title: Low-Complexity Signal Detection for Large-Scale MIMO in Optical Wireless Communications

abstract: Optical wireless communication (OWC) has been a rapidly growing research area in recent years. Applying multiple-input multiple-output (MIMO), particularly large-scale MIMO, into OWC is very promising to substantially increase spectrum efficiency. However, one challenging problem to realize such an attractive goal is the practical signal detection algorithm for optical MIMO systems, whereby the linear signal detection algorithm like minimum mean square error (MMSE) can achieve satisfying performance but involves complicated matrix inversion of large size. In this paper, we first prove a special property that the filtering matrix of the linear MMSE algorithm is symmetric positive definite for indoor optical MIMO systems. Based on this property, a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method is proposed to reduce the overall complexity by one order of magnitude with a negligible performance loss. The performance guarantee of the proposed SOR-based algorithm is analyzed from the following three aspects. First, we prove that the SOR-based algorithm is convergent for indoor large-scale optical MIMO systems. Second, we prove that the SOR-based algorithm with the optimal relaxation parameter can achieve a faster convergence rate than the recently proposed Neumann-based algorithm. Finally, a simple quantified relaxation parameter, which is independent of the receiver location and signal-to-noise ratio, is proposed to guarantee the performance of the SOR-based algorithm in practice. Simulation results verify that the proposed SOR-based algorithm can achieve the exact performance of the classical MMSE algorithm with a small number of iterations.


title: Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice

abstract: The authors developed and implemented transparent machine-learning models that call into question the use of black-box machine-learning models in healthcare and criminal justice applications.


title: Development of Wireless Battery Monitoring for electric vehicle

abstract: A Wireless Battery Monitoring System (WBMS) for electric vehicle has been developed for monitoring voltage, current and temperature of battery. This system consists of hardwares (sensors, a microcontroller, a bluetooth module, an Android smartphone) and software. It was designed on a low cost microcontroller ATMEGA 328 (Arduino UNO). Voltage, current and temperature data are transfered to microcontroller, then data of battery is transfered using bluetooth communication to display. In this research, data of battery monitoring are displayed on Personal Computer (PC) with LabVIEW programme and android smartphone. The monitoring system was able to show real-time data of voltage, current and temperature and display data on android smartphone and PC simultaneously.


title: MeltdownPrime and SpectrePrime: Automatically-Synthesized Attacks Exploiting Invalidation-Based Coherence Protocols

abstract: The recent Meltdown and Spectre attacks highlight the importance of automated verification techniques for identifying hardware security vulnerabilities. We have developed a tool for synthesizing microarchitecture-specific programs capable of producing any user-specified hardware execution pattern of interest. Our tool takes two inputs: a formal description of (i) a microarchitecture in a domain-specific language, and (ii) a microarchitectural execution pattern of interest, e.g. a threat pattern. All programs synthesized by our tool are capable of producing the specified execution pattern on the supplied microarchitecture. We used our tool to specify a hardware execution pattern common to Flush+Reload attacks and automatically synthesized security litmus tests representative of those that have been publicly disclosed for conducting Meltdown and Spectre attacks. We also formulated a Prime+Probe threat pattern, enabling our tool to synthesize a new variant of each---MeltdownPrime and SpectrePrime. Both of these new exploits use Prime+Probe approaches to conduct the timing attack. They are both also novel in that they are 2-core attacks which leverage the cache line invalidation mechanism in modern cache coherence protocols. These are the first proposed Prime+Probe variants of Meltdown and Spectre. But more importantly, both Prime attacks exploit invalidation-based coherence protocols to achieve the same level of precision as a Flush+Reload attack. While mitigation techniques in software (e.g., barriers that prevent speculation) will likely be the same for our Prime variants as for original Spectre and Meltdown, we believe that hardware protection against them will be distinct. As a proof of concept, we implemented SpectrePrime as a C program and ran it on an Intel x86 processor, averaging about the same accuracy as Spectre over 100 runs---97.9% for Spectre and 99.95% for SpectrePrime.


title: Evaluating Visual Conversational Agents via Cooperative Human-AI Games

abstract: As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. ::: In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. ::: We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.


title: Discriminative 3D morphable model fitting

abstract: This paper presents a novel discriminative method for estimating 3D shape from a single image with 3D Morphable Model (3DMM). Until now, most traditional 3DMM fitting methods depend on the analysis-by-synthesis framework which searches for the best parameters by minimizing the difference between the input image and the model appearance. They are highly sensitive to initialization and have to rely on the stochastic optimization to handle local minimum problem, which is usually a time-consuming process. To solve the problem, we find a different direction to estimate the shape parameters through learning a regressor instead of minimizing the appearance difference. Compared with the traditional analysis-by-synthesis framework, the new discriminative approach makes it possible to utilize large databases to train a robust fitting model which can reconstruct shape from image features accurately and efficiently. We compare our method with two popular 3DMM fitting algorithms on FRGC database. Experimental results show that our approach significantly outperforms the state-of-the-art in terms of efficiency, robustness and accuracy.


title: Simultaneous Monitoring of Canine Heart Rate and Respiratory Patterns During Scent Detection Tasks

abstract: Man-made technological odor detection systems continue to improve but still cannot match the tracking efficiency, mobility, and selectivity in the presence of interfering odors achieved by detector dogs. The reliability of dogs as olfactory detectors does not depend solely on their performance but also on the handler’s skill in interpreting the behavior of the dog that signals the detection of the target odor. We present our efforts on a wireless wearable system combining electrocardiogram (ECG) and an electronic stethoscope for direct monitoring of cardiopulmonary events in dogs toward enabling cybernetic dog–machine interfaces. This is, to our knowledge, the first cyber-physical attempt to simultaneously record heart rate, heart rate variability, and continuous auscultation of respiratory behavior in a wearable form factor during scent-detection tasks. In this paper, we present: 1) a method to automate the detection of sniffing episodes based on power spectral density of the respiratory sounds; 2) proof-of-concept extraction and quantification of the ECG and respiratory sounds features that would enable the discovery of physiological patterns associated to scent-detection tasks; and 3) proof-of-concept correlation of such patterns with the presence/absence of target odors. These contributions pave the way for a novel real-time cybernetic olfactory detection monitoring system to provide decision support for handlers in the field in addition to enabling future computer-sniffing dog interfaces.


title: Circularly polarized wide axial ratio beamwidth omnidirectional bifilar helix antennas

abstract: In this paper, analysis and design of omnidirectional single feed circularly polarized, wide axial ratio beamwidth resonant side-fed bifilar helix antennas not requiring ground plane is presented. The simulation and measurement results show that side-fed helix antennas of various radii gives near perfect circular polarization (CP) almost over the whole sphere except at & around nulls, at a certain turn angle that corresponds to the formula A = ρl given in [1], where A is the area of loop, p is the pitch of the helix and ρ is the radianlength. Helix with smaller radii gives axial ratio (AR) close to one at higher values of turn angle with lower value of resonant input impedances but CP is less sensitive to inaccuracy in turn angle. However helix with larger radii results in AR close to one at lower values of turn angle and provides better values of input resistance but its AR is more sensitive to variations in turn angle. The simulated results show that the polarization solid angle beamwidth (for AR ≥ −3dB) varies from 3.96л to 4л steradian. The gain and 3dB beamwidth of the antennas are 2dB and 90° respectively. The polarization bandwidth varies from 34% to 65%. The simulated results are verified by experimental measurements.


title: Validity of the school setting interview for students with special educational needs in regular high school – a Rasch analysis

abstract: Participation in education is a vital component of adolescents’ everyday life and a determinant of health and future opportunities in adult life. The School Setting Interview (SSI) is an instrument which assesses student-environment fit and reflects the potential needs for adjustments to enhance students’ participation in school activities. The aim of the study was to investigate the psychometric properties of the SSI for students with special educational needs in regular high school. A sample of 509 students with special educational needs was assessed with the SSI. The polytomous unrestricted Rasch model was used to analyze the psychometric properties of the SSI regarding targeting, model fit, differential item functioning (DIF), response category functioning and unidimensionality. The SSI generally confirmed fit to assumptions of the Rasch model. Reliability was acceptable (0.73) and the SSI scale was able to separate students into three different levels of student-environment fit. DIF among gender was detected in item “Remember things” and in item “Homework” DIF was detected among students with or without diagnosis. All items had disordered thresholds. The SSI demonstrated unidimensionality and no response dependence was present among items. The results suggest that the SSI is valid for use among students with special educational needs in order to provide and evaluate environmental adjustments. However, the items with the detected DIF and the SSI rating scale with its disordered thresholds needs to be further scrutinized.


title: Super-resolution Using Constrained Deep Texture Synthesis

abstract: Hallucinating high frequency image details in single image super-resolution is a challenging task. Traditional super-resolution methods tend to produce oversmoothed output images due to the ambiguity in mapping between low and high resolution patches. We build on recent success in deep learning based texture synthesis and show that this rich feature space can facilitate successful transfer and synthesis of high frequency image details to improve the visual quality of super-resolution results on a wide variety of natural textures and images.


