time, e.g., presidential, municipal, parliamentary, amongst others. The system caters for integrity of an election process in terms of the functional and non-functional requirements. The functional requirements embedded in the design of the proposed system warrant well-secured identification and authentication processes for the voter through the use of combined simple biometrics. The design of the system guarantees that no votes in favor of a given candidate are lost, due to improper tallying of the voting counts, with the proper incorporation of system FLAGpsilas. Transparency of voting follows through in all phases of an election process to assure the voter that his/her vote went in favor of his/her candidate of choice. Besides its main functional properties, the proposed system is designed to cater for several essential nonfunctional requirements. Of utmost importance are the requirements for correctness, robustness, coherence, consistency, and security. To verify the robustness and reliability of the proposed system, intensive computer simulations were run under varying voting environments, viz. voter density, voter inter-arrival times, introduced acts of malice, etc. Results of the simulations show that security and performance of the system are according to expectations. These results provide the proper grounds that would guide the decision maker in customizing the proposed system to fit his particular voting needs.


title: Intrusion detection techniques and approaches

abstract: Recent security incidents and analysis have demonstrated that manual response to such attacks is no longer feasible. Intrusion detection systems (IDS) offer techniques for modelling and recognising normal and abusive system behaviour. Such methodologies include statistical models, immune system approaches, protocol verification, file and taint checking, neural networks, whitelisting, expression matching, state transition analysis, dedicated languages, genetic algorithms and burglar alarms. This paper describes these techniques including an IDS architectural outline and an analysis of IDS probe techniques finishing with a summary of associated technologies.


title: Active Complex Event Processing infrastructure: Monitoring and reacting to event streams

abstract: State-of-the-art Complex Event Processing technology (CEP), while effective for pattern matching on event streams, is limited in its capability of reacting in real-time to opportunities and risks detected when monitoring the physical or virtual world. We propose to tackle this problem by embedding active rule support within the CEP engine, henceforth called Active Complex Event Processing technology, or short, ACEP. We design an ACEP infrastructure that integrates the active rule component into the CEP kernel. This not only allows fine-grained but also more efficient rule processing. Based on the infrastructure we develop optimization techniques to improve the responsiveness of our system. We demonstrate the power of ACEP technology by applying it to the development of our real-time healthcare system being deployed in Univ. of Massachusetts Medical School hospital. Through performance experiments using real-world workloads collected from the hospital, we show that our ACEP solution is effective and efficient at supporting business process in event-based systems compared to possible alternatives.


title: Quantum advantage with shallow circuits

abstract: Quantum effects can enhance information-processing capabilities and speed up the solution of certain computational problems. Whether a quantum advantage can be rigorously proven in some setting or demonstrated experimentally using near-term devices is the subject of active debate. We show that parallel quantum algorithms running in a constant time period are strictly more powerful than their classical counterparts; they are provably better at solving certain linear algebra problems associated with binary quadratic forms. Our work gives an unconditional proof of a computational quantum advantage and simultaneously pinpoints its origin: It is a consequence of quantum nonlocality. The proposed quantum algorithm is a suitable candidate for near-future experimental realizations, as it requires only constant-depth quantum circuits with nearest-neighbor gates on a two-dimensional grid of qubits (quantum bits).


title: Iterative Graph-Based Filtering for Image Abstraction and Stylization

abstract: In this brief, motivated by the recent advances in graph signal processing, we address the problem of image abstraction and stylization. A novel unified graph-based multi-layer framework is proposed to perform iterative filtering without requiring any weight updates. The proposed graph-based filtering approach is shown to be superior to other existing methods due to iteratively using the filtered Laplacian in order to enhance the smoothened image signal at each layer. In order to render real images into painterly style ones and create a simple stylized format from color images, the low-contrast regions of an image are first smoothened using the proposed iterative graph filters in either vertex or spectral domains. The abstracted image is then quantized and sharpened using the proposed iterative highpass graph filter. The effectiveness of the graph-based image stylization method is verified through several experiments. It is shown that the proposed method can yield significantly improved visual quality for stylized images as compared to other existing methods.


title: Toward Comprehensive Modeling and Large-Angle Tracking Control of a Limited-Angle Torque Actuator With Cylindrical Halbach

abstract: The single-phase limited-angle torque (LAT) motor with cylindrical Halbach magnetic array provides a smaller form factor, lighter moving mass, and higher force throughput within $\pm$ 60 $^\circ$ angle range, compared with the three-phase permanent magnet synchronous motor. Due to the imperfect flux distribution within the air-gap of the Halbach array, as well as the inherent angular dependent characteristics, it is mainly used for point-to-point motion applications in prior literature. In this paper, we would like to deal with above issues, so that it can be extensively used for large-angle tracking applications. First, we derive the ideal model of this LAT actuator. Next, we propose to identify its nominal model parameters by the relay feedback with sufficiently small oscillations near the neutral position. Later, a robust output feedback control with high-gain observer is proposed to handle both model uncertainty and angular-dependent nonlinear current–torque relationship, so that the precision tracking of a defined smooth trajectory is achieved with only position sensing. The real-time experiment on a prototype validates the practical appeal of proposed methods.


