title: A Survey of Small-Scale Unmanned Aerial Vehicles: Recent Advances and Future Development Trends

abstract: This paper provides a brief overview on the recent advances of small-scale unmanned aerial vehicles (UAVs) from the perspective of platforms, key elements, and scientific research. The survey starts with an introduction of the recent advances of small-scale UAV platforms, based on the information summarized from 132 models available worldwide. Next, the evolvement of the key elements, including onboard processing units, navigation sensors, mission-oriented sensors, communication modules, and ground control station, is presented and analyzed. Third, achievements of small-scale UAV research, particularly on platform design and construction, dynamics modeling, and flight control, are introduced. Finally, the future of small-scale UAVs' research, civil applications, and military applications are forecasted.


title: Extracting cyber threat intelligence from hacker forums: Support vector machines versus convolutional neural networks

abstract: Hacker forums and other social platforms may contain vital information about cyber security threats. But using manual analysis to extract relevant threat information from these sources is a time consuming and error-prone process that requires a significant allocation of resources. In this paper, we explore the potential of Machine Learning methods to rapidly sift through hacker forums for relevant threat intelligence. Utilizing text data from a real hacker forum, we compared the text classification performance of Convolutional Neural Network methods against more traditional Machine Learning approaches. We found that traditional machine learning methods, such as Support Vector Machines, can yield high levels of performance that are on par with Convolutional Neural Network algorithms.


title: Medicaid program; state plan home and community-based services, 5-year period for waivers, provider payment reassignment, and home and community-based setting requirements for Community First Choice and home and community-based services (HCBS) waivers. Final rule.

abstract: This final rule amends the Medicaid regulations to define and describe state plan section 1915(i) home and community-based services (HCBS) under the Social Security Act (the Act) amended by the Affordable Care Act. This rule offers states new flexibilities in providing necessary and appropriate services to elderly and disabled populations. This rule describes Medicaid coverage of the optional state plan benefit to furnish home and community based-services and draw federal matching funds. This rule also provides for a 5-year duration for certain demonstration projects or waivers at the discretion of the Secretary, when they provide medical assistance for individuals dually eligible for Medicaid and Medicare benefits, includes payment reassignment provisions because state Medicaid programs often operate as the primary or only payer for the class of practitioners that includes HCBS providers, and amends Medicaid regulations to provide home and community-based setting requirements related to the Affordable Care Act for Community First Choice State plan option. This final rule also makes several important changes to the regulations implementing Medicaid 1915(c) HCBS waivers.


title: Mediator Effects of Positive Emotions on Social Support and Depression among Adolescents Suffering from Mobile Phone Addiction.

abstract: BACKGROUND ::: Depression is a common mental disorder that is widely seen among adolescents suffering from mobile phone addiction. While it is well known that both positive emtions in adolescents wiotions and social support can have a positive impact by helping individuals to maintain a positive attitude, the correlation between positive emotions, social support, and depression among these adolescents remains to be investigated. This study examined the mediator effects of positive emotions on the relationship between social support and depression among adolescents suffering from mobile phone addiction. ::: ::: ::: SUBJECTS AND METHODS ::: For this study, conducted in 2016, we selected 1,346 adolescent students from three middle schools (ranging from Junior Grade One to Senior Grade Three) in Hunan Province of China, to participate in the survey. Participants were selected using the stratified cluster random sampling method, and all participants remained anonymous throughout the study. Each participant completed the Self-made General Situation Questionnaire, the Social Support Rating Scale, the Positive and Negative Affect Schedule, the Center for Epidemiological Studies Depression Scale, and the Mobile Phone Addiction Tendency Scale. ::: ::: ::: RESULTS ::: There was significant positive correlation between positive emotions and social support. Both positive emotions and social support demonstrated significant negative correlation with depression. Positive emotions had partial mediator effects on the relationship between social support and depression (P<0.01). ::: ::: ::: CONCLUSIONS ::: Both social support and positive emotions can lower levels of depression among adolescents suffering from mobile phone addiction. Social support contributes to positive emoth mobile phone addiction, thereby reducing their levels of depression. These findings suggest that more support and care should be given to this particular adolescent population.


