title: Identifying Ghanaian Pre-Service Teachers' Readiness for Computer Use: A Technology Acceptance Model Approach.

abstract: INTRODUCTIONThe use of computers is becoming widespread in Ghanaian education settings. While the students, especially those in the junior and senior high schools, seem ready to embrace this modern technology, it is not easy to identify a similar readiness in teachers. For example, Adu Gyamfi (2011) in his study on the use of Information and Communication Technology (ICT) on pre-service teacher education in Ghana concluded that about 85 percent of the new teachers graduating from Ghanaian pre-service teacher institutions felt they lacked the necessary skills to teach with ICT, implying an inadequacy in the training of the teachers. However, an accumulating body of research has found that teachers' contribution is very important for the implementation and success of any technological innovation in education (Eteokleous-Grigorious, et al., 2012; Lim, Lock & Brook, 2011). Consequently, teachers' familiarity is crucial for a successful introduction of computer technologies into the classroom environment. Again, in his study in Singapore to build a model that predicts the level of technology acceptance by pre-service teachers at the Institute of Education, Teo (2009) found that, the level of technology acceptance among pre-service teachers determines the extent to which new technologies could be integrated into classroom settings. Given that pre-service teachers are the agents of change in the effective and meaningful integration of computer technology in the Ghanaian education system, their readiness to use this modern technology is of paramount importance. For this reason, there is a need to understand factors that influence technology acceptance among pre-service teachers in Ghana. This study extends the technology acceptance model to identify factors that influence technology acceptance among pre-service teachers in Ghana. The findings contribute to the literature by validating the TAM in the Ghanaian context and provide several prominent implications for the research and practice of technology integration development.LITERATURE REVIEW AND THEORETICAL FRAMEWORKThis section encompasses the study's literature review. First, it discusses issues related to pre-service teachers' beliefs about technology acceptance. Second, it addresses the Technology Acceptance Model which provides the conceptual model for the current study.Beliefs about technology useGenerally, when teachers are asked to use technology to facilitate learning, some degree of change in their beliefs is required (Ertmer & Ottenbreit-Leftwich, 2010). Although, a growing number of research studies have revealed that there are a number of factors such as lack of access to ICT(Ertmer, et al.,1999), inadequate technical and administrative support (Sandholtz, 2001), lack of training provided to teachers in integrating ICT(Adams,2005; Lim & Khine(2006) most reform efforts in the past have been unsuccessful, because of top-down approaches, which failed to take teachers' existing beliefs into consideration (Hammond, 2011; Jimoyiannis & Komis, 2007). It is against this background that this study is designed to extend the technology acceptance model, by adding pedagogical beliefs to examine factors that influence technology acceptance among pre-service teachers in Ghana.The pertinence of pedagogical beliefs in educational innovation was captured by Kagan (1992:85) who noted that "the more one reads studies of teacher belief, the more strongly one suspects that this piebald form of personal knowledge lies at the very heart of teaching." In a similar vein, Pajaras (1992) was of the view that understanding teachers beliefs helped improve their professional preparation. This implies that, an understanding of the current beliefs and perspectives that pre-service teachers hold may serve as a good starting point for discussion about their acceptance and adoption of computer technology for instructional purpose.Pedagogical beliefs are defined as the beliefs that teachers hold, pertaining to the nature of teaching and how teaching should be carried out (Teo, Chai, Hung & Lee 2008). …


title: Arch2030: A Vision of Computer Architecture Research over the Next 15 Years

abstract: Application trends, device technologies and the architecture of systems drive progress in information technologies. However, the former engines of such progress - Moore's Law and Dennard Scaling - are rapidly reaching the point of diminishing returns. The time has come for the computing community to boldly confront a new challenge: how to secure a foundational future for information technology's continued progress. The computer architecture community engaged in several visioning exercises over the years. Five years ago, we released a white paper, 21st Century Computer Architecture, which influenced funding programs in both academia and industry. More recently, the IEEE Rebooting Computing Initiative explored the future of computing systems in the architecture, device, and circuit domains. This report stems from an effort to continue this dialogue, reach out to the applications and devices/circuits communities, and understand their trends and vision. We aim to identify opportunities where architecture research can bridge the gap between the application and device domains.


title: Critical Thinking: Frameworks and Models for Teaching

abstract: Developing critical thinking since the educational revolution gave rise to flourishing movements toward embedding critical thinking (CT henceforth) stimulating classroom activities in educational settings. Nevertheless the process faced with complications such as teachability potentiality, lack of practical frameworks concerning actualization of CT tasks, and transferability obstacles, as well as lack of a homogeneous model of conceptualization of CT among educators. The present study made an effort to represent a comprehensive model of CT for educators drawn on the contemporary literaturein order to indicate a uniform delineation of the construct and to offer a comprehensive model of CT for the intention of making boosting learners’ capability of CT possible.


ARTIFICIAL INTELLIGENCE 1 Discourse Strategies for Generating Natural-Language Text*


title: The Singularity: A Philosophical Analysis

abstract: What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I. J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”:


title: Vulnerability, livelihood assets and institutional dynamics in the management of wetlands in Lake Victoria watershed basin

abstract: This paper uses data from 600 households in the Lake Victoria watershed in Tanzania, Kenya, and Uganda to analyze the effects of vulnerabilities and shocks on the management and exploitation of wetlands within the context of agricultural activities and high poverty levels. A multinomial logit model is used to determine variables that influence the perception of wetlands degradation, while a tobit model is used to establish the determinants of willingness to pay for wetland conservation and the imputed value of wetland product extracts. The model results show that although the perception of wetland degradation is modest, it is influenced by attributes of social capital. Variables such as floods, diseases and droughts significantly influence the households’ willingness to pay for wetland conservation. Land size and ownership, education level and household size all influence households’ likelihood to actively engage in wetland resource exploitation and willingness to pay for its conservation. The implications of these results hinge on measures that would moderate the effects of shocks, mobilize collective action, and improve physical infrastructure within the context of sustainable wetland resource use.


title: Feature Extraction using Sparse SVD for Biometric Fusion in Multimodal Authentication

abstract: Token based security (ID Cards) have been used to restrictaccess to the Secured systems. The purpose of Biometrics is to identify / verify the correctness of an individual by using certain physiological or behavioural traits associated with the person. Current biometric systems make use of face, fingerprints, iris, hand geometry, retina, signature, palm print, voiceprint and so on to establis h a person’s identity. Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport, pan card, etc. In this paper, we consider face and fingerprint patterns for identification/verification. Using this data we proposed a novel model for authentication in multimod al biometrics often called ContextSensitive Exponent Associative Memory Model (CS EAM). It provides different stages of security for biometrics fusion patterns. In stage 1, fusion of face and finger patterns using Principal Component Analysis (PCA), in stage 2 by applying Sparse SVD decomposition to extract the feature patterns from the fusion data and face pattern and thenin stage 3, using CSEAM model, the extracted feature vectorscan be encoded. The final key will be stored in the smart cardsas Associative Memory (M) , which is often called Context-Sensitive Associative Memory (CSAM) . In CSEAM model, the CSEAM will be computed using exponential kronecker productfor encoding and verification of the chosen samples from the users. The exponential of matrix can be computed in various ways such as Taylor Series, Pade Approximation and also using Ordinary Differential Equations (O.D.E.). Among these approaches we considered first two methods for computing exponential of a feature space.The result analysis of SVD and Sparse SVD for feature extraction process and also authentication/verification process of the proposed system in terms of performance measures as Mean square error rateswill be presented.