title: Fiscal decentralization and local expenditure policy in China

abstract: Since the tax-sharing reform in 1994, the Chinese fiscal system has exhibited a marked vertical fiscal imbalance—a mismatch between expenditure and revenue assignments—at the local levels, which may cause the common-pool problem in local governments' behavior. Using a large fiscal dataset at the county level from 1997 to 2006, this paper studies the effects of fiscal decentralization on local expenditure policy and analyzes how the vertical fiscal imbalance shapes these effects. The estimation results show that expenditure decentralization increases government spending and leads to a fund allocation with a larger weight on capital construction and smaller weights on education and administration. In contrast, revenue decentralization has little influence on local government expenditures. We show that the differences in the effects of expenditure and revenue decentralization can be attributed to the distortionary effects caused by the vertical fiscal imbalance and, thus, offer support to the importance of the common-pool problem at the county level of China's fiscal system.


title: A tale of two erasure codes in HDFS

abstract: Distributed storage systems are increasingly transitioning to the use of erasure codes since they offer higher reliability at significantly lower storage costs than data replication. However, these codes tradeoff recovery performance as they require multiple disk reads and network transfers for reconstructing an unavailable data block. As a result, most existing systems use an erasure code either optimized for storage overhead or recovery performance. ::: ::: In this paper, we present HACFS, a new erasure-coded storage system that instead uses two different erasure codes and dynamically adapts to workload changes. It uses a fast code to optimize for recovery performance and a compact code to reduce the storage overhead. A novel conversion mechanism is used to efficiently upcode and downcode data blocks between fast and compact codes. We show that HACFS design techniques are generic and successfully apply it to two different code families: Product and LRC codes. ::: ::: We have implemented HACFS as an extension to the Hadoop Distributed File System (HDFS) and experimentally evaluate it with five different workloads from production clusters. The HACFS system always maintains a low storage overhead and significantly improves the recovery performance as compared to three popular single-code storage systems. It reduces the degraded read latency by up to 46%, and the reconstruction time and disk/network traffic by up to 45%.


title: A TDM-based multi-channel analog front-end for wearable dry EEG recording system

abstract: This paper presents a multi-channel analog front-end (AFE) for wearable dry EEG recording system. A novel AFE architecture is proposed to combine Time Division Multiplexing (TDM) together with chopping stabilization to improve the system-level common mode rejection ratio (CMRR) and the AFE input-referred noise. By utilizing TDM technique, single 2nd stage amplifier can be shared by multiple channels, leading to significant reduction in both power consumption and chip area. A 4-channel AFE prototype has been implemented in a standard 0.18-µm CMOS process. The post-layout simulation results show 0.62 µV rms input-referred noise integrated from 0.5 Hz to 100 Hz. The input impedance is boosted to 650 MΩ at 50 Hz by a tunable impedance boosting loop and the simulated intrinsic CMRR is 86 dB. The front-end only consumes 1.25 µW per channel under 1V supply voltage.


title: How do politicians use Facebook? An applied Social Observatory:

abstract: In the age of the digital generation, written public data is ubiquitous and acts as an outlet for today's society. Platforms like Facebook, Twitter, Google+ and LinkedIn have profoundly changed how we communicate and interact. They have enabled the establishment of and participation in digital communities as well as the representation, documentation and exploration of social behaviours, and had a disruptive effect on how we use the Internet. Such digital communications present scholars with a novel way to detect, observe, analyse and understand online communities over time. This article presents the formalization of a Social Observatory: a low latency method for the observation and measurement of social indicators within an online community. Our framework facilitates interdisciplinary research methodologies via tools for data acquisition and analysis in inductive and deductive settings. By focusing our Social Observatory on the public Facebook profiles of 187 federal German politicians we illustrate how we can analyse and measure sentiment, public opinion, and information discourse in advance of the federal elections. To this extent, we analysed 54,665 posts and 231,147 comments, creating a composite index of overall public sentiment and the underlying conceptual discussion themes. Our case study demonstrates the observation of communities at various resolutions: “zooming” in on specific subsets or communities as a whole. The results of the case study illustrate the ability to observe published sentiment and public dialogue as well as the difficulties associated with established methods within the field of sentiment analysis within short informal text.


title: On semi-supervised linear regression in covariate shift problems

abstract: Semi-supervised learning approaches are trained using the full training (labeled) data and available testing (unlabeled) data. Demonstrations of the value of training with unlabeled data typically depend on a smoothness assumption relating the conditional expectation to high density regions of the marginal distribution and an inherent missing completely at random assumption for the labeling. So-called covariate shift poses a challenge for many existing semi-supervised or supervised learning techniques. Covariate shift models allow the marginal distributions of the labeled and unlabeled feature data to differ, but the conditional distribution of the response given the feature data is the same. An example of this occurs when a complete labeled data sample and then an unlabeled sample are obtained sequentially, as it would likely follow that the distributions of the feature data are quite different between samples. The value of using unlabeled data during training for the elastic net is justified geometrically in such practical covariate shift problems. The approach works by obtaining adjusted coefficients for unlabeled prediction which recalibrate the supervised elastic net to compromise: (i) maintaining elastic net predictions on the labeled data with (ii) shrinking unlabeled predictions to zero. Our approach is shown to dominate linear supervised alternatives on unlabeled response predictions when the unlabeled feature data are concentrated on a low dimensional manifold away from the labeled data and the true coefficient vector emphasizes directions away from this manifold. Large variance of the supervised predictions on the unlabeled set is reduced more than the increase in squared bias when the unlabeled responses are expected to be small, so an improved compromise within the bias-variance tradeoff is the rationale for this performance improvement. Performance is validated on simulated and real data.


