title: Fully integrated dual-band power amplifiers with on-chip baluns in 65nm CMOS for an 802.11n MIMO WLAN SoC

abstract: Fully integrated dual-band power amplifiers with on-chip baluns for 802.11n MIMO WLAN applications are presented. With a 3.3v supply, the PAs produce a saturated output power of 28.3dBm and 26.7dBm with peak drain efficiency of 35.3% and 25.3% for the 2.4GHz and 5GHz bands, respectively. By utilizing multiple fully self-contained linearization algorithms, an EVM of −25dB is achieved at 22.4dBm for the 2.4GHz band and 20.5dBm for the 5GHz band while transmitting 54Mbs OFDM. The chip is fabricated in standard 65nm CMOS and the PAs occupy 0.31mm2 (2.4G) and 0.27mm2 (5G) area.


title: A Metrics-Based Approach to Technical Documentation Quality

abstract: Technical documentation is now fully taking the step from stale printed booklets (or electronic versions of these) to interactive and online versions. This provides opportunities to reconsider how we define and assess the quality of technical documentation. This paper suggests an approach based on the Goal-Question-Metric paradigm: predefined quality goals are continuously assessed and visualized by the use of metrics. To test this approach, we perform two experiments. We adopt well known software analysis techniques, e.g., clone detection and test coverage analysis, and assess the quality of two real world documentations, that of a mobile phone and of (parts of) a warship. The experiments show that quality issues can be identified and that the approach is promising.


title: Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

abstract: In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.


title: Decision Tree: Review of Techniques for Missing Values at Training, Testing and Compatibility

abstract: Data mining rely on large amount of data to make learning model and the quality of data is very important. One of the important problem under data quality is the presence of missing values. Missing values can occur in both at the time of training and at the time of testing. There are many methods proposed to deal with missing values in training data. Many of them resort to imputation techniques. However, Very few methods are there to deal with the missing values at testing/prediction time. In this paper, we discuss and summarize various strategies to deal with this problem both at training and testing time. Also, we have discussed the compatibility between various methods at training and testing to achieve better results.


title: Context sensitive synonym discovery for web search queries

abstract: We propose a simple yet effective approach to context sensitive synonym discovery for Web search queries based on co-click analysis; i.e., analyzing queries leading to clicking same documents. In addition to deriving word based synonyms, we also derive concept based synonyms with the help of query segmentation. Evaluation results show that this approach dramatically outperforms the thesaurus based synonym replacement method in keeping search intent, from accuracy of 40% to above 80%.


An Interactive e-Government Question Answering System


title: Optogenetic Brain Interfaces

abstract: The brain is a large network of interconnected neurons where each cell functions as a nonlinear processing element. Unraveling the mysteries of information processing in the complex networks of the brain requires versatile neurostimulation and imaging techniques. Optogenetics is a new stimulation method which allows the activity of neurons to be modulated by light. For this purpose, the cell-types of interest are genetically targeted to produce light-sensitive proteins. Once these proteins are expressed, neural activity can be controlled by exposing the cells to light of appropriate wavelengths. Optogenetics provides a unique combination of features, including multimodal control over neural function and genetic targeting of specific cell-types. Together, these versatile features combine to a powerful experimental approach, suitable for the study of the circuitry of psychiatric and neurological disorders. The advent of optogenetics was followed by extensive research aimed to produce new lines of light-sensitive proteins and to develop new technologies: for example, to control the distribution of light inside the brain tissue or to combine optogenetics with other modalities including electrophysiology, electrocorticography, nonlinear microscopy, and functional magnetic resonance imaging. In this paper, the authors review some of the recent advances in the field of optogenetics and related technologies and provide their vision for the future of the field.


title: An evolutionary many-objective optimisation algorithm with adaptive region decomposition

abstract: When optimizing an multiobjective optimization problem, the evolution of population can be regarded as a approximation to the Pareto Front (PF). Motivated by this idea, we propose an adaptive region decomposition framework: MOEA/D-AM2M for the degenerated Many-Objective optimization problem (MaOP), where degenerated MaOP refers to the optimization problem with a degenerated PF in a subspace of the objective space. In this framework, a complex MaOP can be adaptively decomposed into a number of many-objective optimization subproblems, which is realized by the adaptively direction vectors design according to the present population's distribution. A new adaptive weight vectors design method based on this adaptive region decomposition is also proposed for selection in MOEA/D-AM2M. This strategy can timely adjust the regions and weights according to the population's tendency in the evolutionary process, which serves as a remedy for the inefficiency of fixed and evenly distributed weights when solving MaOP with a degenerated PF. Five degenerated MaOPs with disconnected PFs are generated to identify the effectiveness of proposed MOEA/D-AM2M. Contrast experiments are conducted by optimizing those MaOPs using MOEA/D-AM2M, MOEA/D-DE and MOEA/D-M2M. Simulation results have shown that the proposed MOEA/D-AM2M outperforms MOEA/D-DE and MOEA/D-M2M.


title: Transfer learning in multi-agent systems through parallel transfer

abstract: Transfer Learning(TL) has been shown to signicantly accelerate the convergence of a reinforcement learning process. TL is the process of reusing learned information across tasks. Information is shared between a source and a target task. Previous work has required that the target task wait until the source task has nished learning before transferring information. The execution of the source task prior to the target task considerably extends the time required for the target task to complete. This paper proposes a novel approach allowing both source and target task to learn in parallel. This allows the transfer to be bi-directional, so processes can act as both source and target simultaneously. This, in consequence, allows tasks to learn from each other’s experiences and thereby reduces the learning time required. The proposed approach is evaluated on a multi-agent smartgrid scenario.


title: Expert system for predicting protein localization sites in gram-negative bacteria.

abstract: We have developed an expert system that makes use of various kinds of knowledge organized as "if-then" rules for predicting protein localization sites in Gram-negative bacteria, given the amino acid sequence information alone. We considered four localization sites: the cytoplasm, the inner (cytoplasmic) membrane, the periplasm, and the outer membrane. Most rules were derived from experimental observations. For example, the rule to recognize an inner membrane protein is the presence of either a hydrophobic stretch in the predicted mature protein or an uncleavable N-terminal signal sequence. Lipoproteins are first recognized by a consensus pattern and then assumed present at either the inner or outer membrane. These two possibilities are further discriminated by examining an acidic residue in the mature N-terminal portion. Furthermore, we found an empirical rule that periplasmic and outer membrane proteins were successfully discriminated by their different amino acid composition. Overall, our system could predict 83% of the localization sites of proteins in our database.


title: Fuzzy sentiment classification in social network facebook' statuses mining

abstract: Nowadays, posts expressing opinions in social networks are beneficial for businesses, sway public sentiments and emotions having higher social and political impact. In fact, opinions mining or sentiment classification is an important issue in social networks. Therefore, machine learning methods and natural language processing are very effective for opinion mining which are widely used in social media network. The main purpose of the paper is to demonstrate the feasibility of machine learning and sentiment analysis for studying Tunisian users' statuses. We aim to extract terms related to sentiment and behavior, especially during the “Tunisian Election”. While existing sentiment analysis methods focus only on the extraction of positive and negative opinions, in our work we aim to extract fuzzy opinions. That is why we propose to follow Fuzzy Support Vector Machine during our classification process. This later is compared with the basic Support Vector Machine referring to assessment measures such us accuracy, precision, recall, and F-measure.


title: A simple method for estimating the fractal dimension from digital images: The compression dimension

abstract: Abstract The fractal structure of real world objects is often analyzed using digital images. In this context, the compression fractal dimension is put forward. It provides a simple method for the direct estimation of the dimension of fractals stored as digital image files. The computational scheme can be implemented using readily available free software. Its simplicity also makes it very interesting for introductory elaborations of basic concepts of fractal geometry, complexity, and information theory. A test of the computational scheme using limited-quality images of well-defined fractal sets obtained from the Internet and free software has been performed. Also, a systematic evaluation of the proposed method using computer generated images of the Weierstrass cosine function shows an accuracy comparable to those of the methods most commonly used to estimate the dimension of fractal data sequences applied to the same test problem.


abstract: Multi-agent settings are quickly gathering importance in machine learning. This includes a plethora of recent work on deep multi-agent reinforcement learning, but also can be extended to hierarchical RL, generative adversarial networks and decentralised optimisation. In all these settings the presence of multiple learning agents renders the training problem non-stationary and often leads to unstable training or undesired final results. We present Learning with Opponent-Learning Awareness (LOLA), a method in which each agent shapes the anticipated learning of the other agents in the environment. The LOLA learning rule includes a term that accounts for the impact of one agent's policy on the anticipated parameter update of the other agents. Results show that the encounter of two LOLA agents leads to the emergence of tit-for-tat and therefore cooperation in the iterated prisoners' dilemma, while independent learning does not. In this domain, LOLA also receives higher payouts compared to a naive learner, and is robust against exploitation by higher order gradient-based methods. Applied to repeated matching pennies, LOLA agents converge to the Nash equilibrium. In a round robin tournament we show that LOLA agents successfully shape the learning of a range of multi-agent learning algorithms from literature, resulting in the highest average returns on the IPD. We also show that the LOLA update rule can be efficiently calculated using an extension of the policy gradient estimator, making the method suitable for model-free RL. The method thus scales to large parameter and input spaces and nonlinear function approximators. We apply LOLA to a grid world task with an embedded social dilemma using recurrent policies and opponent modelling. By explicitly considering the learning of the other agent, LOLA agents learn to cooperate out of self-interest. The code is at this http URL.