L ESSONS FROM THE R ADEMACHER C OMPLEXITY FOR D EEP L EARNING


title: Mental health and legal representation for asylum seekers in the ‘legacy caseload’

abstract: This article examines the legal challenges asylum seekers arriving by boat to Australia experience when seeking assistance with their claims and its impact on their mental health. The authors outline the experiences of asylum seekers in the “legacy caseload” group who have been waiting up to four years to have their protection claims assessed. The complex interplay between legal assistance to support refugee claims and the way those making claims inevitably struggle to understand, engage and participate in the process is analysed. It is argued that provision of legal assistance for this group will be essential to ensuring that the refugee status determination process is fair and allows asylum seekers to understand and participate more fully in the process. Recent changes to the assessment of claims combined with a reduction in funding for legal assistance create significant hurdles and combine to compound existing stress and emotional trauma leading to detrimental outcomes on the mental health of asylum seekers.


title: A new FPGA based architecture to improve performance of deflectometry image processing algorithm

abstract: Image processing has gained its popularity over the recent years in areas, such as computer vision, artificial intelligence and automation. Deflectometry image processing technique is developed to inspect defects on reflecting surfaces. FPGA offers flexibility by employing reconfigurability, and furthermore, provides parallelization and pipelining to improve latency and execution time. The performance gain of deflectometry image processing algorithm in terms of execution times can be achieved by using an architecture based on FPGA, as proposed in this paper. The results show the correlation between performance and resource consumption. The simulation results are calculated and arranged in form of comparison between software and hardware implementation, using proposed architecture. Moreover, for the rapid prototyping purpose, Xilinx High Level Synthesis (HLS) tool is selected in the development methodology to realize the proposed design.


title: Effects of Robotic Knee Exoskeleton on Human Energy Expenditure

abstract: A number of studies discuss the design and control of various exoskeleton mechanisms, yet relatively few address the effect on the energy expenditure of the user. In this paper, we discuss the effect of a performance augmenting exoskeleton on the metabolic cost of an able-bodied user/pilot during periodic squatting. We investigated whether an exoskeleton device will significantly reduce the metabolic cost and what is the influence of the chosen device control strategy. By measuring oxygen consumption, minute ventilation, heart rate, blood oxygenation, and muscle EMG during 5-min squatting series, at one squat every 2 s, we show the effects of using a prototype robotic knee exoskeleton under three different noninvasive control approaches: gravity compensation approach, position-based approach, and a novel oscillator-based approach. The latter proposes a novel control that ensures synchronization of the device and the user. Statistically significant decrease in physiological responses can be observed when using the robotic knee exoskeleton under gravity compensation and oscillator-based control. On the other hand, the effects of position-based control were not significant in all parameters although all approaches significantly reduced the energy expenditure during squatting.


Determinization in Monte-Carlo Tree Search for the card game


title: Learning task-specific models for dexterous, in-hand manipulation with simple, adaptive robot hands

abstract: In this paper, we propose a hybrid methodology based on a combination of analytical, numerical and machine learning methods for performing dexterous, in-hand manipulation with simple, adaptive robot hands. A constrained optimization scheme utilizes analytical models that describe the kinematics of adaptive hands and classic conventions for modelling quasistatically the manipulation problem, providing intuition about the problem mechanics. A machine learning (ML) scheme is used in order to split the problem space, deriving task-specific models that account for difficult to model, dynamic phenomena (e.g., slipping). In this respect, the ML scheme: 1) employs the simulation module in order to explore the feasible manipulation paths for a specific hand-object system, 2) feeds the feasible paths to an experimental setup that collects manipulation data in an automated fashion, 3) uses clustering techniques in order to group together similar manipulation trajectories, 4) trains a set of task-specific manipulation models and 5) uses classification techniques in order to trigger a task-specific model based on the user provided task specifications. The efficacy of the proposed methodology is experimentally validated using various adaptive robot hands in 2D and 3D in-hand manipulation tasks.


title: Deep neural networks for Markovian interactive scene prediction in highway scenarios

abstract: In this paper, we compare different deep neural network approaches for motion prediction within a highway entrance scenario. The focus of our work lies on models that operate on limited history of data in order to fulfill the Markov property1 and be usable within an integrated prediction and motion planning framework for automated vehicles. We examine different model structures and feature combinations in order to find a model with a good tradeoff between accuracy and computational performance. We evaluate all models with standard metrics like the negative log-likelihood (NLL) and evaluate the performance of each model within a closed-loop simulation. We find a neural network only operating on spatial features of the current state to have the best closed-loop prediction performance, despite the NLL suggesting otherwise.


title: Façade: An Experiment in Building a Fully-Realized Interactive Drama

abstract: Contemporary games are making significant strides towards offering complex, immersive experiences for players. We can now explore sprawling 3D virtual environments populated by beautifully rendered characters and objects with autonomous behavior, engage in highly visceral action-oriented experiences offering a variety of missions with multiple solutions, and interact in ever-expanding online worlds teeming with physically customizable player avatars.


title: Procedural Generation of 3D Canyons

abstract: This paper presents a non-assisted method for procedural generation of 3D canyon scenes. Our approach combines techniques of computer graphics, computer vision, image processing and graph search algorithms. Our main contribution is a generation approach that uses noise-generated height maps that are carefully transformed and manipulated by data clustering (through the Mean Shift algorithm) and image operations in order to mimic the observed geological features of real canyons. Several parameters are used to guide and constrain the generation of terrains, canyon features, course and shape of rivers, plain areas, soft slope regions, cliffs and plateaus.


title: Investigation of electrically gate-all-around hexagonal nanowire FET (HexFET) architecture for 5 nm node logic and SRAM applications

abstract: This work investigates, in detail, the electrically gate-all-around (eGAA) Hexagonal NW FET (HexFET) which combines the high current drive of FinFETs with the excellent electrostatic robustness of conventional Gate-All-Around Nanowire (GAA NW) FETs. We evaluate HexFET as a potential successor to FinFET for 5nm node logic and SRAM applications using first principles atomistic-based modeling, calibrated 3D numerical device simulations, and circuit-level benchmarking. From this, we conclude that the eGAA HexFET architecture offers superior performance to both FinFET and GAA NW FET for 5nm node applications.


title: Quipper: a scalable quantum programming language

abstract: The field of quantum algorithms is vibrant. Still, there is currently a lack of programming languages for describing quantum computation on a practical scale, i.e., not just at the level of toy problems. We address this issue by introducing Quipper, a scalable, expressive, functional, higher-order quantum programming language. Quipper has been used to program a diverse set of non-trivial quantum algorithms, and can generate quantum gate representations using trillions of gates. It is geared towards a model of computation that uses a classical computer to control a quantum device, but is not dependent on any particular model of quantum hardware. Quipper has proven effective and easy to use, and opens the door towards using formal methods to analyze quantum algorithms.


title: Automated Construction Of Database Interfaces: Intergrating Statistical And Relational Learning For Semantic Parsing

abstract: The development of natural language interfaces (NLI's) for databases has been a challenging problem in natural language processing (NLP) since the 1970's. The need for NLI's has become more pronounced due to the widespread access to complex databases now available through the Internet. A challenging problem for empirical NLP is the automated acquisition of NLI's from training examples. We present a method for integrating statistical and relational learning techniques for this task which exploits the strength of both approaches. Experimental results from three different domains suggest that such an approach is more robust than a previous purely logic-based approach.


title: Design and analysis of low noise amplifier using cadence

abstract: Low Noise Amplifier also known as LNA is one of the most significant component for application in ::: wireless communication system. It is a very important part in RF receiver because it can reduce noise of ::: gain by the amplifier when the noise of the amplifier is received directly. The low noise amplifier has been ::: designed to get the better performance by follow the requirement in this new era consists of high gain, low ::: noise figure, lower power consumption, small chip area, low cost and good input and output matching. In ::: this research, a LNA schematic consists of three stages which are common gate amplifier, common drain ::: amplifier and active inductor is designed to mitigate this constraint. Common gate and common drain are ::: used for input and output stages in every LNA. Both are also used for excellent input and output matching ::: and have a potential to get a lower noise whereas for active inductor, it is used to obtain the lower power ::: consumption and to reduce the chip size in layout design. The results show that the proposed LNA is able to ::: achieve the best performance with a simulated gain of 14.7dB, extremely lower power consumption of ::: 0.8mW, noise figure of 7dB and small chip area 0.26mm². Consequently, this modified LNA is appropriate ::: for low-voltage applications especially in wireless communication system.


title: Multimodal feature analysis for quantitative performance evaluation of endotracheal intubation (ETI)

abstract: Endotracheal intubation (ETI) is a crucial medical procedure performed on critically ill patients. It involves insertion of a breathing tube into the trachea i.e. the windpipe connecting the larynx and the lungs. Often, this procedure is performed by the paramedics (aka providers) under challenging prehospital settings e.g. roadside, ambulances or helicopters. Successful intubations could be lifesaving, whereas, failed intubation could potentially be fatal. Under prehospital environments, ETI success rates among the paramedics are surprisingly low and this necessitates better training and performance evaluation of ETI skills. Currently, few objective metrics exist to quantify the differences in ETI techniques between providers. In this pilot study, we develop a quantitative framework for discriminating the kinematic characteristics of providers with different experience levels. The system utilizes statistical analysis on spatio-temporal multimodal features extracted from optical motion capture, accelerometers and electromyography (EMG) sensors. Our experiments involved three individuals performing intubations on a dummy, each with different levels of training. Quantitative performance analysis on multimodal features revealed distinctive differences among different skill levels. In future work, the feedback from these analysis could potentially be harnessed for enhanced ETI training.