title: Verification and validation of bioinformatics software without a gold standard: a case study of BWA and Bowtie

abstract: BackgroundBioinformatics software quality assurance is essential in genomic medicine. Systematic verification and validation of bioinformatics software is difficult because it is often not possible to obtain a realistic "gold standard" for systematic evaluation. Here we apply a technique that originates from the software testing literature, namely Metamorphic Testing (MT), to systematically test three widely used short-read sequence alignment programs.ResultsMT alleviates the problems associated with the lack of gold standard by checking that the results from multiple executions of a program satisfy a set of expected or desirable properties that can be derived from the software specification or user expectations. We tested BWA, Bowtie and Bowtie2 using simulated data and one HapMap dataset. It is interesting to observe that multiple executions of the same aligner using slightly modified input FASTQ sequence file, such as after randomly re-ordering of the reads, may affect alignment results. Furthermore, we found that the list of variant calls can be affected unless strict quality control is applied during variant calling.ConclusionThorough testing of bioinformatics software is important in delivering clinical genomic medicine. This paper demonstrates a different framework to test a program that involves checking its properties, thus greatly expanding the number and repertoire of test cases we can apply in practice.


title: Fast Hardware Architectures for Supersingular Isogeny Diffie-Hellman Key Exchange on FPGA

abstract: In this paper, we present a constant-time hardware implementation that achieves new speed records for the supersingular isogeny Diffie-Hellman (SIDH), even when compared to highly optimized Haswell computer architectures. We employ inversion-free projective isogeny formulas presented by Costello et al. at CRYPTO 2016 on an FPGA. Modern FPGA’s can take advantage of heavily parallelized arithmetic in \(\mathbb {F}_{p^{2}}\), which lies at the foundation of supersingular isogeny arithmetic. Further, by utilizing many arithmetic units, we parallelize isogeny evaluations to accelerate the computations of large-degree isogenies by approximately 57%. On a constant-time implementation of 124-bit quantum security SIDH on a Virtex-7, we generate ephemeral public keys in 10.6 and 11.6 ms and generate the shared secret key in 9.5 and 10.8 ms for Alice and Bob, respectively. This improves upon the previous best time in the literature for 768-bit implementations by a factor of 1.48. Our 83-bit quantum security implementation improves upon the only other implementation in the literature by a speedup of 1.74 featuring fewer resources and constant-time.


title: Privacy-Aware Dynamic Ride Sharing

abstract: Dynamic ride sharing is a service that enables shared vehicle rides in real time and on short notice. It can be an effective solution to counter the problem of increasing traffic jams at peak hours in cities. The growing use and popularity of smart phones and GPS-enabled devices provides us with tools required to efficiently implement ride sharing and significantly enhance carpooling. However, privacy and safety concerns are the main obstacles faced when encouraging people to use such a service. In this work, we present “Match Maker,” a negotiation-based model that hides exact location information data for system participants while implementing privacy preserving ride sharing. We use the concept of imprecision (not being precise about location of the user out of set of n locations) and follow the idea of obfuscation, which equates a higher degree of imprecision with a higher degree of privacy. We identify two attack types that could circumvent privacy preserving ride sharing. We compare the Match Maker model with the standard central trusted server model collecting precise location data, which we term eBay model. We provide the first comprehensive approach that integrates privacy, safety and trust in a single model. We present a recursive ellipse-based algorithm to compute an optimal driver path as well as three negotiation strategies for drivers and passengers. We conduct extensive experiments on real road networks and compare the strategies for privacy and effectiveness of ride sharing in terms of traffic load and vehicle km reduction. We show that ride sharing saves between 9p and 21p (on average 12p) of vehicle km if drivers are only prepared to accept slight detours of their usual trips. In the city of Melbourne, with 11.6 million trips a weekday and an average trip length of 10.2 km, this would save 14.2 million km per weekday.


title: iSpot analysed: participatory learning and reputation

abstract: We present an analysis of activity on iSpot, a website supporting participatory learning about wildlife through social networking. A sophisticated and novel reputation system provides feedback on the scientific expertise of users, allowing users to track their own learning and that of others, in an informal learning context. We find steeply unequal long-tail distributions of activity, characteristic of social networks, and evidence of the reputation system functioning to amplify the contribution of accredited experts. We argue that there is considerable potential to apply such a reputation system in other participatory learning contexts.