Industry 4.0 and its impact on the functioning of supply chains


title: How the ICT development level and usage influence student achievement in reading, mathematics, and science

abstract: The main purpose of this study is to investigate how national ICT development level and individual ICT usage will influence achievements in reading, mathematics, and science for 4th and 8th grade school students. Large-scale international databases, including TIMSS 2011, PIRLS 2011, and PISA 2012, were employed in the current study. Hierarchical linear models (HLM) were applied to examine both country- and individual-level variables. According to the findings of this study, the national ICT development level is a significant positive predictor for individual academic performance in all three subjects for both 4th grade and 8th grade students, while the national economic development level was controlled for. Such finding indicates a similar trend of the ICT influences for both groups, although there exists a difference in terms of the extent of the relationships. In addition, individual-level ICT use is a significant predictor, even if students' gender and socioeconomic status are controlled for; however, its influence is mixed across different student groups and subjects depending on the ICT usage type. Three Large-scale international assessments including PISA, TIMSS, & PIRLS were used.With national GDP was controlled for, the positive effects of national ICT level on individual achievements were discovered.Similar trend of national ICT influences on individual achievement was observed for both 4th and 8th grade.Personal ICT usage showed mixed influences on student achievement, depending on different usage type.


title: Understanding the Challenges Faced by Neurodiverse Software Engineering Employees: Towards a More Inclusive and Productive Technical Workforce

abstract: Technology workers are often stereotyped as being socially awkward or having difficulty communicating, often with humorous intent; however, for many technology workers with atypical cognitive profiles, such issues are no laughing matter. In this paper, we explore the hidden lives of neurodiverse technology workers, e.g., those with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and/or other learning disabilities, such as dyslexia. We present findings from interviews with 10 neurodiverse technology workers, identifying the challenges that impede these employees from fully realizing their potential in the workplace. Based on the interview findings, we developed a survey that was taken by 846 engineers at a large software company. In this paper, we reflect on the differences between the neurotypical (N = 781) and neurodiverse (N = 59) respondents. Technology companies struggle to attract, develop, and retain talented software developers; our findings offer insight into how employers can better support the needs of this important worker constituency.


title: Trimming process and temperature variation in second-order bandgap voltage reference circuits

abstract: This paper presents the design and experimental performance of a second-order bandgap voltage reference integrated circuit (IC). Experimentally observed nominal reference voltage at room temperature is 1.150V with best temperature performance of 3mV variation over -40 to 120?C. A 5-bit resistor trimming is used to compensate the variation of reference voltage due to layout mismatch and process variation. A trimming methodology is described in this paper to optimize both the temperature performance and reduce the variation of the room temperature voltage over different samples. Even with best temperature performance trim-code, the absolute variation in reference voltage over 20 samples is 85mV which is trimmed to ?11mV (1.3%) using the proposed trimming methodology. The second-order bandgap circuit is designed in a 0.5µm BiCMOS process with less than 50µA current consumption.


title: Catena: Efficient Non-equivocation via Bitcoin

abstract: We present Catena, an efficiently-verifiable Bitcoinwitnessing scheme. Catena enables any number of thin clients, such as mobile phones, to efficiently agree on a log of application-specific statements managed by an adversarial server. Catenaimplements a log as an OP_RETURN transaction chain andprevents forks in the log by leveraging Bitcoin's security againstdouble spends. Specifically, if a log server wants to equivocate ithas to double spend a Bitcoin transaction output. Thus, Catenalogs are as hard to fork as the Bitcoin blockchain: an adversarywithout a large fraction of the network's computational powercannot fork Bitcoin and thus cannot fork a Catena log either. However, different from previous Bitcoin-based work, Catenadecreases the bandwidth requirements of log auditors from 90GB to only tens of megabytes. More precisely, our clients onlyneed to download all Bitcoin block headers (currently less than35 MB) and a small, 600-byte proof for each statement in a block. We implement Catena in Java using the bitcoinj library and use itto extend CONIKS, a recent key transparency scheme, to witnessits public-key directory in the Bitcoin blockchain where it can beefficiently verified by auditors. We show that Catena can securemany systems today, such as public-key directories, Tor directoryservers and software transparency schemes.


title: Harvesting Mid-level Visual Concepts from Large-Scale Internet Images

abstract: Obtaining effective mid-level representations has become an increasingly important task in computer vision. In this paper, we propose a fully automatic algorithm which harvests visual concepts from a large number of Internet images (more than a quarter of a million) using text-based queries. Existing approaches to visual concept learning from Internet images either rely on strong supervision with detailed manual annotations or learn image-level classifiers only. Here, we take the advantage of having massive well organized Google and Bing image data, visual concepts (around 14, 000) are automatically exploited from images using word-based queries. Using the learned visual concepts, we show state-of-the-art performances on a variety of benchmark datasets, which demonstrate the effectiveness of the learned mid-level representations: being able to generalize well to general natural images. Our method shows significant improvement over the competing systems in image classification, including those with strong supervision.


title: Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology

abstract: Patents represent the technological or inventive activity and output across different fields, regions, and time. The analysis of information from patents could be used to help focus efforts in research and the economy; however, the roles of the factors that can be extracted from patent records are still not entirely understood. To better understand the impact of these factors on patent value, machine learning techniques such as feature selection and classification are used to analyze patents in a sample industry, nanotechnology. Each nanotechnology patent was represented by a comprehensive set of numerical features that describe inventors, assignees, patent classification, and outgoing references. After careful design that included selection of the most relevant features, selection and optimization of the accuracy of classification models that aimed at finding most valuable (top-performing) patents, we used the generated models to analyze which factors allow to differentiate between the top-performing and the remaining nanotechnology patents. A few interesting findings surface as important such as the past performance of inventors and assignees, and the count of referenced patents.


Why Can't You Convince Me? Modeling Weaknesses in Unpersuasive Arguments


title: A model of design decision making based on empirical results of interviews with software designers

abstract: Despite the impact of design decisions on software design, we have little understanding about how design decisions are made. This hinders our ability to provide design metrics, processes and training that support inherent design work. By interviewing 25 software designers and using content analysis and explanation building as our analysis technique, we provide qualitative and quantitative results that highlight aspects of rational and naturalistic decision making in software design. Our qualitative multi-case study results in a model of design decision making to answer the question: how do software designers make design decisions? We find the structure of the design problem determines the aspects of rational and naturalistic decision making used. The more structured the design decision, the less a designer considers options.


title: Research Directions for AI in Computer Games

abstract: The computer games industry is now bigger than the film industry. Until recently, technology in games was driven by a desire to achieve real-time, photo-realistic graphics. To a large extent, this has now been achieved. As game developers look for new and innovative technologies to drive games development, AI is coming to the fore. This paper will examine how sophisticated AI techniques, such as those being used in mainstream academic research, can be applied to computer games and introduce three projects doing just that.


title: False data injection attacks identification for smart grids

abstract: False Data Injection Attacks (FDIA) in Smart Grid is considered to be the most threatening cyber-physics attack. According to the variety of measurement categories in power system, a new method for false data detection and identification is presented. The main emphasis of our research is that we have equivalent measurement transformation instead of traditional weighted least squares state estimation in the process of SE and identify false data by the residual researching method. In this paper, one FDIA attack case in IEEE 14 bus system is designed by exploiting the MATLAB to test the effectiveness of the algorithm. Using this method the false data can be effectively dealt with.