title: Predictive Mitigation of Timing Channels - Threat Defense for Machine Codes

abstract: The security of software systems can be threatened by many internal and external threats, including data leakages due to timing channels. Even if developers manage to avoid security threats in the source code or bytecode during development and testing, new threats can arise as the compiler generates machine codes from representations at the binary code level during execution on the processor or due to operating system specifics. Current approaches either do not allow the neutralization of timing channels to be achieved comprehensively with a sufficient degree of security or require an unjustifiable amount of time and/or resources. Herein, a method is demonstrated for the protected execution of software based on a secure virtual execution environment (VEE) that combines the results from dynamic and static analyses to find timing channels through the application of code transformations. This solution complements other available techniques to prevent timing channels from being exploited. This approach helps control the appearance and neutralization of timing channels via just-in-time code modifications during all stages of program development and usage. This work demonstrates the identification of threats using timing channels as an example. The approach presented herein can be expanded to the neutralization of other types of threats.


title: Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA

abstract: Probabilistic topic models have recently attracted much attention because of their successful applications in many text mining tasks such as retrieval, summarization, categorization, and clustering. Although many existing studies have reported promising performance of these topic models, none of the work has systematically investigated the task performance of topic models; as a result, some critical questions that may affect the performance of all applications of topic models are mostly unanswered, particularly how to choose between competing models, how multiple local maxima affect task performance, and how to set parameters in topic models. In this paper, we address these questions by conducting a systematic investigation of two representative probabilistic topic models, probabilistic latent semantic analysis (PLSA) and Latent Dirichlet Allocation (LDA), using three representative text mining tasks, including document clustering, text categorization, and ad-hoc retrieval. The analysis of our experimental results provides deeper understanding of topic models and many useful insights about how to optimize the performance of topic models for these typical tasks. The task-based evaluation framework is generalizable to other topic models in the family of either PLSA or LDA.


title: Control of a center-out reaching task using a reinforcement learning Brain-Machine Interface

abstract: In this work, we develop an experimental primate test bed for a center-out reaching task to test the performance of reinforcement learning based decoders for Brain-Machine Interfaces. Neural recordings obtained from the primary motor cortex were used to adapt a decoder using only sequences of neuronal activation and reinforced interaction with the environment. From a naive state, the system was able to achieve 100% of the targets without any a priori knowledge of the correct neural-to-motor mapping. Results show that the coupling of motor and reward information in an adaptive BMI decoder has the potential to create more realistic and functional models necessary for future BMI control.


Simulated annealing


title: Tendon arrangement and muscle force requirements for human-like force capabilities in a robotic finger

abstract: Human motion can provide a rich source of examples for use in robot grasping and manipulation. Adapting human examples to a robot manipulator is a difficult problem, however, in part due to differences between human and robot hands. Even hands that are anthropomorphic in external design may differ dramatically from the human hand in ability to grasp and manipulate objects due to internal design differences. For example, force transmission mechanisms in robot fingers are generally symmetric about flexion/extension axes, but in human fingers they are focused toward flexion. This paper describes how a tendon driven robot finger can be optimized for force transmission capability equivalent to the human index finger. We show that two distinct tendon arrangements that are similar to those that have been used in robot hands can achieve the same range of forces as the human finger with minimal additional cost in total muscle force requirements.


title: Structure tensor-based WLS filter for adaptive smoothing

abstract: The weighted least squares (WLS) filter is a well-known edge preserving smoothing technique, but its weights highly depend on the image gradients. Thus, it is hard to smooth local textures with large gradients by WLS filter. In this paper, we propose a structure tensor-based WLS (ST-WLS) filter to enhance the smoothing ability of WLS filter while successfully keeping object boundaries. We introduce structure tensor into WLS filter to extract local structures in an image and use them to guide the smoothing process. Specifically, we use structure tensor to measure isotropy of pixels. Then, we calculate the smoothing weights for pixels based on both their isotropy and gradients. Experimental results demonstrate that ST-WLS filter has strong ability of smoothing textures, especially for isotropic textures, while preserving boundaries and structures of objects.


title: Vapor SIMD: Auto-vectorize once, run everywhere

abstract: Just-in-Time (JIT) compiler technology offers portability while facilitating target- and context-specific specialization. Single-Instruction-Multiple-Data (SIMD) hardware is ubiquitous and markedly diverse, but can be difficult for JIT compilers to efficiently target due to resource and budget constraints. We present our design for a synergistic auto-vectorizing compilation scheme. The scheme is composed of an aggressive, generic offline stage coupled with a lightweight, target-specific online stage. Our method leverages the optimized intermediate results provided by the first stage across disparate SIMD architectures from different vendors, having distinct characteristics ranging from different vector sizes, memory alignment and access constraints, to special computational idioms. We demonstrate the effectiveness of our design using a set of kernels that exercise innermost loop, outer loop, as well as straight-line code vectorization, all automatically extracted by the common offline compilation stage. This results in performance comparable to that provided by specialized monolithic offline compilers. Our framework is implemented using open-source tools and standards, thereby promoting interoperability and extendibility.