title: Predicting individual differences in reading comprehension: a twin study

abstract: We examined the Simple View of reading from a behavioral genetic perspective. Two aspects of word decoding (phonological decoding and word recognition), two aspects of oral language skill (listening comprehension and vocabulary), and reading comprehension were assessed in a twin sample at age 9. Using latent factor models, we found that overlap among phonological decoding, word recognition, listening comprehension, vocabulary, and reading comprehension was primarily due to genetic influences. Shared environmental influences accounted for associations among word recognition, listening comprehension, vocabulary, and reading comprehension. Independent of phonological decoding and word recognition, there was a separate genetic link between listening comprehension, vocabulary, and reading comprehension and a specific shared environmental link between vocabulary and reading comprehension. There were no residual genetic or environmental influences on reading comprehension. The findings provide evidence for a genetic basis to the “Simple View” of reading.


title: A Novel Hierarchical Convolutional Neural Network for Question Answering over Paragraphs

abstract: The question of classical Factoid Question Answering (FQA) task is always in the form of a single sentence. There also exists another kind of FQA task, whose question is a descriptive paragraph, such as quiz bowl question answering. Recently, some works try to automatically answer paragraph questions by applying machine learning methods. However, these methods neglect the correlation information between sentences in a paragraph and do not take full advantage of answer embedding information. In this paper, we propose a novel Hierarchical Convolutional Neural Network, called HCNN-E, to settle the task by considering ordinal information of sentences in paragraph and the information of answer embeddings. The experimental results on two public datasets demonstrate the effectiveness of proposed method, and the proposed method can achieve approximately 10% - 20% improvements, when comparing with the baselines.


title: seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention

abstract: Discovering temporal lagged and inter-dependencies in multivariate time series data is an important task. However, in many real-world applications, such as commercial cloud management, manufacturing predictive maintenance, and portfolios performance analysis, such dependencies can be non-linear and time-variant, which makes it more challenging to extract such dependencies through traditional methods such as Granger causality or clustering. In this work, we present a novel deep learning model that uses multiple layers of customized gated recurrent units (GRUs) for discovering both time lagged behaviors as well as inter-timeseries dependencies in the form of directed weighted graphs. We introduce a key component of Dual-purpose recurrent neural network that decodes information in the temporal domain to discover lagged dependencies within each time series, and encodes them into a set of vectors which, collected from all component time series, form the informative inputs to discover inter-dependencies. Though the discovery of two types of dependencies are separated at different hierarchical levels, they are tightly connected and jointly trained in an end-to-end manner. With this joint training, learning of one type of dependency immediately impacts the learning of the other one, leading to overall accurate dependencies discovery. We empirically test our model on synthetic time series data in which the exact form of (non-linear) dependencies is known. We also evaluate its performance on two real-world applications, (i) performance monitoring data from a commercial cloud provider, which exhibit highly dynamic, non-linear, and volatile behavior and, (ii) sensor data from a manufacturing plant. We further show how our approach is able to capture these dependency behaviors via intuitive and interpretable dependency graphs and use them to generate highly accurate forecasts.


Improving the wellbeing and mental health of young people through the delivery of online mentoring and counselling services


title: How to steal a machine learning classifier with deep learning

abstract: This paper presents an exploratory machine learning attack based on deep learning to infer the functionality of an arbitrary classifier by polling it as a black box, and using returned labels to build a functionally equivalent machine. Typically, it is costly and time consuming to build a classifier, because this requires collecting training data (e.g., through crowdsourcing), selecting a suitable machine learning algorithm (through extensive tests and using domain-specific knowledge), and optimizing the underlying hyperparameters (applying a good understanding of the classifier's structure). In addition, all this information is typically proprietary and should be protected. With the proposed black-box attack approach, an adversary can use deep learning to reliably infer the necessary information by using labels previously obtained from the classifier under attack, and build a functionally equivalent machine learning classifier without knowing the type, structure or underlying parameters of the original classifier. Results for a text classification application demonstrate that deep learning can infer Naive Bayes and SVM classifiers with high accuracy and steal their functionalities. This new attack paradigm with deep learning introduces additional security challenges for online machine learning algorithms and raises the need for novel mitigation strategies to counteract the high fidelity inference capability of deep learning.


title: Media Exposure through the Funnel: A Model of Multi-Stage Attribution

abstract: In this paper, we address the problem of advertising attribution by developing a Hidden Markov Model (HMM) of an individual consumer's behavior based on the concept of a conversion funnel. We apply the model to a unique dataset from the online campaign for the launch of a car. We observe that different ad formats, e.g. display and search ads, affect consumers differently based on their states in the decision process. Display ads usually have an early impact on the consumer, moving her from a disengaged state to an state in which she interacts with the campaign. On the other hand, search ads have a pronounced effect across all stages. Further, when the consumer interacts with these ads (e.g. by clicking on them), the likelihood of a conversion increases considerably. Finally, we show that attributing conversions based on the HMM provides fundamentally different insights into ad effectiveness relative to the commonly used approaches for attribution. Contrary to the common belief that display ads are not useful, our results show that display ads have a significant effect on the early stages of the conversion process. Furthermore, we show that only a fraction of online conversions are driven by online ads.


Skeletal Tracking using Microsoft Kinect


title: Interventions to facilitate social interaction for young children with autism: review of available research and recommendations for educational intervention and future research.

abstract: The purpose of this paper is to review the knowledge available from aggregated research (primarily through 2000) on the characteristics of social interactions and social relationships among young children with autism, with special attention to strategies and tactics that promote competence or improved performance in this area. In its commissioning letter for the initial version of this paper, the Committee on Educational Interventions for Children with Autism of the National Research Council requested “a critical, scholarly review of the empirical research on interventions to facilitate the social interactions of children with autism, considering adult–child interactions (where information is available) as well as child–child interactions, and including treatment of [one specific question]: What is the empirical evidence that social irregularities of children with autism are amenable to remediation?” To do this, the paper (a) reviews the extent and quality of empirical literature on social interaction for young children with autism; (b) reviews existing descriptive and experimental research that may inform us of relations between autism and characteristics that support social development, and efforts to promote improved social outcomes (including claims for effectiveness for several specific types of intervention); (c) highlights some possible directions for future research; and (d) summarizes recommendations for educational practices that can be drawn from this research.


title: Personality and job performance: The Big Five revisited.

abstract: Prior meta-analyses investigating the relation between the Big 5 personality dimensions and job performance have all contained a threat to construct validity, in that much of the data included within these analyses was not derived from actual Big 5 measures. In addition, these reviews did not address the relations between the Big 5 and contextual performance. Therefore, the present study sought to provide a meta-analytic estimate of the criterion-related validity of explicit Big 5 measures for predicting job performance and contextual performance. The results for job performance closely paralleled 2 of the previous meta-analyses, whereas analyses with contextual performance showed more complex relations among the Big 5 and performance. A more critical interpretation of the Big 5-performance relationship is presented, and suggestions for future research aimed at enhancing the validity of personality predictors are provided. During the several decades prior to the 1990s, the use of personality testing in employee selection was generally looked down on by personnel selection specialists. This was primarily due to pessimistic conclusions drawn by researchers such as Guion and Gottier (1965) in their qualitative review of the personality testing literature and by Schmitt, Gooding, Noe, and Kirsch (1984) in their quantitative meta-analysis of various personnel selection techniques. The general conclusion drawn by these researchers was that personality tests did not demonstrate adequate predictive validity to qualify their use in personnel selection. In fact, Schmitt et al. (1984) found that personality tests were among the least valid types of selection tests, with an overall mean sample-size weighted correlation of .21 for predicting job performance, and concluded


title: The Social Engineering Personality Framework

abstract: We explore Information and Communication Technology (ICT) security in a socio-technical world and focus in particular on the susceptibility to social engineering attacks. We pursue the question if and how personality traits influence this susceptibility. We use Cialdini's principles of influence to categorise social engineering attacks. First we show with a comprehensive literature review how existent research approaches social engineering susceptibility. Based on this review we construct suggestions for plausible relations between personality traits of the Five-Factor Model (Big 5) and the principles of influence. We propose our -- at this stage theory-based -- "Social Engineering Personality Framework" (SEPF) which we will evaluate in future empiric research. The characteristics of victims' personality traits in the SEPF will support and guide security researchers and practitioners in developing detection, mitigation, and prevention strategies while dealing with human factors in social engineering attacks.


title: A 6–18 GHz integrated HPA-DPDT GaN MMIC

abstract: In this contribution an integrated HPA-DPDT for next generation AESA TRMs is presented. The proposed circuit relies on a concurrent design technique merging switches and HPA matching network. Realized MMIC features a 3×5mm2 outline operating in the 6–18 GHz band with a typical output power of 2W, an associated PAE of 13% and 3dB insertion loss in RX mode.