title: Attribute-Based Secure Data Sharing with Hidden Policies in Smart Grid

abstract: Smart grid uses intelligent transmission and distribution networks to deliver electricity. It aims to improve the electric system's reliability, security, and efficiency through two-way communication of consumption data and dynamic optimization of electric-system operations, maintenance, and planning. The smart grid systems use fine-grained power grid measurements to provide increased grid stability and reliability. Key to achieving this is securely sharing the measurements among grid entities over wide area networks. Typically, such sharing follows policies that depend on data generator and consumer preferences and on time-sensitive contexts. In smart grid, as well as the data, policies for sharing the data may be sensitive because they directly contain sensitive information, and reveal information about underlying data protected by the policy, or about the data owner or recipients. In this study, we propose an attribute-based data sharing scheme in smart grid. Not only the data but also the access policies are obfuscated in grid operators' point of view during the data sharing process. Thus, the data privacy and policy privacy are preserved in the proposed scheme. The access policy can be expressed with any arbitrary access formula. Thus, the expressiveness of the policy is enhanced. The security is also improved such that the unauthorized key generation center or the grid manage systems that store the data cannot decrypt the data to be shared. The computation overhead of recipients are also reduced by delegating most of the laborious decryption operations to the more powerful grid manage systems.


title: Data security in cloud computing

abstract: This paper discusses the security of data in cloud computing. It is a study of data in the cloud and aspects related to it concerning security. The paper will go in to details of data protection methods and approaches used throughout the world to ensure maximum data protection by reducing risks and threats. Availability of data in the cloud is beneficial for many applications but it poses risks by exposing data to applications which might already have security loopholes in them. Similarly, use of virtualization for cloud computing might risk data when a guest OS is run over a hypervisor without knowing the reliability of the guest OS which might have a security loophole in it. The paper will also provide an insight on data security aspects for Data-in-Transit and Data-at-Rest. The study is based on all the levels of SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service).


title: A Spatial Layout and Scale Invariant Feature Representation for Indoor Scene Classification

abstract: Unlike standard object classification, where the image to be classified contains one or multiple instances of the same object, indoor scene classification is quite different since the image consists of multiple distinct objects. Furthermore, these objects can be of varying sizes and are present across numerous spatial locations in different layouts. For automatic indoor scene categorization, large-scale spatial layout deformations and scale variations are therefore two major challenges and the design of rich feature descriptors which are robust to these challenges is still an open problem. This paper introduces a new learnable feature descriptor called “spatial layout and scale invariant convolutional activations” to deal with these challenges. For this purpose, a new convolutional neural network architecture is designed which incorporates a novel “spatially unstructured” layer to introduce robustness against spatial layout deformations. To achieve scale invariance, we present a pyramidal image representation. For feasible training of the proposed network for images of indoor scenes, this paper proposes a methodology, which efficiently adapts a trained network model (on a large-scale data) for our task with only a limited amount of available training data. The efficacy of the proposed approach is demonstrated through extensive experiments on a number of data sets, including MIT-67, Scene-15, Sports-8, Graz-02, and NYU data sets.


title: A systematic mapping study on mining software repositories

abstract: Background: Software repositories provide large amount of data encompassing software changes throughout its evolution. Those repositories can be effectively used to extract and analyze pertinent information and derive conclusions related to the software history or its current snapshot. Objective: This work aims to investigate recent studies on Mining Software Repositories (MSR) approaches collecting evidences about software analysis goals (purpose, focus, and object of analysis), data sources, evaluation methods, tools, and how the area is evolving. Method: A systematic mapping study was performed to identify and analyze research on mining software repositories by analyzing five editions of Working Conference on Mining Software Repositories -- the main conference on this area. Results: MSR approaches have been used for many different goals, mainly for comprehension of defects, analysis of the contribution and behavior of developers, and software evolution comprehension. Besides, some gaps were identified with respect to their goals, focus, and data source type (e.g. lack of usage of comments to identify smells, refactoring, and issues of software quality). Regarding the evaluation method, our analysis pointed out to an extensive usage of some types of empirical evaluation. Conclusion: Studies of the MSR have focused on different goals, however there are still many research opportunities to be explored and issues associated with MSR that should be considered.


title: Green public computer lab using single-board computer and interactive computer reservation system

abstract: Public computer lab is very common in college and university. The main cost for maintenance lab is the power cost. In our university, the public computer lab consume more than 40kWh per day. The earlier study found that most cost comes from a major part: Some users don't shut down the computer after their usage. In this study, we use a low-power single-board computer (Raspberry Pi 2) for interactive kiosk providing computer reservation system and temporary storing the student usage, the VM-Based server providing RESTful web service, and embedded software on the client for shutting down the idled computer. The study found the new reservation system can decrease energy consumption up to 44.32%, lower staff's maintenance time, fair of sharing limited computer resource, and student usages are record accurately.


