title: Using Wearable Sensors for Remote Healthcare Monitoring System

abstract: Recent technological advances in wireless communications and wireless sensor networks have enabled the design of low-cost, intelligent, tiny, and lightweight medical sensor nodes that can be strategically placed on human body, create a wireless body area network (WBAN) to monitor various physiological vital signs for a long period of time and providing real-time feedback to the user and medical staff. WBANs promise to revolutionize health monitoring. In this paper, medical sensors were used to collect physiological data from patients and transmit it to Intelligent Personal digital Assistant (IPDA) using ZigBee/IEEE802.15.4 standard and to medical server using 3G communications. We introduced priority scheduling and data compression into the system to increase transmission rate of physiological critical signals which improve the bandwidth utilization. It also extends the life time of hand-held personal server by reducing power consumption during transmission.


title: A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery

abstract: The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.


title: Implementation of Compact UWB Bandpass Filter With a Notch-Band

abstract: An ultra-wideband (UWB) bandpass filter on microstrip line with notch-band, compact-size and improved upper-stopband performances is presented and implemented. A modified non-uniform resonator is at first formed by transversely attaching three pairs of impedance-stepped stubs. Its resonance behavior is thoroughly characterized so as to allocate the first three resonant frequencies within 3.1-10.6 GHz band while suppressing spurious harmonics in the upper-band. As this resonator is linked at two sides with two feed lines via interdigital coupled lines with enhanced coupling degree, a preferred UWB passband is realized. Then, one of two arms in the coupled-line sections is largely extended and folded, aiming to create a notch band at the frequency of 5.6 GHz by virtue of out-of-phase transmission cancellation. Two filter prototypes are optimized and fabricated to demonstrate the notch-band-included and upper-stopband-improved UWB bandpass performances.


title: A Literature Survey of Benchmark Functions For Global Optimization Problems

abstract: Test functions are important to validate and compare the performance of optimisation algorithms. There have been many test or benchmark functions reported in the literature; however, there is no standard list or set of benchmark functions. Ideally, test functions should have diverse properties to be truly useful to test new algorithms in an unbiased way. For this purpose, we have reviewed and compiled a rich set of 175 benchmark functions for unconstrained optimisation problems with diverse properties in terms of modality, separability, and valley landscape. This is by far the most complete set of functions so far in the literature, and it can be expected that this complete set of functions can be used for validation of new optimisation in the future.


title: Rozzle: De-cloaking Internet Malware

abstract: JavaScript-based malware attacks have increased in recent years and currently represent a signicant threat to the use of desktop computers, smartphones, and tablets. While static and runtime methods for malware detection have been proposed in the literature, both on the client side, for just-in-time in-browser detection, as well as offline, crawler-based malware discovery, these approaches encounter the same fundamental limitation. Web-based malware tends to be environment-specific, targeting a particular browser, often attacking specic versions of installed plugins. This targeting occurs because the malware exploits vulnerabilities in specific plugins and fails otherwise. As a result, a fundamental limitation for detecting a piece of malware is that malware is triggered infrequently, only showing itself when the right environment is present. We observe that, using fingerprinting techniques that capture and exploit unique properties of browser configurations, almost all existing malware can be made virtually impssible for malware scanners to detect. This paper proposes Rozzle, a JavaScript multi-execution virtual machine, as a way to explore multiple execution paths within a single execution so that environment-specific malware will reveal itself. Using large-scale experiments, we show that Rozzle increases the detection rate for offline runtime detection by almost seven times. In addition, Rozzle triples the effectiveness of online runtime detection. We show that Rozzle incurs virtually no runtime overhead and allows us to replace multiple VMs running different browser configurations with a single Rozzle-enabled browser, reducing the hardware requirements, network bandwidth, and power consumption.


title: Scalable Bias-Resistant Distributed Randomness

abstract: Bias-resistant public randomness is a critical component in many (distributed) protocols. Generating public randomness is hard, however, because active adversaries may behave dishonestly to bias public random choices toward their advantage. Existing solutions do not scale to hundreds or thousands of participants, as is needed in many decentralized systems. We propose two large-scale distributed protocols, RandHound and RandHerd, which provide publicly-verifiable, unpredictable, and unbiasable randomness against Byzantine adversaries. RandHound relies on an untrusted client to divide a set of randomness servers into groups for scalability, and it depends on the pigeonhole principle to ensure output integrity, even for non-random, adversarial group choices. RandHerd implements an efficient, decentralized randomness beacon. RandHerd is structurally similar to a BFT protocol, but uses RandHound in a one-time setup to arrange participants into verifiably unbiased random secret-sharing groups, which then repeatedly produce random output at predefined intervals. Our prototype demonstrates that RandHound and RandHerd achieve good performance across hundreds of participants while retaining a low failure probability by properly selecting protocol parameters, such as a group size and secret-sharing threshold. For example, when sharding 512 nodes into groups of 32, our experiments show that RandHound can produce fresh random output after 240 seconds. RandHerd, after a setup phase of 260 seconds, is able to generate fresh random output in intervals of approximately 6 seconds. For this configuration, both protocols operate at a failure probability of at most 0.08% against a Byzantine adversary.


Clozapine Versus Risperidone in Treatment-Refractory Schizophrenia: Possible Impact of Dosing Strategies


title: Genetic profile of second primary tumors and recurrences in head and neck squamous cell carcinomas

abstract: Background. Second primary tumors and recur- rences are an important problem in patients with head and neck squamous cell carcinoma. The purpose of this study was to determine the genetic changes in tumor samples to improve knowledge of tumor progression. Methods. Copy number changes of 37 genes were ana- lyzed by multiplex ligation-dependent probe amplification (MLPA) in 36 primary tumors and their corresponding 21 sec- ond primary tumors and 15 recurrences. Results. CCND1 and EMS1 amplifications and gain of BCL2L1 were the most common genetic alterations in the pri- mary tumor, second primary tumor, and recurrence samples. Gains of ERBB2 and PTPN1 were associated with recurrences. Conclusion. Specific genetic profiles for each group have been found. Similarities between primary tumor and second primary tumor and dissimilarity between primary tumor and re- currence suggest that clinicopathological criteria do not always accurately differentiate these entities. Genetic profiling may aid in the diagnosis and prognosis of these difficult cases. V


title: Cultivating mindfulness in health care professionals: a review of empirical studies of mindfulness-based stress reduction (MBSR).

abstract: Demands faced by health care professionals include heavy caseloads, limited control over the work environment, long hours, as well as organizational structures and systems in transition. Such conditions have been directly linked to increased stress and symptoms of burnout, which in turn, have adverse consequences for clinicians and the quality of care that is provided to patients. Consequently, there exists an impetus for the development of curriculum aimed at fostering wellness and the necessary self-care skills for clinicians. This review will examine the potential benefits of mindfulness-based stress reduction (MBSR) programs aimed at enhancing well-being and coping with stress in this population. Empirical evidence indicates that participation in MBSR yields benefits for clinicians in the domains of physical and mental health. Conceptual and methodological limitations of the existing studies and suggestions for future research are discussed.