title: A Randomized Controlled Comparison of Emotional Freedom Technique and Cognitive-Behavioral Therapy to Reduce Adolescent Anxiety: A Pilot Study

abstract: OBJECTIVE ::: The objective of this pilot study was to compare the efficacy of Emotional Freedom Techniques (EFT) with that of Cognitive-Behavioral Therapy (CBT) in reducing adolescent anxiety. ::: ::: ::: DESIGN ::: Randomized controlled study. ::: ::: ::: SETTINGS ::: This study took place in 10 schools (8 public/2 private; 4 high schools/6 middle schools) in 2 northeastern states in the United States. ::: ::: ::: PARTICIPANTS ::: Sixty-three high-ability students in grades 6-12, ages 10-18 years, who scored in the moderate to high ranges for anxiety on the Revised Children's Manifest Anxiety Scale-2 (RCMAS-2) were randomly assigned to CBT (n = 21), EFT (n = 21), or waitlist control (n = 21) intervention groups. ::: ::: ::: INTERVENTIONS ::: CBT is the gold standard of anxiety treatment for adolescent anxiety. EFT is an evidence-based treatment for anxiety that incorporates acupoint stimulation. Students assigned to the CBT or EFT treatment groups received three individual sessions of the identified protocols from trained graduate counseling, psychology, or social work students enrolled at a large northeastern research university. ::: ::: ::: OUTCOME MEASURES ::: The RCMAS-2 was used to assess preintervention and postintervention anxiety levels in participants. ::: ::: ::: RESULTS ::: EFT participants (n = 20; M = 52.16, SD = 9.23) showed significant reduction in anxiety levels compared with the waitlist control group (n = 21; M = 57.93, SD = 6.02) (p = 0.005, d = 0.74, 95% CI [-9.76, -1.77]) with a moderate to large effect size. CBT participants (n = 21; M = 54.82, SD = 5.81) showed reduction in anxiety but did not differ significantly from the EFT (p = 0.18, d = 0.34; 95% CI [-6.61, 1.30]) or control (p = 0.12, d = 0.53, 95% CI [-7.06, .84]). ::: ::: ::: CONCLUSIONS ::: EFT is an efficacious intervention to significantly reduce anxiety for high-ability adolescents.


title: A pole-zero matching method for EBG surfaces composed of a dipole FSS printed on a grounded dielectric slab

abstract: A method is presented, for the efficient derivation of the dispersion equation associated with electromagnetic bandgap (EBG) structures composed by lossless frequency selective surfaces (FSS) printed on stratified dielectric media. The method, valid for the range of frequency where a single propagating Floquet mode occurs, is based on Foster's reactance theorem applied to an equivalent transmission line network. This theorem implies that the admittance functions of frequency which represent the FSS satisfy the pole-zero analytical properties of the driving point LC admittance functions. By these basic properties and by the full-wave identification of the FSS resonances, an analytical form of the dispersion equation is obtained. This equation is next solved for both surface wave and leaky wave modes by a conventional numerical technique. The results are successfully compared with those from a full-wave analysis.


Synchronous Multi-GPU Deep Learning with Low-Precision Communication: An Experimental Study


title: Ecstasy (MDMA) dependence

abstract: Methylenedioxymethamphetamine (MDMA) is generally described as non-addictive. However, this report describes three cases in which criteria for dependence were met. A wider understanding that MDMA can be addictive in rare cases is important as very heavy use may cause lasting neuronal changes. This risk could be reduced with effective identification and treatment of dependent persons. In one case dependence was linked with self-medication of post-traumatic stress disorder (PTSD). © 1999 Elsevier Science Ireland Ltd. All rights reserved.


Open Data Integration


title: Sign language to speech conversion

abstract: Human beings interact with each other to convey their ideas, thoughts, and experiences to the people around them. But this is not the case for deaf-mute people. Sign language paves the way for deaf-mute people to communicate. Through sign language, communication is possible for a deaf-mute person without the means of acoustic sounds. The aim behind this work is to develop a system for recognizing the sign language, which provides communication between people with speech impairment and normal people, thereby reducing the communication gap between them. Compared to other gestures (arm, face, head and body), hand gesture plays an important role, as it expresses the user's views in less time. In the current work flex sensor-based gesture recognition module is developed to recognize English alphabets and few words and a Text-to-Speech synthesizer based on HMM is built to convert the corresponding text.


title: The force requirements for tooth movement, Part I: Tipping and bodily movement.

abstract: Abstract Specific data on the relation between force and tooth movement is essential not only to facilitate non-traumatic orthodontic treatment but also to establish a sound basis for appliance design. Whilst the broad approaches to orthodontic tooth movement appear to be the application of tipping and/or bodily movement, no scientific comparison between these two types of movement has yet been made. This article describes experiments which repeat investigations of Storey and Smith into the relation between the force applied and the rate of tipping movement and their extension to include bodily movement. Twenty adolescent male patients requiring distal movement of upper cuspid teeth as a part of orthodontic treatment were each fitted with calibrated cuspid retraction springs. The appliances were designed to induce bodily movement on one side of the mouth concurrently with tipping movement on the other. Initial force applications were identical, and the experiments lasted for up to eleven weeks following one activation of the retraction springs. The results of these experiments were compared with those of other workers, and an explanation of the causes of the past controversy regarding various theories of tooth movement is offered.


title: Utopias and Dystopias as Cybernetic Information Systems: Envisioning the Posthuman Neuropolity

abstract: While it is possible to understand utopias and dystopias as particular kinds of sociopolitical systems, in this text we argue that utopias and dystopias can also be understood as particular kinds of information systems in which data is received, stored, generated, processed, and transmitted by the minds of human beings that constitute the system’s ‘nodes’ and which are connected according to specific network topologies. We begin by formulating a model of cybernetic information-processing properties that characterize utopias and dystopias. It is then shown that the growing use of neuroprosthetic technologies for human enhancement is expected to radically reshape the ways in which human minds access, manipulate, and share information with one another; for example, such technologies may give rise to posthuman ‘neuropolities’ in which human minds can interact with their environment using new sensorimotor capacities, dwell within shared virtual cyberworlds, and link with one another to form new kinds of social organizations, including hive minds that utilize communal memory and decision-making. Drawing on our model, we argue that the dynamics of such neuropolities will allow (or perhaps even impel) the creation of new kinds of utopias and dystopias that were previously impossible to realize. Finally, we suggest that it is important that humanity begin thoughtfully exploring the ethical, social, and political implications of realizing such technologically enabled societies by studying neuropolities in a place where they have already been ‘pre-engineered’ and provisionally exist: in works of audiovisual science fiction such as films, television series, and role-playing games


title: Cognitive Wireless Powered Network: Spectrum Sharing Models and Throughput Maximization

abstract: The recent advance in radio-frequency (RF) wireless energy transfer (WET) has motivated the study of wireless powered communication network (WPCN) in which distributed wireless devices are powered via dedicated WET by the hybrid access-point (H-AP) in the downlink (DL) for uplink (UL) wireless information transmission (WIT). In this paper, by utilizing the cognitive radio (CR) technique, we study a new type of CR enabled secondary WPCN, called cognitive WPCN, under spectrum sharing with the primary wireless communication system. In particular, we consider a cognitive WPCN, consisting of one single H-AP with constant power supply and distributed wireless powered users, shares the same spectrum for its DL WET and UL WIT with an existing primary communication link, where the WPCN’s WET/WIT and the primary link’s WIT may interfere with each other. Under this new setup, we propose two coexisting models for spectrum sharing of the two systems, namely underlay - and overlay -based cognitive WPCNs, depending on different types of knowledge on the primary user transmission available for the cognitive WPCN. For each model, we maximize the sum-throughput of the cognitive WPCN by optimizing its transmission under different constraints applied to protect the primary user transmission. Analysis and simulation results are provided to compare the sum-throughput of the cognitive WPCN versus the achievable rate of the primary user under two proposed coexisting models. It is shown that the overlay based cognitive WPCN outperforms the underlay based counterpart, thanks to its fully co-operative WET/WIT design with the primary WIT, while also requiring higher complexity for implementation.


title: Computing beyond Moore's Law

abstract: Photolithography systems are on pace to reach atomic scale by the mid-2020s, necessitating alternatives to continue realizing faster, more predictable, and cheaper computing performance. If the end of Moore's law is real, a research agenda is needed to assess the viability of novel semiconductor technologies and navigate the ensuing challenges.


title: Novel Multi-Stub Loaded Resonators and Their Applications to Various Bandpass Filters

abstract: This paper presents a new class of multi-stub loaded resonators with flexibly controlled resonance modes, which are suitable for both tunable and nontunable filter applications. The proposed resonators, modified from a basic tri-mode resonator, include two quad-mode ones and a six-mode one. One of the quad-mode resonators is utilized to design a reconfigurable dual-band filter with tunable lower passband and fixed upper passband. The other quad-mode resonator is applied to design a bandpass filter (BPF) with very high skirt selectivity. The six-mode resonator is suitable for tri-band BPFs with individually controllable passband frequencies and bandwidths. All three circuits are theoretically analyzed and experimentally demonstrated. Comparisons of the measured and simulated results are presented to verify various applications of the resonators.


