Compiling Bayesian Networks Using Variable Elimination

Compiling Bayesian networks has proven an effective approach for inference that can utilize both global and local network structure. In this paper, we define a new method of compiling based on variable elimination (VE) and Algebraic Decision Diagrams (ADDs). The approach is important for the following reasons. First, it exploits local structure much more effectively than previous techniques based on VE. Second, the approach allows any of the many VE variants to compute answers to multiple queries simultaneously. Third, the approach makes a large body of research into more structured representations of factors relevant in many more circumstances than it has been previously. Finally, experimental results demonstrate that VE can exploit local structure as effectively as state–of–the–art algorithms based on conditioning on the networks considered, and can sometimes lead to much faster compilation times.


An Examination of Undergraduate Student's Perceptions and Predilections of the Use of YouTube in the Teaching and Learning Process

Pervasive social networking and media sharing technologies have augmented perceptual understanding and information gathering and, while text-based resources have remained the standard for centuries, they do not appeal to the hyper-stimulated visual learners of today. In particular, the research suggests that targeted YouTube videos enhance student engagement, depth of understanding, and overall satisfaction in higher education courses. In order to investigate student perceptions and preferences regarding the implications of YouTube, a study was conducted at a Mid-Atlantic minority serving institution that examined student opinions regarding the usage of YouTube videos to augment instruction in online and classroombased courses. According to the findings, use of YouTube in the teaching and learning process enhances instruction with students most likely to visit video sharing services from mobile devices. Further, length has an impact on student decisions whether or not to watch a video, and course delivery format impacts length and audio preferences. Finally, there is no relationship between personal use of social media and the perceived value of the use of YouTube in the instructional process.


Plant-growth-promoting compounds produced by two agronomically important strains of Azospirillum brasilense, and implications for inoculant formulation

We evaluated phytohormone and polyamine biosynthesis, siderophore production, and phosphate solubilization in two strains (Cd and Az39) of Azospirillum brasilense used for inoculant formulation in Argentina during the last 20 years. Siderophore production and phosphate solubilization were evaluated in a chemically defined medium, with negative results. Indole 3-acetic acid (IAA), gibberellic acid (GA3), and abscisic acid (ABA) production were analyzed by gas chromatography-mass spectrometry. Ethylene, polyamine, and zeatin (Z) biosynthesis were determined by gas chromatography-flame ionization detector and high performance liquid chromatography (HPLC-fluorescence and -UV), respectively. Phytohormones IAA, Z, GA3, ABA, ethylene, and growth regulators putrescine, spermine, spermidine, and cadaverine (CAD) were found in culture supernatant of both strains. IAA, Z, and GA3 were found in all two strains; however, their levels were significantly higher (p < 0.01) in Cd (10.8, 2.32, 0.66 μg ml−1). ABA biosynthesis was significantly higher (p < 0.01) in Az39 (0.077 μg ml−1). Ethylene and polyamine CAD were found in all two strains, with highest production in Cd cultured in NFb plus l-methionine (3.94 ng ml−1 h−1) and Az39 cultured in NFb plus l-lysine (36.55 ng ml−1 h−1). This is the first report on the evaluation of important bioactive molecules in strains of A. brasilense as potentially capable of direct plant growth promotion or agronomic yield increase. Az39 and Cd showed differential capability to produce the five major phytohormones and CAD in chemically defined medium. This fact has important technological implications for inoculant formulation as different concentrations of growth regulators are produced by different strains or culture conditions.


A 1.2–6.6GHz LNA using transformer feedback for wideband input matching and noise cancellation in 0.13µm CMOS

A novel transformer feedback featuring wideband input matching and noise cancellation is proposed and demonstrated in a wideband differential LNA for software-defined-radio (SDR) applications. Implemented in 0.13μm CMOS with an area of 0.32mm2, the LNA prototype measures a wideband input matching S11 of less than -10dB from 1.2GHz to 6.6GHz and minimum NF of 1.8dB while consuming 11mA at 1.2V supply.


Hamming Clustering: A New Approach to Rule Extraction

A new algorithm, called Hamming Clustering (HC), is proposed to extract a set of rules underlying a given classification problem. It is able to reconstruct the and-or expression associated with any Boolean function from a training set of


Extracting and Analyzing Hidden Graphs from Relational Databases

Analyzing interconnection structures among underlying entities or objects in a dataset through the use of graph analytics can provide tremendous value in many application domains. However, graphs are not the primary representation choice for storing most data today, and in order to have access to these analyses, users are forced to manually extract data from their data stores, construct the requisite graphs, and then load them into some graph engine in order to execute their graph analysis task. Moreover, in many cases (especially when the graphs are dense), these graphs can be significantly larger than the initial input stored in the database, making it infeasible to construct or analyze such graphs in memory. In this paper we address both of these challenges by building a system that enables users to declaratively specify graph extraction tasks over a relational database schema and then execute graph algorithms on the extracted graphs. We propose a declarative domain specific language for this purpose, and pair it up with a novel condensed, in-memory representation that significantly reduces the memory footprint of these graphs, permitting analysis of larger-than-memory graphs. We present a general algorithm for creating such a condensed representation for a large class of graph extraction queries against arbitrary schemas. We observe that the condensed representation suffers from a duplication issue, that results in inaccuracies for most graph algorithms. We then present a suite of in-memory representations that handle this duplication in different ways and allow trading off the memory required and the computational cost for executing different graph algorithms. We also introduce several novel deduplication algorithms for removing this duplication in the graph, which are of independent interest for graph compression, and provide a comprehensive experimental evaluation over several real-world and synthetic datasets illustrating these trade-offs.


Debugging Machine Learning Tasks

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl’s theory of causation, and uses Pearl’s PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.


Interactive cloth rendering of microcylinder appearance model under environment lighting

This paper proposes an interactive rendering method of cloth fabrics under environment lighting. The outgoing radiance from cloth fabrics in the microcylinder model is calculated by integrating the product of the distant environment lighting, the visibility function, the weighting function that includes shadowing/masking effects of threads, and the light scattering function of threads. The radiance calculation at each shading point of the cloth fabrics is simplified to a linear combination of triple product integrals of two circular Gaussians and the visibility function, multiplied by precomputed spherical Gaussian convolutions of the weighting function. We propose an efficient calculation method of the triple product of two circular Gaussians and the visibility function by using the gradient of signed distance function to the visibility boundary where the binary visibility changes in the angular domain of the hemisphere. Our GPU implementation enables interactive rendering of static cloth fabrics with dynamic viewpoints and lighting. In addition, interactive editing of parameters for the scattering function (e.g. thread’s albedo) that controls the visual appearances of cloth fabrics can be achieved.


GPU Accelerated Sub-Sampled Newton's Method

First order methods, which solely rely on gradient information, are commonly used in diverse machine learning (ML) and data analysis (DA) applications. This is attributed to the simplicity of their implementations, as well as low per-iteration computational/storage costs. However, they suffer from significant disadvantages; most notably, their performance degrades with increasing problem ill-conditioning. Furthermore, they often involve a large number of hyper-parameters, and are notoriously sensitive to parameters such as the step-size. By incorporating additional information from the Hessian, second-order methods, have been shown to be resilient to many such adversarial effects. However, these advantages of using curvature information come at the cost of higher per-iteration costs, which in “big data” regimes, can be computationally prohibitive. In this paper, we show that, contrary to conventional belief, second-order methods, when implemented appropriately, can be more efficient than first-order alternatives in many largescale ML/ DA applications. In particular, in convex settings, we consider variants of classical Newton’s method in which the Hessian and/or the gradient are randomly sub-sampled. We show that by effectively leveraging the power of GPUs, such randomized Newton-type algorithms can be significantly accelerated, and can easily outperform state of the art implementations of existing techniques in popular ML/ DA software packages such as TensorFlow. Additionally these randomized methods incur a small memory overhead compared to first-order methods. In particular, we show that for million-dimensional problems, our GPU accelerated sub-sampled Newton’s method achieves a higher test accuracy in milliseconds as compared with tens of seconds for first order alternatives.