title: The Positive Relationships of Playfulness With Indicators of Health, Activity, and Physical Fitness

abstract: Adult playfulness is a personality trait that enables people to frame or reframe everyday situations in such a way that they experience them as entertaining, intellectually stimulating, or personally interesting. Earlier research supports the notion that playfulness is associated with the pursuit of an active way of life. While playful children are typically described as being active, only limited knowledge exists on whether playfulness in adults is also associated with physical activity. Additionally, existing literature has not considered different facets of playfulness, but only global playfulness. Therefore, we employed a multifaceted model that allows distinguishing among Other-directed, Lighthearted, Intellectual, and Whimsical playfulness. For narrowing this gap in the literature, we conducted two studies addressing the associations of playfulness with health, activity, and fitness. The main aim of Study 1 was a comparison of self-ratings (N = 529) and ratings from knowledgeable others (N = 141). We tested the association of self- and peer-reported playfulness with self- and peer-reported physical activity, fitness, and health behaviors. There was a good convergence of playfulness among self- and peer-ratings (between r = .46 and .55, all p < .001). Data show that both self- and peer-ratings are differentially associated with physical activity, fitness, and health behaviors. For example, self-rated playfulness shared 3% of the variance with self-rated physical fitness and 14% with the pursuit of an active way of life. Study 2 provides data on the association between self-rated playfulness and objective measures of physical fitness (i.e., hand and forearm strength, lower body muscular strength and endurance, cardio-respiratory fitness, back and leg flexibility, and hand and finger dexterity) using a sample of N = 67 adults. Self-rated playfulness was associated with lower baseline and activity (climbing stairs) heart rate and faster recovery heart rate (correlation coefficients were between -.19 and -.24 for global playfulness). Overall, Study 2 supported the findings of Study 1 by showing positive associations of playfulness with objective indicators of physical fitness (primarily cardio-respiratory fitness). The findings represent an important starting point for future studies on the relationships between playfulness, and health, activity, and physical fitness.


title: A Fourier View of REINFORCE

abstract: We show a connection between the Fourier spectrum of Boolean functions and the REINFORCE gradient estimator for binary latent variable models. We show that REINFORCE estimates (up to a factor) the degree-1 Fourier coefficients of a Boolean function. Using this connection we offer a new perspective on variance reduction in gradient estimation for latent variable models: namely, that variance reduction involves eliminating or reducing Fourier coefficients that do not have degree 1. We then use this connection to develop low-variance unbiased gradient estimators for binary latent variable models such as sigmoid belief networks. The estimator is based upon properties of the noise operator from Boolean Fourier theory and involves a sample-dependent baseline added to the REINFORCE estimator in a way that keeps the estimator unbiased. The baseline can be plugged into existing gradient estimators for further variance reduction.


title: Limitations and risks of machine ethics

abstract: Many authors have proposed constraining the behaviour of intelligent systems with ‘machine ethics’ to ensure positive social outcomes from the development of such systems. This paper critically analyses the prospects for machine ethics, identifying several inherent limitations. While machine ethics may increase the probability of ethical behaviour in some situations, it cannot guarantee it due to the nature of ethics, the computational limitations of computational agents and the complexity of the world. In addition, machine ethics, even if it were to be ‘solved’ at a technical level, would be insufficient to ensure positive social outcomes from intelligent systems.


title: Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes

abstract: Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the F1-measure with 0.1084 root mean square error. This is a 56% reduction of mean square error compared to the state of the art (i.e., the CKD-EPI equation: a glomerular filtration rate estimator). We also perform feature selection to determine the most relevant attributes for detecting CKD and rank them according to their predictability. We identify new predictive attributes which have not been used by any previous GFR estimator equations. Finally, we perform a cost-accuracy tradeoff analysis to identify a new CKD detection approach with high accuracy and low cost.


title: An Improved Variable On-Time Control Strategy for a CRM Flyback PFC Converter

abstract: The traditional critical conduction mode (CRM) flyback PFC converter with constant on-time control strategy usually suffers low power factor (PF) and high total harmonic distortion (THD) due to the nonsinusoidal input current waveform. In order to solve this problem, an improved variable on-time control strategy for the CRM flyback PFC converter is proposed in this letter. A simple analog divider circuit consisting of an operational amplifier, two signal switches, and an RC filter is proposed to modulate the turn-on time of the primary switch, and the PF and THD of the CRM flyback PFC converter can be evidently improved. The theoretical analysis is presented and the experiment results verify the advantages of the proposed control scheme.


title: SOCS2: physiological and pathological functions.

abstract: Suppressors of cytokine signalling (SOCS) proteins are modulators of cytokine and growth factor signalling whose aberrant regulation has been linked to a variety of inflammatory and neoplastic diseases. SOCS proteins are able to act as substrate-recruiting component of E3-ubiquitin ligase complexes and target interacting proteins for degradation. At least some of the family members can also directly inhibit tyrosine kinases such as Janus Kinases (JAK). The most studied family members, CIS, SOCS1, SOCS2 and SOCS3 are important regulators of the JAK-STAT pathway. Here, we focus on SOCS2 and review its biological function as well as its implication in pathological processes. Furthermore, we take advantage of the known crystal structures of SOCS2 to discuss the potential effects of a selection of SOCS2 mutations that were identified in tumour tissues.


title: Compressing Deep Convolutional Networks Using K-means Based on Weights Distribution

abstract: For the application of deep neural networks on devices with limited hardware resources, it is necessary to reduce the computational complexity and storage requirement. Compression is an effective way to achieve this goal. One of the available method is to quantize the weights to enforce weight sharing, which can greatly reduce the parameters of each layer. This paper presents an improved k-means clustering algorithm to compress CNN (convolutional neural networks).By taking weights distribution into consideration when choosing clustering centers to quantize weights, this algorithm automatically chooses and revises centers to compress network. Compared with traditional quantification method, this algorithm can maintain accuracy and increase the compression speed at the same time. Experiments on AlexNet show that using k-means based on weights distribution to quantize the weights can improve compression speed by 5% to 10% and improve accuracy by 6% compared to traditional algorithm. This algorithm provides a better way for the application of convolutional neural networks on mobile devices.


title: Lexical Coverage in Taiwan Mandarin Conversation

abstract: Information about the lexical capacity of the speakers of a specific language is indispensible for empirical and experimental studies on the human behavior of using speech as a communicative means. Unlike the increasing number of gigantic text- or web-based corpora that have been developed in recent decades, publicly distributed spoken resources, espcially conversations, are few in number. This article studies the lexical coverage of a corpus of Taiwan Mandarin conversations recorded in three speaking scenarios. A wordlist based on this corpus has been prepared and provides information about frequency counts of words and parts of speech processed by an automatic system. Manual post-editing of the results was performed to ensure the usability and reliability of the wordlist. Syllable information was derived by automatically converting the Chinese characters to a conventional romanization scheme, followed by manual correction of conversion errors and disambiguiation of homographs. As a result, the wordlist contains 405,435 ordinary words and 57,696 instances of discourse particles, markers, fillers, and feedback words. Lexical coverage in Taiwan Mandarin conversation is revealed and is compared with a balanced corpus of texts in terms of words, syllables, and word categories.


title: Collaborative Index Embedding for Image Retrieval

abstract: In content-based image retrieval, SIFT feature and the feature from deep convolutional neural network (CNN) have demonstrated promising performance. To fully explore both visual features in a unified framework for effective and efficient retrieval, we propose a collaborative index embedding method to implicitly integrate the index matrices of them. We formulate the index embedding as an optimization problem from the perspective of neighborhood sharing and solve it with an alternating index update scheme. After the iterative embedding, only the embedded CNN index is kept for on-line query, which demonstrates significant gain in retrieval accuracy, with very economical memory cost. Extensive experiments have been conducted on the public datasets with million-scale distractor images. The experimental results reveal that, compared with the recent state-of-the-art retrieval algorithms, our approach achieves competitive accuracy performance with less memory overhead and efficient query computation.


title: Uniform handling and abstraction of NFV hardware accelerators

abstract: This article outlines some NFV topics related to implementing VNF over NFVI where HWA may be needed. The document highlights the commonalities and differences among various HWA approaches and suggests uniform handling, common architecture, and abstraction layer for the hardware acceleration components. This uniform handling will allow deployment of various hardware accelerators within NFVI and facilitate interoperability between various VNFs and HWAs.