title: Studying agile organizational design to sustain innovation.

abstract: Innovation is a core part of software development companies, frequently determined by organizational design variables including structure, capacity for learning, for change and adaptation. Agile software methods have evolved as approaches to promote agility and innovativeness in software development organizations. However, little research has examined organizational innovativeness and its relationship with organizational design and adoption of agile methods. In this work, we propose a conceptual framework to characterize innovation?s prone and averse patterns on organizational design in agile companies by measuring diffusion and integration of technologies and practices within individual, team, organizational, and environmental levels.


title: Eligibility Traces for Options

abstract: Temporally extended actions not only represent knowledge in the hierarchical setup in reinforcement learning, they also improve exploration while reducing the complexity of choosing actions. The option framework provides a concrete way to implement and reason about temporal abstraction. This work attempts to test the utility of eligibility traces with options and find good ways of doing multi-step intra-option updates. Three algorithms, based on off-policy methods - importance sampling, tree-backup and retrace, are proposed for using eligibility traces with options.


title: Real-Time Raag Recognition for Interactive Music

abstract: We describe a system that can listen to a performance of Indian music and recognize the raag, the fundamental melodic framework that Indian classical musicians improvise within. In addition to determining the most likely raag being performed, the system displays the estimated the likelihood of each of the other possible raags, visualizing the changes over time. The system computes the pitch-class distribution and uses a Bayesian decision rule to classify the resulting twelve dimensional feature vector, where each feature represents the relative use of each pitch class. We show that the system achieves high performance on a variety of sources, making it a viable tool for interactive performance.


title: Informative acoustic feature selection on microphone array wiener filtering for collecting target source on sports ground

abstract: We propose a Wiener filter design method for collecting target sources on a noisy sports field. Because the noise on a sports field, e.g., cheering from the audience, arrives from the same direction as that of the targeted source, it is difficult to accurately design a Wiener filter by simply using spatial cues. This study focused on a combination of spatial cues and acoustic feature modeling. The Wiener filter using our method was designed using a Gaussian-mixture-model-based mapping function with automatically selected informative acoustic features from pre-enhanced observation using spatial cues. Through experiments using two-directional microphones on a mock sports field, it was confirmed that the proposed method outperformed previous methods that used either spatial cues or acoustic feature modeling only.


title: Semantic Information Retrieval: A Comparative Experimental Study of NLP Tools and Language Resources for Arabic

abstract: In this paper, we try to exploit the semantic richness of Arabic language for Information Retrieval (IR). The semantics of Arabic words may be extracted from dictionaries or corpora, which are analyzed and minded using Natural Language Processing (NLP) and text mining tools. This allows modeling the contextual dependencies between words, which help identify the meaning of queries in the search process. Thus, the queries are enriched by semantic knowledge, which enhances search performance. In this context, this paper describes a text mining-based approach for Arabic semantic IR, which considers senses of query terms. Experiments and results based on a standard Arabic Test collection are discussed through this communication. In the one hand, we compare dictionary versus corpus-based approaches for modeling semantics. On the other hand, we compare some Arabic NLP tools in the preprocessing step. Thus, we study the effect of Arabic morphology on the semantic interpretation of queries.


title: DL-BAC: Distributed Ledger Based Access Control for Web Applications

abstract: Since Internet based applications have become the norm for most users, security has become a bigger concern than ever before, especially for applications like social networking and cloud based storage. Access control is one of the key techniques that can mitigate security concerns for web based applications. However, most existing access control mechanisms require a trusted party, which are vulnerable to many threats including malicious insiders and single point failure. In response to these challenges, we propose DL-BAC, a novel access control system based on the distributed ledger. DL-BAC robustly enforces access control policies without depending on a single trusted party. We also provide an extension of DL-BAC that is privacy respecting and evaluate the performance of DL-BAC to show its practicability.


title: The four roles of supply chain management in construction

abstract: It is argued that due to construction peculiarities, supply chain management has four specific roles in construction. Practical initiatives in each role to advance the construction supply chain are analysed. The present status of construction supply chains is investigated by means of case studies and a comparison with previous research. Three main conclusions are drawn regarding the present status. Firstly, even in normal situations the construction supply chain has a large quantity of waste and problems. Secondly, most of these are caused in another stage of the construction supply chain than when detected. Thirdly, waste and problems are largely caused by obsolete, myopic control of the construction supply chain. These results concur with the findings made on make-to-order supply chains in general. Finally, the subjective and objective limitations of the four roles are analysed, this being based on empirical findings and the generic theory of supply chain management.


title: Full on-chip CMOS low dropout voltage regulator using MOS capacitor compensation

abstract: In This paper, we presents a full on-chip and area efficient low-dropout voltage regulator (LDO) which, exploiting the technique nested miller compensation with active capacitor(NMCAC) to eliminate the external capacitor without compromising the stability of the system in the full output current range. The external capacitor is removed allowing for greater power system integration for system on-chip applications. The proposed LDO is that provides a fast transient response, a low quiescent current 51 µA with a dropout voltage of 192 mV and output variation 69 mV when a full load step 0–50 mA is applied. It designed in 0.18 µm CMOS process.


title: Online Collaborative Learning for Open-Vocabulary Visual Classifiers

abstract: We focus on learning open-vocabulary visual classifiers, which scale up to a large portion of natural language vocabulary (e.g., over tens of thousands of classes). In particular, the training data are large-scale weakly labeled Web images since it is difficult to acquire sufficient well-labeled data at this category scale. In this paper, we propose a novel online learning paradigm towards this challenging task. Different from traditional N-way independent classifiers that generally fail to handle the extremely sparse and inter-related labels, our classifiers learn from continuous label embeddings discovered by collaboratively decomposing the sparse image-label matrix. Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers. The algorithm can learn over 30,000 classes of 1,000 training images within 1 second on a standard GPU. Extensively experimental results on four benchmarks demonstrate the effectiveness of our method.


title: Bladder injuries in emergency/expedited laparoscopic surgery in the absence of previous surgery: a case series.

abstract: INTRODUCTION ::: The use of laparoscopy as a diagnostic and therapeutic tool is being used increasingly in the emergency setting with many of these procedures being performed by trainees. While the incidence of iatrogenic injuries is reported to be low, we present six emergency or expedited cases in which the bladder was perforated by the suprapubic trocar. ::: ::: ::: CASES ::: Three cases were related to the management of appendicitis, two to negative diagnostic laparoscopies for lower abdominal pain and one to an ectopic pregnancy. Management of the bladder injuries varied from a urinary catheter alone to laparotomy with debridement of the abdominal wall due to sepsis and later reconstruction. Four of the six cases were performed by registrars. ::: ::: ::: CONCLUSIONS ::: Although the incidence of bladder injury is low, its importance is highlighted by the large number of laparoscopies being performed. In addition to catheterisation of the patient, care must be taken with the insertion of low suprapubic ports and consideration should be made regarding alternative sites. Adequate laparoscopic supervision and training in port site planning is required for surgical trainees.


title: Deep Learning Reconstruction for 9-View Dual Energy CT Baggage Scanner

abstract: For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hidden objects. Among various types of 3D computed tomography (CT) systems to address this issue, this paper is interested in a stationary CT using fixed X-ray sources and detectors. However, due to the limited number of projection views, analytic reconstruction algorithms produce severe streaking artifacts. Inspired by recent success of deep learning approach for sparse view CT reconstruction, here we propose a novel image and sinogram domain deep learning architecture for 3D reconstruction from very sparse view measurement. The algorithm has been tested with the real data from a prototype 9-view dual energy stationary CT EDS carry-on baggage scanner developed by GEMSS Medical Systems, Korea, which confirms the superior reconstruction performance over the existing approaches.


title: An IoT based scholar bus monitoring system

abstract: School transport is used by millions of children worldwide. However, not a substantial effort is done in order to improve the existing school transport systems. This paper presents the development of an IoT based scholar bus monitoring system. The development of new telematics technologies has enabled the development of various Intelligent Transport Systems. However, these are not presented as ITS services to end users. This paper presents the development of an IoT based scholar bus monitoring system that through localization and speed sensors will allow many stakeholders such as parents, the goverment, the school and many other authorities to keep realtime track of the scholar bus behavior, resulting in a better controlled scholar bus.