title: TrPF: A Trajectory Privacy-Preserving Framework for Participatory Sensing

abstract: The ubiquity of the various cheap embedded sensors on mobile devices, for example cameras, microphones, accelerometers, and so on, is enabling the emergence of participatory sensing applications. While participatory sensing can benefit the individuals and communities greatly, the collection and analysis of the participators' location and trajectory data may jeopardize their privacy. However, the existing proposals mostly focus on participators' location privacy, and few are done on participators' trajectory privacy. The effective analysis on trajectories that contain spatial-temporal history information will reveal participators' whereabouts and the relevant personal privacy. In this paper, we propose a trajectory privacy-preserving framework, named TrPF, for participatory sensing. Based on the framework, we improve the theoretical mix-zones model with considering the time factor from the perspective of graph theory. Finally, we analyze the threat models with different background knowledge and evaluate the effectiveness of our proposal on the basis of information entropy, and then compare the performance of our proposal with previous trajectory privacy protections. The analysis and simulation results prove that our proposal can protect participators' trajectories privacy effectively with lower information loss and costs than what is afforded by the other proposals.


title: A General Suspiciousness Metric for Dense Blocks in Multimodal Data

abstract: Which seems more suspicious: 5,000 tweets from 200 users on 5 IP addresses, or 10,000 tweets from 500 users on 500 IP addresses but all with the same trending topic and all in 10 minutes? The literature has many methods that try to find dense blocks in matrices, and, recently, tensors, but no method gives a principled way to score the suspiciouness of dense blocks with different numbers of modes and rank them to draw human attention accordingly. Dense blocks are worth inspecting, typically indicating fraud, emerging trends, or some other noteworthy deviation from the usual. Our main contribution is that we show how to unify these methods and how to give a principled answer to questions like the above. Specifically, (a) we give a list of axioms that any metric of suspicousness should satisfy, (b) we propose an intuitive, principled metric that satisfies the axioms, and is fast to compute, (c) we propose CROSSSPOT, an algorithm to spot dense regions, and sort them in importance ("suspiciousness") order. Finally, we apply CROSSSPOT to real data, where it improves the F1 score over previous techniques by 68% and finds retweet-boosting in a real social dataset spanning 0.3 billion posts.


title: Integrated FSO/PON for Broadband Access Networks: A Comprehensive Protocol Stack Design and Analysis

abstract: In integrated free-space optical/passive optical networks (FSO/PON), providing efficient media access control (MAC) protocol and ensuring high reliable transmission are two major challenging issues. This paper comprehensively addresses these issues through a protocol stack design, which jointly considers the dynamic bandwidth allocation (DBA), adaptive rate (AR) transmission, and automatic repeat request (ARQ) with different retransmission strategies. A 2-D Markov chain model is developed to qualify quality-of-service (QoS) performance metrics in terms of frame loss probability, throughput and delay. Selected numerical results show that the proposed protocol stack design can offer considerable performance improvement over conventional ones.


title: Real-time synthetic aperture radar (SAR) processing with a new subaperture approach

abstract: A time-domain subaperture approach that is suitable for real-time synthetic aperture radar (SAR) processing and that produces high-quality, full-resolution images is presented. The real-time subaperture algorithm is based on an approximation of the phase history correction in each subaperture with a simple linear correction, which can be carried out by an up/down-conversion of the received signal followed by a moving average operation. The characteristics of the impulse response function are improved considerably by means of a frequency overlap of the subapertures and become comparable to a conventional matched filter response. Special algorithms for high-quality SAR processing, such as autofocus, Doppler centroid estimation, and range migration correction, can be efficiently implemented. High flexibility is achieved for multilook processing and no secondary range compression is needed, even for high squint angles. >


title: Dockless Bike-Sharing Reallocation Based on Data Analysis: Solving Complex Problem with Simple Method

abstract: Dockless bike-sharing service is currently revolutionizing the traditional bike-sharing market and it saves on strat-up costs because it gives up the fixed docking stations. This innovation also opens up a new window for applying operations research techniques based on data analysis. Given the growing importance of bike-sharing system nowadays, in this paper we propose an integrated method for reallocating dockless bikes after a whole day operation. Initially, we collect and preprocess the GPS data of dockless bikes and cluster these bikes into several groups. Then we formulate the reallocation of bikes to be an integer programming model. We consider two scenarios, a workday and a weekend day, in our numerical test. The results demonstrate that our method can provide tangible policy recommendations for managing the dockless bikes.


title: A model for social engineering awareness program for schools

abstract: Advancements in security has over the years of technological growth been mainly focused on providing secured technological infrastructure. The developed security measures and counter-measures have played a major role in reducing the surge of cyber-attacks. However, hackers have continued to exploit vulnerabilities due to the human element to gain access into otherwise secured systems. Risks and potential for exploits are more so in schools where the human vulnerability is enhanced by young impressionable pupils. Social engineering, the art of manipulating people so they give up confidential information, is increasingly the approach of choice for hackers who exploit the human element. Social engineers bypass secured systems in schools by directing targeting and exploiting the human vulnerabilities of school's students and staff. Education through awareness campaigns are typically used in countering the threat from social engineering. Such awareness campaigns tend to however be too holistic in focus to lead to the significant and sustainable change in behaviour required to counter social engineering. This paper presents a model for designing and implementing social engineering awareness programmes aimed at fostering behaviour change in schools. It demonstrates the process of designing a social engineering awareness program to meet all types of learning styles by using different multiple communication methods. Evaluation and continuous reinforcement approaches are also presented. A pilot implementation of our proposed model for social engineering awareness programme shows a significant change in behaviour of school's teaching staff.


title: PARAMETRIC STUDY OF A SINGLE FLEXIBLE LINK BASED ON TIMOSHENKO AND EULER-BERNOULLI BEAM THEORIES

abstract: In this research the sensitivity analysis (SA) of the geometric parameters such as: length, thickness and width of a single link flexible manipulator on maximum deflection (MD) of the end effector and vibration energy (VE) of that point are conducted. The motion equation of the system is developed based on Gibbs-Appel (G-A) formulation. Also for modeling the elastic property of the system the assumption of assumed modes method (AMM) is applied. In this study, two theories are used to obtain the end-point MD and VE of the end effector. Firstly, the assumption of Timoshenko beam theory (TBT) has been applied to consider the effects of shear and rotational inertia. After that, EulerBernoulli beam theory (EBBT) is used. Then Sobol’s sensitivity analysis method is applied to determine how VE and end-point MD is influenced by those geometric parameters. At the end of the research, results of two mentioned theories are compared.


title: GPS based portable dual-axis solar tracking system using astronomical equation

abstract: The overall objective of this study is to design and develop a portable dual-axis solar tracking system that focuses on portable and mobility purpose. This paper discusses the design, electronic control system and the algorithm based on the astronomical equation. The tracking system utilizes the GPS module and a digital compass sensor for determining the location and the heading feedback of the system respectively. Moreover, the microcontroller based tracking system is embedded with a PID controller for which will increase the PV positioning accuracy based from the feedback signal of the absolute encoder. Furthermore, this paper also analyses and compares the performance between the fixed-tilted PV panel and the developed portable solar tracking system.


title: Parameter identification and digital control of speed of a permanent magnet DC motors

abstract: The digital control of speed of a permanent magnet DC motor based on a digital computer is analyzed in this paper. Datasheet information about the DC motor was initially unknown. Therefore, the parameter identification and parameter estimation of the motor was done. A type of PID controller, which parameters were optimized (PI), was used in process of controlling the speed of the motor. DAQ acquisition card was used for the purposes of motor's parameter identification and realization of computer control of motor speed in closed feedback loop. Manipulations with the same DAQ card were done using Matlab/Simulink software package. Experimental results were compared with the results obtained by simulation.


title: Gesture-based interaction in domotic environments: State of the art and HCI framework inspired by the diversity

abstract: Applications for the control and automation of residential environments (domotics) are an emerging area of study within Human-Computer Interaction (HCI). One of the related challenges is to design gestural interaction with these applications. This paper explores socio-technical aspects of gestural interaction in intelligent domotic environments. An analysis of literature in the area revealed that some HCI-related aspects are treated in a restricted manner that neglects socio-technical dimensions. We propose a framework for discussing related challenges in an integrated manner, considering the dimensions people, gestural mode of interaction, and domotics. Some of these challenges are addressed by literature outside the area of domotics. Many open research questions remain, e.g. how to design gestural vocabularies that minimize ambiguity and consider cultural and social aspects. The proposed framework might contribute to answering these questions thus to designing meaningful interaction that is intuitive and easy to learn.


title: Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments

abstract: One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors and the inability to perform a successful tracking of interest points because of the bold assumptions in VO, such as brightness constancy. We address this problem from a deep learning perspective, for which we first fine-tune a Deep Neural Network (DNN) with the purpose of obtaining enhanced representations of the sequences for VO. Then, we demonstrate how the insertion of Long Short Term Memory (LSTM) allows us to obtain temporally consistent sequences, as the estimation depends on previous states. However, the use of very deep networks does not allow the insertion into a real-time VO framework; therefore, we also propose a Convolutional Neural Network (CNN) of reduced size capable of performing faster. Finally, we validate the enhanced representations by evaluating the sequences produced by the two architectures in several state-of-art VO algorithms, such as ORB-SLAM and DSO.