Nobody ’ s watching ? Subtle cues affect generosity in an anonymous economic game

Models indicate that opportunities for reputation formation can play an important role in sustaining cooperation and prosocial behavior. Results from experimental economic games support this conclusion, as manipulating reputational opportunities affects prosocial behavior. Noting that some prosocial behavior remains even in anonymous noniterated games, some investigators argue that humans possess a propensity for prosociality independent of reputation management. However, decision-making processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues. Manipulating game parameters alters explicit information employed in overt strategizing but leaves intact cues that may affect intuitive judgments relevant to reputation formation. To explore how subtle cues of observability impact prosocial behavior, we conducted five dictator games, manipulating both auditory cues of the presence of others (via the use of sound-deadening earmuffs) and visual cues (via the presentation of stylized eyespots). Although earmuffs appeared to reduce generosity, this effect was not significant. However, as predicted, eyespots substantially increased generosity, despite no differences in actual anonymity; when using a computer displaying eyespots, almost twice as many participants gave money to their partners compared with the


An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning

This paper proposes a novel anomaly detection system for spacecrafts based on data mining techniques. It constructs a nonlinear probabilistic model w.r.t. behavior of a spacecraft by applying the relevance vector regression and autoregression to massive telemetry data, and then monitors the on-line telemetry data using the model and detects anomalies. A major advantage over conventional anomaly detection methods is that this approach requires little a priori knowledge on the system.


Positron-Emission Tomography and Personality Disorders

This study used positron-emission tomography to examine cerebral metabolic rates of glucose (CMRG) in 17 patients with DSM III-R diagnoses of personality disorder. Within the group of 17 personality disorder patients, there was a significant inverse correlation between a life history of aggressive impulse difficulties and regional CMRG in the frontal cortex of the transaxial plane approximately 40 mm above the canthomeatal line (CML) (r = −.56, p = 0.17). Diagnostic groups included antisocial (n = 6), borderline (n = 6), dependent (n = 2), and narcissistic (n = 3). Regional CMRG in the six antisocial patients and in the six borderline patients was compared to a control group of 43 subjects using an analysis of covariance with age and sex as covariates. In the borderline personality disorder group, there was a significant decrease in frontal cortex metabolism in the transaxial plane approximately 81 mm above the CML and a significant increase in the transaxial plane approximately 53 mm above the CML (F[1,45] = 8.65, p = .005; and F[1,45] = 7.68, p = .008, respectively).


Benchmarking personal cloud storage

Personal cloud storage services are data-intensive applications already producing a significant share of Internet traffic. Several solutions offered by different companies attract more and more people. However, little is known about each service capabilities, architecture and -- most of all -- performance implications of design choices. This paper presents a methodology to study cloud storage services. We apply our methodology to compare 5 popular offers, revealing different system architectures and capabilities. The implications on performance of different designs are assessed executing a series of benchmarks. Our results show no clear winner, with all services suffering from some limitations or having potential for improvement. In some scenarios, the upload of the same file set can take seven times more, wasting twice as much capacity. Our methodology and results are useful thus as both benchmark and guideline for system design.


On-line Fault Detection of Sensor Measurements

On-line fault detection in sensor networks is of paramount importance due to the convergence of a variety of challenging technological, application, conceptual, and safety related factors. We introduce a taxonomy for classi£cation of faults in sensor networks and the £rst on-line model-based testing technique. The approach is generic in the sense that it can be applied on an arbitrary system of heterogeneous sensors with an arbitrary type of fault model, while it provides a ¤exible tradeoff between accuracy and latency. The key idea is to formulate on-line testing as a set of instances of a non-linear function minimization and consequently apply nonparametric statistical methods to identify the sensors that have the highest probability to be faulty. The optimization is conducted using the Powell nonlinear function minimization method. The effectiveness of the approach is evaluated in the presence of random noise using a system of light sensors.


Development of support system for forward tilting of the upper body

We have been developing a wearable robot that directly and physically supports human movement. A ldquomuscle suitrdquo for the arm that will provide muscular support for the paralyzed or those otherwise unable to move unaided is one example. Whereas low back pain is one of the most serious issue for the manual worker and in this paper, we focus on developing the support system for forward tilting of the upper body. In this paper, the concept and mechanical structure of the support system is shown. From the experimental result to keep forward tilting posture with load indicates our system is very effective for keeping forward tilting posture and it becomes clear that more than 60% reduction of muscular power is possible.


Randomized Controlled Caregiver Mediated Joint Engagement Intervention for Toddlers with Autism

This study aimed to determine if a joint attention intervention would result in greater joint engagement between caregivers and toddlers with autism. The intervention consisted of 24 caregiver-mediated sessions with follow-up 1 year later. Compared to caregivers and toddlers randomized to the waitlist control group the immediate treatment (IT) group made significant improvements in targeted areas of joint engagement. The IT group demonstrated significant improvements with medium to large effect sizes in their responsiveness to joint attention and their diversity of functional play acts after the intervention with maintenance of these skills 1 year post-intervention. These are among the first randomized controlled data to suggest that short-term parent-mediated interventions can have important effects on core impairments in toddlers with autism. Clinical Trials #: NCT00065910.


Developmental dyslexia: genetic dissection of a complex cognitive trait

Developmental dyslexia, a specific impairment of reading ability despite adequate intelligence and educational opportunity, is one of the most frequent childhood disorders. Since the first documented cases at the beginning of the last century, it has become increasingly apparent that the reading problems of people with dyslexia form part of a heritable neurobiological syndrome. As for most cognitive and behavioural traits, phenotypic definition is fraught with difficulties and the genetic basis is complex, making the isolation of genetic risk factors a formidable challenge. Against such a background, it is notable that several recent studies have reported the localization of genes that influence dyslexia and other language-related traits. These investigations exploit novel research approaches that are relevant to many areas of human neurogenetics.


Neural Probabilistic Model for Non-projective MST Parsing

In this paper, we propose a probabilistic parsing model that defines a proper conditional probability distribution over nonprojective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTMCNNs, which automatically benefits from both wordand character-level representations, by using a combination of bidirectional LSTMs and CNNs. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over nonprojective trees. By exploiting Kirchhoff’s Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straightforward end-to-end model training procedure via back-propagation. We evaluate our model on 17 different datasets, across 14 different languages. Our parser achieves state-of-the-art parsing performance on nine datasets.


Private memoirs of a smart meter

Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers.


Audio-Visual Speech Enhancement based on Multimodal Deep Convolutional Neural Network

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus on addressing audio information only. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNN (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. In the proposed AVDCNN SE model, audio and visual features are first processed using individual CNNs, and then, fused into a joint network to generate enhanced speech at an output layer. The AVDCNN model is trained in an end-toend manner, and parameters are jointly learned through backpropagation. We evaluate enhanced speech using five objective criteria. Results show that the AVDCNN yields notably better performance as compared to an audio-only CNN-based SE model, confirming the effectiveness of integrating visual information into the SE process.


Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches

Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like Go. While RL is emerging as a practical component in reallife systems, most successes have been in Single Agent domains. This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments. The report concludes with advances in the paradigm of training Multi-Agent Systems called Decentralized Actor, Centralized Critic, based on an extension of MDPs called Decentralized Partially Observable MDPs, which has seen a renewed interest lately. 1 ar X iv :1 80 7. 09 42 7v 1 [ cs .A I] 2 5 Ju l 2 01 8


Introduction to Robotics: Mechanics and Control

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Development of humanoid robot platform KHR-2 (KAIST humanoid robot-2)

We are presenting the mechanical and electrical system design and system integration of controllers including sensory devices of the humanoid, KHR-2 in this paper. The concept and the objective of the design will be described. We have developed KHR-2 since 2003 which has 41 DOF (degree of freedom). Each arm including a hand and a wrist has 11 DOF (5+2 DOF/hand (finger + wrist), 4 DOF/arm) and each leg has 6 DOF. Head and trunk have 6 DOF (2 DOF/eye and 2 DOF/neck) and 1 DOF respectively. The mechanical part of the robot is designed to have human friendly appearance and wide movable angle range. Joint actuators are designed to have negligible uncertainties such as backlash. To control all axes, distributed control architecture is adopted to reduce the computation burden of the main controller (PC) and to expand the devices easily. We developed a sub-controller which is a servo motor controller and sensor interfacing devices using microprocessor. The main controller (PC) attached on the back of the robot communicates with sub-controllers in real-time by using CAN (controller area network). Windows XP is used for OS (operating system) for fast development of main control program. RTX (real time extension) HAL extension software is used to realize the real-time control in Windows XP environment. KHR-2 has several sensor types, which are 3-axis F/T (force/torque) sensors at foot and wrist, inertia sensor system (accelerometer and rate gyro) and CCD camera. The F/T sensor at the foot is the most fundamental sensor for stable walking. The inertia sensor system is essential for determining the inclination between the ground and the robot. Finally, we used the CCD camera for navigation and stabilization of the robot in the future. We described the details of the KHR-2 in this paper.