Global Economic Impacts Associated with Artificial Intelligence


title: Implementation of deep-learning based image classification on single board computer

abstract: In this paper, a deep-learning agorithm based on convolutional neural-network is implemented using python and tflearn for image classification. A large number of different images which contains two types of animals, namely cat and dog are used for classification. Two different structures of CNN are used, namely with two and five layers. It is shown that the CNN with higher layer performs classification process with much higher accuracy. The best CNN model with high accuracy and small loss function deployed in single board computer.


title: An efficient approach for device identification and traffic classification in IoT ecosystems

abstract: Internet of Things arises as a computational paradigm that promotes the interconnection of objects to the Internet and enables interaction, operational efficiency, and communication. With the increasing inclusion in the network of intelligent objects that have characteristics such as diversity, heterogeneity, mobility and low computational power, it is fundamental to develop mechanisms that allow management and control. In addition, it is important to identify whether the assets are working properly or have anomalies. Traffic classification techniques are important to aid in network analysis and to handle many other key aspects such as security, management, access control, provisioning, and resource allocation. In order to promote the identification of network devices, especially IoT, this article presents a technique that uses Random Forest, a supervised automatic learning algorithm, together with the inspection of the contents of the packages for this purpose. Also, we use the same algorithm to perform the classification of network traffic. In the end, the identification of the devices showed an accuracy of approximately 99%.


title: PDMS-based Triboelectric and Transparent Nanogenerators with ZnO Nanorod Arrays

abstract: Vertically-grown ZnO nanorod arrays (NRAs) on indium tin oxide (ITO)-coated polyethylene terephthalate (PET), as a top electrode of nanogenerators, were investigated for the antireflective property as well as an efficient contact surface in bare polydimethysiloxane (PDMS)-based triboelectric nanogenerators. Compared to conventional ITO-coated PET (i.e., ITO/PET), the ZnO NRAs considerably suppressed the reflectance from 20 to 9.7% at wavelengths of 300–1100 nm, creating a highly transparent top electrode, as demonstrated by theoretical analysis. Also, the interval time between the peaks of generated output voltage under external pushing forces was significantly decreased from 1.84 to 0.19 s because the reduced contact area of the PDMS by discrete surfaces of the ZnO NRAs on ITO/PET causes a rapid sequence for triboelectric charge generation process including rubbing and separating. Therefore, the use of this top electrode enabled to operate the transparent PDMS-based triboelectric nanogenerator at high fr...


title: Digital Signal Processing by Virtual Instrumentation of a MEMS Magnetic Field Sensor for Biomedical Applications

abstract: We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) card, and a virtual instrument. It allows the development of a semi-portable prototype with the capacity to filter small electromagnetic interference signals through digital signal processing. The virtual instrument includes an algorithm to implement different configurations of infinite impulse response (IIR) filters. The PCB


title: Non-negative Tensor Factorization with missing data for the modeling of gene expressions in the Human Brain

abstract: Non-negative Tensor Factorization (NTF) has become a prominent tool for analyzing high dimensional multi-way structured data. In this paper we set out to analyze gene expression across brain regions in multiple subjects based on data from the Allen Human Brain Atlas [1] with more than 40 % data missing in our problem. Our analysis is based on the non-negativity constrained Canonical Polyadic (CP) decomposition where we handle the missing data using marginalization considering three prominent alternating least squares procedures; multiplicative updates, column-wise, and row-wise updating of the component matrices. We examine three gene expression prediction scenarios based on data missing at random, whole genes missing and whole areas missing within a subject. We find that the column-wise updating approach also known as HALS performs the most efficient when fitting the model. We further observe that the non-negativity constrained CP model is able to predict gene expressions better than predicting by the subject average when data is missing at random. When whole genes and whole areas are missing it is in general better to predict by subject averages. However, we find that when whole genes are missing from all subjects the model based predictions are useful. When analyzing the structure of the components derived for one of the best predicting model orders the components identified in general constitute localized regions of the brain. Non-negative tensor factorization based on marginalization thus forms a promising framework for imputing missing values and characterizing gene expression in the human brain. However, care also has to be taken in particular when predicting the genetic expression levels at a whole region of the brain missing as our analysis indicates that this requires a substantial amount of subjects with data for this region in order for the model predictions to be reliable.


title: Learning Depth from Single Monocular Images

abstract: We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured outdoor environments which include forests, trees, buildings, etc.) and their corresponding ground-truth depthmaps. Then, we apply supervised learning to predict the depthmap as a function of the image. Depth estimation is a challenging problem, since local features alone are insufficient to estimate depth at a point, and one needs to consider the global context of the image. Our model uses a discriminatively-trained Markov Random Field (MRF) that incorporates multiscale local- and global-image features, and models both depths at individual points as well as the relation between depths at different points. We show that, even on unstructured scenes, our algorithm is frequently able to recover fairly accurate depthmaps.


title: A review on regenerative braking in electric vehicle

abstract: Conventional vehicles running on fossil fuels is one of the cause for global pollution and as a result creating adverse effects on the environment. The electric vehicle is becoming a reality and seen as the promising solution, as it offers great advantage of regenerative braking by which driving range of the vehicle is increased by harvesting braking energy. Braking control systems such as Hybrid braking system and fully Electrified Regenerative braking system are normally replacing conventional frictional braking system to stop the electric vehicles. In such systems stability, precision control and soft transition from one state to another depends on control strategies. Classical controllers have limitations which are overcome by robust and intelligent controller. In this paper implementation methodology of Electrical motors, analysis and advancements of various controllers like Sliding mode controller, H2/H∞ controller, an Adaptive controller, Fuzzy logic controller in combination with various algorithm and control techniques, Artificial neural network control and Neuro-fuzzy with PID Controllers are discussed.