title: What ERP systems can tell us about Sarbanes‐Oxley

abstract: Purpose – To provide background for senior and middle management in information technology organizations who may be in the implementation phase of compliance for Sarbanes‐Oxley (SOX). As the information technology (IT) organization looks forward to additional compliance or other IT control frameworks such as COBIT, the paper can help construct a roadmap. Other audiences include senior management, accountants, internal auditors, and academics who may wish to evaluate the impact of SOX on the information technology organization.Design/methodology/approach – SOX is surveyed to understand the four major compliance areas that must be supported in the IT organization. Recently published works are integrated into an evaluation of enterprise resource planning (ERP) research to identity several ongoing themes that point to practical advice for implementing SOX. The private sector of US business is saturated with ERP applications and provides a useful benchmark of what to expect with SOX compliance. The sections of...


title: General Theory of Security and a Study Case in Internet of Things

abstract: This paper studies the problems of “security meridian-collateral” and “security confrontation” systematically and refreshes the traditional concept of security. On the one hand, based on the model of “meridians” in Chinese traditional medicine for the first time, this paper proves the following results strictly by the probability method. There is a complete “meridian-collateral diagram” in Internet of things and any finite system, so that any “sickness” of the system can be cured effectively. On the other hand, this paper studies the network attack and defense from the perspective of the information theory. Through mathematical modeling, based on the famous Shannon’s coding theorem, the research on the ability problem of attacker and defender is transformed into the research on the channels of attacker and defender. From the perspective of the channel capacity in information theory for the first time, this paper gives the reachable theoretical limitation of the attack ability of hacker and the defend ability of honker precisely.


title: Dynamic texture segmentation

abstract: We address the problem of segmenting a sequence of images of natural scenes into disjoint regions that are characterized by constant spatio-temporal statistics. We model the spatio-temporal dynamics in each region by Gauss-Markov models, and infer the model parameters as well as the boundary of the regions in a variational optimization framework. Numerical results demonstrate that - in contrast to purely texture-based segmentation schemes - our method is effective in segmenting regions that differ in their dynamics even when spatial statistics are identical.


title: The suppressive influence of SMA on M1 in motor imagery revealed by fMRI and dynamic causal modeling

abstract: Although motor imagery is widely used for motor learning in rehabilitation and sports training, the underlying mechanisms are still poorly understood. Based on fMRI data sets acquired with very high temporal resolution (300 ms) under motor execution and imagery conditions, we utilized Dynamic Causal Modeling (DCM) to determine effective connectivity measures between supplementary motor area (SMA) and primary motor cortex (M1). A set of 28 models was tested in a Bayesian framework and the by-far best-performing model revealed a strong suppressive influence of the motor imagery condition on the forward connection between SMA and M1. Our results clearly indicate that the lack of activation in M1 during motor imagery is caused by suppression from the SMA. These results highlight the importance of the SMA not only for the preparation and execution of intended movements, but also for suppressing movements that are represented in the motor system but not to be performed.


title: Real-time PCR in the microbiology laboratory

abstract: Use of PCR in the field of molecular diagnostics has increased to the point where it is now accepted as the standard method for detecting nucleic acids from a number of sample and microbial types. However, conventional PCR was already an essential tool in the research laboratory. Real-time PCR has catalysed wider acceptance of PCR because it is more rapid, sensitive and reproducible, while the risk of carryover contamination is minimised. There is an increasing number of chemistries which are used to detect PCR products as they accumulate within a closed reaction vessel during real-time PCR. These include the non-specific DNA-binding fluorophores and the specific, fluorophore-labelled oligonucleotide probes, some of which will be discussed in detail. It is not only the technology that has changed with the introduction of real-time PCR. Accompanying changes have occurred in the traditional terminology of PCR, and these changes will be highlighted as they occur. Factors that have restricted the development of multiplex real-time PCR, as well as the role of real-time PCR in the quantitation and genotyping of the microbial causes of infectious disease, will also be discussed. Because the amplification hardware and the fluorogenic detection chemistries have evolved rapidly, this review aims to update the scientist on the current state of the art. Additionally, the advantages, limitations and general background of real-time PCR technology will be reviewed in the context of the microbiology laboratory.


title: ABC: An Academic Industrial-Strength Verification Tool

abstract: ABC is a public-domain system for logic synthesis and formal verification of binary logic circuits appearing in synchronous hardware designs ABC combines scalable logic transformations based on And-Inverter Graphs (AIGs), with a variety of innovative algorithms A focus on the synergy of sequential synthesis and sequential verification leads to improvements in both domains This paper introduces ABC, motivates its development, and illustrates its use in formal verification.


title: Modified cuckoo search algorithm with self adaptive parameter method

abstract: The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been applied to solve a wide range of real-world optimization problem. In this paper, the proposed method uses two new mutation rules based on the rand and best individuals among the entire population. In order to balance the exploitation and exploration of the algorithm, the new rules are combined through a linear decreasing probability rule. Then, self adaptive parameter setting is introduced as a uniform random value to enhance the diversity of the population based on the relative success number of the proposed two new parameters in the previous period. To verify the performance of SACS, 16 benchmark functions chosen from literature are employed. Experimental results indicate that the proposed method performs better than, or at least comparable to state-of-the-art methods from literature when considering the quality of the solutions obtained. In the last part, experiments have been conducted on Lorenz system and Chen system to estimate the parameters of these two chaotic systems. Simulation results further demonstrate the proposed method is very effective.


title: Mapping the Antigenic and Genetic Evolution of Influenza Virus

abstract: The antigenic evolution of influenza A (H3N2) virus was quantified and visualized from its introduction into humans in 1968 to 2003. Although there was remarkable correspondence between antigenic and genetic evolution, significant differences were observed: Antigenic evolution was more punctuated than genetic evolution, and genetic change sometimes had a disproportionately large antigenic effect. The method readily allows monitoring of antigenic differences among vaccine and circulating strains and thus estimation of the effects of vaccination. Further, this approach offers a route to predicting the relative success of emerging strains, which could be achieved by quantifying the combined effects of population level immune escape and viral fitness on strain evolution.


title: A Survey on Methods and Theories of Quantized Neural Networks

abstract: Deep neural networks are the state-of-the-art methods for many real-world tasks, such as computer vision, natural language processing and speech recognition. For all its popularity, deep neural networks are also criticized for consuming a lot of memory and draining battery life of devices during training and inference. This makes it hard to deploy these models on mobile or embedded devices which have tight resource constraints. Quantization is recognized as one of the most effective approaches to satisfy the extreme memory requirements that deep neural network models demand. Instead of adopting 32-bit floating point format to represent weights, quantized representations store weights using more compact formats such as integers or even binary numbers. Despite a possible degradation in predictive performance, quantization provides a potential solution to greatly reduce the model size and the energy consumption. In this survey, we give a thorough review of different aspects of quantized neural networks. Current challenges and trends of quantized neural networks are also discussed.


Point process models for sub-daily rainfall simulation


Course Enrollment Recommender System


title: Vibration measurements to study the effect of cryogen flow in a superconducting quadrupole

abstract: The conceptual design of compact superconducting magnets for the International Linear Collider final focus is presently under development. A primary concern in using superconducting quadrupoles is the potential for inducing additional vibrations from cryogenic operation. We have employed a Laser Doppler Vibrometer system to measure the vibrations in a spare RHIC quadrupole magnet under cryogenic conditions. Some preliminary results of these studies were limited in resolution due to a rather large motion of the laser head as well as the magnet. As a first step towards improving the measurement quality, a new set up was used that reduces the motion of the laser holder. The improved setup is described, and vibration spectra measured at cryogenic temperatures, both with and without helium flow, are presented.


title: Target Estimation in Colocated MIMO Radar via Matrix Completion

abstract: We consider a colocated MIMO radar scenario, in which the receive antennas forward their measurements to a fusion center. Based on the received data, the fusion center formulates a matrix which is then used for target parameter estimation. When the receive antennas sample the target returns at Nyquist rate, and assuming that there are more receive antennas than targets, the data matrix at the fusion center is low-rank. When each receive antenna sends to the fusion center only a small number of samples, along with the sample index, the receive data matrix has missing elements, corresponding to the samples that were not forwarded. Under certain conditions, matrix completion techniques can be applied to recover the full receive data matrix, which can then be used in conjunction with array processing techniques, e.g., MUSIC, to obtain target information. Numerical results indicate that good target recovery can be achieved with occupancy of the receive data matrix as low as 50%.