Do KPIs used by CIOs Decelerate Digital Business Transformation? The Case of ITIL


title: Use of fresh vegetable discards as sea urchin diet: effect on gonad index and quality

abstract: This study evaluated the exploitation of unprocessed agricultural discards in the form of fresh vegetable leaves as a diet for the sea urchin Paracentrotus lividus through the assessment of their effects on gonad yield and quality. A stock of wild-caught P. lividus was fed on discarded leaves from three different species (Beta vulgaris, Brassica oleracea, and Lactuca sativa) and the macroalga Ulva lactuca for 3 months under controlled conditions. At the beginning and end of the experiment, total and gonad weight were measured, while gonad and diet total carbon (C%), nitrogen (N%), δ13C, δ15N, carbohydrates, lipids, and proteins were analyzed. The results showed that agricultural discards provided for the maintenance of gonad index and nutritional value (carbohydrate, lipid, and protein content) of initial specimens. L. sativa also improved gonadic color. The results of this study suggest that fresh vegetable discards may be successfully used in the preparation of more balanced diets for sea urchin aquaculture. The use of agricultural discards in prepared diets offers a number of advantages, including an abundant resource, the recycling of discards into new organic matter, and reduced pressure on marine organisms (i.e., macroalgae) in the production of food for cultured organisms.


title: Elliptic Curve Cryptosystems

abstract: We discuss analogs based on elliptic curves over finite fields of public key cryptosystems which use the multiplicative group of a finite field. These elliptic curve cryptosystems may be more secure, because the analog of the discrete logarithm problem on elliptic curves is likely to be harder than the classical discrete logarithm problem, especially over GF(2'). We discuss the question of primitive points on an elliptic curve modulo p, and give a theorem on nonsmoothness of the order of the cyclic subgroup generated by a global point.


title: Quantum coherence enabled determination of the energy landscape in light-harvesting complex II.

abstract: The near-unity efficiency of energy transfer in photosynthesis makes photosynthetic light-harvesting complexes a promising avenue for developing new renewable energy technologies. Knowledge of the energy landscape of these complexes is essential in understanding their function, but its experimental determination has proven elusive. Here, the observation of quantum coherence using two-dimensional electronic spectroscopy is employed to directly measure the 14 lowest electronic energy levels in light-harvesting complex II (LHCII), the most abundant antenna complex in plants containing approximately 50% of the world's chlorophyll. We observe that the electronically excited states are relatively evenly distributed, highlighting an important design principle of photosynthetic complexes that explains the observed ultrafast intracomplex energy transfer in LHCII.


title: Unwrapping the eye for visible-spectrum gaze tracking on wearable devices

abstract: Wearable devices with gaze tracking can assist users in many daily-life tasks. When used for extended periods of time, it is desirable that such devices do not employ active illumination for safety reasons and to minimize interference from other light sources such as the sun. Most non active-illumination methods for gaze tracking attempt to locate the iris contour by fitting an ellipse. Although the camera projection causes the iris to appear as an ellipse in the eye image, it is actually a circle on the eye surface. Instead of searching for an ellipse in the eye image, the method proposed in this paper searches for a circle on the eye surface. To this end, the method calibrates a three-dimensional eye model based on the location of the corners of the eye. Using the 3D eye model, an input image is first transformed so that the eye's spherical surface is warped into a plane, thus “unwrapping” the eye. The iris circle is then detected on the unwrapped image by a three-step robust circle-fitting procedure. The location of the circle corresponds to the gaze orientation on the outside image. The method is fast to calibrate and runs in realtime. Extensive experimentation on embedded hardware and comparisons with alternative methods demonstrate the effectiveness of the proposed solution.