title: Fast Traffic Sign Recognition via High-Contrast Region Extraction and Extended Sparse Representation

abstract: In this paper, we propose a high-performance traffic sign recognition (TSR) framework to rapidly detect and recognize multiclass traffic signs in high-resolution images. This framework includes three parts: a novel region-of-interest (ROI) extraction method called the high-contrast region extraction (HCRE), the split-flow cascade tree detector (SFC-tree detector), and a rapid occlusion-robust traffic sign classification method based on the extended sparse representation classification (ESRC). Unlike the color-thresholding or extreme region extraction methods used by previous ROI methods, the ROI extraction method of the HCRE is designed to extract ROI with high local contrast, which can keep a good balance of the detection rate and the extraction rate. The SFC-tree detector can detect a large number of different types of traffic signs in high-resolution images quickly. The traffic sign classification method based on the ESRC is designed to classify traffic signs with partial occlusion. Instead of solving the sparse representation problem using an overcomplete dictionary, the classification method based on the ESRC utilizes a content dictionary and an occlusion dictionary to sparsely represent traffic signs, which can largely reduce the dictionary size in the occlusion-robust dictionaries and achieve high accuracy. The experiments demonstrate the advantage of the proposed approach, and our TSR framework can rapidly detect and recognize multiclass traffic signs with high accuracy.


title: Probabilistic Image Colorization

abstract: We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset.


title: Optimal Charging in Wireless Rechargeable Sensor Networks

abstract: Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - e) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/e) ::: 2 ::: ). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.


title: The Role of Mental Health Professionals in Gender Reassignment Surgeries: Unjust Discrimination or Responsible Care?

abstract: OBJECTIVE ::: Recent literature has raised an important ethical concern relating to the way in which surgeons approach people with gender dysphoria (GD): it has been suggested that referring transsexual patients to mental assessment can constitute a form of unjust discrimination. The aim of this paper is to examine some of the ethical issues concerning the role of the mental health professional in gender reassignment surgeries (GRS). ::: ::: ::: METHOD ::: The role of the mental health professional in GRS is analyzed by presenting the Standards of Care by the World Professional Association of Transgender Health, and discussing the principles of autonomy and non-discrimination. ::: ::: ::: RESULTS ::: Purposes of psychotherapy are exploring gender identity; addressing the negative impact of GD on mental health; alleviating internalized transphobia; enhancing social and peer support; improving body image; promoting resilience; and assisting the surgeons with the preparation prior to the surgery and the patient's follow-up. Offering or requesting psychological assistance is in no way a form of negative discrimination or an attack to the patient's autonomy. Contrarily, it might improve transsexual patients' care, and thus at the most may represent a form of positive discrimination. To treat people as equal does not mean that they should be treated in the same way, but with the same concern and respect, so that their unique needs and goals can be achieved. ::: ::: ::: CONCLUSIONS ::: Offering or requesting psychological assistance to individuals with GD is a form of responsible care, and not unjust discrimination. ::: ::: ::: LEVEL OF EVIDENCE V ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: The Mythos of Model Interpretability

abstract: Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer myriad notions of what attributes render models interpretable. Despite this ambiguity, many papers proclaim interpretability axiomatically, absent further explanation. In this paper, we seek to refine the discourse on interpretability. First, we examine the motivations underlying interest in interpretability, finding them to be diverse and occasionally discordant. Then, we address model properties and techniques thought to confer interpretability, identifying transparency to humans and post-hoc explanations as competing notions. Throughout, we discuss the feasibility and desirability of different notions, and question the oft-made assertions that linear models are interpretable and that deep neural networks are not.


title: Modeling and Analysis of Performance Under Interference in the Cloud

abstract: One of the key performance challenges in cloud computing is the problem of interference, or resource contention, among colocated VMs. While prior work has empirically analyzed interference for specific workloads under specific settings, there is a need for a generic approach to estimate application performance under any interference condition.In this paper, we present an analytical model to estimate performance as a function of various workload, system, and interference conditions, including the intensity and length ofinterference, for single- and multi-VM systems. Comparisons with empirical results under various scenarios show that our model can provide accurate latency estimations (less than 5% error). We employ our model to analyze systems under interference, and derive useful results to aid practitioners.


title: Fly4SmartCity: A cloud robotics service for smart city applications

abstract: In this paper a possible implementation of a cloud-robotics service in a smart city scenario is proposed, in which small Unmanned Aerial Vehicles (UAVs) are employed for emergency management operations. The emerging cloud robotics paradigm considers robots as simple agents connected to a remote network infrastructure; this networked approach allows them to benefit from off-board powerful computational and storage resources on one hand, and to leverage open data about the urban fabric and common knowledge on the other hand. This work describes how the integration of a capable, reliable and stable cloud platform, small unmanned vehicles, high-bandwidth/low-latency mobile networks and open data publicly available on the internet makes the design of a real-world UAV-based emergency management service possible. Simulated and real experiments show the feasibility of the approach. Legal and operational issues which at present still prevent a practical application of these techniques are also addressed.


A Cluster Analysis Approach To Describing Tax Data


title: A four-fingered robot hand with shape memory alloys

abstract: This paper presents the design and implementation of a four-fingered human-size robotic hand intended for dexterous and grasping manipulation applications. The prototype hand has 3 degrees of freedom (DOF) per finger and 2 DOF in the thumb for a total of 11 DOF. Shape memory alloys (SMAs) micro-coils are embedded intrinsically within the hand structure to power the joints while exhibiting some advantageous features such as low-cost, lightweight, compactness and clean silent operation. To increase efficiency and decrease power dissipation, the SMA micro-actuators integrate magnetic bi-stable structures. Mechanical design, actuation approach and first prototype are presented and discussed.


title: Fast reciprocal nearest neighbors clustering

abstract: This paper presents a novel approach for accelerating the popular reciprocal nearest neighbors (RNN) clustering algorithm, i.e. the fast-RNN. We speed up the nearest neighbor chains construction via a novel dynamic slicing strategy for the projection search paradigm. We detail an efficient implementation of the clustering algorithm along with a novel data structure, and present extensive experimental results that illustrate the excellent performance of fast-RNN in low- and high-dimensional spaces. A C++ implementation has been made publicly available.


title: Interaction of the histone mRNA hairpin with stem-loop binding protein (SLBP) and regulation of the SLBP-RNA complex by phosphorylation and proline isomerization.

abstract: In metazoans, the majority of histone proteins are generated from replication-dependent histone mRNAs. These mRNAs are unique in that they are not polyadenylated but have a stem-loop structure in their 3' untranslated region. An early event in 3' end formation of histone mRNAs is the binding of stem-loop binding protein (SLBP) to the stem-loop structure. Here we provide insight into the mechanism by which SLBP contacts the histone mRNA. There are two binding sites in the SLBP RNA binding domain for the histone mRNA hairpin. The first binding site (Glu129-Val158) consists of a helix-turn-helix motif that likely recognizes the unpaired uridines in the loop of the histone hairpin and, upon binding, destabilizes the first G-C base pair at the base of the stem. The second binding site lies between residues Arg180 and Pro200, which appears to recognize the second G-C base pair from the base of the stem and possibly regions flanking the stem-loop structure. We show that the SLBP-histone mRNA complex is regulated by threonine phosphorylation and proline isomerization in a conserved TPNK sequence that lies between the two binding sites. Threonine phosphorylation increases the affinity of SLBP for histone mRNA by slowing the off rate for complex dissociation, whereas the adjacent proline acts as a critical hinge that may orient the second binding site for formation of a stable SLBP-histone mRNA complex. The nuclear magnetic resonance and kinetic studies presented here provide a framework for understanding how SLBP recognizes histone mRNA and highlight possible structural roles of phosphorylation and proline isomerization in RNA binding proteins in remodeling ribonucleoprotein complexes.


title: Computational imaging using lightweight diffractive-refractive optics

abstract: Diffractive optical elements (DOE) show great promise for imaging optics that are thinner and more lightweight than conventional refractive lenses while preserving their light efficiency. Unfortunately, severe spectral dispersion currently limits the use of DOEs in consumer-level lens design. In this article, we jointly design lightweight diffractive-refractive optics and post-processing algorithms to enable imaging under white light illumination. Using the Fresnel lens as a general platform, we show three phase-plate designs, including a super-thin stacked plate design, a diffractive-refractive-hybrid lens, and a phase coded-aperture lens. Combined with cross-channel deconvolution algorithm, both spherical and chromatic aberrations are corrected. Experimental results indicate that using our computational imaging approach, diffractive-refractive optics is an alternative candidate to build light efficient and thin optics for white light imaging.


title: DDoS in the IoT: Mirai and Other Botnets

abstract: The Mirai botnet and its variants and imitators are a wake-up call to the industry to better secure Internet of Things devices or risk exposing the Internet infrastructure to increasingly disruptive distributed denial-of-service attacks.


title: To personalize or not to personalize: modeling queries with variation in user intent

abstract: In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user intent using both explicit relevance judgments and large-scale log analysis of user behavior patterns. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can also affect the variation in behavior. We characterize queries using a variety of features of the query, the results returned for the query, and people's interaction history with the query. Using these features we build predictive models to identify queries that can benefit from personalization.