title: A deeper look into bug fixes: patterns, replacements, deletions, and additions

abstract: Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of bug-fixing commits in Java projects, focusing on assumptions underlying common search-based repair approaches. We make observations that can be leveraged to guide high quality automatic software repair to target Java specifically, including common and uncommon statement modifications in human patches and the applicability of previously-proposed patch construction operators in the Java context.


title: Fast Userspace Packet Processing

abstract: In recent years, we have witnessed the emergence of high speed packet I/O frameworks, bringing unprecedented network performance to userspace. Using the Click modular router, we first review and quantitatively compare several such packet I/O frameworks, showing their superiority to kernel-based forwarding. We then reconsider the issue of software packet processing, in the context of modern commodity hardware with hardware multi-queues, multi-core processors and non-uniform memory access. Through a combination of existing techniques and improvements of our own, we derive modern general principles for the design of software packet processors. Our implementation of a fast packet processor framework, integrating a faster Click with both Netmap and DPDK, exhibits up-to about 2.3x speed-up compared to other software implementations, when used as an IP router.


title: CIM: Community-Based Influence Maximization in Social Networks

abstract: Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.


title: Dodecagonal circularly polarized patch antenna for RFID applications

abstract: A compact design of a single feed circularly polarized microstrip antenna for RFID applications is proposed. This structure fabricated on FR4 substrate offers an area reduction of 10%, minimum axial ratio of 0.5 dB, axial ratio bandwidth of 116 MHz ( 5%) with a broadside radiation characteristic in the entire band and a gain of 4.9 dBi, 10dB impedance bandwidth of 5.4% (130MHz) at a resonant frequency of 2.4168 GHz. The structure is suitable for RFID reader antenna applications.


title: Towards a flexible functional split for cloud-RAN networks

abstract: Very dense deployments of small cells are one of the key enablers to tackle the ever-growing demand on mobile bandwidth. In such deployments, centralization of RAN functions on cloud resources is envisioned to overcome severe inter-cell interference and to keep costs acceptable. However, RAN back-haul constraints need to be considered when designing the functional split between RAN front-ends and centralized equipment. In this paper we analyse constraints and outline applications of flexible RAN centralization.


title: Towards a Conceptual Framework for Big Data Adoption in Organizations

abstract: Big data potential value across business sectors has received tremendous attention from the practitioner and academia world. The huge amount of data collected in different forms in organizations promises to radically transform the business landscape globally. The impact of big data, which is spreading across all business sectors, has potential to create new opportunities for growth. With organizations now able to store huge diverse amounts of data from different sources and forms, big data is expected to deliver tremendous value across business sectors. This paper focuses on building a business case for big data adoption in organizations. This paper discusses some of the opportunities and potential benefits associated with big data adoption across various business sectors globally. The discussion is important for making a business case for big data investment in organizations, which is major challenge for its adoption globally. The paper uses the IT strategic grid to understand the current and future potential benefits of big data for different business sectors. The results of the study suggest that there is no one-size-fits-all to big data adoption potential benefits in organizations.


title: Design of A Push-Push and Push-Pull Oscillator Based on SIW/SICL Technique

abstract: In this letter, the design of a Push-Push and Push-Pull oscillator based on substrate integrated waveguide (SIW) and substrate integrated coaxial line (SICL) technique is presented. Differential-phase lock of the sub-oscillators is realized with the SIW cavity at fundamental ${\rm TE}_{110}$ mode, thus simplifying the oscillator design procedure. Based on the SIW and SICL technique, a broadband magic-T is utilized for combining the desired signal and cancelling the undesired signal for both Push-Push and Push-Pull output. The prototype of the Push-Push and Push-Pull oscillator is designed and fabricated. Measured results show that the proposed oscillator has $f_{0}$ suppression of $-$ 13.1 dBc at the Push-Push port, and $2f_{0}$ suppression of $-$ 24.3 dBc at the Push-Pull port. Compared with the conventional single oscillator, the phase noise is improved by 2.5 $\sim$ 3.2 dB at 100 kHz through 1 MHz offset for the desired $f_{0}$ and $2f_{0}$ output signals.


title: Intracranial Error Detection via Deep Learning

abstract: Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG). However, these methods were so far only rarely evaluated for use in intracranial EEG. We employed convolutional neural networks (CNNs) to classify and characterize the error-related brain response as measured in 24 intracranial EEG recordings. Decoding accuracies of CNNs were significantly higher than those of a regularized linear discriminant analysis. Using time-resolved deep decoding, it was possible to classify errors in various regions in the human brain, and further to decode errors over 200 ms before the actual erroneous button press, e.g., in the precentral gyrus. Moreover, deeper networks performed better than shallower networks in distinguishing correct from error trials in all-channel decoding. In single recordings, up to 100 % decoding accuracy was achieved. Visualization of the networks' learned features indicated that multivariate decoding on an ensemble of channels yields related, albeit non-redundant information compared to single-channel decoding. In summary, here we show the usefulness of deep learning for both intracranial error decoding and mapping of the spatio-temporal structure of the human error processing network.


title: Transversal Pneumatic Artificial Muscles

abstract: The paper presents an innovative construction of Pneumatic Artificial Muscles. Due to transverse deformation of the muscle, it allows obtaining a linear displacement by changing a diameter of a muscle. The new type of drive, which is represented by this solution, can be used in a robot designed for exploration of a human intestine. On the basis of investigation, there are presented results of a research, differences between traditional and transversal pneumatic muscles and profits of the new construction.


title: Facial expression recognition using optimized active regions

abstract: In this paper, we report an effective facial expression recognition system for classifying six or seven basic expressions accurately. Instead of using the whole face region, we define three kinds of active regions, i.e., left eye regions, right eye regions and mouth regions. We propose a method to search optimized active regions from the three kinds of active regions. A Convolutional Neural Network (CNN) is trained for each kind of optimized active regions to extract features and classify expressions. In order to get representable features, histogram equalization, rotation correction and spatial normalization are carried out on the expression images. A decision-level fusion method is applied, by which the final result of expression recognition is obtained via majority voting of the three CNNs’ results. Experiments on both independent databases and fused database are carried out to evaluate the performance of the proposed system. Our novel method achieves higher accuracy compared to previous literature, with the added benefit of low latency for inference.


title: Quality of Services in Assets of Smart Parking

abstract: Technology integration with physical environment is need of the hour. Since it is estimated that with the momentum people migrating to urban areas, 843 people million people are expected to live in cities by 2050 [1]. And to sustain the growth Intelligent System are to be developed. These give birth to the concept of “smartness”. Smartness in the economy, living standards, education, business, infrastructure and much more. One asset of smartness can be smartness on the road that is smart traffic monitoring, smart parking etc. Government is investing a huge sum of money for implementing this smart parking. There are several service providers, which are offering service. So, for the maximization of satisfaction for the user and selection of best service provider, it necessary to define a certain set of parameters. In this paper, we have given the QoS metrics for smart parking from user as well as service provider aspect. These attributes are helpful for service analysis and better decision making.


title: Rethinking Historical Trauma

abstract: Recent years have seen the rise of historical trauma as a construct to describe the impact of colonization, cultural suppression, and historical oppression of Indigenous peoples in North America (e.g., Native Americans in the United States, Aboriginal peoples in Canada). The discourses of psychiatry and psychology contribute to the conflation of disparate forms of violence by emphasizing presumptively universal aspects of trauma response. Many proponents of this construct have made explicit analogies to the Holocaust as a way to understand the transgenerational effects of genocide. However, the social, cultural, and psychological contexts of the Holocaust and of post-colonial Indigenous “survivance” differ in many striking ways. Indeed, the comparison suggests that the persistent suffering of Indigenous peoples in the Americas reflects not so much past trauma as ongoing structural violence. The comparative study of genocide and other forms of massive, organized violence can do much to illuminate both common mechanisms and distinctive features, and trace the looping effects from political processes to individual experience and back again. The ethics and pragmatics of individual and collective healing, restitution, resilience, and recovery can be understood in terms of the self-vindicating loops between politics, structural violence, public discourse, and embodied experience.