title: Testing a walkthrough methodology for theory-based design of walk-up-and-use interfaces

abstract: The value of theoretical analyses in user interface design has been hotly debated. All sides agree that it is difficult to apply current theoretical models within the constraints of real-world development projects. We attack this problem in the context of bringing the theoretical ideas within a model of exploratory learning [19] to bear on the evaluation of alternative interfaces for walk-up-and-use systems. We derived a “cognitive walkthrough” procedure for systematically evaluating features of an interface in the context of the theory. Four people independently applied this procedure to four alternative interfaces for which we have empirical usability data. Consideration of the walkthrough sheds light on the consistency with which such a procedure can be applied as well as the accuracy of the results.


title: Recurrent Reinforcement Learning: A Hybrid Approach

abstract: Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and infer hidden states as they often depend on the agent's entire interaction history and may require substantial domain knowledge. In this work, we investigate a deep-learning approach to learning the representation of states in partially observable tasks, with minimal prior knowledge of the domain. In particular, we propose a new family of hybrid models that combines the strength of both supervised learning (SL) and reinforcement learning (RL), trained in a joint fashion: The SL component can be a recurrent neural networks (RNN) or its long short-term memory (LSTM) version, which is equipped with the desired property of being able to capture long-term dependency on history, thus providing an effective way of learning the representation of hidden states. The RL component is a deep Q-network (DQN) that learns to optimize the control for maximizing long-term rewards. Extensive experiments in a direct mailing campaign problem demonstrate the effectiveness and advantages of the proposed approach, which performs the best among a set of previous state-of-the-art methods.


title: A Generative Entity-Mention Model for Linking Entities with Knowledge Base

abstract: Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entity-mention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s\e), and the distribution of possible contexts of a specific entity P(c\e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s\e) and P(c\e). Experimental results show that our method can significantly outperform the traditional methods.


title: Augmenting Microsoft's HoloLens with vuforia tracking for neuronavigation

abstract: Major hurdles for Microsoft's HoloLens as a tool in medicine have been accessing tracking data, as well as a relatively high-localisation error of the displayed information; cumulatively resulting in its limited use and minimal quantification. The following work investigates the augmentation of HoloLens with the proprietary image processing SDK Vuforia, allowing integration of data from its front-facing RGB camera to provide more spatially stable holograms for neuronavigational use. Continuous camera tracking was able to maintain hologram registration with a mean perceived drift of 1.41 mm, as well as a mean sub 2-mm surface point localisation accuracy of 53%, all while allowing the researcher to walk about a test area. This represents a 68% improvement for the later and a 34% improvement for the former compared with a typical HoloLens deployment used as a control. Both represent a significant improvement on hologram stability given the current state-of-the-art, and to the best of the authors knowledge are the first example of quantified measurements when augmenting hologram stability using data from the RGB sensor.


title: Semi-Supervised Semantic Image Segmentation with Self-correcting Networks

abstract: Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak set). Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weak set and a self-correction module that improves the generated labels during training using the increasingly accurate primary model. We introduce two variants of the self-correction module using either linear or convolutional functions. Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models trained with a small fully supervised set perform similar to, or better than, models trained with a large fully supervised set while requiring ~7x less annotation effort.


title: A high-harmonic gyro-klystron amplifier: theory and experiment†

abstract: Abstract Experimental results of a high-harmonic gyro-klystron amplifier are presented along with small-signal theory and large-signal simulation data. In this device, axis-encircling electrons interact synchronously with high-order azimuthal cylindrical-cavity TE modes. Gain in excess of 20 dB has been achieved at the fifth and sixth harmonics of the cyclotron frequency. The requisite high-energy beam is provided by a gyro-resonant RF accelerator. Harmonic operation together with RF acceleration potentially constitute a compact high-power high-frequency amplifier.


title: Integration site selection by retroviruses and transposable elements in eukaryotes

abstract: Next-generation sequencing has facilitated the study of how transposable elements and retroviruses select their diverse genomic sites of integration, and revealed integration site preferences that range from specific nucleotide sequences to particular chromatin states. The authors review the various mechanisms of integration site selection in eukaryotes, as well as the molecular and cellular determinants that guide this process.


title: Internet Addiction: A Systematic Review of Epidemiological Research for the Last Decade

abstract: In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.


title: Privacy, identity and security in ambient intelligence: a scenario analysis

abstract: The success of ambient intelligence (AmI) will depend on how secure it can be made, how privacy and other rights of individuals can be protected and how individuals can come to trust the intelligent world that surrounds them and through which they move. This article addresses these issues by analysing scenarios for ambient intelligence applications that have been developed over the last few years. It elaborates the assumptions that promotors make about the likely use of the technology and possibly unwanted side effects. It concludes with a number of threats for personal privacy that become evident.