A SURVEY OF VIDEO MOVING OBJECT DETECTION


title: On the State of the Art of Evaluation in Neural Language Models

abstract: Ongoing innovations in recurrent neural network architectures have provided a steady influx of apparently state-of-the-art results on language modelling benchmarks. However, these have been evaluated using differing code bases and limited computational resources, which represent uncontrolled sources of experimental variation. We reevaluate several popular architectures and regularisation methods with large-scale automatic black-box hyperparameter tuning and arrive at the somewhat surprising conclusion that standard LSTM architectures, when properly regularised, outperform more recent models. We establish a new state of the art on the Penn Treebank and Wikitext-2 corpora, as well as strong baselines on the Hutter Prize dataset.


title: Learning Discriminative Aggregation Network for Video-Based Face Recognition

abstract: In this paper, we propose a discriminative aggregation network (DAN) method for video face recognition, which aims to integrate information from video frames effectively and efficiently. Unlike existing aggregation methods, our method aggregates raw video frames directly instead of the features obtained by complex processing. By combining the idea of metric learning and adversarial learning, we learn an aggregation network that produces more discriminative synthesized images compared to raw input frames. Our framework reduces the number of frames to be processed and significantly speed up the recognition procedure. Furthermore, low-quality frames containing misleading information are filtered and denoised during the aggregation process, which makes our system more robust and discriminative. Experimental results show that our method can generate discriminative images from video clips and improve the overall recognition performance in both the speed and accuracy on three widely used datasets.


title: Learning Contact-Rich Manipulation Skills with Guided Policy Search

abstract: Autonomous learning of object manipulation skills can enable robots to acquire rich behavioral repertoires that scale to the variety of objects found in the real world. However, current motion skill learning methods typically restrict the behavior to a compact, low-dimensional representation, limiting its expressiveness and generality. In this paper, we extend a recently developed policy search method [1] and use it to learn a range of dynamic manipulation behaviors with highly general policy representations, without using known models or example demonstrations. Our approach learns a set of trajectories for the desired motion skill by using iteratively refitted time-varying linear models, and then unifies these trajectories into a single control policy that can generalize to new situations. To enable this method to run on a real robot, we introduce several improvements that reduce the sample count and automate parameter selection. We show that our method can acquire fast, fluent behaviors after only minutes of interaction time, and can learn robust controllers for complex tasks, including putting together a toy airplane, stacking tight-fitting lego blocks, placing wooden rings onto tight-fitting pegs, inserting a shoe tree into a shoe, and screwing bottle caps onto bottles.


title: Demonstration of 300 Mbit/s free space optical link with commercial visible LED

abstract: Recently, we have introduced a method to improve modulation rate of a light emitting diode (LED) by using simple 2-cascaded Schottky diodes-capacitance current-shaping circuit. In this work, we demonstrate a high-speed free space optical link with a commercial 660 nm LED and a PIN (Si) photodiode. The system achieves an error-free data transmission at the rate of 300 Mbit/s over 11 m using on-off keying non-return-to-zero modulation.


title: Analysis and validation of proteomic data generated by tandem mass spectrometry

abstract: The analysis of the large amount of data generated in mass spectrometry–based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.


title: Computing illumination-invariant descriptors of spatially filtered color image regions.

abstract: Spatial filters provide a useful and efficient means of analyzing an input color image into components that capture different spatial properties. Representations based on spatial filtering have restricted usefulness for recognition, however, because the output of a spatial filter across an image depends on the scene illumination conditions. We use a physically accurate linear model for spectral reflectance to derive invariants of distributions in spatially filtered color images that do not depend on the scene illumination. These invariants can be used for the illumination-invariant recognition of regions following an arbitrary linear filtering operation. We describe a method for illumination correction based on color distributions and introduce an illumination change consistency constraint that is useful for verifying matches obtained using the invariants. We show, using a set of classification experiments, that the filtered distribution invariants can significantly improve the capability of a recognition system in environments where illumination cannot be controlled.


title: Preoperative determinants of common bile duct stones during laparoscopic cholecystectomy.

abstract: INTRODUCTION ::: The aim of this study is to determine whether there are any clinical or biochemical predictors of common bile duct (CBD) stones in patients undergoing laparoscopic cholecystectomy. ::: ::: ::: METHODS ::: A prospective database of nearly 1000 laparoscopic cholecystectomies performed under the care of a single surgeon with a standardised technique between 1999 and 2006, was analysed. Clinical presentation, ultrasound and immediate preoperative biochemical results as well as the operative cholangiogram findings were reviewed. Routine cholangiography was attempted in most patients and the primary outcome variable was the detection of bile duct stones. The data was analysed using chi-squared test for categorical variables. The significant variables on univariate analysis were further characterised to identify the independent predictors of bile duct stones using a logistic regression model (significance p < 0.05). ::: ::: ::: RESULTS ::: A total of 757 of 988 patients (77%) underwent cholangiography. Male-to-female ratio was 1 : 3 with a median age of 54 years (range: 17-93). Ten per cent of patients had bile duct stones identified on cholangiography. On univariate analysis, jaundice (p = 0.019), cholangitis (p < 0.001), alanine transaminase > 100 (p = 0.024), alkaline phosphatase (ALP) > 350 (p < 0.001) and CBD > 10 mm (p = 0.01) were significant markers for predicting bile duct stones. Bilirubin > 30 (x2 normal) was found not to be significant (p = 0.145). On a logistic regression model, ALP > 350 and/or cholangitis were found to be independent predictive factors of CBD stones (odds ratio 6.1). ::: ::: ::: CONCLUSIONS ::: If a policy of routine intra-operative cholangiography is not adopted, a history of cholangitis or a raised ALP immediately preoperatively should lead to a high suspicion of CBD stones.


title: Performance Evaluation of Analog Beamforming with Hardware Impairments for mmW Massive MIMO Communication in an Urban Scenario

abstract: The use of massive multiple-input multiple-output (MIMO) techniques for communication at millimeter-Wave (mmW) frequency bands has become a key enabler to meet the data rate demands of the upcoming fifth generation (5G) cellular systems. In particular, analog and hybrid beamforming solutions are receiving increasing attention as less expensive and more power efficient alternatives to fully digital precoding schemes. Despite their proven good performance in simple setups, their suitability for realistic cellular systems with many interfering base stations and users is still unclear. Furthermore, the performance of massive MIMO beamforming and precoding methods are in practice also affected by practical limitations and hardware constraints. In this sense, this paper assesses the performance of digital precoding and analog beamforming in an urban cellular system with an accurate mmW channel model under both ideal and realistic assumptions. The results show that analog beamforming can reach the performance of fully digital maximum ratio transmission under line of sight conditions and with a sufficient number of parallel radio-frequency (RF) chains, especially when the practical limitations of outdated channel information and per antenna power constraints are considered. This work also shows the impact of the phase shifter errors and combiner losses introduced by real phase shifter and combiner implementations over analog beamforming, where the former ones have minor impact on the performance, while the latter ones determine the optimum number of RF chains to be used in practice.