title: Did First-Mover Advantage Survive the Dot-Com Crash ? *

abstract: Widespread expectations of first-mover advantages contributed to the rush of market entry during the early growth phase of the Internet sector. After the shakeout, however, such beliefs came into question. This study assesses the magnitude and sources of firstmover advantages in 46 Internet markets, based on data for more than 200 publicly traded entrants. The findings show a large premium in stock market capitalization and revenue for early entrants in markets with network effects, and for pioneers with patented innovations. Absent these factors, first-mover advantages appear minimal. First-movers had higher propensity to patent, and their patents were far more likely to be cited. This suggests that early entrants played a disproportionate role in promoting new technology, even though many innovative pioneers failed to survive. In general, the findings imply that first-mover advantages arose within the Internet sector but were contingent upon market and firm characteristics.


title: Better Summarization Evaluation with Word Embeddings for ROUGE

abstract: ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank coefficients.


title: A secure architecture for the range-level command and control system of a national cyber range testbed

abstract: In recent years, cyber security researchers have become burdened by the time and cost necessary to instantiate secure testbeds suitable for analyzing new threats or evaluating emerging technologies [1]. To alleviate this, DARPA initiated the National Cyber Range (NCR) program to develop the architecture and software tools needed for a secure, self-contained cyber testing facility. Among NCR's goals was the development of a range capable of rapid and automated reconfiguration of resources, broad scalability, and support for running simultaneous experiments at different security levels [2]. ::: ::: In this paper we present our architecture for the Range-level Command & Control System (RangeC2) developed as part of the Johns Hopkins University Applied Physics Laboratory's implementation of the NCR [3]. Our discussion includes the RangeC2's functional and non-functional requirements, the rationale behind its partitioning into layered subsystems, an analysis of each subsystem's fundamental mechanisms, and an in-depth look at their processing paradigms and data flows. ::: ::: To meet the demands of this range, the RangeC2 was required to perform three primary jobs: 1) management of all range resources; 2) management of numerous concurrent experiments; and 3) enforcement of each experiment's resource security and perimeter isolation. Our discussion of the architecture will show how these requirements were met while overcoming the RangeC2's most critical challenges.


title: Fast texture synthesis using tree-structured vector quantization

abstract: Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.


title: Constant-slope rate allocation for distributed real-world encoding

abstract: Parallel encoding systems face the problem of distributed coding decisions, an example of which is rate allocation between different encoding chunks. Distributed encoding is traditionally performed by assigning a fixed bitrate or quality setting to each chunk, after which the chunk uses rate control to achieve that particular target. In this paper, we discuss the use of constant-slope encoding, resulting in chunks which all operate at the same quality-rate trade-off. The (Lagrangian) optimization can be performed independent of the chosen codec or distortion/quality metric. In this paper, we report results for mobile resolutions for VP9 encodes, on both open-source and Netflix catalog original and licensed content titles. BD-rate gains are obtained of 6.8 % on average, and a more constant quality across titles is observed.


title: Comparison of scenario-based software architecture evaluation methods

abstract: Software engineering community has proposed several methods to evaluate software architectures with respect to desired quality attributes such as maintainability, performance, and so on. There is, however, little effort on systematically comparing such methods to discover similarities and differences between existing approaches. In this paper, we compare four well known scenario-based SA evaluation methods using an evaluation framework. The framework considers each method from the point of view of method context, stakeholders, structure, and reliability. The comparison reveals that most of the studied methods are structurally similar but there are a number of differences among their activities and techniques. Therefore, some methods overlap, which guides us to identify five common activities that can form a generic process model.


title: A forwarding strategy based on reinforcement learning for Content-Centric Networking

abstract: This paper proposes a packet forwarding strategy for Information-Centric Networking. Our proposal is based on reinforcement learning techniques and aims at balancing the exploration of new paths and exploiting the data acquired from previous explorations. The output interfaces of a node are classified according to the content retrieval time and all interests that share the same prefix with contents previously forwarded are sent through the interface with the lowest mean retrieval time. The exploration of new paths is probabilistic. Each node sends the same interest through the best interface and through another interface chosen at random simultaneously. The goal is to retrieve the content by using the best path found until present moment and at the same time explore copies that are recently stored in the cache of nearest nodes. Simulation results show that the proposed strategy reduces up to 28% the number of hops traversed by received contents and up to 80% the interest load per node in comparison to other forwarding strategies.


Inferring User Interests From Microblogs Ceren Budak Anitha


title: Multi-modal Mutual Topic Reinforce Modeling for Cross-media Retrieval

abstract: As an important and challenging problem in the multimedia area, multi-modal data understanding aims to explore the intrinsic semantic information across different modalities in a collaborative manner. To address this problem, a possible solution is to effectively and adaptively capture the common cross-modal semantic information by modeling the inherent correlations between the latent topics from different modalities. Motivated by this task, we propose a supervised multi-modal mutual topic reinforce modeling (M$^3$R) approach, which seeks to build a joint cross-modal probabilistic graphical model for discovering the mutually consistent semantic topics via appropriate interactions between model factors (e.g., categories, latent topics and observed multi-modal data). In principle, M$^3$R is capable of simultaneously accomplishing the following two learning tasks: 1) modality-specific (e.g., image-specific or text-specific ) latent topic learning; and 2) cross-modal mutual topic consistency learning. By investigating the cross-modal topic-related distribution information, M$^3$R encourages to disentangle the semantically consistent cross-modal topics (containing some common semantic information across different modalities). In other words, the semantically co-occurring cross-modal topics are reinforced by M$^3$R through adaptively passing the mutually reinforced messages to each other in the model-learning process. To further enhance the discriminative power of the learned latent topic representations, M$^3$R incorporates the auxiliary information (i.e., categories or labels) into the process of Bayesian modeling, which boosts the modeling capability of capturing the inter-class discriminative information. Experimental results over two benchmark datasets demonstrate the effectiveness of the proposed M$^3$R in cross-modal retrieval.


title: Random Search Algorithms

abstract: Random search algorithms are useful for many ill-structured global optimization problems with continuous and/or discrete variables. Typically random search algorithms sacrifice a guarantee of optimality for finding a good solution quickly with convergence results in probability. Random search algorithms include simulated annealing, tabu search, genetic algorithms, evolutionary programming, particle swarm optimization, ant colony optimization, cross-entropy, stochastic approximation, multistart and clustering algorithms, to name a few. They may be categorized as global (exploration) versus local (exploitation) search, or instance-based versus model-based. However, one feature these methods share is the use of a random element embedded in their iterative procedures. This article provides an overview of random search algorithms used to solve black-box global optimization problems. ::: ::: ::: Keywords: ::: ::: random search algorithm; ::: single-point generators; ::: multiple-point generators; ::: multistart algorithm; ::: global optimization


title: Low Resource Dependency Parsing: Cross-lingual Parameter Sharing in a Neural Network Parser

abstract: Training a high-accuracy dependency parser requires a large treebank. However, these are costly and time-consuming to build. We propose a learning method that needs less data, based on the observation that there are underlying shared structures across languages. We exploit cues from a different source language in order to guide the learning process. Our model saves at least half of the annotation effort to reach the same accuracy compared with using the purely supervised method.


title: Novelty and interestingness measures for design-space exploration

abstract: Measures of novelty and interestingness are frequently encountered in the context of developmental robotics, being derived from human psychology. This work addresses these measures from the viewpoint of enhancing design-space exploration in black-box optimization. We provide a unifying notational and naming scheme with the intent of facilitating comparison, implementation, and application in the domain of design optimization. Initial analysis shows a promising interestingness measure for being tried on real-world design problems.


title: Finding Maximum Disjoint Set of Boundary Rectangles With Application to PCB Routing

abstract: Motivated by the bus escape routing problem in printed circuit boards (PCBs), we study the following optimization problem: given a set of rectangles attached to the boundary of a rectangular region, find a subset of nonoverlapping rectangles with maximum total weight. We present an efficient algorithm that solves this problem optimally in ${O(n^{4})}$ time, where ${n}$ is the number of rectangles in the input instance. This improves over the best previous ${O(n^{6})}$ -time algorithm available for the problem. We also present two efficient approximation algorithms for the problem that find near-optimal solutions with guaranteed approximation factors. The first algorithm finds a 2-approximate solution in ${O(n^{2})}$ time, and the second one computes a ${4/3}$ -approximation in ${O(n^{3})}$ time. The experimental results demonstrate the efficiency of both our exact and approximation algorithms.


title: Priority and Synchronization Support for ROS

abstract: Robot Operating System (ROS) is a software component framework developed for robot applications. It provides a rich set of software libraries and tools to construct robot components, including implementation of many popular perception, planning, and control algorithms. The state of the art in ROS, however, does not fundamentally support priority and synchronization among tasks, so-called nodes in ROS. Therefore, ROS may not be appropriate as a platform of real-time multi-tasking environments, despite the fact that most robot applications are multi-tasking and running under real-time constraints. In this paper, we explore real-time performance of ROS. We present a priority-based message transmission mechanism to reduce the execution time and time variance of high-priority ROS nodes. We also present a synchronization mechanism to harmonize multiple ROS nodes running at different frequencies. The presented mechanisms can be both used with legacy ROS applications without modification. Experiments using an autonomous driving system show a 62.3% reduction in a worst-case execution time and indicate that time differences between nodes are guaranteed below a constant time. In addition, the total CPU utilization is saved by up to about 10%.