Development of a biped walking robot compensating for three-axis moment by trunk motion

The authors have been using the ZMP (@ro Mfoment&oint) as a criterion to distinguish the stability of walking for a biped walking robot which has a trunk. T6 the authors introduce a control method of dynamic biped walking for a biped walking robot to compensate for the three-axis (pitch, roll and yaw-axis) moment on an arbitraryplanned ZMP by trunk motion. The authors developed a biped walking robot and performed a walking experiment with the robot using the control method. The result was a fast dynamic biped walking a t the walking speed of 0.54 slstep with a 0.3 m step on a flat floor. This walking speed is about 50 percent faster than that with the robot which compensates for only the twoaxis (pitch and roll-axis) moment by t runk motion. -z In 1986, the authors developed a biped walking robot WL12 (Waseda Leg No. 12) which has a trunk for the stabilization of walking. At the same time the authors proposed a control method of dynamic biped walking for a biped walking robot to compensate for the two-axis (pitch and roll-axis) moment on an arbitrary planned ZMP by the trunk[l]-[3). In 1990, by using this walking control method, the authors achieved dynamic biped walking at the fastest walking speed of 0.8 s/step with 0.3 m step length on a flat floor[5]. The method was based on the assumption that the contact point between robot foot and the floor would not slide. That is to say, only the pitch-axis and roll-axis moments were compensated for by trunk motion, and the yaw-axis moment was not taken into account in the stabilization of walking. In the walking experiment, however, as the robot walked faster, the yaw-axis moment began to give the robot a spin on the yaw-axis, which became a considerable problem that affected the stability of walking. In contrast, i r was considered that human beings compensate not only for the pitch and roll-axis moment but also for the yaw-axis moment by swinging both arms and rotating the waist to maintain the total stability of walking. 0-7803-0823-9p33/$3.00 (C) 1993 IEEE __ _I------" I -__-.*-Fig. 1 Biped walking robot WL-12RV Therefore, the objective of this study was to develop a biped walking robot which has an ability to compensate for the three-axis moment by trunk motion, to work out a control method of dynamic biped walking for the robot and to realize faster walking than before.


The Development of Honda Humanoid Robot

I n this paper. we present the mechanism, system cor$guration, basic control algorithm and integrated ,functions of the Nonda humanoid robot. Like its human counterpart, this robot lzas the ability to move forward and backward, sideways to the right or the left, as well as diagonally. I n addition, the robot can turn in any direction, walk up and down stairs continuously. Furthermore, due to its unique posture stability control, the robot is able to maintain its balance despite unexpected complications such as uneven ground su faces As a part of its integrated functions, this robot is able to inove on a planned path autonomously and to perform simple operations via wireless tele-operation.


Design of prototype humanoid robotics platform for HRP

This paper presents a prototype humanoid robotics platform developed for HRP-2. HRP-2 is a new humanoid robotics platform, which we have been developing in phase two of HRP. HRP is a humanoid robotics project, which has been lunched by Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with rough terrain in the open air and can prevent the possible damages to a humanoid robot’s own self in the event of tipping over. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot’s own self even tough HRP-2 tips over. In this paper, the mechanisms and specifications of developed prototype humanoid robotics platform, and its electrical system are introduced.


Un-Crafting: De-Constructive Engagements with Interactive Artifacts

Crafting interactive artifacts is typically associated with synthesizing, making, constructing, putting things together. In this paper, we discuss how these activities can be contrasted with de-synthesizing activities that revolve around decomposition, dissection, and taking things apart. Based on framings that emphasize how related practices are valuable in engineering processes, we aim to unlock the potential of de-constructive engagements with interactive technologies as a material driven (design) practice. We propose un-crafting as a framework of four modes of taking interactive artifacts apart (i.e., un-crafting for material exposition, material inspiration, material inquiry, and material exploration) aiming to pinpoint de-constructive episodes inherent to design processes, as well as to encourage the refinement of respective techniques and methods.


Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions

The rhythmic components of heart rate variability (HRV) can be separated and quantitatively assessed by means of power spectral analysis. The powers of high frequency (HF) and low frequency (LF) components of HRV have been shown to estimate cardiac vagal and sympathetic activities. The reliability of these spectral indices, as well as that of LF/HF ratio as a marker of autonomic interaction at rest and during exercise, is briefly reviewed. Modifications in autonomic activities induced by different physiological conditions, e.g. hypoxia exposure, training, and water immersion, have been found in HRV power spectra at rest. The changes in HF and LF powers and in LF/HF ratio observed during exercise have been shown not to reflect the decrease in vagal activity and the activation of sympathetic system occurring at increasing loads. HF peak was recognised in power spectra in the entire range of relative intensity, being responsible for the most part of HR variability at maximal load. LF power did not change during low intensity exercise and decreased to negligible values at medium–high intensity, where sympathetic activity was enhanced. There was no influence from factors such as fitness level, age, hypoxia, and blood distribution. In contrast, a dramatic effect of body position has been suggested by the observation that LF power increased at medium–high intensities when exercising in the supine position. The increased respiratory activity due to exercise would be responsible of HF modulation of HR via a direct mechanical effect. The changes in LF power observed at medium–high intensity might be the expression of the modifications in arterial pressure control mechanisms occurring with exercise. The finding of opposite trends for LF rhythm in supine and sitting exercises suggests that different readjustments might have occurred in relation to different muscular inputs in the two positions.


Signal compensation and extraction of high resolution position for sinusoidal magnetic encoders

MEs (magnetic encoders) are widely used in industrial control systems because of its significant advantages, such as high resolution, high speed, operation in harsh environments, and low cost. This paper describes a method to extract the position information with high resolution for sinusoidal MEs. A code compensator based on optimization theories is applied to correct non-ideal signals of the encoder outputs. This algorithm can effectively eliminate the DC offset, phase-shift, amplitude difference and sinusoidal deformation from these outputs. Then, a size-reduced look-up table is built-up off-line to generate the high resolution quadrature pulses suitable for standard incremental encoder signals. These LUTs (Look-up tables) rely on the approximate linear property of the amplitude of sinusoids in section [-sin(pi/4) ; sin(pi/4)] and is decreased by converting the values of LUTs into binary values (0 and 1). The firmware is implemented on a 16-bit DSP (digital signal processor) TMS320F2812 hardware platform with minimum computation. The experimental results are also presented in this paper.


Love, hate, arousal and engagement: exploring audience responses to performing arts

Understanding audience responses to art and performance is a challenge. New sensors are promising for measurement of implicit and explicit audience engagement. However, the meaning of biometric data, and its relationship to engagement, is unclear. We conceptually explore the audience engagement domain to uncover opportunities and challenges in the assessment and interpretation of audience engagement data. We developed a display that linked performance videos with audience biometric data and presented it to 7 performing arts experts, to explore the measurement, interpretation and application of biometric data. Experts were intrigued by the response data and reflective in interpreting it. We deepened our inquiry with an empirical study with 49 participants who watched a video of a dance performance. We related temporal galvanic skin response (GSR) data to two self-report scales, which provided insights on interpreting this measure. Our findings, which include strong correlations, support the interpretation of GSR as a valid representation of audience engagement.


Micro-Doppler effect in radar: phenomenon, model, and simulation study

When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.