title: Sentence-Level and Document-Level Sentiment Mining for Arabic Texts

abstract: In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.


title: Comparison of eleven heavy metals in moringa oleifera lam. products

abstract: Eleven heavy metals in various products of Moringa oleifera were analyzed to determine eleven heavy metals (Al, As, Cd, Cr, Cu, Fe, Pb, Mn, Hg, Ni, and Zn) using Inductively Coupled Plasma-Mass Spectrometry. The products of M. oleifera were purchased in Nakhon Pathom, Thailand. All products were digested with nitric acid solution before determining the concentrations of heavy metals. The recoveries of all heavy metals were found to be in the range of 99.89-103.05%. Several criteria such as linearity, limits of detection, limits of quantification, specificity, precision under repeatability conditions and intermediate precision reproducibility were evaluated. Results indicate that this method could be used in the laboratory for determination of eleven heavy metals in M. oleifera products with acceptable analytical performance. The results of analysis showed that the highest concentrations of As, Cr, Hg, and Mn were found in tea leaves while the highest concentrations of Al, Cd, Cu, Fe, Ni, Pb, and Zn were found in leaf capsules. Continuous monitoring of heavy metals in M. oleifera products is crucial for consumer health.


title: Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification

abstract: Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations. In this work, we present a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks (CNNs). When training a CNN with data from all the domains, some neurons learn representations shared across several domains, while some others are effective only for a specific one. Based on this important observation, we propose a Domain Guided Dropout algorithm to improve the feature learning procedure. Experiments show the effectiveness of our pipeline and the proposed algorithm. Our methods on the person re-identification problem outperform stateof-the-art methods on multiple datasets by large margins.


title: Remittances, school quality, and household education expenditures in Nepal

abstract: A heightened interest in understanding the remitting practices of immigrants and their impact on a variety of economic indicators has emerged as remittances to developing countries have risen substantially over the past decade. If remittances primarily enhance consumption, they may have no lasting impact on economic growth. However, through asset accumulation and human capital investment, remittances may serve as a vehicle for growth. In this paper, we use the 2010 Nepal Living Standards Survey III (NLSS III) to examine how remittances affect household expenditures on human capital investment. Overall, our findings suggest that at the margin, remittances do contribute to human capital investment, but this effect varies substantially by school quality within Nepal. In addition, our results indicate that internal remittances (remittances from household members migrating internally) have a greater impact on education than do external remittances. We posit that this may be due to a higher value placed on Nepali education by internal migrants as compared to the education needed for foreign job opportunities by migrants abroad.


title: Health literacy–listening skill and patient questions following cancer prevention and screening discussions

abstract: Objective Patient question-asking is essential to shared decision making. We sought to describe patients’ questions when faced with cancer prevention and screening decisions, and to explore differences in question-asking as a function of health literacy with respect to spoken information (health literacy–listening). Methods Four-hundred and thirty-three (433) adults listened to simulated physician–patient interactions discussing (i) prophylactic tamoxifen for breast cancer prevention, (ii) PSA testing for prostate cancer and (iii) colorectal cancer screening, and identified questions they would have. Health literacy–listening was assessed using the Cancer Message Literacy Test-Listening (CMLT-Listening). Two authors developed a coding scheme, which was applied to all questions. Analyses examined whether participants scoring above or below the median on the CMLT-Listening asked a similar variety of questions. Results Questions were coded into six major function categories: risks/benefits, procedure details, personalizing information, additional information, decision making and credibility. Participants who scored higher on the CMLT-Listening asked a greater variety of risks/benefits questions; those who scored lower asked a greater variety of questions seeking to personalize information. This difference persisted after adjusting for education. Conclusion Patients’ health literacy–listening is associated with distinctive patterns of question utilization following cancer screening and prevention counselling. Providers should not only be responsive to the question functions the patient favours, but also seek to ensure


title: Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting

abstract: The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins. At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browser-identifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.


title: Dynamic Average-Value Modeling of Direct Power-Controlled Active Front-End Rectifiers

abstract: Active front-end (AFE) rectifiers are becoming widely used in medium-to-high-power adjustable speed drives (ASDs) to achieve regenerative operation and meet the energy efficiency and harmonic requirements. The typical control methods used with AFE rectifiers include voltage-oriented control (VOC), direct power control (DPC) and virtual-flux-based methods. This paper presents a dynamic average-value model (AVM) of the AFE rectifier system which is based on the voltage-source converter (VSC) operated with DPC. The developed AVM and the modeling methodology presented in this paper highlight the specifics of the hysteresis bang-bang type DPC that is used to track the real and reactive power references. The proposed AVM of the AFE rectifier system is demonstrated to significantly reduce the computing effort while accurately capturing the fast transient performance of the system.


Recognizing Car Fluents from Video Supplementary Material


title: Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

abstract: Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches.


title: Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions

abstract: This paper presents a novel traffic classification scheme to improve classification performance when few training data are available. In the proposed scheme, traffic flows are described using the discretized statistical features and flow correlation information is modeled by bag-of-flow (BoF). We solve the BoF-based traffic classification in a classifier combination framework and theoretically analyze the performance benefit. Furthermore, a new BoF-based traffic classification method is proposed to aggregate the naive Bayes (NB) predictions of the correlated flows. We also present an analysis on prediction error sensitivity of the aggregation strategies. Finally, a large number of experiments are carried out on two large-scale real-world traffic datasets to evaluate the proposed scheme. The experimental results show that the proposed scheme can achieve much better classification performance than existing state-of-the-art traffic classification methods.


title: Evidence relating to Object-Oriented software design: A survey

abstract: There is little empirical knowledge of the effectiveness of the object-oriented paradigm. To conduct a systematic review of the literature describing empirical studies of this paradigm. We undertook a Mapping Study of the literature. 138 papers have been identified and classified by topic, form of study involved, and source. The majority of empirical studies of OO concentrate on metrics, relatively few consider effectiveness.


title: A Survey Paper on E-Learning Recommender System

abstract: A recommender system in an e-learning context is a software agent that tries to ”intelligently” recommend actions to a learner based on the actions of previous learners. This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be simply a web resource. These recommendation systems have been tried in ecommerce to entice purchasing of goods, but haven’t been tried in e-learning. This paper suggests the use of web mining techniques to build such an agent that could recommend online learning activities or shortcuts in a course web site based on learners’ access history to improve course material navigation as well as assist the online learning process. These techniques are considered integrated web mining as opposed to off-line web mining used by expert users to discover online access patterns


title: Application-centric security policies on unmodified Android,” UCLA Computer Science Department

abstract: AbstractGoogle’s Android platform uses a fairly standard resource-centric permission model toprotect resources such as the camera, GPS, and Internet connection. We claim that a much bet-ter permission model for developers and users would be application-centric, with a vocabularythat directly relates to application-level functionality, e.g., one permission could allow camerause, but only for barcode scanning; another could allow Internet access, but only to certain do-mains. Despite the large apparent gap between resource- and application-centric permissions,we argue that Android already provides the necessary mechanisms to support an expressiveand practical form of application-centric policies. Speciﬁcally, each application-centric per-mission can be represented by a new Android permission and can be enforced by coupling thepermission with a trusted service running in its own process. We present a survey of the top 24free Android apps and show that a small vocabulary of application-centric permissions coversmuch of the functionality of those apps. We also describe a prototype implementation of ourapproach.


title: A Review of Resolution Losses for AR/VR Foveated Imaging Applications

abstract: Foveated imaging is of great interest for Augmented and Virtual Reality applications. The resolution losses off-axis simulated in foveated imaging are modelled using cone density on the retina. This article reviews the other factors limiting the resolution off-axis in AR/VR, in particular the impact of the eye lens. Several off-axis resolution simulations are proposed and compared in order to provide some theoretical compression ratios for 4K and 8K display systems. A model taking into account both the cone density across the retina and the optical performance of the eye lens is proposed and evaluated. The variability and challenges of modelling the human eye resolution losses are also discussed in particular in the case of age-dependence.