title: Performance analysis of various classifiers on deep learning network for breast cancer detection

abstract: Cancer is a deadly disease that plagues mankind. 12.5%of women acquire breast cancer in their lifetime. Though, the mortality rate in women due to breast cancer is the second highest behind lung cancer, periodic clinical checkups and self-tests help in early detection and thereby significantly increases the odds of survival. Mammography is considered as the “Gold Standard” of non-invasive breast cancer detection technique. Screening for cancer through manual analysis of the medical images is tedious, time-consuming and impractical for large data. Thus, an automated, accurate and efficient detection system is needed to diagnose breast cancer within a short period of time. Deep learning techniques can revolutionize modern medicine by automating the detection process without compromising its accuracy. In this work, an automated system is proposed for achieving error-free detection of breast cancer using a Sparse Autoencoder (SAE) which learns feature representations from the mammogram and a classifier which is cascaded with the SAE performs the classification based on these learned features. Different classifiers like decision trees, K-nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest classifier are used and their performances are compared. From the analysis, it is observed that the Random Forest Classifier performs better compared to other methods.


title: AmITV: Enhancing the Role of TV in Ambient Intelligence Environments

abstract: The proliferation of Internet of Things (IoT) devices and services and their integration in Ambient Intelligence (AmI) Environments revealed a new range of roles that TVs are expected to play so as to improve quality of life. This work introduces AmITV, an integrated multimodal system that permits end-users to use the TV not only as a traditional entertainment center, but also as (i) a control center for manipulating any intelligent device, (ii) an intervention host that presents appropriate content when they need help or support, (iii) an intelligent agent that communicates with the users in a natural manner and assists them throughout their daily activities, (iv) a notification medium that informs them about interesting or urgent events, and (v) a communication hub that permits them to exchange messages in real-time or asynchronously. This paper presents two motivational scenarios inspired from Home and Hotel Intelligent Environments and the infrastructure behind AmITV. Additionally, it describes how it realizes the newly emerged roles of TVs as a multimodal, intelligent and versatile interaction hub with the ambient facilities of the entire technologically-augmented environment.


title: Towards Optimal Transport with Global Invariances

abstract: Many problems in machine learning involve calculating correspondences between sets of objects, such as point clouds or images. Discrete optimal transport provides a natural and successful approach to such tasks whenever the two sets of objects can be represented in the same space, or at least distances between them can be directly evaluated. Unfortunately neither requirement is likely to hold when object representations are learned from data. Indeed, automatically derived representations such as word embeddings are typically fixed only up to some global transformations, for example, reflection or rotation. As a result, pairwise distances across two such instances are ill-defined without specifying their relative transformation. In this work, we propose a general framework for optimal transport in the presence of latent global transformations. We cast the problem as a joint optimization over transport couplings and transformations chosen from a flexible class of invariances, propose algorithms to solve it, and show promising results in various tasks, including a popular unsupervised word translation benchmark.


title: Electronic Commerce Customer Relationship Management: A Research Agenda

abstract: In this paper, we approach electronic commerce Customer Relationship Management (e-CRM) from the perspective of five research areas. Our purpose is to define a conceptual framework to examine the relationships among and between these five research areas within e-CRM and to propose how they might be integrated to further research this area. We begin with a discussion of each of the research areas through brief reviews of relevant literature for each and a discussion of the theoretical and strategic implications associated with some CRM technologies and research areas. Next we present our framework, which focuses on e-CRM from the five research perspectives. We then present a theoretical framework for e-CRM in terms of the five research areas and how they affect one another, as well as e-CRM processes and both performance and non-performance outcomes.


title: Neurobiology of the Adolescent Brain and Behavior: Implications for Substance Use Disorders

abstract: Objective Adolescence is a developmental period that entails substantial changes in risk-taking behavior and experimentation with alcohol and drugs. Understanding how the brain is changing during this period relative to childhood and adulthood and how these changes vary across individuals are key in predicting risk for later substance abuse and dependence. Method This review discusses recent human imaging and animal work in the context of an emerging view of adolescence as characterized by a tension between early emerging "bottom-up" systems that express exaggerated reactivity to motivational stimuli and later maturing "top-down" cognitive control regions. Behavioral, clinical, and neurobiological evidences are reported for dissociating these two systems developmentally. The literature on the effects of alcohol and its rewarding properties in the brain is discussed in the context of these two systems. Results Collectively, these studies show curvilinear development of motivational behavior and the underlying subcortical brain regions, with a peak inflection from 13 to 17 years. In contrast, prefrontal regions, important in top-down regulation of behavior, show a linear pattern of development well into young adulthood that parallels that seen in behavioral studies of impulsivity. Conclusions The tension or imbalance between these developing systems during adolescence may lead to cognitive control processes being more vulnerable to incentive-based modulation and increased susceptibility to the motivational properties of alcohol and drugs. As such, behavior challenges that require cognitive control in the face of appetitive cues may serve as useful biobehavioral markers for predicting which teens may be at greater risk for alcohol and substance dependence.


title: Simulation of a compact dual-band microstrip bandstop filter having three spurlines

abstract: A compact dual-band bandstop filter (BSF) is presented. It combines a conventional open-stub BSF and three spurlines. This filter generates two stopbands at 2.0 GHz and 3.0 GHz with the same circuit size as the conventional BSF.


Understanding Principal Component Analysis Using a Visual Analytics Tool


title: News Sentiment Analysis Based on Cross-Domain Sentiment Word Lists and Content Classifiers

abstract: The main task of sentiment classification is to automatically judge sentiment polarity (positive or negative) of published sentiment data (e.g. news or reviews). Some researches have shown that supervised methods can achieve good performance for blogs or reviews. However, the polarity of a news report is hard to judge. Web news reports are different from other web documents. The sentiment features in news are less than the features in other Web documents. Besides, the same words in different domains have different polarity. So we propose a self-growth algorithm to generate a cross-domain sentiment word list, which is used in sentiment classification of Web news. This paper considers some previously undescribed features for automatically classifying Web news, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms, and also validates the self-growth algorithm for the cross-domain word list.


title: InP Single-Ended Transimpedance Amplifier with 92-GHz Transimpedance Bandwidth

abstract: A single-ended InP transimpedance amplifier (TIA) for next generation high-bandwidth optical fiber communication systems is presented. The TIA exhibits 48 dB-Omega transimpedance and has a 3-dB bandwidth of 92 GHz. The input-referred current noise is 20 pA/radicHz and the transimpedance group delay is below 10 ps over the entire measured frequency range.


title: Deciding How to Decide: Dynamic Routing in Artificial Neural Networks

abstract: We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar. We find that, in dynamically-routed networks trained to classify images, layers and branches become specialized to process distinct categories of images. Additionally, given a fixed computational budget, dynamically-routed networks tend to perform better than comparable statically-routed networks.


A Two-stage Approach for Extending Event Detection to New Types via Neural Networks


title: Q: Exploit Hardening Made Easy

abstract: Prior work has shown that return oriented programming (ROP) can be used to bypass W⊕X, a software defense that stops shellcode, by reusing instructions from large libraries such as libc. Modern operating systems have since enabled address randomization (ASLR), which randomizes the location of libc, making these techniques unusable in practice. However, modern ASLR implementations leave smaller amounts of executable code unrandomized and it has been unclear whether an attacker can use these small code fragments to construct payloads in the general case. ::: ::: In this paper, we show defenses as currently deployed can be bypassed with new techniques for automatically creating ROP payloads from small amounts of unrandomized code. We propose using semantic program verification techniques for identifying the functionality of gadgets, and design a ROP compiler that is resistant to missing gadget types. To demonstrate our techniques, we build Q, an end-to-end system that automatically generates ROP payloads for a given binary. Q can produce payloads for 80% of Linux /usr/bin programs larger than 20KB. We also show that Q can automatically perform exploit hardening: given an exploit that crashes with defenses on, Q outputs an exploit that bypasses both W⊕X and ASLR. We show that Q can harden nine real-world Linux and Windows exploits, enabling an attacker to automatically bypass defenses as deployed by industry for those programs.


title: Parallel Efficient Sparse Matrix-Matrix Multiplication on Multicore Platforms

abstract: Sparse matrix-matrix multiplication (SpGEMM) is a key kernel in many applications in High Performance Computing such as algebraic multigrid solvers and graph analytics. Optimizing SpGEMM on modern processors is challenging due to random data accesses, poor data locality and load imbalance during computation. In this work, we investigate different partitioning techniques, cache optimizations (using dense arrays instead of hash tables), and dynamic load balancing on SpGEMM using a diverse set of real-world and synthetic datasets. We demonstrate that our implementation outperforms the state-of-the-art using Intel\(^{{\textregistered }}\) Xeon\(^{{\textregistered }}\) processors. We are up to 3.8X faster than Intel\(^{{\textregistered }}\) Math Kernel Library (MKL) and up to 257X faster than CombBLAS. We also outperform the best published GPU implementation of SpGEMM on nVidia GTX Titan and on AMD Radeon HD 7970 by up to 7.3X and 4.5X, respectively on their published datasets. We demonstrate good multi-core scalability (geomean speedup of 18.2X using 28 threads) as compared to MKL which gets 7.5X scaling on 28 threads.