title: Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping

abstract: We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57% compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.


title: Applying the connectionist inductive learning and logic programming system to power system diagnosis

abstract: The connectionist inductive learning and logic programming system, C-IL/sup 2/P, integrates the symbolic and connectionist paradigms of artificial intelligence through neural networks that perform massively parallel logic programming and inductive learning from examples and background knowledge. This work presents an extension of C-IL/sup 2/P that allows the implementation of extended logic programs in neural networks. This extension makes C-IL/sup 2/P applicable to problems where the background knowledge is represented in a default logic. As a case example, we have applied the system for fault diagnosis of a simplified power system generation plant, obtaining good preliminary results.


title: VisGets: Coordinated Visualizations for Web-based Information Exploration and Discovery

abstract: In common Web-based search interfaces, it can be difficult to formulate queries that simultaneously combine temporal, spatial, and topical data filters. We investigate how coordinated visualizations can enhance search and exploration of information on the World Wide Web by easing the formulation of these types of queries. Drawing from visual information seeking and exploratory search, we introduce VisGets - interactive query visualizations of Web-based information that operate with online information within a Web browser. VisGets provide the information seeker with visual overviews of Web resources and offer a way to visually filter the data. Our goal is to facilitate the construction of dynamic search queries that combine filters from more than one data dimension. We present a prototype information exploration system featuring three linked VisGets (temporal, spatial, and topical), and used it to visually explore news items from online RSS feeds.


title: The wave kernel signature: A quantum mechanical approach to shape analysis

abstract: We introduce the Wave Kernel Signature (WKS) for characterizing points on non-rigid three-dimensional shapes. The WKS represents the average probability of measuring a quantum mechanical particle at a specific location. By letting vary the energy of the particle, the WKS encodes and separates information from various different Laplace eigenfrequencies. This clear scale separation makes the WKS well suited for a large variety of applications. Both theoretically and in quantitative experiments we demonstrate that the WKS is substantially more discriminative and therefore allows for better feature matching than the commonly used Heat Kernel Signature (HKS). As an application of the WKS in shape analysis we show results on shape matching.


title: Sentiment analysis using neural network

abstract: With the rapid growth in use of social networking sites in the past decade, it has become a notable medium for people to express their views or opinions. This has fostered & promoted sentiment analysis as a dynamic & potential area of research where new techniques & models need to be explored for continuous improvement in result accuracy. In this paper, we propose a probabilistic neural network (PNN) with a self-adaptive approach to perform sentiment analysis on tweets. Probabilistic Neural Network as a multi-layered feed-forward neural network is an apt choice because of its prominent features of adaptive learning, fault tolerance, parallelism and generalization which provide a superior performance. Also, the smoothing parameter of PNN plays a great role for predicting an accurate class of classifier. So a self-adaptive algorithm is used to calculate and optimize the smoothing parameter in our research. Two types of Probabilistic Neural Network models are implemented in the proposed approach. First model of PNN, also called as PNNS has single value of smoothing parameter for whole network. Second model, also called as PNNC has different values of smoothing parameter for each class. The training and testing dataset is collected from Twitter using Twitter API. Accuracy of both model PNNS and PNNC is calculated and result shows that the PNNC has a better performance than PNNS.


title: Analogy of autonomic computing: An AIS (artificial immune systems) overview

abstract: The goal of Autonomic Computing paradigm is to set up an environment for computing devices and its applications such that the systems can manage themselves in highly complex situations. This concept is inspired from the behavior of human nervous system adopting its autonomic properties. In our study we observe that almost every property of Autonomic Computing matches with the properties of immune memory cells. However those properties play a key role in developing various AIS models to encounter many critical problems in computing. This paper addresses to the commonability of both Autonomic Computation and AIS models. Combining ideas from different fields which may lead to explore new methodologies of autonomic computation in the context of Artificial immune systems(AIS).


title: A study of sentiment analysis using deep learning techniques on Thai Twitter data

abstract: Sentiment analysis is very important for social listening, especially, when there are millions of Twitter users in Thailand nowadays. Almost all prior works are based on classical classification techniques, e.g., SVM, Naive Bayes, etc. Recently, the deep learning techniques have shown promising accuracy in this domain on English tweet corpus. In this paper, we propose the first study that applies deep learning techniques to classify sentiment of Thai Twitter data. There are two deep learning techniques included in our study: Long Short Term Memory (LSTM) and Dynamic Convolutional Neural Network (DCNN). A proper data preprocessing has been conducted. Moreover, we also investigate an effect of word orders in Thai tweets. The results show that the deep learning techniques significantly outperform many classical techniques: Naive Bayes and SVM, except Maximum Entropy.