A Wideband Sequential-Phase Fed Circularly Polarized Patch Array

This communication presents a wideband circularly polarized (CP) 2 × 2 patch array using a sequential-phase feeding network. By combining three operating modes, both axial ratio (AR) and impedance bandwidths are enhanced and wider than those of previous published sequential-fed single-layer patch arrays. These three CP operating modes are tuned and matched by optimizing the truncated corners of patch elements and the sequential-phase feeding network. A prototype of the proposed patch array is built to validate the design experimentally. The measured -10-dB impedance bandwidth is 1.03 GHz (5.20-6.23 GHz), and the measured 3-dB AR bandwidth is 0.7 GHz (5.25-5.95 GHz), or 12.7% corresponding to the center frequency of 5.5 GHz. The measured peak gain is about 12 dBic and the gain variation is less than 3 dB within the AR bandwidth.


The effects of feedback on energy conservation: A meta-analysis.

Feedback has been studied as a strategy for promoting energy conservation for more than 30 years, with studies reporting widely varying results. Literature reviews have suggested that the effectiveness of feedback depends on both how and to whom it is provided; yet variations in both the type of feedback provided and the study methodology have made it difficult for conclusions to be drawn. The current article analyzes past theoretical and empirical research on both feedback and proenvironmental behavior to identify unresolved issues, and utilizes a meta-analysis of 42 feedback studies published between 1976 and 2010 to test a set of hypotheses about when and how feedback about energy usage is most effective. Results indicate that feedback is effective overall, r = .071, p < .001, but with significant variation in effects (r varied from -.080 to .480). Several treatment variables were found to moderate this relationship, including frequency, medium, comparison message, duration, and combination with other interventions (e.g., goal, incentive). Overall, results provide further evidence of feedback as a promising strategy to promote energy conservation and suggest areas in which future research should focus to explore how and for whom feedback is most effective.


Modeling Cellular-to-UAV Path-Loss for Suburban Environments

Operating unmanned aerial vehicle (UAV) over cellular networks would open the barriers of remote navigation and far-flung flying by combining the benefits of UAVs and the ubiquitous availability of cellular networks. In this letter, we provide an initial insight on the radio propagation characteristics of cellular-to-UAV (CtU) channel. In particular, we model the statistical behavior of the path-loss from a cellular base station toward a flying UAV. Where we report the value of the path-loss as a function of the depression angle and the terrestrial coverage beneath the UAV. The provided model is derived based on extensive experimental data measurements conducted in a typical suburban environment for both terrestrial (by drive test) and aerial coverage (using a UAV). The model provides simple and accurate prediction of CtU path-loss that can be useful for both researchers and network operators alike.


Behavioral Game Theory on Online Social Networks : Colonel Blotto is on Facebook

We show how online social networks such as Facebook can be used in Behavioral Game Theory research. We report the deployment of a Facebook application ‘Project Waterloo’ that allows users to play the Colonel Blotto game against their friends and strangers. Unlike conventional studies performed in the laboratory environment, which rely on monetary incentives to attract human subjects to play games, our framework does not use money and instead relies on reputation and entertainment incentives. We describe the Facebook application we created for conducting this experiment, and perform a preliminary analysis of the data collected in the game. We conclude by discussing the advantages of our approach and list some ideas for future work.


Historical vignettes of the thyroid gland.

Although "glands" in the neck corresponding to the thyroid were known for thousands of years, they were mainly considered pathological when encountered. Recognition of the thyroid gland as an anatomical and physiological entity required human dissection, which began in earnest in the 16th century. Leonardo Da Vinci is generally credited as the first to draw the thyroid gland as an anatomical organ. The drawings were subsequently "lost" to medicine for nearly 260 years. The drawings were probably of a nonhuman specimen. Da Vinci vowed to produce an anatomical atlas, but it was never completed. Michelangelo Buonarroti promised to complete drawings for the anatomical work of Realdus Columbus, De Re Anatomica, but these were also never completed. Andreas Vesalius established the thyroid gland as an anatomical organ with his description and drawings in the Fabrica. The thyroid was still depicted in a nonhuman form during this time. The copper etchings of Bartholomew Eustachius made in the 1560s were obviously of humans, but were not actually published until 1714 with a description by Johannes Maria Lancisius. These etchings also depicted some interesting anatomy, which we describe. The Adenographia by Thomas Wharton in 1656 named the thyroid gland for the first time and more fully described it. The book also attempted to assign a function to the gland. The thyroid gland's interesting history thus touches a number of famous men from diverse backgrounds.


Database updating through user feedback in fingerprint-based Wi-Fi location systems

Wi-Fi fingerprinting is a technique which can provide location in GPS-denied environments, relying exclusively on Wi-Fi signals. It first requires the construction of a database of “fingerprints”, i.e. signal strengths from different access points (APs) at different reference points in the desired coverage area. The location of the device is then obtained by measuring the signal strengths at its location, and comparing it with the different reference fingerprints in the database. The main disadvantage of this technique is the labour required to build and maintain the fingerprints database, which has to be rebuilt every time a significant change in the wireless environment occurs, such as installation or removal of new APs, changes in the layout of a building, etc. This paper investigates a new method to utilise user feedback as a way of monitoring changes in the wireless environment. It is based on a system of “points” given to each AP in the database. When an AP is switched off, the number of points associated with that AP will gradually reduce as the users give feedback, until it is eventually deleted from the database. If a new AP is installed, the system will detect it and update the database with new fingerprints. Our proposed system has two main advantages. First it can be used as a tool to monitor the wireless environment in a given place, detecting faulty APs or unauthorised installation of new ones. Second, it regulates the size of the database, unlike other systems where feedback is only used to insert new fingerprints in the database.


Interleaved-Boost Converter With High Voltage Gain

This paper presents an interleaved-boost converter, magnetically coupled to a voltage-doubler circuit, which provides a voltage gain far higher than that of the conventional boost topology. Besides, this converter has low-voltage stress across the switches, natural-voltage balancing between output capacitors, low-input current ripple, and magnetic components operating with the double of switching frequency. These features make this converter suitable to applications where a large voltage step-up is demanded, such as grid-connected systems based on battery storage, renewable energies, and uninterruptible power system applications. Operation principle, main equations, theoretical waveforms, control strategy, dynamic modeling, and digital implementation are provided. Experimental results are also presented validating the proposed topology.


Internal Coupled-Fed Dual-Loop Antenna Integrated With a USB Connector for WWAN/LTE Mobile Handset

A coupled-fed dual-loop antenna capable of providing eight-band WWAN/LTE operation and suitable to integrate with a USB connector in the mobile handset is presented. The antenna integrates with a protruded ground, which is extended from the main ground plane of the mobile handset to accommodate a USB connector functioning as a data port of the handset. To consider the presence of the integrated protruded ground, the antenna uses two separate shorted strips and a T-shape monopole encircled therein as a coupling feed and a radiator as well. The shorted strips are short-circuited through a common shorting strip to the protruded ground and coupled-fed by the T-shape monopole to generate two separate quarter-wavelength loop resonant modes to form a wide lower band to cover the LTE700/GSM850/900 operation (704-960 MHz). An additional higher-order loop resonant mode is also generated to combine with two wideband resonant modes contributed by the T-shape monopole to form a very wide upper band of larger than 1 GHz to cover the GSM1800/1900/UMTS/LTE2300/2500 operation (1710-2690 MHz). Details of the proposed antenna are presented. For the SAR (specific absorption rate) requirement in practical mobile handsets to meet the limit of 1.6 W/kg for 1-g human tissue, the SAR values of the antenna are also analyzed.


Optimal spatial filtering of single trial EEG during imagined hand movement.

The development of an electroencephalograph (EEG)-based brain-computer interface (BCI) requires rapid and reliable discrimination of EEG patterns, e.g., associated with imaginary movement. One-sided hand movement imagination results in EEG changes located at contra- and ipsilateral central areas. We demonstrate that spatial filters for multichannel EEG effectively extract discriminatory information from two populations of single-trial EEG, recorded during left- and right-hand movement imagery. The best classification results for three subjects are 90.8%, 92.7%, and 99.7%. The spatial filters are estimated from a set of data by the method of common spatial patterns and reflect the specific activation of cortical areas. The method performs a weighting of the electrodes according to their importance for the classification task. The high recognition rates and computational simplicity make it a promising method for an EEG-based brain-computer interface.