title: The General Term of the Prime Number Sequence and the Smarandache Prime Function.

abstract: Let I s consider the function d(i) = number of divisors of the positive integer number i. We have found the following expression for this function:


title: Seebeck effects in a graphene nanoribbon coupled to two ferromagnetic leads

abstract: We theoretically investigate the Seebeck effects for the system of a narrow graphene nanoribbon between two ferromagnetic (FM) electrodes with noncollinear magnetic moments. Both zigzag-edge graphene nanoribbons (ZGNRs) and armchair-edge graphene nanoribbons (AGNRs) have been considered. By using the nonequilibrium Green's function method combining with the tight-binding Hamiltonian, it is demonstrated that, the Seebeck coefficients are sensitive to the chirality and width of the nanoribbon in the absence of magnetic field. Compared with 22-ZGNR and metallic 17-AGNR systems, semiconducting 15-AGNR system is found to posses superior thermoelectric performance, its Seebeck coefficients can be improved by two orders of magnitude. Meanwhile, the Seebeck coefficients for both 22-ZGNR and metallic 17-AGNR systems are the same order as that of graphene system. Furthermore, the Seebeck coefficients are strongly dependent on the magnetization M as well as magnetic configuration of the two FM leads. Particularly, t...


On Cloud-Based Software Processes


title: The Log Handwriting Program Improved Children’s Writing Legibility: A Pretest–Posttest Study

abstract: OBJECTIVE. We determined the feasibility and outcomes of the Log Handwriting Program (Raynal, 1990), an 8-week training program based on task-specific practice of handwriting. METHOD. We used a pretest–posttest design involving 16 first- and second-grade Australian students. Handwriting training sessions occurred in schools for 45 min per week over 8 weeks, in groups of 2 or 3. Weekly homework was provided. The primary outcome measure was the Minnesota Handwriting Assessment (range = 0 to 34; Reisman, 1999). Legibility, form, alignment, size, spacing, and speed were measured. RESULTS. All six assessment subscales showed statistically significant differences. Legibility improved by a mean of 4.1 points (95% confidence interval = 2.5 to 5.7); form, 5.3 points; alignment, 7.8 points; size, 7.9 points; and space, 5.3 points. Speed decreased by 3.9 points. CONCLUSION. Preliminary evidence indicates that an 8-week Log Handwriting Program is feasible and improved handwriting in primary school children. Mackay, N., McCluskey, A., & Mayes, R. (2010). The Log Handwriting Program improved children’s writing legibility: A


title: Music Education and the Knowledge Economy: Developing Creativity, Strengthening Communities

abstract: A process for distinguishing nucleic acid segments on the basis of nucleotide differences, thereby providing a rapid, convenient means for detecting mutation, is disclosed. The present process comprises synthesizing separately complementary nucleic acid strands on each of at least two target nucleic acid templates using a nucleic acid polymerase and nucleoside triphosphate substrates, wherein at least one of the natural nucleoside triphosphate substrates is replaced with a mobility-shifting analog; denaturing the synthesized strands from the templates if necessary; and comparing the mobility of the separately synthesized strands through a size-fractionation medium. A difference in mobility indicates that the target nucleic acid templates contain different numbers of total nucleotides and/or different numbers of nucleotides complementary to the mobility-shifting analogs. Thus, the process of the present invention can be used to distinguish homologous DNAs or RNAs differing by nucleotide substitutions that affect the number of mobility-shifting analog residues incorporated into synthesized strands, as well as to distinguish differences due to nucleotide insertion or deletion.


title: Mobile Cloud Business Process Management System for the Internet of Things: A Survey

abstract: The Internet of Things (IoT) represents a comprehensive environment that consists of a large number of smart devices interconnecting heterogeneous physical objects to the Internet. Many domains such as logistics, manufacturing, agriculture, urban computing, home automation, ambient assisted living, and various ubiquitous computing applications have utilized IoT technologies. Meanwhile, Business Process Management Systems (BPMSs) have become a successful and efficient solution for coordinated management and optimized utilization of resources/entities. However, past BPMSs have not considered many issues they will face in managing large-scale connected heterogeneous IoT entities. Without fully understanding the behavior, capability, and state of the IoT entities, the BPMS can fail to manage the IoT integrated information systems. In this article, we analyze existing BPMSs for IoT and identify the limitations and their drawbacks based on a Mobile Cloud Computing perspective. Later, we discuss a number of open challenges in BPMS for IoT.