Helping people make better decisions using optimal gamification.


title: Modelling power line communication using network simulator-3

abstract: Power line communications (PLC) plays an important role in the blend of communication technologies considered for enabling the smart grid vision. However, different from, e.g., wireless communications, for which ready-to-use modules are included in several simulation packages, there are no freely available software tools to simulate PLC networks. In particular, software modules which emulate the PLC channel are needed to capture the specifics of PLC in a communication network simulation. In this paper, we aim at closing this gap and present a PLC channel simulator written using the network simulator-3 (ns-3) library and made available for free use.


title: BitNet: Bit-Regularized Deep Neural Networks

abstract: We present a novel optimization strategy for training neural networks which we call "BitNet". The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over all real values. Our key idea is to limit the expressive power of the network by dynamically controlling the range and set of values that the parameters can take. We formulate this idea using a novel end-to-end approach that circumvents the discrete parameter space by optimizing a relaxed continuous and differentiable upper bound of the typical classification loss function. The approach can be interpreted as a regularization inspired by the Minimum Description Length (MDL) principle. For each layer of the network, our approach optimizes real-valued translation and scaling factors and arbitrary precision integer-valued parameters (weights). We empirically compare BitNet to an equivalent unregularized model on the MNIST and CIFAR-10 datasets. We show that BitNet converges faster to a superior quality solution. Additionally, the resulting model has significant savings in memory due to the use of integer-valued parameters.


title: Single-Negative Properties Based on the Bandgaps of One-Dimensional Phononic Crystal

abstract: Phononic Crystals (PCs) have important potential application in engineering by the properties of bandgaps. In the paper, the bandgap characteristic of one-dimensional PCs is attributed to the results of Single-Negative (SN) properties, e.g. negative modulus or negative density. The effective parameters of the 1D PCs were predicted by the equivalent layer concept with considering viscous damping, and the results showed that during the frequency regions of the bandgaps, the negative parameters appeared. This will be a reference for the design of acoustic negative refraction metamaterials.


title: Towards Accurate Handwritten Word Recognition for Hindi and Bangla

abstract: Building accurate lexicon free handwritten text recognizers for Indic languages is a challenging task, mostly due to the inherent complexities in Indic scripts in addition to the cursive nature of handwriting. In this work, we demonstrate an end-to-end trainable CNN-RNN hybrid architecture which takes inspirations from recent advances of using residual blocks for training convolutional layers, along with the inclusion of spatial transformer layer to learn a model invariant to geometric distortions present in handwriting. In this work we focus building state of the art handwritten word recognizers for two popular Indic scripts – Devanagari and Bangla. To address the need of large scale training data for such low resources languages, we utilize synthetically rendered data for pre-training the network and later fine tune it on the real data. We outperform the previous lexicon based, state of the art methods on the test set of Devanagari and Bangla tracks of RoyDB by a significant margin.


title: ABRIR at NTCIR-9 GeoTime Task Usage of Wikipedia and GeoNames for Handling Named Entity Information

abstract: In the previous NTCIR8-GeoTime task, ABRIR (Appropriate Boolean query Reformulation for Information Retrieval) proved to be one of the most effective systems for retrieving documents with Geographic and Temporal constraints. However, failure analysis showed that the identification of named entities and relationships between these entities and the query is important in improving the quality of the system. In this paper, we propose to use Wikipedia and GeoNames as resources for extracting knowledge about named entities. We also modify our system to use such information.


title: Lateral crural strut graft: technique and clinical applications in rhinoplasty.

abstract: The lateral crural strut graft is a new, versatile technique for reshaping, repositioning, or reconstructing the lateral crura. These grafts are strips of autogenous cartilage that are sutured to the deep surface of the lateral crura. Lateral crural struts are useful in the correction of the boxy nasal tip, malpositioned lateral crura, alar rim retraction, alar rim collapse, and concave lateral crura. We describe the surgical technique and demonstrate representative results of our experience with these grafts in 118 patients.


title: Vision: towards real time epidemic vigilance through online social networks: introducing SNEFT -- social network enabled flu trends

abstract: Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.


title: Application of Novel Extraction Technologies for Bioactives from Marine Algae

abstract: Marine algae are a rich source of bioactive compounds. This paper outlines the main bioactive compounds in marine algae and recent advances in novel technologies for extracting them. Novel extraction technologies reviewed include enzyme-assisted extraction, microwave-assisted extraction, ultrasound-assisted extraction, supercritical fluid extraction, and pressurized liquid extraction. These technologies are reviewed with respect to principles, benefits, and potential applications for marine algal bioactives. Advantages of novel technologies include higher yield, reduced treatment time, and lower cost compared to traditional solvent extraction techniques. Moreover, different combinations of novel techniques used for extraction and technologies suitable for thermolabile compounds are identified. The limitations of and challenges to employing these novel extraction technologies in industry are also highlighted.


title: Static analysis for probabilistic programs: inferring whole program properties from finitely many paths

abstract: We propose an approach for the static analysis of probabilistic programs that sense, manipulate, and control based on uncertain data. Examples include programs used in risk analysis, medical decision making and cyber-physical systems. Correctness properties of such programs take the form of queries that seek the probabilities of assertions over program variables. We present a static analysis approach that provides guaranteed interval bounds on the values (assertion probabilities) of such queries. First, we observe that for probabilistic programs, it is possible to conclude facts about the behavior of the entire program by choosing a finite, adequate set of its paths. We provide strategies for choosing such a set of paths and verifying its adequacy. The queries are evaluated over each path by a combination of symbolic execution and probabilistic volume-bound computations. Each path yields interval bounds that can be summed up with a "coverage" bound to yield an interval that encloses the probability of assertion for the program as a whole. We demonstrate promising results on a suite of benchmarks from many different sources including robotic manipulators and medical decision making programs.


title: Transformational Teaching: Theoretical Underpinnings, Basic Principles, and Core Methods

abstract: Approaches to classroom instruction have evolved considerably over the past 50 years. This progress has been spurred by the development of several learning principles and methods of instruction, including active learning, student-centered learning, collaborative learning, experiential learning, and problem-based learning. In the present paper, we suggest that these seemingly different strategies share important underlying characteristics and can be viewed as complimentary components of a broader approach to classroom instruction called transformational teaching. Transformational teaching involves creating dynamic relationships between teachers, students, and a shared body of knowledge to promote student learning and personal growth. From this perspective, instructors are intellectual coaches who create teams of students who collaborate with each other and with their teacher to master bodies of information. Teachers assume the traditional role of facilitating students’ acquisition of key course concepts, but do so while enhancing students’ personal development and attitudes toward learning. They accomplish these goals by establishing a shared vision for a course, providing modeling and mastery experiences, challenging and encouraging students, personalizing attention and feedback, creating experiential lessons that transcend the boundaries of the classroom, and promoting ample opportunities for preflection and reflection. We propose that these methods are synergistically related and, when used together, maximize students’ potential for intellectual and personal growth.


title: VAULT: Reducing Paging Overheads in SGX with Efficient Integrity Verification Structures

abstract: Intel's SGX offers state-of-the-art security features, including confidentiality, integrity, and authentication (CIA) when accessing sensitive pages in memory. Sensitive pages are placed in an Enclave Page Cache (EPC) within the physical memory before they can be accessed by the processor. To control the overheads imposed by CIA guarantees, the EPC operates with a limited capacity (currently 128 MB). Because of this limited EPC size, sensitive pages must be frequently swapped between EPC and non-EPC regions in memory. A page swap is expensive (about 40K cycles) because it requires an OS system call, page copying, updates to integrity trees and metadata, etc. Our analysis shows that the paging overhead can slow the system on average by 5×, and other studies have reported even higher slowdowns for memory-intensive workloads. The paging overhead can be reduced by growing the size of the EPC to match the size of physical memory, while allowing the EPC to also accommodate non-sensitive pages. However, at least two important problems must be addressed to enable this growth in EPC: (i) the depth of the integrity tree and its cacheability must be improved to keep memory bandwidth overheads in check, (ii) the space overheads of integrity verification (tree and MACs) must be reduced. We achieve both goals by introducing a variable arity unified tree (VAULT) organization that is more compact and has lower depth. We further reduce the space overheads with techniques that combine MAC sharing and compression. With simulations, we show that the combination of our techniques can address most inefficiencies in SGX memory access and improve overall performance by 3.7×, relative to an SGX baseline, while incurring a memory capacity over-head of only 4.7%.


title: Big Data Analysis in Smart Manufacturing: A Review

abstract: The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.


title: A Denial of Service Attack Method for an IoT System

abstract: In recent years, Internet of things (IoT) is widely used in various domains. However, the security of the IoT system becomes a challenge. If the IoT system is attacked, a great property loss will happen. In this paper, a denial of service (DoS) attack to an IoT system is shown. The attack tool is Kali Linux, A Denial of Service (DOS) attack is launched by using 3 different methods. The comparison between the 3 DoS attack methods is also given.