A Semantic Web Methodology for Situation-Aware Curative Food Service Recommendation System

Recently curative food is becoming more popular, as more people realize its benefits. Based on the theory of Chinese medicine, food itself is medicine. The Curative food which is an ideal nutritious food can help to loss weight, increase immunity and is also good for curative effects in patients. In this paper, we propose a new curative food service (CFS) recommendation system, the designed Situation-aware Curative Food Service Recommendation System (SCFSRS), which provides the cooperation web-based platform for all related mobile users (MUs) and curative food service providers (CFSPs), could strengthen the ability of CFS suggestion. SCFSRS is a five-tier system composed of the MUs, UDDI registries (UDDIRs), CFSPs, curative food services server (CFSS), and database server (DS).


Pattern-based synonym and antonym extraction

Many research studies adopt manually selected patterns for semantic relation extraction. However, manually identifying and discovering patterns is time consuming and it is difficult to discover all potential candidates. Instead, we propose an automatic pattern construction approach to extract verb synonyms and antonyms from English newspapers. Instead of relying on a single pattern, we combine results indicated by multiple patterns to maximize the recall.


Improving Back-Propagation by Adding an Adversarial Gradient

The back-propagation algorithm is widely used for learning in artificial neural networks. A challenge in machine learning is to create models that generalize to new data samples not seen in the training data. Recently, a common flaw in several machine learning algorithms was discovered: small perturbations added to the input data lead to consistent misclassification of data samples. Samples that easily mislead the model are called adversarial examples. Training a ”maxout” network on adversarial examples has shown to decrease this vulnerability, but also increase classification performance. This paper shows that adversarial training has a regularizing effect also in networks with logistic, hyperbolic tangent and rectified linear units. A simple extension to the back-propagation method is proposed, that adds an adversarial gradient to the training. The extension requires an additional forward and backward pass to calculate a modified input sample, or mini batch, used as input for standard back-propagation learning. The first experimental results on MNIST show that the ”adversarial back-propagation” method increases the resistance to adversarial examples and boosts the classification performance. The extension reduces the classification error on the permutation invariant MNIST from 1.60% to 0.95% in a logistic network, and from 1.40% to 0.78% in a network with rectified linear units. Based on these promising results, adversarial back-propagation is proposed as a stand-alone regularizing method that should be further investigated.


EVOLUTION OF SOLID WASTE MANAGEMENT IN MALAYSIA

This paper seeks to examine the policy evolution of solid waste management in Malaysia and to determine its challenges and opportunities by assessing policy gaps, trends and stakeholders perception of solid waste management in Malaysia. Malaysian solid waste generation has been increasing drastically where solid waste generation was projected to increase from about 9.0 million tonnes in 2000 to about 10.9 million tonnes in 2010, to about 12.8 million tonnes in 2015 and finally to about 15.6 million tonnes in 2020 though national recycling rate is only about 3-5 %. This projected increasing rate of solid waste generation is expected to burden the country’s resources and environment in managing these wastes in a sustainable manner. Solid waste management policies in Malaysia has evolved from simple informal policies to supplementary provision in legislation such as the Local Government Act, 1976 and the Environmental Quality Act, 1974 to formal policies such as the National Strategic Plan for Solid Waste Management (NSP) 2005, Master Plan on National Waste Minimization (MWM) in 2006, National Solid Waste Management Policy 2006 and the Solid Waste and Public Cleansing Management Act (SWMA) 2007. Policy gap analysis indicates challenges in the area of policy implementation potentially due to lack of political will, weak stakeholder acceptance and policy impracticality due to direct adoption of policy practices from developed countries while potential opportunities are in the area of legislation for mandatory recycling and source separation as well as government green procurement initiatives. In conclusion, policy evolution of solid waste management in Malaysia may be shifting from a focus on basic solid waste management issues of proper collection, disposal and infrastructure requirements towards sustainable waste management.


The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000

SWISS-PROT is a curated protein sequence database which strives to provide a high level of annotation (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases. Recent developments of the database include format and content enhancements, cross-references to additional databases, new documentation files and improvements to TrEMBL, a computer-annotated supplement to SWISS-PROT. TrEMBL consists of entries in SWISS-PROT-like format derived from the translation of all coding sequences (CDSs) in the EMBL Nucleotide Sequence Database, except the CDSs already included in SWISS-PROT. We also describe the Human Proteomics Initiative (HPI), a major project to annotate all known human sequences according to the quality standards of SWISS-PROT. SWISS-PROT is available at: http://www.expasy.ch/sprot/ and http://www.ebi.ac.uk/swissprot/


MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.


WorkerRank: Using Employer Implicit Judgements to Infer Worker Reputation

In online labor marketplaces two parties are involved; employers and workers. An employer posts a job in the marketplace to receive applications from interested workers. After evaluating the match to the job, the employer hires one (or more workers) to accomplish the job via an online contract. At the end of the contract, the employer can provide his worker with some rating that becomes visible in the worker online profile. This form of explicit feedback guides future hiring decisions, since it is indicative of worker true ability. In this paper, first we discuss some of the shortcomings of the existing reputation systems that are based on the end-of-contract ratings. Then we propose a new reputation mechanism that uses Bayesian updates to combine employer implicit feedback signals in a link-analysis approach. The new system addresses the shortcomings of existing approaches, while yielding better signal for the worker quality towards hiring decision.


Implementing deep neural networks for financial market prediction on the Intel Xeon Phi

Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to financial market prediction has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. A critical step in the viability of the approach in practice is the ability to effectively deploy the algorithm on general purpose high performance computing infrastructure. Using an Intel Xeon Phi co-processor with 61 cores, we describe the process for efficient implementation of the batched stochastic gradient descent algorithm and demonstrate a 11.4x speedup on the Intel Xeon Phi over a serial implementation on the Intel Xeon.


Twitter Geolocation and Regional Classification via Sparse Coding

Experimental Methodology ! Three types of embedding schemes (binary bagof-words, word-counts, word-sequence) ! For a fair comparison, we follow the identical experimental methodology as Eisenstein et al. (2010) ! Twitter’s location service enables users to add location information to their Tweets ! Location information of message is useful (e.g., consumer marketing) Ø However, < 3% of messages are geotagged† ! Machine learning can geolocate based on message content Ø Best results are from supervised modelbased approaches ! We focus on unsupervised data-driven approach, namely sparse coding, to take advantage of abundance of unlabeled messages to geolocate Twitter


The Golden Beauty: Brain Response to Classical and Renaissance Sculptures

Is there an objective, biological basis for the experience of beauty in art? Or is aesthetic experience entirely subjective? Using fMRI technique, we addressed this question by presenting viewers, naïve to art criticism, with images of masterpieces of Classical and Renaissance sculpture. Employing proportion as the independent variable, we produced two sets of stimuli: one composed of images of original sculptures; the other of a modified version of the same images. The stimuli were presented in three conditions: observation, aesthetic judgment, and proportion judgment. In the observation condition, the viewers were required to observe the images with the same mind-set as if they were in a museum. In the other two conditions they were required to give an aesthetic or proportion judgment on the same images. Two types of analyses were carried out: one which contrasted brain response to the canonical and the modified sculptures, and one which contrasted beautiful vs. ugly sculptures as judged by each volunteer. The most striking result was that the observation of original sculptures, relative to the modified ones, produced activation of the right insula as well as of some lateral and medial cortical areas (lateral occipital gyrus, precuneus and prefrontal areas). The activation of the insula was particularly strong during the observation condition. Most interestingly, when volunteers were required to give an overt aesthetic judgment, the images judged as beautiful selectively activated the right amygdala, relative to those judged as ugly. We conclude that, in observers naïve to art criticism, the sense of beauty is mediated by two non-mutually exclusive processes: one based on a joint activation of sets of cortical neurons, triggered by parameters intrinsic to the stimuli, and the insula (objective beauty); the other based on the activation of the amygdala, driven by one's own emotional experiences (subjective beauty).


Fine Grained Classification of Named Entities

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and Ontology generation. We examine one coarse category of named entities, persons, and describe a method for automatically classifying person instances into eight finergrained subcategories. We present a supervised learning method that considers the local context surrounding the entity as well as more global semantic information derived from topic signatures and WordNet. We reinforce this method with an algorithm that takes advantage of the presence of entities in multiple contexts.