title: High power and high efficiency GaN-HEMT for microwave communication applications

abstract: Microwaves have been widely used for the modern communication systems, which have advantages in high bit rate transmission and the easiness of compact circuit and antenna design. Gallium Nitride (GaN), featured with high breakdown and high saturation velocity, is one of the promising material for high power and high frequency devices, and a kW-class output power has already been achieved [1]. We have developed the high power and high efficiency GaN HEMTs [2–5], targeting the amplifier for the base transceiver station (BTS). This presentation summarizes our recent works, focusing on the developments for the efficiency boosting and the robustness in high power RF operation.


title: A (sub)graph isomorphism algorithm for matching large graphs

abstract: We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.


title: Pattern Recognition Combining De-Noising and Linear Discriminant Analysis within a Real World Application

abstract: Computer aided systems based on image analysis have become popular in zoological systematics in the recent years. For insects in particular, the difficult taxonomy and the lack of experts greatly hampers studies on conservation and ecology. This problem was emphasized at the UN Conference of Environment, Rio 1992, leading to a directive to intensify efforts to develop automated identification systems for pollinating insects. We have developed a system for the automated identification of bee species which employs image analysis to classify bee forewings. Using the knowledge of a zoological expert to create learning sets of images together with labels indicating the species membership, we have formulated this problem in the framework of supervised learning. While the image analysis process is documented in [5], we describe in this paper a new model for classification that consists of a combination of Linear Discriminant Analysis with a de-noising technique based on a nonlinear generalization of principal component analysis, called Kernel PCA. This model combines the property of visualization provided by Linear Discriminant Analysis with powerful feature extraction and leads to significantly improved classification performance.


title: Computer-aided detection of breast cancer on mammograms : A swarm intelligence optimized wavelet neural network approach

abstract: PSOWNN - Particle Swarm Optimized Wavelet Neural Network. DB - Database.Display Omitted We propose a CAD system for detecting breast cancer in mammograms.Swarm intelligence optimized wavelet neural network detects the cancers.We focus on optimized wavelet neural network to enhance the detection accuracy.Experiments are carried out on real clinical database collected from screening centers.Our method yielded better performance than other existing approaches. Breast cancer is the second leading cause of cancer death in women. Accurate early detection can effectively reduce the mortality rate caused by breast cancer. Masses and microcalcification clusters are an important early signs of breast cancer. However, it is often difficult to distinguish abnormalities from normal breast tissues because of their subtle appearance and ambiguous margins. Computer aided diagnosis (CAD) helps the radiologist in detecting the abnormalities in an efficient way. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using Particle Swarm Optimized Wavelet Neural Network (PSOWNN). The proposed abnormality detection algorithm is based on extracting Laws Texture Energy Measures from the mammograms and classifying the suspicious regions by applying a pattern classifier. The method is applied to real clinical database of 216 mammograms collected from mammogram screening centers. The detection performance of the CAD system is analyzed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.96853 with a sensitivity 94.167% of and specificity of 92.105%.


title: YALMIP : a toolbox for modeling and optimization in MATLAB

abstract: The MATLAB toolbox YALMIP is introduced. It is described how YALMIP can be used to model and solve optimization problems typically occurring in systems and control theory. In this paper, free MATLAB toolbox YALMIP, developed initially to model SDPs and solve these by interfacing eternal solvers. The toolbox makes development of optimization problems in general, and control oriented SDP problems in particular, extremely simple. In fact, learning 3 YALMIP commands is enough for most users to model and solve the optimization problems


title: Potentialities of dual-polarized interrogation for spectral domain chipless tags

abstract: The paper proposes an encoding mechanism for chipless RFID relying on two orthogonally polarized measurements. The paper shows that a dual polarized acquisition, which can be made simultaneously, can be a promising and viable solution for solving the typical calibration-dependent reading which limits the practical applicability of chipless RFID technology.


title: Visual object tracking via one-class SVM

abstract: In this paper, we propose a new visual object tracking approach via one-class SVM (OC-SVM), inspired by the fact that OCSVM's support vectors can form a hyper-sphere, whose center can be regarded as a robust object estimation from samples. In the tracking approach, a set of tracking samples are constructed in a predefined searching window of a video frame. And then a threshold strategy is proposed to select examples from the tracking sample set. Selected examples are used to train an OC-SVM model which estimates a hyper-sphere encircling most of the examples. Finally, we locate the center of the hyper sphere as the tracked object in the current frame. Extensive experiments demonstrate the effectiveness and robustness of the proposed approach in complex background.


title: Improved multitask learning through synaptic intelligence

abstract: Deep learning has led to remarkable advances when applied to problems where the data distribution does not change over the course of learning. In stark contrast, biological neural networks continually adapt to changing domains, and solve a diversity of tasks simultaneously. Furthermore, synapses in biological neurons are not simply real-valued scalars, but possess complex molecular machinery enabling non-trivial learning dynamics. In this study, we take a first step toward bringing this biological complexity into artificial neural networks. We introduce a model of intelligent synapses that accumulate task relevant information over time, and exploit this information to efficiently consolidate memories of old tasks to protect them from being overwritten as new tasks are learned. We apply our framework to learning sequences of related classification problems, and show that it dramatically reduces catastrophic forgetting while maintaining computational efficiency.


title: Dynamic thermal management of air cooled data centers

abstract: Increases in server power dissipation time placed significant pressure on traditional data center thermal management systems. Traditional systems utilize computer room air conditioning (CRAC) units to pressurize a raised floor plenum with cool air that is passed to equipment racks via ventilation tiles distributed throughout the raised floor. Temperature is typically controlled at the hot air return of the CRAC units away from the equipment racks. Due primarily to a lack of distributed environmental sensing, these CRAC systems are often operated conservatively resulting in reduced computational density and added operational expense. This paper introduces a data center environmental control system that utilizes a distributed sensor network to manipulate conventional CRAC units within an air-cooled environment. The sensor network is attached to standard racks and provides a direct measurement of the environment in close proximity to the computational resources. A calibration routine is used to characterize the response of each sensor in the network to individual CRAC actuators. A cascaded control algorithm is used to evaluate the data from the sensor network and manipulate supply air temperature and flow rate from individual CRACs to ensure thermal management while reducing operational expense. The combined controller and sensor network has been deployed in a production data center environment. Results from the algorithm will be presented that demonstrate the performance of the system and evaluate the energy savings compared with conventional data center environmental control architecture.


title: Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture

abstract: Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks (Eedn) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.


title: Alliance for Aging Research AD Biomarkers Work Group: structural MRI

abstract: Biomarkers of Alzheimer's disease (AD) are increasingly important. All modern AD therapeutic trials employ AD biomarkers in some capacity. In addition, AD biomarkers are an essential component of recently updated diagnostic criteria for AD from the National Institute on Aging--Alzheimer's Association. Biomarkers serve as proxies for specific pathophysiological features of disease. The 5 most well established AD biomarkers include both brain imaging and cerebrospinal fluid (CSF) measures--cerebrospinal fluid Abeta and tau, amyloid positron emission tomography (PET), fluorodeoxyglucose (FDG) positron emission tomography, and structural magnetic resonance imaging (MRI). This article reviews evidence supporting the position that MRI is a biomarker of neurodegenerative atrophy. Topics covered include methods of extracting quantitative and semiquantitative information from structural MRI; imaging-autopsy correlation; and evidence supporting diagnostic and prognostic value of MRI measures. Finally, the place of MRI in a hypothetical model of temporal ordering of AD biomarkers is reviewed.


title: Efficient exploitation of similar subexpressions for query processing

abstract: Complex queries often contain common or similar subexpressions, either within a single query or among multiple queries submitted as a batch. If so, query execution time can be improved by evaluating a common subexpression once and reusing the result in multiple places. However, current query optimizers do not recognize and exploit similar subexpressions, even within the same query. We present an efficient, scalable, and principled solution to this long-standing optimization problem. We introduce a light-weight and effective mechanism to detect potential sharing opportunities among expressions. Candidate covering subexpressions are constructed and optimization is resumed to determine which, if any, such subexpressions to include in the final query plan. The chosen subexpression(s) are computed only once and the results are reused to answer other parts of queries. Our solution automatically applies to optimization of query batches, nested queries, and maintenance of multiple materialized views. It is the first comprehensive solution covering all aspects of the problem: detection, construction, and cost-based optimization. Experiments on Microsoft SQL Server show significant performance improvements with minimal overhead.


title: Differences in unwinding of supercoiled DNA induced by the two enantiomers of anti-benzo[a]pyrene diol epoxide.