title: Learning dynamic stream weights for coupled-HMM-based audio-visual speech recognition

abstract: With the increasing use of multimedia data in communication technologies, the idea of employing visual information in automatic speech recognition (ASR) has recently gathered momentum. In conjunction with the acoustical information, the visual data enhances the recognition performance and improves the robustness of ASR systems in noisy and reverberant environments. In audio-visual systems, dynamic weighting of audio and video streams according to their instantaneous confidence is essential for reliably and systematically achieving high performance. In this paper, we present a complete framework that allows blind estimation of dynamic stream weights for audio-visual speech recognition based on coupled hidden Markov models (CHMMs). As a stream weight estimator, we consider using multilayer perceptrons and logistic functions to map multidimensional reliability measure features to audiovisual stream weights. Training the parameters of the stream weight estimator requires numerous input-output tuples of reliability measure features and their corresponding stream weights. We estimate these stream weights based on oracle knowledge using an expectation maximization algorithm. We define 31-dimensional feature vectors that combine model-based and signal-based reliability measures as inputs to the stream weight estimator. During decoding, the trained stream weight estimator is used to blindly estimate stream weights. The entire framework is evaluated using the Grid audio-visual corpus and compared to state-of-the-art stream weight estimation strategies. The proposed framework significantly enhances the performance of the audio-visual ASR system in all examined test conditions.


title: On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics

abstract: In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution audience not to take this assumption for granted, and present empirical evidence of its ineffectiveness from a machine learning perspective. Our examination is based on a setting where binding profiles are used as features to train predictive models; we show (1) prediction performance degrades when the assumption fails and (2) explicit recovery of unreported profiles improves prediction performance. In particular, we propose a framework that jointly recovers profiles and learns predictive model, and show it achieves further performance improvement. The presented study not only suggests applying matrix recovery methods to recover unreported profiles, but also initiates a new missing feature problem which we called Learning with Positive and Unknown Features.


title: Teaching and Working with Robots as a Collaboration

abstract: New applications for autonomous robots bring them into the human environment where they are to serve as helpful assistants to untrained users in the home or office, or work as capable members of human-robot teams for security, military, and space efforts. These applications require robots to be able to quickly learn how to perform new tasks from natural human instruction, and to perform tasks collaboratively with human teammates. Using joint intention theory as our theoretical framework, our approach integrates learning and collaboration through a goal based task structure. Specifically, we use collaborative discourse with accompanying gestures and social cues to teach a humanoid robot a structurally complex task. Having learned the representation for the task, the robot then performs it shoulder-to-shoulder with a human partner, using social communication acts to dynamically mesh its plans with those of its partner, according to the relative capabilities of the human and the robot.


Pattern Classification (2nd Edition)


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

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


title: Cyber Security Standards

abstract: The goal of cyber security standards is to improve the security of information technology (IT) systems, networks, and critical infrastructures. A cyber security standard defines both functional and assurance requirements within a product, system, process, or technology environment. Well-developed cyber security standards enable consistency among product developers and serve as a reliable metric for purchasing security products. Cyber security standards cover a broad range of granularity, from the mathematical definition of a cryptographic algorithm to the specification of security features in a web browser, and are typically implementation independent. A standard must address user needs, but must also be practical since cost and technological limitations must be considered in building products to meet the standard. Additionally, a standard's requirements must be verifiable; otherwise, users cannot assess security even when products are tested against the standard. ::: ::: ::: Keywords: ::: ::: cyber security; ::: information technology; ::: standards; ::: standardization


title: AmbiMax: Autonomous Energy Harvesting Platform for Multi-Supply Wireless Sensor Nodes

abstract: AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs


Do Hardware Cache Flushing Operations Actually Meet Our Expectations?


title: The state of network organization: a survey in three frameworks

abstract: This article reviews the literature on network organizations and interprets explanations for its behaviors in terms of established analytical principles. Tools from computer science, economics, and sociology give three markedly different interpretations of its core attributes but they also settle on a handful of common themes. The proposed benefits are a clarification of what it means for an organization to be network structured, a few insights into its origins, and a suggestion of where the boundaries to some of its different forms might lie.


Universal Generator of Ultra-Wideband Pulses


title: An integrated charger using segmented windings of interior permanent magnet motor based on 3 phase with 9 windings

abstract: Connecting the plug of electric vehicles (EVs) into the grid source directly means to charge the battery. The electric power generated from the grid goes through the electric motor, crosses the bidirectional inverter used as three-boost rectifier and is received by the load (battery). An innovative fast-integrated charger based on segmented windings of permanent magnet synchronous motor (PMSM) is introduced for plug-in electric vehicles (PEV). The three-phase with nine windings of PMSM is used during the traction and charging modes. Three (or single) phases of grid source are directly connected to the neutral points of segmented PMSM windings to provide high power density to the battery. The system configuration and operation of charging mode are explained in detail for proposed integrated on-board charger. The simulation models are designed by using Ansoft Maxwell, Ansys Maxwell circuit editor, and Matlab/Simulink softwares, and simulation results are presented to verify the performance of the proposed system. In addition, the experimental setup of the integrated system is under progress.


The Role of Emotions in Learning


title: Wavelet denoising of partial discharge signals and their pattern classification using artificial neural networks and support vector machines

abstract: This paper presents two pattern recognition approaches using Partial Discharges fingerprints as input features to classify PD patterns. A multi-layer perceptron (MLP) backpropagation neural network and a support vector machine (SVM) were trained to recognize three types of PD patterns. Experimental results showed that the algorithms can achieve high recognition rates. Moreover, the Discrete wavelet transform (DWT) was used to denoise PD signals as a prior stage to the classification process. Different mother wavelets were tested for different levels of decomposition in order to find appropriate wavelet parameters for better signal to noise ratio (SNR) and less distortion after the denoising process.


title: Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation

abstract: This paper focuses on structured-output learning using deep neural networks for 3D human pose estimation from monocular images. Our network takes an image and 3D pose as inputs and outputs a score value, which is high when the image-pose pair matches and low otherwise. The network structure consists of a convolutional neural network for image feature extraction, followed by two sub-networks for transforming the image features and pose into a joint embedding. The score function is then the dot-product between the image and pose embeddings. The image-pose embedding and score function are jointly trained using a maximum-margin cost function. Our proposed framework can be interpreted as a special form of structured support vector machines where the joint feature space is discriminatively learned using deep neural networks. We also propose an efficient recurrent neural network for performing inference with the learned image-embedding. We test our framework on the Human3.6m dataset and obtain state-of-the-art results compared to other recent methods. Finally, we present visualizations of the image-pose embedding space, demonstrating the network has learned a high-level embedding of body-orientation and pose-configuration.


title: Linear Feature Encoding for Reinforcement Learning

abstract: Feature construction is of vital importance in reinforcement learning, as the quality of a value function or policy is largely determined by the corresponding features. The recent successes of deep reinforcement learning (RL) only increase the importance of understanding feature construction. Typical deep RL approaches use a linear output layer, which means that deep RL can be interpreted as a feature construction/encoding network followed by linear value function approximation. This paper develops and evaluates a theory of linear feature encoding. We extend theoretical results on feature quality for linear value function approximation from the uncontrolled case to the controlled case. We then develop a supervised linear feature encoding method that is motivated by insights from linear value function approximation theory, as well as empirical successes from deep RL. The resulting encoder is a surprisingly effective method for linear value function approximation using raw images as inputs.


title: Abstraction Selection in Model-based Reinforcement Learning

abstract: State abstractions are often used to reduce the complexity of model-based reinforcement learning when only limited quantities of data are available. However, choosing the appropriate level of abstraction is an important problem in practice. Existing approaches have theoretical guarantees only under strong assumptions on the domain or asymptotically large amounts of data, but in this paper we propose a simple algorithm based on statistical hypothesis testing that comes with a finite-sample guarantee under assumptions on candidate abstractions. Our algorithm trades off the low approximation error of finer abstractions against the low estimation error of coarser abstractions, resulting in a loss bound that depends only on the quality of the best available abstraction and is polynomial in planning horizon.