User Association for Load Balancing in Heterogeneous Cellular Networks

For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.


A practical traffic management system for integrated LTE-WiFi networks

Mobile operators are leveraging WiFi to relieve the pressure posed on their networks by the surging bandwidth demand of applications. However, operators often lack intelligent mechanisms to control the way users access their WiFi networks. This lack of sophisticated control creates poor network utilization, which in turn degrades the quality of experience (QoE). To meet user traffic demands, it is evident that operators need solutions that optimally balance user traffic across cellular and WiFi networks. Motivated by the lack of practical solutions in this space, we design and implement ATOM - an end-to-end system for adaptive traffic offloading for WiFi-LTE deployments. ATOM has two novel components: (i) A network interface selection algorithm that maps user traffic across WiFi and LTE to optimize user QoE and (ii) an interface switching service that seamlessly re-directs ongoing user sessions in a cost-effective and standards-compatible manner. Our evaluations on a real LTE-WiFi testbed using YouTube traffic reveals that ATOM reduces video stalls by 3-4 times compared to naive solutions.


Distributed Antennas for Indoor Radio Communications

The idea of implementing an indoor radio communications system serving an entire building from a single central antenna appears to be an attractive proposition. However, based on various indoor propagation measurements of the signal attenuation and the multipath delay spread, such a centralized approach appears to be limited to small buildings and to narrow-band FDMA-type systems with limited reliability and flexibility. In this paper, we present the results of indoor radio propagation measurements of two signal distribution approaches that improve the picture dramatically. In the first, the building is divided into many small cells, each served from an antenna located in its own center, and with adjacent cells operating in different frequency bands. In the second approach, the building is divided into one or more large cells, each served from a distributed antenna system or a “leaky feeder” that winds its way through the hallways. This approach eliminates the frequency cell handoff problem that is bound to exist in the first approach, while still preserving the dramatic reductions in multipath delay spread and signal attenuation compared to a centralized system. For example, the measurements show that, with either approach, the signal attenuation can be reduced by as much as a few tens of decibels and the rms delay spread becomes limited to 20 to 50 ns, even in large buildings. This can make possible the implementation of sophisticated broad-band TDMA-type systems that are flexible, robust, and virtually building-independent.


Design, Implementation and Evaluation of Congestion Control for Multipath TCP

Multipath TCP, as proposed by the IETF working group mptcp, allows a single data stream to be split across multiple paths. This has obvious benefits for reliability, and it can also lead to more efficient use of networked resources. We describe the design of a multipath congestion control algorithm, we implement it in Linux, and we evaluate it for multihomed servers, data centers and mobile clients. We show that some ‘obvious’ solutions for multipath congestion control can be harmful, but that our algorithm improves throughput and fairness compared to single-path TCP. Our algorithm is a drop-in replacement for TCP, and we believe it is safe to deploy.


Rate control for communication networks: shadow prices, proportional fairness and stability

Rate Control for Communication Networks: Shadow Prices, Proportional Fairness and Stability Author(s): F. P. Kelly, A. K. Maulloo and D. K. H. Tan Source:  The Journal of the Operational Research Society, Vol. 49, No. 3 (Mar., 1998), pp. 237-252 Published by: on behalf of the Palgrave Macmillan Journals Operational Research Society Stable URL: http://www.jstor.org/stable/3010473 Accessed: 07-10-2015 21:10 UTC


Internet of Things and LoRa™ Low-Power Wide-Area Networks: A survey

Nowadays there is a lot of effort on the study, analysis and finding of new solutions related to high density sensor networks used as part of the IoT (Internet of Things) concept. LoRa (Long Range) is a modulation technique that enables the long-range transfer of information with a low transfer rate. This paper presents a review of the challenges and the obstacles of IoT concept with emphasis on the LoRa technology. A LoRaWAN network (Long Range Network Protocol) is of the Low Power Wide Area Network (LPWAN) type and encompasses battery powered devices that ensure bidirectional communication. The main contribution of the paper is the evaluation of the LoRa technology considering the requirements of IoT. After introduction in Section II the main obstacles of IoT development are discussed, section III addresses the challenges and entails the need for solutions to different problems in WSN research. Section IV presents the LoRaWAN communication protocol architecture requirements while in Section V the LoRa modulation performance is evaluated and discussed. In conclusion LoRa can be considered a good candidate in addressing the IoT challenges.


The Impact of Performance-Contingent Rewards on Perceived Autonomy and Competence 1

Two studies examined the impact of performance-contingent rewards on perceived autonomy, competence, and intrinsic motivation. Autonomy was measured in terms of both decisional and affective reports. The first study revealed an undermining effect of performance-contingent rewards on affective reports of autonomy among university students, and an increase in reports of competence. Decisional autonomy judgements were unaffected by rewards. The second study replicated this pattern of findings among elementary school children. These results help resolve Cognitive Evaluation Theory’s (E. L. Deci & R. M. Ryan, 1985; R. M. Ryan, V. Mims, & R. Koestner, 1983) and Eisenberger, Rhoades, et al.’s (R. Eisenberger, L. Rhoades, & J. Cameron, 1999) divergent positions on the impact of performance-contingent rewards on autonomy. The studies also included measures of intrinsic motivation.


A Multi-tenant Web Application Framework for SaaS

Software as a Service (SaaS) is a software delivery model in which software resources are accessed remotely by users. Enterprises find SaaS attractive because of its low cost. SaaS requires sharing of application servers among multiple tenants for low operational costs. Besides the sharing of application servers, customizations are needed to meet requirements of each tenant. Supporting various levels of configuration and customization is desirable for SaaS frameworks. This paper describes a multi-tenant web application framework for SaaS. The proposed framework supports runtime customizations of user interfaces and business logics by use of file-level namespaces, inheritance, and polymorphism. It supports various client-side web application technologies.


Comparing deep neural networks against humans: object recognition when the signal gets weaker

Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algorithms called deep neural networks (DNNs) that achieve human-level classification performance on object recognition tasks. Furthermore, a growing number of studies report similarities in the way DNNs and the human visual system process objects, suggesting that current DNNs may be good models of human visual object recognition. Yet there clearly exist important architectural and processing differences between stateof-the-art DNNs and the primate visual system. The potential behavioural consequences of these differences are not well understood. We aim to address this issue by comparing human and DNN generalisation abilities towards image degradations. We find the human visual system to be more robust to image manipulations like contrast reduction, additive noise or novel eidolon-distortions. In addition, we find progressively diverging classification error-patterns between man and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition. We envision that our findings as well as our carefully measured and freely available behavioural datasets1 provide a new useful benchmark for the computer vision community to improve the robustness of DNNs and a motivation for neuroscientists to search for mechanisms in the brain that could facilitate this robustness. Data and materials available at https://github.com/rgeirhos/object-recognition 1 ar X iv :1 70 6. 06 96 9v 1 [ cs .C V ] 2 1 Ju n 20 17


Retrograde amnesia and remote memory impairment

-The status of remote memory in case N.A., patients receiving bilateral ECT, and patients with KorsakoBsyndrome was assessed using seven different tests. The extent and pattern of remote memory impairment depended upon the etiology of amnesia, appearing as a relatively brief retrograde amnesia for N.A. and patients receiving ECT and as an extensive impairment covering many decades for patients with KorsakoB syndrome. Differences in remote memory impairment could not be explained by differences in the severity ofanterograde amnesia, indicating that amnesia is not a unitary disorder. We suggest that brief retrograde amnesia is typically present whenever amnesia occurs and that extensive remote memory impairment is a distinct entity related to superimposed cognitive deficits. These ideas and a review of previous studies lead to a comprehensive proposal for understanding retrograde amnesia and remote memory impairment. THE AMNESIC syndrome has been the subject of much interest in neuropsychology because of what it might reveal about the organization and neurological foundations of normal memory. The phenomenon of retrograde amnesia, that loss of memory can occur for the period before the precipitating incident, has had a particularly large impact on ideas about normal memory function [l-5]. However, conflicting views exist regarding both the extent and pattern of retrograde amnesia and these have been used to support different ideas about the nature of amnesia. Specifically, clinical assessment of memory loss in patients with medial temporal lobectomy has suggested that a relatively brief retrograde amnesia extends at least to several months and at most to a few years prior to surgery [6, 71. Formal testing of patients receiving bilateral electroconvulsive therapy (ECT) has revealed a brief retrograde amnesia affecting memory for public events and former television programs that occurred during the few years prior to treatment [8-lo]. These findings, together with studies of anterograde amnesia, are consistent with theories of amnesia that emphasize deficiency in the storage or consolidation of new information [l, 11, 121. By contrast, formal testing of patients with Korsakoff syndrome has revealed an extensive impairment of remote memory for public events [13-l 51, famous faces [2,13,15], and famous voices [16] that covers several decades. These *Now at Department of Psychology, Massachusetts Institute of Technology, Cambridge, MA 02139. ICorrespondence to Larry R. Squire, Veterans Administration Medical Center, 3350 La Jolla Village Drive, V-l 16A, San Diego, CA 92161, U.S.A.