Immediate implant placement: treatment planning and surgical steps for successful outcome.


title: H-bridge based multilevel inverter using PWM switching function

abstract: This paper proposes an efficient PWM switching function for modified H-bridge based multilevel inverter. The prior H-bridge based multilevel inverter can increase the number of output voltage levels by adding switch components and dc input voltage sources. If it employs seven switches and three dc sources, the number of output voltage levels becomes seven. Although its THD characteristics are improved, it needs output filter to meet general output voltage THD requirement, i.e., 5 % below. To mitigate this problem, we propose an efficient PWM switching method for the prior H-bridge based multilevel inverter. By adding PWM switching schemes to the operation of the prior H-bridge switches, it can synthesize more sinusoidal waveform. By this simple alteration in the switching scheme, it can improve the output voltage THD requirement. To verify the high performance of the proposed switching scheme, we implemented computer-aided simulations and experiment using a prototype.


title: Analysis of PROFINET IO Communication Protocol

abstract: The field bus systems have been applied to the industrial automation. PROFINET is an optimized industrial network standard. It provides the service definition and protocols for real-time communication based on Ethernet. The basic characteristics and protocol analysis are presented in this paper. This paper depicts a topology model using Wire shark and Siemens PLC to capture PROFINET IO communication packets and make a deep analysis.


title: Assessment of the effectiveness of interferential current therapy and TENS in the management of carpal tunnel syndrome: a randomized controlled study

abstract: We assessed the effectiveness of interferential current (IFC) and transcutaneous electrical nerve stimulation (TENS) therapies in the management of carpal tunnel syndrome (CTS) compared with splint therapy, a standard treatment modality for CTS. This was a prospective, single-blinded, single-center, randomized, three-group parallel intervention study of 3 weeks duration. Efficacy was examined in the third week after the end of treatments. Subjects were assigned randomly to one of three groups: group I patients received splint therapy, group II patients received TENS applied on the palmar surface of the hand and the carpal tunnel, and group III patients underwent IFC therapy applied on the palmar surface of the hand and the volar surface of the forearm. TENS and ICF treatments were applied five times weekly for a total of 15 sessions. Group 1 patients were stabilized with volar wrist splints for 3 weeks. The efficacy of the therapies was assessed before initiation of therapy and at 3 weeks after completion of therapy using a visual analog scale (VAS), a symptom severity scale, the functional capacity scale of the BCTQ, and measurement of median nerve motor distal latency (mMDL) and median sensory nerve conduction velocity (mSNCV). Groups were compared pairwise using the Mann–Whitney U test to identify the source of differences between groups. The Wilcoxon test was used to analyze changes in variables over time within a group. In the VAS, BCTQ, MDL, and mSNCV, no significant difference was observed between the groups (p > 0.05). In the VAS, BCTQ, and mSNCV, statistically significant improvements were detected in all groups (p < 0.05). There was no statistically significant difference between TENS and splint therapy with respect to improvement in clinical scores, whereas IFC therapy provided a significantly greater improvement in VAS, mMDL, and mSNCV values than splint therapy (VAS: 4.80 ± 1.18 and 6.37 ± 1.18; p = 0.001, mMDL: 3.89 ± 0.88 and 4.06 ± 0.61; p = 0.001, mSNCV: 41.80 ± 1.76 and 40.75 ± 1.48; p = 0.010). IFC therapy provided a significantly greater improvement in VAS, symptom severity, functional capacity, and mMDL and mSNCV values than TENS therapy (VAS: 4.80 ± 1.18 and 6.68 ± 1.42; p < 0.001, symptom severity: 2.70 ± 1.03 and 3.37 ± 1.21; p = 0.015, functional capacity: 1.90 ± 1.21 and 2.50 ± 0.78; p = 0.039, mMDL: 3.89 ± 0.88 and 4.06 ± 0.88; p = 0.003, and mSNCV: 41.80 ± 1.76 and 41.38 ± 1.78; p = 0.021). IFC may be considered a new and safe therapeutic option for the treatment of CTS.


title: Design of a Haptic Gas Pedal for Active Car-Following Support

abstract: The research presented in this paper focuses on the design of a driver support system for the manual longitudinal control of a car during car-following. The aim of the design was to develop a system that would cooperate with the driver in comfortably maintaining (safe) separation with a lead vehicle. Three important design issues for a haptic gas pedal feedback system can be distinguished: 1) quantification of intervehicle separation parameters; 2) the type of haptic feedback; and 3) the relation between haptic feedback and intervehicle separation. Because of the inverse relationship between time-to-contact (TTC) and time-headway (THW)-the smaller the THW, the more important the avoidance of high TTC-THW should act as an amplifier for the haptic gas pedal feedback based on TTC. Using gas pedal stiffness feedback is expected to better facilitate the manual control of intervehicle separation changes, quantified by THW and TTC, because stiffness feedback allows perception of force and force-slope changes. The force changes inform drivers of instantaneous changes in the environment. Force-slope changes prevent drivers from input to the car that would continue to reduce the following gap in situations where this would be undesirable. A review of fixed-base simulator and field tests confirms that haptic gas pedal feedback improves driver vigilance during car-following without increasing the workload.


title: Reduced duty-cycle loss and output inductor current ripple in a ZVS switched three-phase isolated PWM rectifier

abstract: In this paper a PWM method for zero voltage switched three-phase isolated rectifier is presented. The presented PWM scheme features lower maximum output inductor current ripple and lower duty-cycle loss by more than half. Therefore, a smaller output inductor can be used and the ZVS operation can be extended at wider load range for MOSFET devices by increasing the leakage inductance of the transformer. The steady state analysis of duty-cycle loss and inductor current ripple are addressed and verified by the simulation and experimental results for two different PWM schemes.


title: Geometry of neural network loss surfaces via random matrix theory

abstract: Understanding the geometry of neural network loss surfaces is important for the development of improved optimization algorithms and for building a theoretical understanding of why deep learning works. In this paper, we study the geometry in terms of the distribution of eigenvalues of the Hessian matrix at critical points of varying energy. We introduce an analytical framework and a set of tools from random matrix theory that allow us to compute an approximation of this distribution under a set of simplifying assumptions. The shape of the spectrum depends strongly on the energy and another key parameter, ϕ, which measures the ratio of parameters to data points. Our analysis predicts and numerical simulations support that for critical points of small index, the number of negative eigenvalues scales like the 3/2 power of the energy. We leave as an open problem an explanation for our observation that, in the context of a certain memorization task, the energy of minimizers is well-approximated by the function 1/2(1 - ϕ)2.


title: FoveAR: Combining an Optically See-Through Near-Eye Display with Projector-Based Spatial Augmented Reality

abstract: Optically see-through (OST) augmented reality glasses can overlay spatially-registered computer-generated content onto the real world. However, current optical designs and weight considerations limit their diagonal field of view to less than 40 degrees, making it difficult to create a sense of immersion or give the viewer an overview of the augmented reality space. We combine OST glasses with a projection-based spatial augmented reality display to achieve a novel display hybrid, called FoveAR, capable of greater than 100 degrees field of view, view dependent graphics, extended brightness and color, as well as interesting combinations of public and personal data display. We contribute details of our prototype implementation and an analysis of the interactive design space that our system enables. We also contribute four prototype experiences showcasing the capabilities of FoveAR as well as preliminary user feedback providing insights for enhancing future FoveAR experiences.


title: A Control Methodology and Characterization of Dynamics for a Photovoltaic (PV) System Interfaced With a Distribution Network

abstract: This paper proposes a control strategy for a single-stage, three-phase, photovoltaic (PV) system that is connected to a distribution network. The control is based on an inner current-control loop and an outer DC-link voltage regulator. The current-control mechanism decouples the PV system dynamics from those of the network and the loads. The DC-link voltage-control scheme enables control and maximization of the real power output. Proper feedforward actions are proposed for the current-control loop to make its dynamics independent of those of the rest of the system. Further, a feedforward compensation mechanism is proposed for the DC-link voltage-control loop, to make the PV system dynamics immune to the PV array nonlinear characteristic. This, in turn, permits the design and optimization of the PV system controllers for a wide range of operating conditions. A modal/sensitivity analysis is also conducted on a linearized model of the overall system, to characterize dynamic properties of the system, to evaluate robustness of the controllers, and to identify the nature of interactions between the PV system and the network/loads. The results of the modal analysis confirm that under the proposed control strategy, dynamics of the PV system are decoupled from those of the distribution network and, therefore, the PV system does not destabilize the distribution network. It is also shown that the PV system dynamics are not influenced by those of the network (i.e., the PV system maintains its stability and dynamic properties despite major variations in the line length, line X/R ratio, load type, and load distance from the PV system).