Forecasting Cash Withdrawals in the ATM Network Using a Combined Model based on the Holt-Winters Method and Markov Chains


title: GreenCloud: a new architecture for green data center

abstract: Nowadays, power consumption of data centers has huge impacts on environments. Researchers are seeking to find effective solutions to make data centers reduce power consumption while keep the desired quality of service or service level objectives. Virtual Machine (VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We present the GreenCloud architecture, which aims to reduce data center power consumption, while guarantee the performance from users' perspective. GreenCloud architecture enables comprehensive online-monitoring, live virtual machine migration, and VM placement optimization. To verify the efficiency and effectiveness of the proposed architecture, we take an online real-time game, Tremulous, as a VM application. Evaluation results show that we can save up to 27% of the energy when applying GreenCloud architecture.


title: PortLand: a scalable fault-tolerant layer 2 data center network fabric

abstract: This paper considers the requirements for a scalable, easily manageable, fault-tolerant, and efficient data center network fabric. Trends in multi-core processors, end-host virtualization, and commodities of scale are pointing to future single-site data centers with millions of virtual end points. Existing layer 2 and layer 3 network protocols face some combination of limitations in such a setting: lack of scalability, difficult management, inflexible communication, or limited support for virtual machine migration. To some extent, these limitations may be inherent for Ethernet/IP style protocols when trying to support arbitrary topologies. We observe that data center networks are often managed as a single logical network fabric with a known baseline topology and growth model. We leverage this observation in the design and implementation of PortLand, a scalable, fault tolerant layer 2 routing and forwarding protocol for data center environments. Through our implementation and evaluation, we show that PortLand holds promise for supporting a ``plug-and-play" large-scale, data center network.


title: Plan 9, A Distributed System

abstract: Plan 9 is a computing environment physically distributed across many machines. The distribution itself is transparent to most programs giving both users and administrators wide latitude in configuring the topology of the environment. Two properties make this possible: a per process group name space and uniform access to all resources by representing them as files.


title: Producing Linked Data for Smart Cities: the case of Catania

abstract: Abstract Semantic Web technologies and in particular Linked Open Data provide a means for sharing knowledge about cities as physical, social, and technical systems, so enabling the development of smart city applications. This paper presents a prototype based on the case of Catania with the aim of sharing the lessons learnt, which can be reused as reference practices in other cases with similar requirements. The importance of achieving syntactic as well as semantic interoperability – as a result of transforming heterogeneous sources into Linked Data – is discussed: semantic interoperability is solved at data level in order to ease further development on top. We present a comprehensive data model for smart cities that integrates several data sources, including, geo-referenced data, public transportation, urban fault reporting, road maintenance and municipal waste collection. We show some novel ontology design patterns for modeling public transportation, urban fault reporting and road maintenance. Domain practitioners and general members of the public have been asked to play with the prototype, and fill out a survey with questions and feedbacks. A computational experiment has been also conducted to evaluate the performance of our data model in terms of practical scalability over increasing data and efficiency under complex queries. All produced data, models, prototype and questionnaire results are publicly accessible online.


title: The effects of cross-link density and chemical exchange on magnetization transfer in polyacrylamide gels.

abstract: The effects of polymer structure and water-macromolecule interactions on proton relaxation in an aqueous model polymer have been investigated using quantitative measurements of magnetization transfer. Polyacrylamide gels composed of 95% water, 5% comonomers acrylamide and N,N'-methylene-bis-acrylamide were studied. The structure and rigidity were varied by changing the cross-linking density of the polymer. The polymer showed a biphasic change in transverse relaxation with increasing cross-linking density which was accompanied by a sudden increase in magnetization transfer above 40% cross linking. This change may be attributed to the formation of rigid domains in the polymer which exhibit solid-like behavior with a short T2 (11 microseconds) and a Gaussian lineshape. Water-macromolecule interactions were controlled by varying the pH of the gel. At high pH (> 8), there was an increase in magnetization transfer and transverse relaxivity consistent with a chemical-exchange-mediated interaction between water protons and the polymer. By analyzing the system as two proton reservoirs coupled by magnetization exchange, the proton populations, intrinsic relaxation rates, and exchange rates were estimated, for different degrees of cross linking and pH. This model affords useful insights into the relevance of both supramolecular structure and chemical exchange on relaxation in tissues.


title: First impressions: users' judgments of virtual agents' personality and interpersonal attitude in first encounters

abstract: In first encounters people quickly form impressions of each other's personality and interpersonal attitude. We conducted a study to investigate how this transfers to first encounters between humans and virtual agents. In the study, subjects' avatars approached greeting agents in a virtual museum rendered in both first and third person perspective. Each agent exclusively exhibited nonverbal immediacy cues (smile, gaze and proximity) during the approach. Afterwards subjects judged its personality (extraversion) and interpersonal attitude (hostility/friendliness). We found that within only 12.5 seconds of interaction subjects formed impressions of the agents based on observed behavior. In particular, proximity had impact on judgments of extraversion whereas smile and gaze on friendliness. These results held for the different camera perspectives. Insights on how the interpretations might change according to the user's own personality are also provided.


title: Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics

abstract: We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to create a new performance model. Using the model, we quantify the trade-off between different metrics and propose to use it to tune the parameters of recommender algorithms without the need for online testing. Another application for the model is a self-adjusting algorithm blend that optimizes a recommender's parameters over time. We evaluate our findings on data and experiments from news websites.


title: A Switched

abstract: This paper presents a new single stage dc-dc boost converter topology with very large gain conversion ratio as a switched inductor multilevel boost converter (SIMLBC). It is a PWM-based dc-dc converter which combines the Switched-Inductor Structures and the switching capacitor function to provide a very large output voltage with different output dc levels which makes it suitable for multilevel inverter applications. The proposed topology has only single switch like the conventional dc-dc converter which can be controlled in a very simple way. In addition to, two inductors, 2N+2 diodes, N is the number of output dc voltage levels, and 2N-1 dc capacitors. A high switching frequency is employed to decrease the size of these components and thus much increasing the dynamic performance. The proposed topology has been compared with the existence dc-dc boost converters and it gives a higher voltage gain conversion ratio. The proposed converter has been analyzed, simulated and a prototype has been built and experimentally tested. Simulation and experimental results have been provided for validation.


title: Development of suitable photobioreactors for CO2 sequestration addressing global warming using green algae and cyanobacteria.

abstract: CO(2) sequestration by cyanobacteria and green algae are receiving increased attention in alleviating the impact of increasing CO(2) in the atmosphere. They, in addition to CO(2) capture, can produce renewable energy carriers such as carbon free energy hydrogen, bioethanol, biodiesel and other valuable biomolecules. Biological fixation of CO(2) are greatly affected by the characteristics of the microbial strains, their tolerance to temperature and the CO(2) present in the flue gas including SO(X), NO(X). However, there are additional factors like the availability of light, pH, O(2) removal, suitable design of the photobioreactor, culture density and the proper agitation of the reactor that will affect significantly the CO(2) sequestration process. Present paper deals with the photobioreactors of different geometry available for biomass production. It also focuses on the hybrid types of reactors (integrating two reactors) which can be used for overcoming the bottlenecks of a single photobioreactor.