Multiview Photometric Stereo

This paper addresses the problem of obtaining complete, detailed reconstructions of textureless shiny objects. We present an algorithm which uses silhouettes of the object, as well as images obtained under changing illumination conditions. In contrast with previous photometric stereo techniques, ours is not limited to a single viewpoint but produces accurate reconstructions in full 3D. A number of images of the object are obtained from multiple viewpoints, under varying lighting conditions. Starting from the silhouettes, the algorithm recovers camera motion and constructs the object's visual hull. This is then used to recover the illumination and initialize a multiview photometric stereo scheme to obtain a closed surface reconstruction. There are two main contributions in this paper: First, we describe a robust technique to estimate light directions and intensities and, second, we introduce a novel formulation of photometric stereo which combines multiple viewpoints and, hence, allows closed surface reconstructions. The algorithm has been implemented as a practical model acquisition system. Here, a quantitative evaluation of the algorithm on synthetic data is presented together with complete reconstructions of challenging real objects. Finally, we show experimentally how, even in the case of highly textured objects, this technique can greatly improve on correspondence-based multiview stereo results.


Bicycle-Sharing System Analysis and Trip Prediction

Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.


Wavelet-Based Neural Network for Power Disturbance Recognition and Classification

In this paper, a prototype wavelet-based neuralnetwork classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval’s theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.


A time window neural network based framework for Remaining Useful Life estimation

This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.


Going Deeper into Action Recognition: A Survey

We provide a detailed review of the work on human action recognition over the past decade. We refer to “actions” as meaningful human motions. Starting with methods that are based on handcrafted representations, we review the impact of revamped deep neural networks on action recognition. We follow a systematic taxonomy of action recognition approaches to present a coherent discussion over their improvements and fall-backs.


Color attributes for object detection

State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-of-the-art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.


Cyber security for home users: A new way of protection through awareness enforcement

We are currently living in an age, where the use of the Internet has become second nature to millions of people. Not only do businesses depend on the Internet for all types of electronic transactions, but more and more home users are experiencing the immense benefit of the Internet. However, this dependence and use of the Internet bring new and dangerous risks. This is due to increasing attempts from unauthorised third parties to compromise private information for their own benefit – the whole wide area of cyber crime. It is therefore essential that all users understand the risks of using the Internet, the importance of securing their personal information and the consequences if this is not done properly. It is well known that home users are specifically vulnerable, and that cyber criminals have such users squarely in their target. This vulnerability of home users are due to many factors, but one of the most important ones is the fact that such home users are in many cases not aware of the risks of using the Internet, and often venture into cyber space without any awareness preparation for this journey. This paper specifically investigates the position of the home user, and proposes a new model, The E-Awareness Model (E-AM), in which home users can be forced to acquaint themselves with the risks involved in venturing into cyber space. The E-AM consists of two components : the awareness component housed in the E-Awareness Portal, and the enforcement component. This model proposes a way to improve information security awareness amongst home users by presenting some information security content and enforcing the absorption of this content. The main difference between the presented model and other existing information security awareness models, is that in the presented model the acquiring/absorption of the awareness content is compulsory the user is forced to proceed via the E-Awareness Portal without the option of bypassing it.


A RSSI-based and calibrated centralized localization technique for wireless sensor networks

This paper presents a multi-hop localization technique for WSNs exploiting acquired received signal strength indications. The proposed system aims at providing an effective solution for the self localization of nodes in static/semi-static wireless sensor networks without requiring previous deployment information


Loosely-Coupled Benchmark Framework Automates Performance Modeling on IaaS Clouds

Cloud computing is under rapid development, which brings the urgent need of evaluation and comparison of cloud systems. The performance testers often struggle in tedious manual operations, when carrying out lots of similar experiments on the cloud systems. However, few of current benchmark tools provide both flexible workflow controlling methodology and extensible workload abstraction at the same time. We present a modeling methodology to compare the performance from multiple aspects based on a loosely coupled benchmark framework, which automates experiments under agile workflow controlling and achieves broad cloud supports, as well as good workload extensibility. With several built-in workloads and scenario templates, we performed a series of tests on Amazon EC2 services and our private Open Stack-based cloud, and analyze the elasticity and scalability based on the performance models. Experiments show the robustness and compatibility of our framework, which makes remarkable guarantee that it can be leveraged in practice for researchers and testers to perform their further study.


An automatic irrigation system using ZigBee in wireless sensor network

Wireless Sensing Technology is widely used everywhere in the current scientific world. As the technology is growing and changing rapidly, Wireless sensing Network (WSN) helps to upgrade the technology. In the research field of wireless sensor networks the power efficient time is a major issue. This problem can be overcome by using the ZigBee technology. The main idea of this is to understand how data travels through a wireless medium transmission using wireless sensor network and monitoring system. This paper design an irrigation system which is automated by using controllable parameter such as temperature, soil moisture and air humidity because they are the important factors to be controlled in PA(Precision Agricultures).


Winner-Take-All Autoencoders

In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoencoders which use mini-batch statistics to directly enforce a lifetime sparsity in the activations of the hidden units. We then propose the convolutional winner-take-all autoencoder which combines the benefits of convolutional architectures and autoencoders for learning shift-invariant sparse representations. We describe a way to train convolutional autoencoders layer by layer, where in addition to lifetime sparsity, a spatial sparsity within each feature map is achieved using winner-take-all activation functions. We will show that winner-take-all autoencoders can be used to to learn deep sparse representations from the MNIST, CIFAR-10, ImageNet, Street View House Numbers and Toronto Face datasets, and achieve competitive classification performance.


Joint optimization of LCMV beamforming and acoustic echo cancellation

Full-duplex hands-free acoustic human/machine interfaces often require the combination of acoustic echo cancellation and speech enhancement in order to suppress acoustic echoes, local interference, and noise. In order to optimally exploit positive synergies between acoustic echo cancellation and speech enhancement, we present in this contribution a combined least-squares (LS) optimization criterion for the integration of acoustic echo cancellation and adaptive linearly-constrained minimum variance (LCMV) beamforming. Based on this optimization criterion, we derive a computationally efficient system based on the generalized sidelobe canceller (GSC), which effectively deals with scenarioes with time-varying acoustic echo paths and simultaneous presence of double-talk of acoustic echoes, local interference, and desired speakers.