A Conceptual Framework on Informal Work and Informal Worker Organizing


title: Dynamic whole-body mobile manipulation with a torque controlled humanoid robot via impedance control laws

abstract: Service robotics is expected to be established in human households and environments within the next decades. Therefore, dexterous and flexible behavior of these systems as well as guaranteeing safe interaction are crucial for that progress. We address these issues in terms of control strategies for the whole body of DLR's humanoid Justin. Via impedance control laws, we enable the robot to realize main tasks compliantly while, at the same time, taking care of aspects like physical limitations and collision avoidance with its own structure and the environment autonomously. The controller provides a natural redundancy resolution between the arms, the torso and the wheeled platform. A low-dimensional task space interface is proposed that can be used by planning tools. Thereby, planning time can be saved significantly. Experimental results on DLR's Justin are presented to validate our approach.


title: Building Context-Aware Recommendation Systems: A Software Engineering Point of View

abstract: Most publications of Context-Aware Recommender Systems (CARS) do not follow a Software Engineering process while building their systems, rather they focus on proposing and testing algorithms. We believe that CARS can be built following a structured development process, therefore this paper reviews Context-Aware Recommender System literature, identify the performed activities, and map such activities to the ISO/IEC 29110 Software Implementation activities. The results shows that even when no publication follows all the activities of the 29110 standard, all together, the publications cover most of the proposed activities of the ISO/IEC 29110 standard.


title: Adolescents’ heightened risk-seeking in a probabilistic gambling task

abstract: This study investigated adolescent males’ decision-making under risk, and the emotional response to decision outcomes, using a probabilistic gambling task designed to evoke counterfactually mediated emotions (relief and regret). Participants were 20 adolescents (aged 9–11), 26 young adolescents (aged 12–15), 20 mid-adolescents (aged 15–18) and 17 adults (aged 25–35). All were male. The ability to maximize expected value improved with age. However, there was an inverted U-shaped developmental pattern for risk-seeking. The age at which risk-taking was highest was 14.38 years. Although emotion ratings overall did not differ across age, there was an increase between childhood and young adolescence in the strength of counterfactually mediated emotions (relief and regret) reported after receiving feedback about the gamble outcome. We suggest that continuing development of the emotional response to outcomes may be a factor contributing to adolescents’ risky behaviour.


A Correlated Topic Model Using Word Embeddings


title: Interacting With Virtual Reality

abstract: Interacting with virtual reality is fundamentally different from interacing with traditional desktop graphics. The three features that characterize virtual reality interaction are immersion, rich interaction and presence; I define these features. To achieve them, virtual reality system designers need to address many different issues. I discuss some of these issues, in particular multiple inputs, multiple outputs, multiple participants, dynamic virtual worlds, user interface paradigms and performance.


Identification of the Mechanical Properties of Tires for Wheelchair Simulation


PLC BASED ADAPTIVE FUZZY PID SPEED CONTROL OF DC BELT CONVEYOR SYSTEM


title: Turing's test and believable AI in games

abstract: The Turing test is perhaps the most famous, most quoted, and probably most often misrepresented and misunderstood measure of machine intelligence. In this article we'll briefly review the Turing test and some of its key criticisms. In particular, we will try to answer whether the Turing test -- or something derived from it -- can be of use in developing and assessing game AI. We will also present a brief overview of a methodology for conducting believability testing for games and highlight some of the problems inherent in any attempt to categorically determine whether or not some AI behavior is capable of convincing, life-like behavior.


title: Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing

abstract: Digital game-based learning (DGBL) has become a viable instructional option in recent years due to its support of learning motivation. Recent studies have mostly focused on identifying motivational factors in digital games (e.g., curiosity, rules, control) that support intrinsic motivation. These findings, however, are limited in two fronts. First, they did not depict the interactive nature of the motivational processing in DGBL. Second, they excluded the outcome processing (learners' final effort versus performance evaluation) as a possible motivation component to sustain the iterative game playing cycle. To address these problems, situated in the integrative theory of Motivation, Volition, and Performance (MVP), this study examined the relationship between motivational processing and outcome processing in an online instructional game. The study surveyed 264 undergraduate students after playing the Trade Ruler online game. Based on the data collected by ARCS-based Instructional Materials Motivational Survey (IMMS), a regression analysis revealed a significant model between motivational processing (attention, relevance, and confidence) and the outcome processing (satisfaction). The finding preliminarily suggests that both motivational processing and outcome processing need to be considered when designing DGBL. Furthermore, the finding implies a potential relationship between intrinsic motives and extrinsic rewards in DGBL.


title: A 1.2V 1.33Gb/s/pin 8Tb NAND flash memory multi-chip package employing F-chip for low power and high performance storage applications

abstract: A 1.2 V 1.33Gb/s/pin 8Tb NAND flash memory multi-chip package incorporating 16-die stacked 512-Gb NAND flash memories and F-Chip is presented. To meet the performance requirements of storage devices for higher capacity and faster data throughput, the 2nd generation F-Chip is developed. The F-Chip presents a dual bi-directional transceiver architecture including data retiming and training techniques to adaptively improve signal integrity. Besides, the F-Chip supports 1.2 V I/O for low power storage applications. This work, as a result, shows 33% improvement of eye-opening performances and 41% reduction of I/O power consumption compared to the previous generation.


title: Digital image forensics: a booklet for beginners

abstract: Digital visual media represent nowadays one of the principal means for communication. Lately, the reliability of digital visual information has been questioned, due to the ease in counterfeiting both its origin and content. Digital image forensics is a brand new research field which aims at validating the authenticity of images by recovering information about their history. Two main problems are addressed: the identification of the imaging device that captured the image, and the detection of traces of forgeries. Nowadays, thanks to the promising results attained by early studies and to the always growing number of applications, digital image forensics represents an appealing investigation domain for many researchers. This survey is designed for scholars and IT professionals approaching this field, reviewing existing tools and providing a view on the past, the present and the future of digital image forensics.


title: A review of RFID localization: Applications and techniques

abstract: Indoor localization has been actively researched recently due to security and safety as well as service matters. Previous research and development for indoor localization includes infrared, wireless LAN and ultrasonic. However, these technologies suffer either from the limited accuracy or lacking of the infrastructure. Radio Frequency Identification (RFID) is very attractive because of reasonable system price, and reader reliability. The RFID localization can be categorized into tag and reader localizations. In this paper, major localization techniques for both tag and reader localizations are reviewed to provide the readers state of the art of the indoor localization algorithms. The advantage and disadvantage of each technique for particular applications were also discussed.


Learning from Labeled and Unlabeled Data with Label Propagation


title: Digital signatures

abstract: Many traditional and newer businesses and applications have recently been carrying out enormous amounts of electronic transactions, which have led to a critical need for protecting the information from being maliciously altered, for ensuring the authenticity, and for supporting nonrepudiation. Just as signatures facilitate validation and verification of the authenticity of paper documents, digital signatures serve the purpose of validation and authentication of electronic documents. This technology is rather new and emerging and is expected to experience growth and widespread use in the coming years.


title: A review of eye-tracking applications as tools for training

abstract: Substantial literature exists regarding how eye-tracking systems can be used to measure cognitive load and how these measurements can be useful for adapting training in real time. Much of the published literature discusses the applications and limitations of the research and typically provides recommendations for improvement. This review assesses these articles collectively to provide a clearer solution for implementing eye-tracking systems into a training environment. Although limitations exist for using eye tracking as an interface tool, gaze and pupillary response have been successfully used to reflect changes in cognitive load and are starting to be incorporated into adaptive training systems, although issues are still present with differentiating pupil responses from simultaneous psychological effects. Additionally, current eye-tracking systems and data analysis software have proven accurate enough for general use, but issues including system cost and software integration prevent this technology from becoming commercialized for use in common instructional settings.


title: Predicting Personality Traits with Instagram Pictures

abstract: Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying photo filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users take pictures and apply filters to them. To investigate this relationship, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and the way users want to make their pictures look. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.


ARTDroid: A Virtual-Method Hooking Framework on Android ART Runtime


title: A Low Profile and Low Sidelobe Wideband Slot Antenna Array Feb by an Amplitude-Tapering Waveguide Feed-Network

abstract: A low profile slot antenna array is designed to operate over a wide bandwidth with low sidelobe levels. Taylor synthesis is used to taper the power distribution among array aperture. An efficient approach to design an equal-phase but unequal-power symmetric waveguide divider is proposed for constructing an amplitude-tapering feed-network for the array. Phase differences are balanced by adjusting the waveguide phase velocities. The basic radiator is a 2 $\,\times\,$ 2 slot subarray, uniformly fed by such a waveguide divider. A 16 $\,\times\,$ 16 array is then constructed by 8 $\,\times\,$ 8 of such subarrays. A 1-to-64 way corporate-feed waveguide network is designed to excite all subarrays instead of individual slots, and the required power distribution is obtained by adopting a 30 dB Taylor $\overline N=4$ synthesis. Measured results indicate that the array can achieve a 13.8% bandwidth and a gain of more than 29.5 dBi. The first sidelobe level is $ -26.5~{\rm dB} $ in the $E$ -plane and $ -30.4~{\rm dB} $ in the $H$ -plane. The holistic sidelobe levels in both planes are better than $ -25~{\rm dB} $ with a better than $ -40~{\rm dB} $ cross-polarization.


title: The Probable Strong Decline of the Great Bustard Otis tarda tarda Population in North-Western China

abstract: The Great Bustard population in Xinjiang Province, the Chinese stronghold of Otis tarda tarda, is suspect to be declining, but little is known about its trends and numbers. Using the same methods, we compared two former published counts from the province (1988 and 1992) with ours (2013–2016), exhaustively covering the two known bustard areas of Tacheng (summer) and Qapqal (winter). A strong decline (92%) is reported, with a minimum 19 birds found in Tacheng and none in Qapqal. Cooperation between scientists is needed to study the causes of decline in O. T. tarda in Xinjiang and elsewhere.—Wang, M., Gonzalez, M.A., Yang, W., Neuhaus, P., Blanco-Fontao, B. & Ruckstuhl, E.K. (2018). The probable strong decline of the Great Bustard Otis tarda tarda population in north-western China. Ardeola, 65: 291–297.