abstract: The unwinding of supercoiled phi X174 RFI DNA induced by the tumorigenic (+) and non-tumorigenic (-) enantiomers of trans-7,8-dihydroxy-anti-9,10-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BPDE) has been investigated by agarose slab-gel and ethidium titration tube gel electrophoresis. The differences in adduct conformations were verified by flow linear dichroism techniques. Both enantiomers cause a reversible unwinding by the formation of noncovalent intercalative complexes. The effects of covalently bound BPDE residues on the electrophoretic mobilities of the RF I DNA form in agarose gels were investigated in detail in the range of binding ratios rb approximately 0.0-0.06 (covalently bound BPDE residues/nucleotide). In this range of rb values, there is a striking difference in the mobilities of (+)-BPDE- and (-)-BPDE-adducted phi X174 DNA in agarose slab-gels, the covalently bound (+)-BPDE residues causing a significantly greater retardation than (-)-BPDE residues. Increasing the level of covalent adducts beyond rb approximately 0.06 in the case of the (+)-BPDE enantiomer, leads to further unwinding and a minimum in the mobilities (corresponding to comigration of the nicked form and the covalently closed relaxed modified form) at rb 0.10 +/- 0.01; at still higher rb values, rewinding of the modified DNA in the opposite sense is observed. From the minimum in the mobility, a mean unwinding angle (per BPDE residue) of theta = 12 +/- 1.5 degrees is determined, which is in good agreement the value of theta = 11 +/- 1.8 degrees obtained by the tube gel titration method. Using this latter method, values of theta = 6.8 +/- 1.7 degrees for (-)-BPDE-phi X174 adducts are observed. It is concluded that agarose slab gel techniques are not suitable for determining unwinding angles for (-)-BPDE-modified phi X174 DNA because the alterations in the tertiary structures for rb < 0.06 are too small to cause sufficiently large changes in the electrophoretic mobilities. The major trans (+)-BPDE-N2-guanosine covalent adduct is situated at external binding sites and the mechanisms of unwinding are therefore different from those relevant to noncovalent intercalative BPDE-DNA complexes or to classical intercalating drug molecules; a flexible hinge joint and a widening of the minor groove at the site of the lesion may account for the observed unwinding effects. The more heterogeneous (-)-BPDE-nucleoside adducts (involving cis and trans N2-guanosine, and adenosine adducts) are less effective in causing unwinding of supercoiled DNA for reasons which remain to be elucidated.


title: Learning a sparse codebook of facial and body microexpressions for emotion recognition

abstract: Obtaining a compact and discriminative representation of facial and body expressions is a difficult problem in emotion recognition. Part of the difficulty is capturing microexpressions, i.e., short, involuntary expressions that last for only a fraction of a second: at a micro-temporal scale, there are so many other subtle face and body movements that do not convey semantically meaningful information. We present a novel approach to this problem by exploiting the sparsity of the frequent micro-temporal motion patterns. Local space-time features are extracted over the face and body region for a very short time period, e.g., few milliseconds. A codebook of microexpressions is learned from the data and used to encode the features in a sparse manner. This allows us to obtain a representation that captures the most salient motion patterns of the face and body at a micro-temporal scale. Experiments performed on the AVEC 2012 dataset show our approach achieving the best published performance on the arousal dimension based solely on visual features. We also report experimental results on audio-visual emotion recognition, comparing early and late data fusion techniques.


title: Delphi application for face detection using the sobel operator

abstract: The subject of this work is a face detection. Face detection is the process of determining the positions and sizes of human faces in photos using computer technology. This is a very complex problem which has many solutions with different algorithms. Automatic face detection is the first step in recognizing faces, which leads to much greater opportunities, such as face recognition instead of password for email accounts, or automated banking machine.


title: Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning

abstract: Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research ques...


title: Integrating LTE-D2D and VLC techniques to support V2V communication

abstract: In recent years, with the rapid development of Device-to-Device communication (D2D) underlaying cellular network, numerous researchers propose to use LTE-D2D technique for Vehicle to Vehicle (V2V) communications (LTE-V2V), so as to compensate for the disadvantage of Dedicated Short Range Communication (DSRC) technology. Although it may have some effects, Some problems still existed. The main constrain is the interference between cellular links and LTE-V2V links. Focusing on this question, we propose a hybrid architecture which integrates LTE-V2V and Visible Light Communication based V2V communication (VLC-V2V) to improve system performance. Based on the hybrid system, we develop a novel distributed resource allocation algorithm to reduce the interference between cellular links and LTE-V2V links as well as to make full use of cellular frequency spectrum. Finally, the performance of the proposed system is simulated on MATLAB, and compared with a typical LTE-V2V system under different situations on a urban road. The simulation results prove that the hybrid system can significantly improve the system performance especially in the dense area.


title: Network representation learning with rich text information

abstract: Representation learning has shown its effectiveness in many tasks such as image classification and text mining. Network representation learning aims at learning distributed vector representation for each vertex in a network, which is also increasingly recognized as an important aspect for network analysis. Most network representation learning methods investigate network structures for learning. In reality, network vertices contain rich information (such as text), which cannot be well applied with algorithmic frameworks of typical representation learning methods. By proving that DeepWalk, a state-of-the-art network representation method, is actually equivalent to matrix factorization (MF), we propose text-associated DeepWalk (TADW). TADW incorporates text features of vertices into network representation learning under the framework of matrix factorization. We evaluate our method and various baseline methods by applying them to the task of multi-class classification of vertices. The experimental results show that, our method outperforms other baselines on all three datasets, especially when networks are noisy and training ratio is small. The source code of this paper can be obtained from https://github.com/albertyang33/TADW.


title: Cyber situational awareness through network anomaly detection: state of the art and new approaches

abstract: With a major change in the attack landscape, away from well-known attack vectors towards unique and highly tailored attacks, limitations of common rule- and signature-based security systems become more and more obvious. Novel security mechanisms can provide the means to extend existing solutions in order to provide a more sophisticated security approach. As critical infrastructures get increasingly accessible from public networks they show up on attackers’ radars. As a consequence, establishing cyber situational awareness on a higher level through incident information sharing is vital for assessing the increased risk to national security in the cyber space. But legal obligations and economical considerations limit the motivation of companies to pursue information sharing initiatives. To support companies and governmental initiatives, novel security mechanisms should inherently address limiting factors. One novel approach, AECID, is presented that accounts for the limitations of many common intrusion and anomaly detection mechanisms; and which further provides the features to support privacy-aware information sharing for cyber situational awareness.


title: Introduction to agile processes and extreme programming

abstract: Extreme Programming is one of the most discussed subjects in the software development community. But what makes XP extreme? And how does it fit into the New World of agile methodologies? This tutorial will establish the underpinnings of agile methodology and explain why you might want to try one. Then we will see how XP uses a set of practices to build an effective software development team that produces quality software in a predictable and repeatable manner.


title: Watching together: integrating text chat with video

abstract: Watching video online is becoming increasingly popular, and new video streaming technologies have the potential to transform video watching from a passive, isolating experience into an active, socially engaging experience. However, the viability of an active social experience is unclear: both chatting and watching video require attention, and may interfere with one another and detract from the experience. In this paper, we empirically examine the activity of chatting while watching video online. We examine how groups of friends and strangers interact, and find that chat has a positive influence on social relationships, and people chat despite being distracted. We discuss the benefits and opportunities provided by mixing chat and video, uncover some of the attentional and social challenges inherent in this combination of media, and provide guidance for structuring the viewing experience.


title: Query dependent pseudo-relevance feedback based on wikipedia

abstract: Pseudo-relevance feedback (PRF) via query-expansion has been proven to be e®ective in many information retrieval (IR) tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for PRF. One problem with this approach is that one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback process. Besides, existing methods generally do not take into account the significantly different types of queries that are often entered into an IR system. Intuitively, Wikipedia can be seen as a large, manually edited document collection which could be exploited to improve document retrieval effectiveness within PRF. It is not obvious how we might best utilize information from Wikipedia in PRF, and to date, the potential of Wikipedia for this task has been largely unexplored. In our work, we present a systematic exploration of the utilization of Wikipedia in PRF for query dependent expansion. Specifically, we classify TREC topics into three categories based on Wikipedia: 1) entity queries, 2) ambiguous queries, and 3) broader queries. We propose and study the effectiveness of three methods for expansion term selection, each modeling the Wikipedia based pseudo-relevance information from a different perspective. We incorporate the expansion terms into the original query and use language modeling IR to evaluate these methods. Experiments on four TREC test collections, including the large web collection GOV2, show that retrieval performance of each type of query can be improved. In addition, we demonstrate that the proposed method out-performs the baseline relevance model in terms of precision and robustness.