Critical Thinking : A Literature


title: Keep it brief: Scalable creation of compressed localization maps

abstract: Robust, scalable localization unlocks path-planning, obstacle avoidance as well as manipulation and thus is a core competency for many robotic applications. However, as we leave the lab and move out in the world, models of the environment no longer span distances of meters but kilometers in length. Now, gigabytes instead of megabytes of memory are required to hold the model of the environment required for localization. Discarding data and keeping the map representation compact is thus essential for any meaningful application. This paper presents and evaluates a map compression algorithm that approaches this data-reduction as an constrained optimization problem. At the core of the algorithm is the concept of a Summary Map, a reduced map representation that includes only the landmarks that are deemed most useful for place recognition. To assign landmarks to the map we have to satisfy the conflicting goals of map coverage and localizability as well as our tight memory budget. While using an optimization approach for compression is not novel, in this paper we propose adaptations to drastically reduce the computational requirements. Our approach improves scalability from trajectories of a few tens of meters manageable by the state of the art to virtually unlimited dataset sizes in our system. We evaluate the performance of various compression levels as well as several methods for selecting the best localization landmarks from outdoor datasets.


title: Characterizing and explaining spatio-temporal variation of water quality in a highly disturbed river by multi-statistical techniques

abstract: Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran's index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin.


title: Performance analysis of idle programs

abstract: This paper presents an approach for performance analysis of modern enterprise-class server applications. In our experience, performance bottlenecks in these applications differ qualitatively from bottlenecks in smaller, stand-alone systems. Small applications and benchmarks often suffer from CPU-intensive hot spots. In contrast, enterprise-class multi-tier applications often suffer from problems that manifest not as hot spots, but as idle time, indicating a lack of forward motion. Many factors can contribute to undesirable idle time, including locking problems, excessive system-level activities like garbage collection, various resource constraints, and problems driving load. We present the design and methodology for WAIT, a tool to diagnosis the root cause of idle time in server applications. Given lightweight samples of Java activity on a single tier, the tool can often pinpoint the primary bottleneck on a multi-tier system. The methodology centers on an informative abstraction of the states of idleness observed in a running program. This abstraction allows the tool to distinguish, for example, between hold-ups on a database machine, insufficient load, lock contention in application code, and a conventional bottleneck due to a hot method. To compute the abstraction, we present a simple expert system based on an extensible set of declarative rules. WAIT can be deployed on the fly, without modifying or even restarting the application. Many groups in IBM have applied the tool to diagnosis performance problems in commercial systems, and we present a number of examples as case studies.


Survey and Performance Evaluation of DBSCAN Spatial Clustering Implementations for Big Data and High-Performance Computing Paradigms


The influence of Chomsky on the neuroscience of language


title: Atomic snapshots using lattice agreement

abstract: The snapshot object is an important tool for constructing wait-free asynchronous algorithms. We relate the snapshot object to the lattice agreement decision problem. It is shown that any algorithm for solving lattice agreement can be transformed into an implementation of a snapshot object. The overhead cost of this transformation is only a linear number of read and write operations on atomic single-writer multi-reader registers. The transformation uses an unbounded amount of shared memory. We present a deterministic algorithm for lattice agreement that used O(log2 n) operations on 2-processor Test & Set registers, plus O(n) operations on atomic single-writer multi-reader registers. The shared objects are used by the algorithm in a dynamic mode, that is, the identity of the processors that access each of the shared objects is determined dynamically during the execution of the algorithm. By a randomized implementation of 2-processors Test & Set registers from atomic registers, this algorithm implies a randomized algorithm for lattice agreement that uses an expected number of O(n) operations on (dynamic) atomic single-writer multi-reader registers. Combined with our transformation this yields implementations of atomic snapshots with the same complexity.


title: Planning Problems in Intermodal Freight Transport: Accomplishments and Prospects

abstract: Intermodal freight transport has received increased attention due to problems of road congestion, environmental concerns and traffic safety. A growing recognition of the strategic importance of speed and agility in the supply chain is forcing firms to reconsider traditional logistic services. As a consequence, research interest in intermodal freight transportation problems is growing. This paper provides an overview of planning decisions in intermodal freight transport and solution methods proposed in the scientific literature. Planning problems are classified according to the type of decision maker and decision level. General conclusions are given and subjects for further research are identified.


The State Changing its Mind? Australian and New Zealand Governments' Postwar Policy on Married Women's Paid Employment


title: Investigation on seal-ring rules for IC product reliability in 0.25-mum CMOS technology

abstract: The distance between active region and the seal-ring location has been investigated in a 0.25-μm CMOS process. From the experimental results, this distance can be shrunk to only 5 μm without increasing leakage current and decreasing ESD robustness of the ESD protection devices after reliability tests of High-Accelerated Stress Test (HAST) and Temperature Cycling Test (TCT).


title: Perspectives and Visions of Computer Science Education in Primary and Secondary (K-12) Schools

abstract: In view of the recent developments in many countries, for example, in the USA and in the UK, it appears that computer science education (CSE) in primary or secondary schools (K-12) has reached a significant turning point, shifting its focus from ICT-oriented to rigorous computer science concepts. The goal of this special issue is to offer a publication platform for soundly based in-depth experiences that have been made around the world with concepts, approaches, or initiatives that aim at supporting this shift. For this purpose, the article format was kept as large as possible, enabling the authors to explain many facets of their concepts and experiences in detail. Regarding the structure of the articles, we had encouraged the authors to lean on the Darmstadt Model, a category system that was developed to support the development, improvement, and investigation of K-12 CSE across regional or national boundaries. This model could serve as a unifying framework that might provide a proper structure for a well-founded critical discussion about the future of K-12 CSE. Curriculum designers or policy stakeholders, who have to decide, which approach an upcoming national initiative should follow, could benefit from this discussion as well as researchers who are investigating K12 CSE in any regard. With this goal in mind, we have selected six extensive and two short case studies from the UK, New Zealand, USA/Israel, France, Sweden, Georgia (USA), Russia, and Italy that provide an in-depth analysis of K-12 CSE in their respective country or state.


title: Acoustic barcodes: passive, durable and inexpensive notched identification tags

abstract: We present acoustic barcodes, structured patterns of physical notches that, when swiped with e.g., a fingernail, produce a complex sound that can be resolved to a binary ID. A single, inexpensive contact microphone attached to a surface or object is used to capture the waveform. We present our method for decoding sounds into IDs, which handles variations in swipe velocity and other factors. Acoustic barcodes could be used for information retrieval or to triggering interactive functions. They are passive, durable and inexpensive to produce. Further, they can be applied to a wide range of materials and objects, including plastic, wood, glass and stone. We conclude with several example applications that highlight the utility of our approach, and a user study that explores its feasibility.


An Integration Approach for XML Query Parallelization on Multi-thread Systems


title: Forensic analysis of xbox one and playstation 4 gaming consoles

abstract: This paper highlights the challenges faced due to non-availability of trusted specialized forensic tools for conducting investigation on gaming consoles. We have developed a framework to examine existing state-of-the-art forensic acquisition and analysis tools by exploring their applicability to eighth generation gaming consoles such as the Xbox One and PlayStation 4. The framework is used to validate the acquired images, compare the retrieved artifacts before and after restoring the console to the factory settings, and to conduct network forensics on both devices. The paper reveals the need of specialized forensic tools for forensic analysis of these devices.


title: Extracting decision trees from trained neural networks

abstract: Neural Networks are successful in acquiring hidden knowledge in datasets. Their biggest weakness is that the knowledge they acquire is represented in a form not understandable to humans. Researchers tried to address this problem by extracting rules from trained Neural Networks. Most of the proposed rule extraction methods required specialized type of Neural Networks; some required binary inputs and some were computationally expensive. Craven proposed extracting MofN type Decision Trees from Neural Networks. We believe MofN type Decision Trees are only good for MofN type problems and trees created for regular high dimensional real world problems may be very complex. In this paper, we introduced a new method for extracting regular C4.5 like Decision Trees from trained Neural Networks. We showed that the new method (DecText) is effective in extracting high fidelity trees from trained networks. We also introduced a new discretization technique to make DecText be able to handle continuous features and a new pruning technique for finding simplest tree with the highest fidelity.