title: DLPlib: A Library for Deep Learning Processor

abstract: Recently, deep learning processors have become one of the most promising solutions of accelerating deep learning algorithms. Currently, the only method of programming the deep learning processors is through writing assembly instructions by bare hands, which costs a lot of programming efforts and causes very low efficiency. One solution is to integrate the deep learning processors as a new back-end into one prevalent high-level deep learning framework (e.g., TPU (tensor processing unit) is integrated into Tensorflow directly). However, this will obstruct other frameworks to profit from the programming interface. The alternative approach is to design a framework-independent low-level library for deep learning processors (e.g., the deep learning library for GPU, cuDNN). In this fashion, the library could be conveniently invoked in high-level programming frameworks and provides more generality. In order to allow more deep learning frameworks to gain benefits from this environment, we envision it as a low-level library which could be easily embedded into current high-level frameworks and provide high performance. Three major issues of designing such a library are discussed. The first one is the design of data structures. Data structures should be as few as possible while being able to support all possible operations. This will allow us to optimize the data structures easier without compromising the generality. The second one is the selection of operations, which should provide a rather wide range of operations to support various types of networks with high efficiency. The third is the design of the API, which should provide a flexible and user-friendly programming model and should be easy to be embedded into existing deep learning frameworks. Considering all the above issues, we propose DLPlib, a tensor-filter based library designed specific for deep learning processors. It contains two major data structures, tensor and filter, and a set of operators including basic neural network primitives and matrix/vector operations. It provides a descriptor-based API exposed as a C++ interface. The library achieves a speedup of 0.79x compared with the performance of hand-written assembly instructions.


title: Twenty years of particle image velocimetry

abstract: The development of the method of particle image velocimetry (PIV) is traced by describing some of the milestones that have enabled new and/or better measurements to be made. The current status of PIV is summarized, and some goals for future advances are addressed.


title: Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders

abstract: We examine unsupervised machine learning techniques to learn features that best describe configurations of the two-dimensional Ising model and the three-dimensional XY model. The methods range from principal component analysis over manifold and clustering methods to artificial neural-network-based variational autoencoders. They are applied to Monte Carlo-sampled configurations and have, a priori, no knowledge about the Hamiltonian or the order parameter. We find that the most promising algorithms are principal component analysis and variational autoencoders. Their predicted latent parameters correspond to the known order parameters. The latent representations of the models in question are clustered, which makes it possible to identify phases without prior knowledge of their existence. Furthermore, we find that the reconstruction loss function can be used as a universal identifier for phase transitions.


title: Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier

abstract: This paper presents an algorithm for classification of non-melanoma skin lesions based on a novel hierarchical K-Nearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that chooses the most relevant feature subsets at each node of the hierarchy. Colour and texture features are extracted from skin lesions. The accuracy of the proposed hierarchical scheme is higher than 93% in discriminating cancer and pre-malignant lesions from benign lesions, and it reaches an overall classification accuracy of 74% over five common classes of skin lesions, including two non-melanoma cancer types. This is the most extensive published result on non-melanoma skin cancer classification from colour images acquired by a standard camera (non-dermoscopy).


title: Adaptive Cities: A Cybernetic Perspective on Urban Systems

abstract: Cities are changing constantly. All urban systems face different conditions from day to day. Even when averaged regularities can be found, urban systems will be more efficient if they can adapt to changes at the same speeds at which these occur. Technology can assist humans in achieving this adaptation. Inspired by cybernetics, we propose a description of cities as adaptive systems. We identify three main components: information, algorithms, and agents, which we illustrate with current and future examples. The implications of adaptive cities are manifold, with direct impacts on mobility, sustainability, resilience, governance, and society. Still, the potential of adaptive cities will not depend so much on technology as on how we use it.


title: Photovoltaic-Battery-Powered DC Bus System for Common Portable Electronic Devices

abstract: Renewable energy sources based on photovoltaic along with battery-based energy storage necessitate power conditioning to meet load requirements and/or be connected to the electrical grid. The power conditioning is achieved via a DC-DC converter and a DC-AC inverter stages to produce the desired AC source. This is also the case even when the load is of DC type such as the typical electronic devices which require AC adaptors to be powered from the AC mains. The paper presents a hybrid photovoltaic-battery powered DC bus system that eliminates the DC-AC conversion stage resulting in lower cost and improved efficiency. It is also shown experimentally that the AC adaptors associated with the various commonly used electronic devices can be reused with the proposed system and in some cases offer higher operating efficiencies when powered from a DC bus instead. A novel high-gain modified boost converter is also introduced with several times higher voltage conversion ratio than the conventional boost converter topology. This arrangement results in higher DC bus levels and lower cable conduction losses. Moreover, the voltage stress on the modified boost converter power switch is within half the output voltage. Experimental results taken from a laboratory prototype are presented to confirm the effectiveness of the proposed approach.