title: ROBOLIFT: a vision guided autonomous fork-lift for pallet handling

abstract: The paper describes an integrated mobile robot which has been designed and realised on top of a conventional fork lift carrier, to perform autonomous missions as well as to allow a human operator to drive the vehicle in the conventional way. The most challenging goal of the project has been to provide the vehicle with the sensory capability and local intelligence, which is typical of the human driver, without altering the conventional driving control, such as the pedal control of the accelerator (forward and backward), the emergency brake, the direct steering wheel control. Computer vision is proved to be the main sensory system for both autonomous navigation and pallet recognition. ROBOLlFT is the result of a joint co-operation between Elsag Bailey Telerobot and Fiat OM Carrelli Elevatori SpA.


title: Coded M-DPSK with built-in time diversity for fading channels

abstract: Good coded modulation for fading channels requires built-in time diversity. Under a constraint on the interleaving delay, the authors construct and compare three categories of coded M-DPSK (M-ary differential phase-shift keying) schemes with 4 >


title: Radian: Visual Exploration of Traceroutes

abstract: Several projects deploy probes in the Internet. Probes are systems that continuously perform traceroutes and other networking measurements (e.g., ping) towards selected targets. Measurements can be stored and analyzed to gain knowledge on several aspects of the Internet, but making sense of such data requires suitable methods and tools for exploration and visualization. We present Radian, a tool that allows to visualize traceroute paths at different levels of detail and to animate their evolution during a selected time interval. We also describe extensive tests of the tool using traceroutes performed by RIPE Atlas Internet probes.


title: Automated monitoring and detection of resource-limited NFV-based services

abstract: The growing demand for flexibility and cost reduction in the telecommunication landscape directs the focus of service development heavily to programmability and softwarization. In the domain of Network Function Virtualization (NFV), one of the goals is to replace dedicated hardware devices (such as switches, routers, firewalls) with software-based network functionalities, showing comparable performance when deployed on common servers. In this paper, we discuss how current VNF implementation and deployment strategies impact the efficient monitoring of their resources. In a multi-tenant, NFV-based ecosystem, different Service Providers deploy VNFs on a shared infrastructure, where the Infrastructure Provider exposes only VNF specific metrics and little information about the physical hosts where the VNFs are eventually orchestrated. Especially in the situation where datacenters are overcommitted, detecting the risk of e.g. CPU starvation is not straight-forward, when no information from the physical host is available. A new monitoring technique is introduced, based on the skewness of the measured probability distribution of the VNF resource consumption. Our measurements show that this metric is a good indicator for the (un)availability of the required CPU resources in the datacenter.


title: Text Understanding from Scratch

abstract: This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.


title: Chapter 7 TRANSFER LEARNING FOR TEXT MINING

abstract: Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem. Various transfer learning methods are designed to extract the useful knowledge from different but related auxiliary domains. In its connection to text mining, transfer learning has found novel and useful applications. In this chapter, we will review some most recent developments in transfer learning for text mining, explain related algorithms in detail, and project future developments of this field. We focus on two important topics: cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images.


title: Meta-level sentiment models for big social data analysis

abstract: People react to events, topics and entities by expressing their personal opinions and emotions. These reactions can correspond to a wide range of intensities, from very mild to strong. An adequate processing and understanding of these expressions has been the subject of research in several fields, such as business and politics. In this context, Twitter sentiment analysis, which is the task of automatically identifying and extracting subjective information from tweets, has received increasing attention from the Web mining community. Twitter provides an extremely valuable insight into human opinions, as well as new challenging Big Data problems. These problems include the processing of massive volumes of streaming data, as well as the automatic identification of human expressiveness within short text messages. In that area, several methods and lexical resources have been proposed in order to extract sentiment indicators from natural language texts at both syntactic and semantic levels. These approaches address different dimensions of opinions, such as subjectivity, polarity, intensity and emotion. This article is the first study of how these resources, which are focused on different sentiment scopes, complement each other. With this purpose we identify scenarios in which some of these resources are more useful than others. Furthermore, we propose a novel approach for sentiment classification based on meta-level features. This supervised approach boosts existing sentiment classification of subjectivity and polarity detection on Twitter. Our results show that the combination of meta-level features provides significant improvements in performance. However, we observe that there are important differences that rely on the type of lexical resource, the dataset used to build the model, and the learning strategy. Experimental results indicate that manually generated lexicons are focused on emotional words, being very useful for polarity prediction. On the other hand, lexicons generated with automatic methods include neutral words, introducing noise in the detection of subjectivity. Our findings indicate that polarity and subjectivity prediction are different dimensions of the same problem, but they need to be addressed using different subspace features. Lexicon-based approaches are recommendable for polarity, and stylistic part-of-speech based approaches are meaningful for subjectivity. With this research we offer a more global insight of the resource components for the complex task of classifying human emotion and opinion.


Analysis of NSL-KDD Dataset for Fuzzy Based Intrusion Detection System


title: An ultra low-power rail-to-rail comparator for ADC designs

abstract: This paper presents a novel ultra low-power rail-to-rail comparator which can be suitably used in low-to-medium speed A/D converters. This comparator adopts a preamplifier followed by a dynamic latch structure to achieve fast-decision, high-resolution as well as reduced kick-back noise. A new adaptive power control technique is used to reduce the power consumption of the preamplifier. The circuit is designed and simulated in Global Foundries 65nm CMOS technology. The simulation results have shown that the power consumption of 12-bit comparator is 191.2 nW at the clock frequency of 15 MHz and 0.8 V supply voltage with a delay of 1.17nS.


title: A Real–Complex Hybrid Modulation Approach for Scaling Up Multiuser MIMO Detection

abstract: In this paper, a novel approach, namely, real–complex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals (UTs). By RCHM, we mean that UTs transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown that, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing tradeoff that minimizes the required transmit-power given a sum rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average bit-error-rate as well as extensive computer simulations considering the case with single or multiple base stations.


title: Generative Model for Heterogeneous Inference

abstract: Generative models (GMs) such as Generative Adversary Network (GAN) and Variational Auto-Encoder (VAE) have thrived these years and achieved high quality results in generating new samples. Especially in Computer Vision, GMs have been used in image inpainting, denoising and completion, which can be treated as the inference from observed pixels to corrupted pixels. However, images are hierarchically structured which are quite different from many real-world inference scenarios with non-hierarchical features. These inference scenarios contain heterogeneous stochastic variables and irregular mutual dependences. Traditionally they are modeled by Bayesian Network (BN). However, the learning and inference of BN model are NP-hard thus the number of stochastic variables in BN is highly constrained. In this paper, we adapt typical GMs to enable heterogeneous learning and inference in polynomial time.We also propose an extended autoregressive (EAR) model and an EAR with adversary loss (EARA) model and give theoretical results on their effectiveness. Experiments on several BN datasets show that our proposed EAR model achieves the best performance in most cases compared to other GMs. Except for black box analysis, we've also done a serial of experiments on Markov border inference of GMs for white box analysis and give theoretical results.


title: Real-time motion tracking for dance visualization using Kalman filters

abstract: This paper explores an application of Kalman filters for real-time dance visualization within the Unity3d framework. Experiments performed with camera-based motion sensors indicate that Kalman filter implementation can benefit the process of content development for real-time visualization of dance performances. Moreover, we showed that Kalman filters could be used for generating real-time visuals of dance performances. While the scope of this project is limited to dance visualizations our prototype can be extended to become a useful tool for practitioners to identify any movement dysfunctions, and to educate patients with musculoskeletal impairments on their movements.


title: Research guidelines for the Delphi survey technique

abstract: Consensus methods such as the Delphi survey technique are being employed to help enhance effective decision-making in health and social care. The Delphi survey is a group facilitation technique, which is an iterative multistage process, designed to transform opinion into group consensus. It is a flexible approach, that is used commonly within the health and social sciences, yet little guidance exists to help researchers undertake this method of data collection. This paper aims to provide an understanding of the preparation, action steps and difficulties that are inherent within the Delphi. Used systematically and rigorously, the Delphi can contribute significantly to broadening knowledge within the nursing profession. However, careful thought must be given before using the method; there are key issues surrounding problem identification, researcher skills and data presentation that must be addressed. The paper does not claim to be definitive; it purports to act as a guide for those researchers who wish to exploit the Delphi methodology.