title: Salient Object Detection via Structured Matrix Decomposition

abstract: Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.


title: The Exchange of Knowledge Using Cloud Robotics

abstract: To enable robots to perform human-level tasks flexibly in varying conditions, we need a mechanism that allows them to exchange knowledge between themselves for crowd-sourcing the knowledge gap problem. One approach to achieve this is to equip a cloud application with a range of encyclopedic knowledge (i.e. ontologies) and execution logs of different robots performing the same tasks in different environments. In this paper, we show how knowledge exchange between robots can be done using OPENEASE as the cloud application. We equipped OPENEASE with ontologies about the kitchen domain, execution logs of three robots operating in two different kitchens, and semantic descriptions of both environments. By addressing two different use cases, we show that two PR2 robots and one Fetch robot can successfully adapt each other's plan parameters and sub symbolic data to the experiments that they are conducting.


title: Stability-Indicating HPLC Method for the Simultaneous Determination of HIV Tablet Containing Emtricitabine, Tenofovir Disoproxil Fumarate, and Rilpivirine Hydrochloride in Pharmaceutical Dosage Forms

abstract: A simple, accurate, rapid, and stability-indicating RP-HPLC method for a combination of tenofovir disoproxil fumarate, emtricitabine, and rilpivirine has been developed and subsequently validated in commercial tablets. The proposed HPLC method utilizes Phenomenex Gemini C18 column (150 mm × 4.6 mm i.d., 5 µm) and mobile phase consisting of MeCN, potassium dihydrogen phosphate buffer (20 mM, pH 3.3), and triethylamine 58.72 : 41.23 : 0.05 (v/v) at a flow rate of 1.7 mL/min. Quantitation was achieved with UV detection at 270 nm. The method was validated in terms of accuracy, precision, linearity, limits of detection, limits of quantitation, and robustness. This optimized method has been successively applied to pharmaceutical formulation and no interference from the tablet excipients was found. TDF, EMT, and RPV and their combination drug product were subjected to acid, base, neutral hydrolysis, oxidation, dry heat, and photolytic stress conditions and the stressed samples were analyzed by the proposed method. As the proposed LC method could effectively separate the drugs from its degradation products, it can be employed as stability-indicating method for the determination of instability of these drugs in bulk and commercial tablets.


Using deep learning based approaches for bearing remaining useful life prediction


title: The Effect of Technology Usage Habits on Consumers' Intention to Continue Use Mobile Payments

abstract: With the arrival of the mobile commerce era, mobile payments have become one of the critical drivers for mobile commerce success. However, the percentage of consumers who use mobile payments frequently is still low. This research views IT acceptance from the IT ecosystem perspective and adopt transfer of learning theories as the theoretical background to investigate how technology usage habits affect consumers’ intention to continue use mobile payments. Results indicate that mobile service usage habit influence consumers’ behavioral intention directly and indirectly through mobile payment usage habit. In addition, consumers’ online shopping habit and cell phone usage habit influence their behavioral intention indirectly through mobile payment usage habit. Theoretical and practical implications of the findings are also presented.


title: Evaluation of Emerging Modular Multilevel Converters for BESS Applications

abstract: The power conversion system for a battery-energy storage system typically employs a conventional voltage-source converter with battery strings directly connected to the dc bus. This system configuration presents several issues, such as limited efficiency of two-level converter systems and the limited reliability associated with the use of long battery strings. This paper examines three viable multilevel converter solutions for integrating battery energy storage that offer the potential for enhanced efficiency and reliability. These solutions are the modular multilevel converter (MMLC) with battery energy storage distributed into its submodules, the cascaded converter, and the MMLC with battery energy storage centralized on its dc link. The three systems are compared in terms of efficiency, reliability, and module redundancy. It is determined that the MMLC with distributed battery energy storage must operate differently from conventional MMLC systems. Its operation is therefore studied in detail and validated through simulation to demonstrate its suitability for distributed energy-storage integration. The analysis shows that the MMLC with distributed battery energy storage requires the largest number of semiconductor devices for a given power level; however, it also provides the most efficient, reliable, and versatile solution of energy-storage integration.


title: Terms Mining in Document-Based NoSQL: Response to Unstructured Data

abstract: Unstructured data mining has become topical recently due to the availability of high-dimensional and voluminous digital content (known as "Big Data") across the enterprise spectrum. The Relational Database Management Systems (RDBMS) have been employed over the past decades for content storage and management, but, the ever-growing heterogeneity in today's data calls for a new storage approach. Thus, the NoSQL database has emerged as the preferred storage facility nowadays since the facility supports unstructured data storage. This creates the need to explore efficient data mining techniques from such NoSQL systems since the available tools and frameworks which are designed for RDBMS are often not directly applicable. In this paper, we focused on topics and terms mining, based on clustering, in document-based NoSQL. This is achieved by adapting the architectural design of an analytics-as-a-service framework and the proposal of the Viterbi algorithm to enhance the accuracy of the terms classification in the system. The results from the pilot testing of our work show higher accuracy in comparison to some previously proposed techniques such as the parallel search.


Question Answering Using Bi-Directional rNN


title: High Performance Indoor Location Wi-Fi Fingerprinting using Invariant Received Signal Strength

abstract: The instability of Wi-Fi received signal strength (RSS) incurred by mutable channel characteristics hampers a wide-spread adoption of RSS based location fingerprinting to real world indoor localization applications. To overcome RSS instability, we propose a new approach based on the concept of "invariant RSS statistics". By invariant RSS statistics, we mean the RSS samples collected at each calibration location, especially, under minimal random spatiotemporal disturbances. The proposed method forms the reference pattern classes for individual calibration locations with the invariant RSS statistics thus obtained. Fingerprinting is done by identifying the reference pattern class that maximally supports the RSS readings collected at an unknown location for available Wi-Fi sources. The support of RSS readings is defined here as the sum of the likelihood probabilities of individual RSS readings. Unlike conventional methods, the proposed method allows only those readings high in statistical confidence to participate in the sum, while excluding other readings. This is to screen out the influence of those readings contaminated by time-varying disturbances on classification. Experimental results show that the proposed method provides superior performance to conventional ones with the success rate higher by 17%, the printing resolution finer by 30% and, naturally, no performance degradation in time without recalibration.


title: Optimization of Watt's Six-Bar Linkage to Generate Straight and Parallel Leg Motion

abstract: This paper considers optimal synthesis of a special type of four-bar linkages. Combination of this optimal four-bar linkage with on of it's cognates and elimination of two redundant cognates will result in a Watt's six-bar mechanism, which generates straight and parallel motion. This mechanism can be utilized for legged machines. The advantage of this mechanism is that the leg remains straight during it's contact period and because of it's parallel motion, the legs can be as wide as desired to increase contact area and decrease the number of legs required to keep body's stability statically and dynamically. "Genetic algorithm" optimization method is used to find optimal lengths. It is especially useful for problems like the coupler curve equation which are completely nonlinear or extremely difficult to solve.


title: Variable-Frequency Grid-Sequence Detector Based on a Quasi-Ideal Low-Pass Filter Stage and a Phase-Locked Loop

abstract: This paper proposes a filtered-sequence phase-locked loop (FSPLL) structure for detection of the positive sequence in three-phase systems. The structure includes the use of the Park transformation and moving average filters (MAF). Performance of the MAF is mathematically analyzed and represented in Bode diagrams. The analysis allows a proper selection of the window width of the optimal filter for its application in the dq transformed variables. The proposed detector structure allows fast detection of the grid voltage positive sequence (within one grid voltage cycle). The MAF eliminates completely any oscillation multiple of the frequency for which it is designed; thus, this algorithm is not affected by the presence of imbalances or harmonics in the electrical grid. Furthermore, the PLL includes a simple-frequency detector that makes frequency adaptive the frequency depending blocks. This guarantees the proper operation of the FSPLL under large frequency changes. The performance of the entire PLL-based detector is verified through simulation and experiment. It shows very good performance under several extreme grid voltage conditions.


title: Congruent leadership: values in action

abstract: AIM(S) ::: To discuss the significance of an appropriate leadership theory in order to develop an understanding of clinical leadership. ::: ::: ::: BACKGROUND ::: Leadership theories developed from management and related paradigms, particularly transformational leadership, may be ineffective in supporting nurses to gain insights into clinical leadership or to develop and implement clinical leadership skills. Instead, congruent leadership theory, based on a match between the clinical leaders' actions and their values and beliefs about care and nursing, may offer a more firm theoretical foundation on which clinical nurses can build an understanding of and capacity to implement clinical leadership or become clinical leaders. Evaluation The information used is drawn from the contemporary literature and a study conducted by the author. ::: ::: ::: KEY ISSUE(S) ::: Leadership can be better understood when an appropriate theoretical foundation is employed. ::: ::: ::: CONCLUSIONS ::: With regard to clinical leadership, congruent leadership is proposed as the most appropriate theory. ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: It is important to recognize that leadership theories based on a management paradigm may not be appropriate for all clinical applications. Education should be aimed specifically at clinical leaders, recognizing that clinical leaders are followed not for their vision or creativity (even if they demonstrate these), but because they translate their values and beliefs about care into action.