Toward Improving Digital Attribution Model Accuracy


title: Applying regression test selection for COTS-based applications

abstract: ABB incorporates a variety of commercial-off-the-shelf (COTS) components in its products. When new releases of these components are made available for integration and testing, source code is often not provided. Various regression test selection processes have been developed and have been shown to be cost effectiveness. However, the majority of these test selection techniques rely on access to source code for change identification. In this paper we present the application of the lightweight Integrated - Black-box Approach for Component Change Identification (I-BACCI) Version 3 process that select regression tests for applications that use COTS components. Two case studies, examining a total of nine new component releases, were conducted at ABB on products written in C/C++ to determine the effectiveness of I-BACCI. The results of the case studies indicate this process can reduce the required number of regression tests at least 70% without sacrificing the regression fault exposure.


title: Defending users against smartphone apps: techniques and future directions

abstract: Smartphone security research has become very popular in response to the rapid, worldwide adoption of new platforms such as Android and iOS. Smartphones are characterized by their ability to run third-party applications, and Android and iOS take this concept to the extreme, offering hundreds of thousands of “apps” through application markets. In response, smartphone security research has focused on protecting users from apps. In this paper, we discuss the current state of smartphone research, including efforts in designing new OS protection mechanisms, as well as performing security analysis of real apps. We offer insight into what works, what has clear limitations, and promising directions for future research.


title: Energy-Efficient Wireless MAC Protocols for Railway Monitoring Applications

abstract: Recent advances in wireless sensor networking (WSN) techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems. These have the potential for use in the monitoring of railway signaling systems and rail tracks. Energy efficiency is one of the most important design factors for the WSNs as the typical sensor nodes are equipped with limited power batteries. In earlier research, an energy-efficient cluster-based adaptive time-division multiple-access (TDMA) medium-access-control (MAC) protocol, named EA-TDMA, has been developed by the authors for the purpose of communication between the sensors placed in a railway wagon. This paper proposes another new protocol, named E-BMA, which achieves even better energy efficiency for low and medium traffic by minimizing the idle time during the contention period. In addition to railway applications, the EA-TDMA and E-BMA protocols are suitable for generic wireless data communication purposes. Both analytical and simulation results for the energy consumption of TDMA, EA-TDMA, BMA, and E-BMA have been presented in this paper to demonstrate the superiority of the EA-TDMA and E-BMA protocols.


title: The extended argument dependency model: A neurocognitive approach to sentence comprehension across languages

abstract: Real-time language comprehension is a principal cognitive ability and thereby relates to central properties of the human cognitive architecture. Yet how do the presumably universal cognitive and neural substrates of language processing relate to the astounding diversity of human languages (over 5,000)? The authors present a neurocognitive model of online comprehension, the extended argument dependency model (eADM), that accounts for cross-linguistic unity and diversity in the processing of core constituents (verbs and arguments). The eADM postulates that core constituent processing proceeds in three hierarchically organized phases: (1) constituent structure building without relational interpretation, (2) argument role assignment via a restricted set of cross-linguistically motivated information types (e.g., case, animacy), and (3) completion of argument interpretation using information from further domains (e.g., discourse context, plausibility). This basic architecture is assumed to be universal, with crosslinguistic variation deriving primarily from the information types applied in Phase 2 of comprehension. This conception can derive the appearance of similar neurophysiological and neuroanatomical processing correlates in seemingly disparate structures in different languages and, conversely, of cross-linguistic differences in the processing of similar sentence structures.


Dynamic simulation: a case study


title: A modular toolbox for gRNA–Cas9 genome engineering in plants based on the GoldenBraid standard

abstract: BACKGROUND ::: The efficiency, versatility and multiplexing capacity of RNA-guided genome engineering using the CRISPR/Cas9 technology enables a variety of applications in plants, ranging from gene editing to the construction of transcriptional gene circuits, many of which depend on the technical ability to compose and transfer complex synthetic instructions into the plant cell. The engineering principles of standardization and modularity applied to DNA cloning are impacting plant genetic engineering, by increasing multigene assembly efficiency and by fostering the exchange of well-defined physical DNA parts with precise functional information. ::: ::: ::: RESULTS ::: Here we describe the adaptation of the RNA-guided Cas9 system to GoldenBraid (GB), a modular DNA construction framework being increasingly used in Plant Synthetic Biology. In this work, the genetic elements required for CRISPRs-based editing and transcriptional regulation were adapted to GB, and a workflow for gRNAs construction was designed and optimized. New software tools specific for CRISPRs assembly were created and incorporated to the public GB resources site. ::: ::: ::: CONCLUSIONS ::: The functionality and the efficiency of gRNA-Cas9 GB tools were demonstrated in Nicotiana benthamiana using transient expression assays both for gene targeted mutations and for transcriptional regulation. The availability of gRNA-Cas9 GB toolbox will facilitate the application of CRISPR/Cas9 technology to plant genome engineering.