title: A practical guide to neonatal volume guarantee ventilation

abstract: A recent systematic review and meta-analysis shows that volume-targeted ventilation (VTV) compared with pressure-limited ventilation (PLV) reduce death and bronchopulmonary dysplasia, pneumothorax, hypocarbia and severe cranial ultrasound abnormalities. In this paper, we present published research and our experience with volume guarantee (VG) ventilation, a VTV mode available on the Dräger Babylog 8000plus and VN500 ventilators. The VG algorithm measures the expired tidal volume (V(T)) for each inflation and adjusts the peak inflating pressure for the next inflation to deliver a V(T) set by the clinician. The advantage of controlling expired V(T) is that this is less influenced by endotracheal tube leak than inspired V(T). VG ventilation can be used with an endotracheal tube leak up to ∼50%. Initial set V(T) for infants with respiratory distress syndrome should be 4.0 to 5.0 ml kg(-1). The set V(T) should be adjusted to maintain normocapnoea. Setting the peak inflating pressure limit well above the working pressure is important to enable the ventilator to deliver the set V(T), and to avoid frequent alarms. This paper provides a practical guide on how to use VG ventilation.


title: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

abstract: Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson's correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure that focuses on improving recommendation performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. Experiments using three different datasets show the superiority of the measure in new user cold-start conditions.


title: Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies

abstract: Stories can have tremendous power -- not only useful for entertainment, they can activate our interests and mobilize our actions. The degree to which a story resonates with its audience may be in part reflected in the emotional journey it takes the audience upon. In this paper, we use machine learning methods to construct emotional arcs in movies, calculate families of arcs, and demonstrate the ability for certain arcs to predict audience engagement. The system is applied to Hollywood films and high quality shorts found on the web. We begin by using deep convolutional neural networks for audio and visual sentiment analysis. These models are trained on both new and existing large-scale datasets, after which they can be used to compute separate audio and visual emotional arcs. We then crowdsource annotations for 30-second video clips extracted from highs and lows in the arcs in order to assess the micro-level precision of the system, with precision measured in terms of agreement in polarity between the system's predictions and annotators' ratings. These annotations are also used to combine the audio and visual predictions. Next, we look at macro-level characterizations of movies by investigating whether there exist `universal shapes' of emotional arcs. In particular, we develop a clustering approach to discover distinct classes of emotional arcs. Finally, we show on a sample corpus of short web videos that certain emotional arcs are statistically significant predictors of the number of comments a video receives. These results suggest that the emotional arcs learned by our approach successfully represent macroscopic aspects of a video story that drive audience engagement. Such machine understanding could be used to predict audience reactions to video stories, ultimately improving our ability as storytellers to communicate with each other.


title: Scheduling de-icing vehicles within airport logistics: a heuristic algorithm and performance evaluation

abstract: Most delays in the air transport occur at the airport. A particular reason is the complexity of managing the large number of supporting flows in airport logistics. We consider the optimisation problem of scheduling de-icing vehicles that is one of the key supporting logistic flows in the turn-around process of aircraft. The objective is to minimise the delay of flights due to de-icing, and the travel distance of the de-icing vehicles. We study the complexity of the problem, and develop a solution algorithm using greedy randomised adaptive search. A case study of real-life data from Stockholm Arlanda Airport shows that optimised schedule leads to significantly better performance in comparison to intuitive and simple scheduling strategies. The benefit of optimisation in reducing the waiting time for de-icing is further demonstrated via dynamic simulations.


title: Emotional processing of fear: exposure to corrective information.

abstract: In this article we propose mechanisms that govern the processing of emotional information, particularly those involved in fear reduction. Emotions are viewed as represented by information structures in memory, and anxiety is thought to occur when an information structure that serves as program to escape or avoid danger is activated. Emotional processing is denned as the modification of memory structures that underlie emotions. It is argued that some form of exposure to feared situations is common to many psychotherapies for anxiety, and that confrontation with feared objects or situations is an effective treatment. Physiological activation and habituation within and across exposure sessions are cited as indicators of emotional processing, and variables that influence activation and habituation of fear responses are examined. These variables and the indicators are analyzed to yield an account of what information must be integrated for emotional processing of a fear structure. The elements of such a structure are viewed as cognitive representations of the stimulus characteristic of the fear situation, the individual's responses in it, and aspects of its meaning for the individual. Treatment failures are interpreted with respect to the interference of cognitive defenses, autonomic arousal, mood state, and erroneous ideation with reformation of targeted fear structures. Applications of the concepts advanced here to therapeutic practice and to the broader study of psychopathology are discussed.


title: Radiology information system: a workflow-based approach

abstract: Purpose ::: Introducing workflow management technology in healthcare seems to be prospective in dealing with the problem that the current healthcare Information Systems cannot provide sufficient support for the process management, although several challenges still exist. The purpose of this paper is to study the method of developing workflow-based information system in radiology department as a use case.


title: Integration of acoustic and visual speech signals using neural networks

abstract: Results from a series of experiments that use neural networks to process the visual speech signals of a male talker are presented. In these preliminary experiments, the results are limited to static images of vowels. It is demonstrated that these networks are able to extract speech information from the visual images and that this information can be used to improve automatic vowel recognition. The structure of speech and its corresponding acoustic and visual signals are reviewed. The specific data that was used in the experiments along with the network architectures and algorithms are described. The results of integrating the visual and auditory signals for vowel recognition in the presence of acoustic noise are presented. >


title: Knowledge Graph Embedding Based Question Answering

abstract: Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge graph (KG) to answer natural language questions. It helps end users more efficiently and more easily access the substantial and valuable knowledge in the KG, without knowing its data structures. QA-KG is a nontrivial problem since capturing the semantic meaning of natural language is difficult for a machine. Meanwhile, many knowledge graph embedding methods have been proposed. The key idea is to represent each predicate/entity as a low-dimensional vector, such that the relation information in the KG could be preserved. The learned vectors could benefit various applications such as KG completion and recommender systems. In this paper, we explore to use them to handle the QA-KG problem. However, this remains a challenging task since a predicate could be expressed in different ways in natural language questions. Also, the ambiguity of entity names and partial names makes the number of possible answers large. To bridge the gap, we propose an effective Knowledge Embedding based Question Answering (KEQA) framework. We focus on answering the most common types of questions, i.e., simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified. To answer a simple question, instead of inferring its head entity and predicate directly, KEQA targets at jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. Based on a carefully-designed joint distance metric, the three learned vectors' closest fact in the KG is returned as the answer. Experiments on a widely-adopted benchmark demonstrate that the proposed KEQA outperforms the state-of-the-art QA-KG methods.


Aligning Superintelligence with Human Interests : A Technical Research Agenda


title: Serratus anterior or pectoralis minor: Which muscle has the upper hand during protraction exercises?

abstract: Abstract Background The Serratus Anterior (SA) has a critical role in stabilizing the scapula against the thorax. Research has linked shoulder and neck disorders to impairments in the SA activation. Exercises that target the SA are included in the rehabilitation of shoulder or neck pain and mostly include a protraction component. The Pectoralis Minor (PM) functions as a synergist of the SA. From the literature it is unclear to what extent PM is activated during SA exercises. Objectives To determine the activity of SA and PM during different protraction exercises. Design Controlled laboratory study. Method 26 subjects performed 3 exercises: Modified Push-Up Plus (Wall Version), Modified Knee Push-Up Plus (Floor version) and Serratus Punch. Electromyographic (EMG) data was collected from the SA (surface) and PM (fine-wire EMG). Results During the Serratus Punch the SA activity was significantly higher than the PM activity. During the Modified Push-Up Plus exercises (both Wall and Floor version), the SA and PM activity were comparable. The PM showed the highest activity during the Serratus Punch and the Modified Push-Up Plus (Floor), which was significantly higher than during the Modified Push-Up Plus (Wall). The SA showed the highest activity during the Serratus Punch, which was significantly higher than during the Modified Push-Up Plus (Floor) which was in turn significantly higher than the activity during the Modified Push-Up Plus (Wall). Conclusions All exercises activated the PM between 15 and 29% Maximum Voluntary Isometric Contraction and the SA between 15 and 43%. The Modified Push-Up Plus exercise against the wall and the floor activated the SA and PM to a similar degree. When maximum activation of the SA with minimal activation of the PM is desired in healthy subjects, the "Serratus punch" seems to be the optimal exercise.


title: Visual vs. DOM-Based Web Locators: An Empirical Study

abstract: Automation in Web testing has been successfully supported by DOM-based tools that allow testers to program the interactions of their test cases with the Web application under test. More recently a new generation of visual tools has been proposed where a test case interacts with the Web application by recognising the images of the widgets that can be actioned upon and by asserting the expected visual appearance of the result.


Deep Learning with K-Means Applied to Community Detection in Networks