Nonstationary Function Optimization Using Genetic Algorithms with Dominance and Diploidy

Specifically, we apply genetic algorithms that include diploid genotypes and dominance operators to a simple nonstationary problem in function optimization: an oscillating, blind knapsack problem. In doing this, we find that diploidy and dominance induce a form of long term distributed memory that stores and occasionally remembers good partial solutions that were once desirable. This memory permits faster adaptation to drastic environmental shifts than is possible without the added structures and operators. This paper investigates the use of diploid representations and dominance operators in genetic algorithms (GAs) to improve performance in environments that vary with time. The mechanics of diploidy and dominance in natural genetics are briefly discussed, and the usage of these structures and operators in other GA investigations is reviewed. An extension of the schema theorem is developed which illustrates the ability of diploid GAs with dominance to hold alternative alleles in abeyance. Both haploid and diploid GAs are applied to a simple time varying problem: an oscillating, blind knapsack problem. Simulation results show that a diploid GA with an evolving dominance map adapts more quickly to the sudden changes in this problem environment than either a haploid GA or a diploid GA with a fixed dominance map. These proof-of-principle results indicate that diploidy and dominance can be used to induce a form of long term distributed memory within a population of structures. In the remainder of this paper, we explore the mechanism, theory, and implementation of dominance and diploidy in artificial genetic search. We start by examining the role of diploidy and dominance in natural genetics, and we briefly review examples of their usage in genetic algorithm circles. We extend the schema theorem to analyze the effect of these structures and mechanisms. We present results from computational experiments on a 17-object, oscillating, blind 0-1 knapsack problem. Simulations with adaptive dominance maps and diploidy are able to adapt more quickly to sudden environmental shifts than either a haploid genetic algorithm or a diploid genetic algorithm with fixed dominance map. These results are encouraging and suggest the investigation of dominance and diploidy in other GA applications in search and machine learning. INTRODUCTION Real world problems are seldom independent of time. If you don't like the weather, wait five minutes and it will change. If this week gasoline costs $1.30 a gallon, next week it may cost $0.89 a gallon or perhaps $2.53 a gallon. In these and many more complex ways, real world environments are both nonstationary and noisy. Searching for good solutions or good behavior under such conditions is a difficult task; yet, despite the perpetual change and uncertainty, all is not lost. History does repeat itself, and what goes around does come around. The horrors of Malthusian extrapolation rarely come to pass, and solutions that worked well yesterday are at least somewhat likely to be useful when circumstances are somewhat similar tomorrow or the day after. The temporal regularity implied in these observations places a premium on search augmented by selective memory. In other words, a system which does not learn the lessons of its history is doomed to repeat its mistakes. THE MECHANICS OF NATURAL DOMINANCE AND DIPLOIDY It is surprising to some genetic algorithm newcomers that tpe most commonly used GA is modeled after the mechanics of haploid genetics. After all, don't most elementary genetics textbooks start off with a discussion of Mendel's pea plants and some mention of diploidy and dominance? The reason for this disparity between genetic algorithm practice and genetics textbook coverage is due to the success achieved by early GA investigators (Hollstien, 1971; De Jong, 1975) using haploid chromosome models on stationary problems. It was found that surprising efficacy and efficiency could be obtained using single stranded (haploid) chromosomes under the action of reproduction and crossover. As a result, later investigators of artificial genetic search have tended to ignore diploidy and dominance. In this section we examine the mechanics of diploidy and dominance to understand their roles in shielding alternate In this paper, we investigate the behavior of a genetic algorithm augmented by structures and operators capable of exploiting the regularity and repeatability of many nonstationary environments.


Spin Transfer Torques

This tutorial article introduces the physics of spin transfer torques in magnetic devices. We provide an elementary discussion of the mechanism of spin transfer torque, and review the theoretical and experimental progress in this field. Our intention is to be accessible to beginning graduate students. This is the introductory paper for a cluster of “Current Perspectives” articles on spin transfer torques published in volume 320 of the Journal of Magnetism and Magnetic Materials. This article is meant to set the stage for the others which follow it in this cluster; they focus in more depth on particularly interesting aspects of spin-torque physics and highlight unanswered questions that might be productive topics for future research.


Group-sensitive multiple kernel learning for object categorization

In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation “group” between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories. For each category, images within a group share a set of kernel weights while images from different groups may employ distinct sets of kernel weights. In GS-MKL, such group-sensitive kernel combinations together with the multi-kernels based classifier are optimized in a joint manner to seek a trade-off between capturing the diversity and keeping the invariance for each category. Extensive experiments show that our proposed GS-MKL method has achieved encouraging performance over three challenging datasets.


MicroRNA Expression Changes during Interferon-Beta Treatment in the Peripheral Blood of Multiple Sclerosis Patients

MicroRNAs (miRNAs) are small non-coding RNA molecules acting as post-transcriptional regulators of gene expression. They are involved in many biological processes, and their dysregulation is implicated in various diseases, including multiple sclerosis (MS). Interferon-beta (IFN-beta) is widely used as a first-line immunomodulatory treatment of MS patients. Here, we present the first longitudinal study on the miRNA expression changes in response to IFN-beta therapy. Peripheral blood mononuclear cells (PBMC) were obtained before treatment initiation as well as after two days, four days, and one month, from patients with clinically isolated syndrome (CIS) and patients with relapsing-remitting MS (RRMS). We measured the expression of 651 mature miRNAs and about 19,000 mRNAs in parallel using real-time PCR arrays and Affymetrix microarrays. We observed that the up-regulation of IFN-beta-responsive genes is accompanied by a down-regulation of several miRNAs, including members of the mir-29 family. These differentially expressed miRNAs were found to be associated with apoptotic processes and IFN feedback loops. A network of miRNA-mRNA target interactions was constructed by integrating the information from different databases. Our results suggest that miRNA-mediated regulation plays an important role in the mechanisms of action of IFN-beta, not only in the treatment of MS but also in normal immune responses. miRNA expression levels in the blood may serve as a biomarker of the biological effects of IFN-beta therapy that may predict individual disease activity and progression.


The potential environmental impact of engineered nanomaterials

With the increased presence of nanomaterials in commercial products, a growing public debate is emerging on whether the environmental and social costs of nanotechnology outweigh its many benefits. To date, few studies have investigated the toxicological and environmental effects of direct and indirect exposure to nanomaterials and no clear guidelines exist to quantify these effects.


SDN-based solutions for Moving Target Defense network protection

Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.


OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection

Malware is any computer software potentially harmful to both computers and networks. The amount of malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new malware. Supervised machine learning has been adopted to solve this issue. There are two types of features that supervised malware detectors use: (i) static features and (ii) dynamic features. Static features are extracted without executing the sample whereas dynamic ones requires an execution. Both approaches have their advantages and disadvantages. In this paper, we propose for the first time, OPEM, an hybrid unknown malware detector which combines the frequency of occurrence of operational codes (statically obtained) with the information of the execution trace of an executable (dynamically obtained). We show that this hybrid approach enhances the performance of both approaches when run separately.


An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.


Toward Automated Dynamic Malware Analysis Using CWSandbox

Malware is notoriously difficult to combat because it appears and spreads so quickly. In this article, we describe the design and implementation of CWSandbox, a malware analysis tool that fulfills our three design criteria of automation, effectiveness, and correctness for the Win32 family of operating systems


Exploring Multiple Execution Paths for Malware Analysis

Malicious code (or Malware) is defined as software that fulfills the deliberately harmful intent of an attacker. Malware analysis is the process of determining the behavior and purpose of a given Malware sample (such as a virus, worm, or Trojan horse). This process is a necessary step to be able to develop effective detection techniques and removal tools. Currently, Malware analysis is mostly a manual process that is tedious and time-intensive. To mitigate this problem, a number of analysis tools have been proposed that automatically extract the behavior of an unknown program by executing it in a restricted environment and recording the operating system calls that are invoked. The problem of dynamic analysis tools is that only a single program execution is observed. Unfortunately, however, it is possible that certain malicious actions are only triggered under specific circumstances (e.g., on a particular day, when a certain file is present, or when a certain command is received). In this paper, we propose a system that allows us to explore multiple execution paths and identify malicious actions that are executed only when certain conditions are met. This enables us to automatically extract a more complete view of the program under analysis and identify under which circumstances suspicious actions are carried out. Our experimental results demonstrate that many Malware samples show different behavior depending on input read from the environment. Thus, by exploring multiple execution paths, we can obtain a more complete picture of their actions.


Optimizing number of hidden neurons in neural networks

In this paper, a novel and effective criterion based on the estimation of the signal-to-noise-ratio figure (SNRF) is proposed to optimize the number of hidden neurons in neural networks to avoid overfitting in the function approximation. SNRF can quantitatively measure the useful information left unlearned so that overfitting can be automatically detected from the training error only without use of a separate validation set. It is illustrated by optimizing the number of hidden neurons in a multi-layer perceptron (MLP) using benchmark datasets. The criterion can be further utilized in the optimization of other parameters of neural networks when overfitting needs to be considered.