title: A unified model for human activity recognition using spatial distribution of gradients and difference of Gaussian kernel

abstract: Understanding of human action and activity from video data is growing field and received rapid importance due to surveillance, security, entertainment and personal logging. In this work, a new hybrid technique is proposed for the description of human action and activity in video sequences. The unified framework endows a robust feature vector wrapping both global and local information strengthening discriminative depiction of action recognition. Initially, entropy-based texture segmentation is used for human silhouette extraction followed by construction of average energy silhouette images (AEIs). AEIs are the 2D binary projection of human silhouette frames of the video sequences, which reduces the feature vector generation time complexity. Spatial Distribution Gradients are computed at different levels of resolution of sub-images of AEI consisting overall shape variations of human silhouette during the activity. Due to scale, rotation and translation invariant properties of STIPs, the vocabulary of DoG-based STIPs are created using vector quantization which is unique for each class of the activity. Extensive experiments are conducted to validate the performance of the proposed approach on four standard benchmarks, i.e., Weizmann, KTH, Ballet Movements, Multi-view IXMAS. Promising results are obtained when compared with the similar state of the arts, demonstrating the robustness of the proposed hybrid feature vector for different types of challenges—illumination, view variations posed by the datasets.


title: A Slope-Based Multipath Estimation technique for mitigating short-delay multipath in GNSS receivers

abstract: The everlasting public interest on location and positioning services has originated a demand for a high performance Global Navigation Satellite System (GNSS), such as the Global Positioning System or the future European satellite navigation system, Galileo. The performance of GNSS is subject to several errors, such as ionosphere delay, troposphere delay, receiver noise and multipath. Among all these errors, multipath is the main limiting factor in precision-oriented GNSS applications. In order to mitigate the multipath influence on navigation receivers, the multipath problem has been approached from several directions, including the development of novel signal processing techniques. Many of these techniques rely on modifying the tracking loop discriminator in order to make it resistant to multipath. These techniques have proved very efficient against multipath having a medium or large delay with respect to the Line-Of-Sight (LOS) signal. In general, the multipath errors are largely reduced for multipath delays greater than around 0.1 chips (which is about 29.3 meters for Galileo E1 Open Service (OS) signal). Theoretically, this constitutes a remarkable improvement as compared to simpler techniques such as narrow Early-Minus-Late (nEML) tracking loop. However, in practice, most of the multipath signals enter the receiver with short-delay with respect to LOS signal, making most of these mitigation techniques partially ineffective. In this paper, we propose a new multipath estimation technique, namely the Slope-Based Multipath Estimation (SBME), which is capable of mitigating the short-delay multipath (i.e., multipath delays less than 0.35 chips) quite well compared with other state-of-the-art mitigation techniques, such as the nEML and the High Resolution Correlators (HRC). The proposed SBME first derives a multipath estimation equation by utilizing the correlation shape of the ideal normalized correlation function of a Binary Phase Shift Keying (BPSK)- or Multiplexed Binary Offset Carrier (MBOC)-modulated signal, which is then used to compensate for the multipath bias of a nEML tracking loop. It is worth to mention here that the SBME requires an additional correlator at the late side of the correlation function, and it is used in-conjunction with a nEML tracking loop. The multipath performance of the above-mentioned mitigation techniques is presented for Galileo E1 OS and GPS L1 C/A signals from theoretical as well as simulation perspective.