title: IT OFFSHORING: History, Prospects and Challenges

abstract: e Abstract Offshore provision of IS/IT related services has been growing rapidly in recent years and seems firmly set to continue. This trend is fueled by the many advantages of offshore service procurement; however, there are dangers in this practice. Furthermore, offshoring requires adaptation of the IS function and IS management. This, in turn suggests the need for modifications of IS curricula in order to prepare graduates for the new environment. The advantages of offshoring are those of outsourcing in general - cost saving and allowing the organization to focus on its core activities. The main dangers include loss of possibly-important business skills and reliance on remote suppliers who face risks that are unfamiliar to the client firm. The loss of jobs due to offshoring also introduces political considerations. Offshore IS activities are generally the responsibility of an organization's CIO. This management responsibility requires awareness of cultural and legal differences and of risks associated with offshoring and outsourcing in general. Offshoring has an effect on job opportunities for graduates of


title: Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links

abstract: Traceability is the only means to ensure that the source code of a system is consistent with its requirements and that all and only the specified requirements have been implemented by developers. During software maintenance and evolution, requirement traceability links become obsolete because developers do not/cannot devote effort to updating them. Yet, recovering these traceability links later is a daunting and costly task for developers. Consequently, the literature has proposed methods, techniques, and tools to recover these traceability links semi-automatically or automatically. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code. However, IR techniques lack accuracy (precision and recall). In this paper, we show that mining software repositories and combining mined results with IR techniques can improve the accuracy (precision and recall) of IR techniques and we propose Trustrace, a trust-based traceability recovery approach. We apply Trustrace on four medium-size open-source systems to compare the accuracy of its traceability links with those recovered using state-of-the-art IR techniques from the literature, based on the Vector Space Model and Jensen-Shannon model. The results of Trustrace are up to 22.7 percent more precise and have 7.66 percent better recall values than those of the other techniques, on average. We thus show that mining software repositories and combining the mined data with existing results from IR techniques improves the precision and recall of requirement traceability links.


title: Yet another MicroArchitectural Attack:: exploiting I-Cache

abstract: MicroArchitectural Attacks (MA), which can be considered as a special form of Side-Channel Analysis, exploit microarchitectural functionalities of processor implementations and can compromise the security of computational environments even in the presence of sophisticated protection mechanisms like virtualization and sandboxing. This newly evolving research area has attracted significant interest due to the broad application range and the potentials of these attacks. Cache Analysis and Branch Prediction Analysis were the only types of MA that had been known publicly. In this paper, we introduce Instruction Cache (I-Cache) as yet another source of MA and present our experimental results which clearly prove the practicality and danger of I-Cache Attacks.


title: A survey of communication technologies for smart grid connectivity

abstract: The traditional electric power system is examining a transformation process to an intelligent, efficient, and cost-effective smart grid (SG) system. The SG has different subsystems for its accurate functionality. Among these subsystems, the communication subsystem plays a vital role for real-time data sharing between devices and systems connected in SG domain. In this paper, a survey of smart communication subsystem is provided and several communication technologies that have strong potential for implementation in future SG applications by electric utility companies are discussed. The advantages and disadvantages of each communication technology in the SG domain are also presented. Finally, a hybrid communication model is suggested for reliable communication between smart meters and control system in the SG.


title: Personal Learning Environments: Challenging the Dominant Design of Educational Systems

abstract: Current systems used in education follow a consistent design pattern, one that is not supportive of lifelong learning or personalization, is asymmetric in terms of user capability, and which is disconnected from the global ecology of Internet services. In this paper we propose an alternative design pattern for educational systems that emphasizes symmetric connections with a range of services both in formal and informal learning, work, and leisure, and identify strategies for implementation and experimentation.


title: A Mars VTOL Aerobot - Preliminary Design, Dynamics and Control

abstract: In this paper, we propose the use of a novel fixed-wing vertical take-off and landing (VTOL) aerobot. A mission profile to investigate the Isidis Planitia region of Mars is proposed based on the knowledge of the planet's geophysical characteristics, its atmosphere and terrain. The aerobot design is described from the aspects of vehicle selection, its propulsion system, power system, payload, thermal management, structure, mass budget, and control strategy and sensor suite. The aerobot proposed in this paper is believed to be a practical and realistic solution to the problem of investigating the Martian surface. A six-degree-of-freedom flight simulator has been created to support the aerobot design process by providing performance evaluations. The nonlinear dynamics is then linearized to a state-space formulation at a certain trimmed equilibrium point. Basic autopilot modes are developed for the aerobot based on the linearized state-space model. The results of the simulation show the aerobot is stable and controllable.


title: Constructing a biodiversity terminological inventory

abstract: The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications.


title: Color invariant chroma keying and color spill neutralization for dynamic scenes and cameras

abstract: In this article we show how temporal backdrops that alternately change their color rapidly at recording rate can aid chroma keying by transforming color spill into a neutral background illumination. Since the chosen colors sum up to white, the chromatic (color) spill component is neutralized when integrating over both backdrop states. The ability to separate both states additionally allows to compute high-quality alpha mattes. Besides the neutralization of color spill, our method is invariant to foreground colors and supports applications with real-time demands. In this article, we explain different realizations of temporal backdrops and describe how keying and color spill neutralization are carried out, how artifacts resulting from rapid motion can be reduced, and how our approach can be implemented to be compatible with common real-time post-production pipelines.


title: Variable step size Perturb and observe MPPT algorithm for standalone solar photovoltaic system

abstract: The maximum power obtained from a solar photovoltaic system varies due to the variation in the atmospheric condition. Maximum Power Point Tracking (MPPT) controller is implemented to track the maximum power with respect to the changes in the weather conditions to efficiently operate the solar Photovoltaic (PV) system. In this paper, a comparative study between different P&O algorithms such as conventional Perturbation & Observation (P&O) MPPT and variable step size Perturb & Observe (VSS P&O) MPPT are presented. The performance study of standalone solar PV system is carried out under various weather conditions with different MPPT controller. DC-DC boost converter is adopted as power conditioning unit.


title: Technology, Organization, and Structure---A Morphogenetic Approach

abstract: This article relates Archer's morphogenetic approach, derived from the philosophical tradition of critical realism, to the use of information and communication technology in organizations. Three gains are seen to accrue from this approach: greater clarity about the material properties of technology, links to broader structural conditions arising from the conceptualization of the relationship between agency and structure, and the potential to explore the importance of reflexivity in contemporary organizations, especially in conditions of the widespread use of information and communication technology. The importance of disaggregating the artifacts of this technology into levels and features is stressed to enable analysis to explore the specific impacts of particular combinations. This is developed through a discussion of data warehousing in connection with the attention being given to the importance of analytics in organizational strategies. Key features are in wider aspects of the cultural and structural context, demonstrating the fruitfulness of a morphogenetic approach.


Emotion Analysis on Twitter: The Hidden Challenge.


title: Anatomy of Successful Business Models for Complex Services: Insights from the Telemedicine Field

abstract: AbstractTelemedicine services may improve the quality of life of individuals while also reducing the costs of service provisioning. They represent an important but as yet understudied type of complex services that integrates many stakeholders acting in service value networks. These complex services typically comprise a combination of information technology (IT) services and highly person-oriented, non-IT services, and are characterized by long service delivery periods. In such an environment, it is particularly difficult to generate successful and sustainable business models, which are necessary for the widespread provision of telemedicine services. Following a design research approach, we develop and evaluate the CompBizMod framework, a morphological box allowing for: (1) the analysis, description, and classification of telemedicine business models, (2) the identification of white spots for future business opportunities, (3) and the identification of patterns for successful business models. We contribute...


title: Gender specific classification of road accident patterns through data mining techniques

abstract: Road accident analysis is very challenging task and investigating the dependencies between the attributes become complex because of many environmental and road related factors. In this research work we applied data mining classification techniques to carry out gender based classification of which RndTree and C4.5 using AdaBoost Meta classifier gives high accurate results. The training dataset used for the research work is obtained from Fatality Analysis Reporting System (FARS) which is provided by the University of Alabama's Critical Analysis Reporting Environment (CARE) system. The results reveal that AdaBoost used with RndTree improvised the classifier's accuracy.


Mathematical Induction: A Focus on the Conceptual Framework