title: A CMOS buffer without short-circuit power consumption

abstract: A new CMOS buffer without short-circuit power consumption is proposed. The gate-driving signal of the output pull-up (pull-down) transistor is fed back to the output pull-down (pull-up) transistor to get tri-state output momentarily, eliminating the short-circuit power consumption, The HSPICE simulation results verified the operation of the proposed buffer and showed the power-delay product is about 15% smaller than conventional tapered CMOS buffer.


title: Searchable ciphertext‐policy attribute‐based encryption with revocation in cloud storage

abstract: Summary ::: ::: To protect the sensitive data outsourced to cloud server, outsourcing data in an encrypted way has become popular nowadays. However, it is not easy to find the corresponding ciphertext efficiently, especially the large ciphertext stored on cloud server. Besides, some data owners do not want those users who attempt to decrypt to know the sensitive access structure of the ciphertext because of some business or private reasons. In addition, the user attributes revocation and key updating are important issues, which affect application of ciphertext-policy attribute-based encryption (CP-ABE) in cloud storage systems. To overcome the previous problems in cloud storage, we present a searchable CP-ABE with attribute revocation, where access structures are partially hidden so that receivers cannot extract sensitive information from the ciphertext. The security of our scheme can be reduced to the decisional bilinear Diffie–Hellman (DBDH) assumption and decisional linear (DL) assumption. Copyright © 2015 John Wiley & Sons, Ltd.


title: Hate Crimes and Stigma-Related Experiences Among Sexual Minority Adults in the United States: Prevalence Estimates From a National Probability Sample

abstract: Using survey responses collected via the Internet from a U.S. national probability sample of gay, lesbian, and bisexual adults (N = 662), this article reports prevalence estimates of criminal victimization and related experiences based on the target's sexual orientation. Approximately 20% of respondents reported having experienced a person or property crime based on their sexual orientation; about half had experienced verbal harassment, and more than 1 in 10 reported having experienced employment or housing discrimination. Gay men were significantly more likely than lesbians or bisexuals to experience violence and property crimes. Employment and housing discrimination were significantly more likely among gay men and lesbians than among bisexual men and women. Implications for future research and policy are discussed.


title: Optimizing a Battery Energy Storage System for Frequency Control Application in an Isolated Power System

abstract: This paper presents a method for optimal sizing and operation of a battery energy storage system (BESS) used for spinning reserve in a small isolated power system. Numerical simulations are performed on a load-frequency control (LFC) dynamic simulator of the isolated network. A novel control algorithm using adjustable state of charge limits is implemented and tested on a BESS dynamic model. An optimal sizing procedure of the BESS is developed for an isolated power system in order to achieve highest expected profitability of the device. The BESS can increase significantly the power system stability, the grid security, and the planning flexibility for a small isolated power system with low grid inertia. Meanwhile, it fulfills the frequency control requirements with a high expected economic profitability.


title: Towards Automated RESTful Web Service Composition

abstract: Emerging as the popular choice for leading Internet companies to expose internal data and resources, Restful Web services are attracting increasing attention in the industry.While automating WSDL/SOAP based Web service composition has been extensively studied in the research community, automated RESTful Web service composition in the context of service-oriented architecture (SOA), to the best of our knowledge, is less explored. As an early paper addressing this problem, this paper discusses the challenges of composing RESTful Web services and proposes a formal model for describing individual Web services and automating the composition. It demonstrates our approach by applying it to a real-world RESTful Web service composition problem. This paper represents our initial efforts towards the problem of automated RESTful Web service composition.We are hoping that it will draw interests from the research community on Web services, and engage more researchers in this challenge.


title: Subtypes, Dimensions, Levels, and Mental States in Narcissism and Narcissistic Personality Disorder

abstract: Various conceptualizations of subtypes, levels, and dimensions of narcissism and narcissistic personality disorder (NPD) are considered with a particular focus on overt grandiose presentations and covert vulnerable presentations. Evidence supporting this distinction and clinical vignettes to illustrate it are presented as well as their implications for clinical work with NPD patients. The research and clinical evidence points to the conclusion that these broad categorical subtypes are better conceptualized as dimensions on which individual patients vary on relative levels, thus suggesting that grandiose and vulnerable presentations represent two sides of the same coin. A case example and clinical implications are provided and discussed.


title: Equivalent-Circuit Models for the Design of Metamaterials Based on Artificial Magnetic Inclusions

abstract: In this paper, we derive quasi-static equivalent-circuit models for the analysis and design of different types of artificial magnetic resonators-i.e., the multiple split-ring resonator, spiral resonator, and labyrinth resonator-which represent popular inclusions to synthesize artificial materials and metamaterials with anomalous values of the permeability in the microwave and millimeter-wave frequency ranges. The proposed models, derived in terms of equivalent circuits, represent an extension of the models presented in a recent publication. In particular, the extended models take into account the presence of a dielectric substrate hosting the metallic inclusions and the losses due to the finite conductivity of the conductors and the finite resistivity of the dielectrics. Exploiting these circuit models, it is possible to accurately predict not only the resonant frequency of the individual inclusions, but also their quality factor and the relative permeability of metamaterial samples made by given arrangements of such inclusions. Finally, the three models have been tested against full-wave simulations and measurements, showing a good accuracy. This result opens the door to a quick and accurate design of the artificial magnetic inclusions to fabricate real-life metamaterial samples with anomalous values of the permeability.


title: A Model Integrating Fire Prediction and Detection for Rural-Urban Interface

abstract: This paper proposes a model that integrates new smoke detection and fire prediction techniques for the rural-urban interface area. The model aims to predict fire risk from weather parameters, and to detect smoke using video monitoring systems. Further, the fire danger index (FDI) provided by the prediction algorithm would be utilized to enhance the certainty of smoke detection and reduce false alarm rates. Experimental results illustrate that our prediction algorithm successfully predicts fire risk on a five-point scale with mean accuracy of 94.92% and the detection algorithm more effectively detects smoke compared to other algorithms by achieving 97% average accuracy.


title: Musically Informed Sonification for Chronic Pain Rehabilitation: Facilitating Progress & Avoiding Over-Doing

abstract: In self-directed chronic pain physical rehabilitation it is important that the individual can progress as physical capabilities and confidence grow. However, people with chronic pain often struggle to pass what they have identified as safe boundaries. At the same time, over-activity due to the desire to progress fast or function more normally, may lead to setbacks. We investigate how musically-informed movement sonification can be used as an implicit mechanism to both avoid overdoing and facilitate progress during stretching exercises. We sonify an end target-point in a stretch exercise, using a stable sound (i.e., where the sonification is musically resolved) to encourage movements ending and an unstable sound (i.e., musically unresolved) to encourage continuation. Results on healthy participants show that instability leads to progression further beyond the target-point while stability leads to a smoother stop beyond this point. We conclude discussing how these findings should generalize to the CP population.


title: Cryptocurrencies without Proof of Work

abstract: We study decentralized cryptocurrency protocols in which the participants do not deplete physical scarce resources. Such protocols commonly rely on Proof of Stake, i.e., on mechanisms that extend voting power to the stakeholders of the system. We offer analysis of existing protocols that have a substantial amount of popularity. We then present our novel pure Proof of Stake protocols, and argue that they help in mitigating problems that the existing protocols exhibit.


title: Sleep timing is more important than sleep length or quality for medical school performance

abstract: Overwhelming evidence supports the importance of sleep for memory consolidation. Medical students are often deprived of sufficient sleep due to large amounts of clinical duties and university load, we therefore investigated how study and sleep habits influence university performance. We performed a questionnaire-based study with 31 medical students of the University of Munich (second and third clinical semesters; surgery and internal medicine). The students kept a diary (in 30-min bins) on their daily schedules (times when they studied by themselves, attended classes, slept, worked on their thesis, or worked to earn money). The project design involved three 2-wk periods (A: during the semester; B: directly before the exam period—pre-exam; C: during the subsequent semester break). Besides the diaries, students completed once questionnaires about their sleep quality (Pittsburgh Sleep Quality Index [PSQI]), their chronotype (Munich Chronotype Questionnaire [MCTQ]), and their academic history (previous grades...