title: Formal Security Analysis of Neural Networks using Symbolic Intervals

abstract: Due to the increasing deployment of Deep Neural Networks (DNNs) in real-world security-critical domains including autonomous vehicles and collision avoidance systems, formally checking security properties of DNNs, especially under different attacker capabilities, is becoming crucial. Most existing security testing techniques for DNNs try to find adversarial examples without providing any formal security guarantees about the non-existence of such adversarial examples. Recently, several projects have used different types of Satisfiability Modulo Theory (SMT) solvers to formally check security properties of DNNs. However, all of these approaches are limited by the high overhead caused by the solver. In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers. Instead, we leverage interval arithmetic to compute rigorous bounds on the DNN outputs. Our approach, unlike existing solver-based approaches, is easily parallelizable. We further present symbolic interval analysis along with several other optimizations to minimize overestimations of output bounds. We design, implement, and evaluate our approach as part of ReluVal, a system for formally checking security properties of Relu-based DNNs. Our extensive empirical results show that ReluVal outperforms Reluplex, a state-of-the-art solver-based system, by 200 times on average. On a single 8-core machine without GPUs, within 4 hours, ReluVal is able to verify a security property that Reluplex deemed inconclusive due to timeout after running for more than 5 days. Our experiments demonstrate that symbolic interval analysis is a promising new direction towards rigorously analyzing different security properties of DNNs.


title: Sensorless optimization of dead times in DC-DC converters with synchronous rectifiers

abstract: This paper introduces an approach to achieve optimum dead times in dc–dc converters with synchronous rectifiers without sensing any of the power-stage signals other than the output voltage. The dead times are adjusted adaptively to minimize the duty-cycle command, which results in maximization of the converter efficiency. The method is particularly well suited for digital controller implementation, requiring no additional analog components or modifications of standard gate-drive circuitry. Experimental results for a digitally controlled 5 V-to-1 V, 5-A synchronous buck converter demonstrate practical implementation of the sensorless dead-time optimization algorithm.


title: Automatic Road Crack Detection Using Random Structured Forests

abstract: Cracks are a growing threat to road conditions and have drawn much attention to the construction of intelligent transportation systems. However, as the key part of an intelligent transportation system, automatic road crack detection has been challenged because of the intense inhomogeneity along the cracks, the topology complexity of cracks, the inference of noises with similar texture to the cracks, and so on. In this paper, we propose CrackForest, a novel road crack detection framework based on random structured forests, to address these issues. Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high-performance crack detector, which can identify arbitrarily complex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively. In addition, our method is faster and easier to parallel. Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.


Algorithm Runtime Prediction : Methods & Evaluation ( Extended


Developmental Niche


title: Image Processing - Principles and Applications

abstract: This PDF file contains the editorial “Image Processing: Principles and Applications” for JEI Vol. 15 Issue 03


title: Memex: A Browsing Assistant for Collaborative Archiving and Mining of Surf Trails

abstract: Keyword indices, topic directories, and link-based rankings are used to search and structure the rapidly growing Web today. Surprisingly little use is made of years of browsing experience of millions of people. Indeed, this information is routinely discarded by browsers. Even deliberate bookmarks are stored in a passive and isolated manner. All this goes against Vannevar Bush’s dream of the Memex : an enhanced supplement to personal and community memory. We propose to demonstrate the beginnings of a ‘Memex’ for the Web: a browsing assistant for individuals and groups with focused interests. Memex blurs the articial distinction between browsing history and deliberate bookmarks. The resulting glut of data is analyzed in a number of ways at the individual and community levels. Memex constructs a topic directory customized to the community, mapping their interests naturally to nodes in this directory. This lets the user recall topic-based browsing contexts by asking questions like \What trails was I following when I was last surng about classical music?" and \What are some popular pages in or near my community’s recent trail graph related to music?" 1 Motivation Three paradigms have emerged for exploring the Web: keyword search, directory browsing, and following links. Popular search engine and directory sites are visited tens of millions of times per day. We speculate that the total number of clicks per day is orders of magnitude larger. This third source of information, the browsing history of millions of Web users over several years, an information source that dwarfs the scale of the Web itself, is almost entirely discarded by browsers as ‘history’. Deliberate ‘bookmarks’ are preserved, but passively, in browser-dependent formats; this separates them from the dominant world of HTML hypermedia, even if their owners were willing to share them (as they are, in our experience, with all but a small section of their browsing activity).


title: A Novel Sparsity Measure for Tensor Recovery

abstract: In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor. The proposed sparsity measure has a natural physical meaning which is intrinsically the size of the fundamental Kronecker basis to express the tensor. By embedding the sparsity measure into the tensor completion and tensor robust PCA frameworks, we formulate new models to enhance their capability in tensor recovery. Through introducing relaxation forms of the proposed sparsity measure, we also adopt the alternating direction method of multipliers (ADMM) for solving the proposed models. Experiments implemented on synthetic and multispectral image data sets substantiate the effectiveness of the proposed methods.


title: Spam mail detection through data mining techniques

abstract: In todays electronic world a huge part of communication, both professional and private, takes place in the form of electronic mails or emails. However, due to advertising agencies and social networking websites most of the emails circulated contain unwanted information which is not relevant to the user. Spam emails are a type of electronic mail where the user receives unsolicited messages via email. Spam emails cause inconvenience and financial loss to the recipients so there is a need to filter them and separate them from the legitimate emails. Many algorithms and filters have been developed to detect the spam emails but spammers continuously evolve and sophisticate their spamming techniques due to which the existing filters are becoming less effective. The method proposed in this paper involves creating a spam filter using binary and continuous probability distributions. The algorithms implemented in building the classifier model are Naive Bayes and Decision Trees. The effect of overfitting on the performance and accuracy of decision trees is analyzed. Finally, the better classifier model is identified based on its accuracy to correctly classify spam and non-spam emails.


title: Assessment and early diagnosis of dysphagia.

abstract: Dysphagia is defined as difficulty in swallowing food (semi-solid or solid), liquid, or both. Neurological, muscular, anatomical, and/or psychological factors may predispose a person to difficulty in swallowing. Difficulty in swallowing or dysphagia can lead to serious complications including aspiration pneumonia, malnutrition, and death if not diagnosed early. Health care providers who are knowledgeable in assessing and diagnosing individuals with or at risk for dysphagia can in collaboration with an interdisciplinary team ameliorate the complications of dysphagia and optimize outcomes through focused interventions. In addition, health care costs would be reduced through decreased need for hospitalization, length of stay when hospitalized, and overall health care expenditures. This article provides a review of the epidemiology of dysphagia, the normal swallowing process, pathophysiology, signs and symptoms, and diagnostics. Implications for practice also are discussed.


title: A survey on attack detection on cloud using supervised learning techniques

abstract: Cloud computing has brought a revolution in the field of computing. Apart from the merits of cloud computing, it possesses several demerits also. There are several types of attacks on cloud. All the attacks are focused on a particular layer of cloud architecture. Cloud computing architecture composed of three layers Infrastructure as a service (IaaS), platform as a service (PaaS) and Application as a service (AaaS). If Iaas is vulnerable then all above layers can't be secure. The principal concern of security in Iaas is Virtualization. There are several attacks on virtualization in IaaS layer like attack on VM image sharing. VM isolation violation, insecure VM migration and VM escape. In this paper all such attacks are studied and the solutions are also discussed.


title: A new meniscus injury diagnostic model using fuzzy cognitive maps

abstract: The aim of this paper is to create an integrated meniscus injury diagnostic model, which by exploiting information about patient history and clinical examination made by physicians concludes to a final diagnosis using Fuzzy Cognitive Maps. Mechanisms of injury and symptoms combined with test results constitute the first level modelled with FCM. The output of this level decides if the patient has a meniscus tear or not. The second level decides if the injury is acute or degenerative with the help of risk factors using Competitive Fuzzy Cognitive Maps in order to conclude to only one decision. The results obtained by the model are very promising without requiring sending the patient for MRI (Magnetic Resonance Imaging) which is of utmost importance in diagnosing soft tissue injuries.


title: BlazingText: Scaling and Accelerating Word2Vec using Multiple GPUs

abstract: Word2Vec is a popular algorithm used for generating dense vector representations of words in large corpora using unsupervised learning. The resulting vectors have been shown to capture semantic relationships between the corresponding words and are used extensively for many downstream natural language processing (NLP) tasks like sentiment analysis, named entity recognition and machine translation. Most open-source implementations of the algorithm have been parallelized for multi-core CPU architectures including the original C implementation by Mikolov et al. [1] and FastText [2] by Facebook. A few other implementations have attempted to leverage GPU parallelization but at the cost of accuracy and scalability. In this work, we present BlazingText, a highly optimized implementation of word2vec in CUDA, that can leverage multiple GPUs for training. BlazingText can achieve a training speed of up to 43M words/sec on 8 GPUs, which is a 9x speedup over 8-threaded CPU implementations, with minimal effect on the quality of the embeddings.