title: Automatically identifying apps in mobile traffic

abstract: With the rapid development of smartphones in recent years, we have witnessed an exponential growth of the number of mobile apps. Considering the security and management issues, network operators need to have a clear visibility into the apps running in the network. To this end, this paper presents a novel approach to generating the fingerprints for mobile apps from network traffic. The fingerprints that characterize the unique behaviors of specific mobile apps can be used to identify mobile apps from the real network traffic. In order to handle the large volume of traffic efficiently, we use non-negative matrix factorization NMF to perform traffic analysis to cluster similar network traffic into groups. Then, access patterns of individual apps that are extracted from each group can be used as fingerprints distinguishing apps from others uniquely. The experimental evaluations show that the proposed approach can identify the mobile apps from random and mixed network traffic with high precision. Copyright © 2015 John Wiley & Sons, Ltd.


title: Sexuality in adolescent boys with autism spectrum disorder: self-reported behaviours and attitudes.

abstract: Differences in sexual functioning of adolescents with and without autism spectrum disorder (ASD) are understudied. In the current study, self-reported sexual behaviours, interests and attitudes of 50 adolescent boys, aged 15–18, with at least average intelligence and diagnosed with ASD, were compared with a matched general population control group of 90 boys. Results demonstrated substantial similarity between the groups in terms of sexual behaviours. The only significant difference was that boys with ASD reacted more tolerant towards homosexuality compared to the control group. Results reveal that sexuality is a normative part of adolescent development in high-functioning boys with ASD. Hence, attention should be given to this topic in education and mental health care.


title: Maximum likelihood from incomplete data via the EM algorithm

abstract: Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the first mass of a pair of said unbalanced masses being rigidly fastened to said shaft and with said second mass of said pair being movably arranged relative to said first mass, means for controlling and regulating the conveying rate during conveyer operation by varying the rotational speed of said motor between predetermined minimum and maximum values, said second mass being movably outwardly by centrifugal force against the pressure of spring means, said spring means being prestressed in such a manner that said second mass is, at rotational motor speeds lower than said minimum speed, held in its initial position, and at motor speeds between said lower and upper values in positions which are radially offset with respect to the axis of said motor to an extent depending on the value of said rotational motor speed.


title: Domestication evolution, genetics and genomics in wheat

abstract: Domestication of plants and animals is the major factor underlying human civilization and is a gigantic evolutionary experiment of adaptation and speciation, generating incipient species. Wheat is one of the most important grain crops in the world, and consists mainly of two types: the hexaploid bread wheat (Triticum aestivum) accounting for about 95% of world wheat production, and the tetraploid durum wheat (T. durum) accounting for the other 5%. In this review, we summarize and discuss research on wheat domestication, mainly focusing on recent findings in genetics and genomics studies. T. aestivum originated from a cross between domesticated emmer wheat T. dicoccum and the goat grass Aegilops tauschii, most probably in the south and west of the Caspian Sea about 9,000 years ago. Wild emmer wheat has the same genome formula as durum wheat and has contributed two genomes to bread wheat, and is central to wheat domestication. Domestication has genetically not only transformed the brittle rachis, tenacious glume and non-free threshability, but also modified yield and yield components in wheat. Wheat domestication involves a limited number of chromosome regions, or domestication syndrome factors, though many relevant quantitative trait loci have been detected. On completion of the genome sequencing of diploid wild wheat (T. urartu or Ae. tauschii), domestication syndrome factors and other relevant genes could be isolated, and effects of wheat domestication could be determined. The achievements of domestication genetics and robust research programs in Triticeae genomics are of greatly help in conservation and exploitation of wheat germplasm and genetic improvement of wheat cultivars.


title: What Is Marine Biodiversity? Towards Common Concepts and Their Implications for Assessing Biodiversity Status

abstract: ‘Biodiversity’ is one of the most common keywords used in environmental sciences, spanning from research to management, nature conservation and consultancy. Despite this, our understanding of the underlying concepts varies greatly, between and within disciplines as well as among the scientists themselves. Biodiversity can refer to descriptions or assessments of the status and condition of all or selected groups of organisms, from the genetic variability, to the species, populations, communities, and ecosystems. However, a concept of biodiversity also must encompass understanding the interactions and functions on all levels from individuals up to the whole ecosystem, including changes related to natural and anthropogenic environmental pressures. While biodiversity as such is an abstract and relative concept rooted in the spatial domain, it is central to most international, European and national governance initiatives aimed at protecting the marine environment. These rely on status assessments of biodiversity which typically require numerical targets and specific reference values, to allow comparison in space and/or time, often in association with some external structuring factors such as physical and biogeochemical conditions. Given that our ability to apply and interpret such assessments requires a solid conceptual understanding of marine biodiversity, here we define this and show how the abstract concept can and needs to be interpreted and subsequently applied in biodiversity assessments.


title: Metaheuristics in large-scale global continues optimization

abstract: Metaheuristic algorithms are extensively recognized as effective approaches for solving high-dimensional optimization problems. These algorithms provide effective tools with important applications in business, engineering, economics, and science. This paper surveys state-of-the-art metaheuristic algorithms and their current applications in the field of large-scale global optimization. The paper mainly covers the fundamental algorithmic frameworks such as decomposition and non-decomposition methods. More than 200 papers are carefully reviewed to prepare the current comprehensive survey.