title: The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention

abstract: Evidence has amassed from both animal intracranial recordings and human electrophysiology that neural oscillatory mechanisms play a critical role in a number of cognitive functions such as learning, memory, feature binding and sensory gating. The wide availability of high-density electrical and magnetic recordings (64-256 channels) over the past two decades has allowed for renewed efforts in the characterization and localization of these rhythms. A variety of cognitive effects that are associated with specific brain oscillations have been reported, which range in spectral, temporal, and spatial characteristics depending on the context. Our laboratory has focused on investigating the role of alpha-band oscillatory activity (8-14 Hz) as a potential attentional suppression mechanism, and this particular oscillatory attention mechanism will be the focus of the current review. We discuss findings in the context of intersensory selective attention as well as intrasensory spatial and feature-based attention in the visual, auditory, and tactile domains. The weight of evidence suggests that alpha-band oscillations can be actively invoked within cortical regions across multiple sensory systems, particularly when these regions are involved in processing irrelevant or distracting information. That is, a central role for alpha seems to be as an attentional suppression mechanism when objects or features need to be specifically ignored or selected against.


title: A compact CPW fed modified circular patch antenna with stub for UWB applications

abstract: In this paper, a new coplanar waveguide CPW fed Modified Circular Patch Antenna (MCPA) is presented. The proposed antenna is designed to operate from 3 GHz to 12 GHz. It consists of a circular patch which is truncated from two sides with respect to feed line. A stub of optimized dimension is attached to other side at a proper location to achieve ultra wide band characteristics. The proposed antenna is printed on a dielectric substrate glass epoxy FR 4 substrate and fed by a 50 Ω CPW on the same layer, which can yield an ultra wide bandwidth (-10 dB reflection coefficient) with satisfactory radiation patterns. The radiation patterns in the x-z plane are relatively omnidirectional, whereas the y-z plane patterns are bidirectional similar to that of a monopole antenna. The performance and characteristics of the antenna are investigated in order to understand its operation. It is proposed that this antenna can be part of various ultra wideband communication systems.


title: Road traffic sign detection and classification

abstract: A vision-based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown.


title: Mapping slums using spatial features in Accra, Ghana

abstract: In order to map the spatial extent and location of slum settlements multiple methodologies have been devised including remote sensing based methods and field based methods using surveys and census data. In this study we utilize spatial, structural, and contextual features (e.g., PanTex, Histogram of Oriented Gradients, Line Support Regions, Hough transforms and others) calculated at multiple spatial scales from high spatial resolution satellite data to map slum areas and compare these estimates to three field based slum maps: one from the UN Habitat/Accra Metropolitan Assembly (UNAMA) and two census data derived maps based on the UN Habitat definition of a slum, a simple slum/non-slum dichotomy map, and a slum index map. When comparing the remotely sensed derived slum areas to the UNAMA slum definition results indicate an overall accuracy of 94.3% and a Kappa of 0.91. When compared to the dichotomous, census derived slum maps the results are not as accurate. This reduced accuracy is due to the substantial over prediction of slums, especially if only one criterion was missing, using the census data. In relation to the slum index, the remote sensing estimates of slums were significantly correlated with an r2 of 0.45 and when population density was taken into account, the correlation increased to an r2 of 0.78. Overall, the remote sensing methodology provides a reasonable estimate of slum areas and variations within the city.


title: QuERy: A Framework for Integrating Entity Resolution with Query Processing

abstract: This paper explores an analysis-aware data cleaning architecture for a large class of SPJ SQL queries. In particular, we propose QuERy, a novel framework for integrating entity resolution (ER) with query processing. The aim of QuERy is to correctly and efficiently answer complex queries issued on top of dirty data. The comprehensive empirical evaluation of the proposed solution demonstrates its significant advantage in terms of efficiency over the traditional techniques for the given problem settings.


title: Maximum Power Point Tracking of PV System Using ANFIS Prediction and Fuzzy Logic Tracking

abstract: The maximum operating point of solar Photovoltaic (PV) panels changes with environmen- tal conditions. Many methods have been proposed to locate and track the maximum power point (MPP) of PV cells. The diculties that face these methods are the rapid changes in solar radiation and the va- riety in cell temperature which aects the MPP set- ting. External sensors are used in many approaches to measure solar irradiation and ambient temperature to estimate the MPP as a function of data measured. In this paper, linear correlation is proposed to ana- lyze the experimental data to select the appropriate PV parameters that can recognize the MPP location. Short circuit current (Isc) and open circuit voltage (Voc) are selected as inputs factors instead of environ- mental influences. The paper demonstrates how these simple factors are necessary to locate accurate MPP under wide changes in environmental conditions. The statistical analysis is used to classify the data in ap- propriate fuzzy memberships. The proposed maxi- mum power point tracking (MPPT) model depends on an Adaptive Neuro-Fuzzy Inference System (AN- FIS) which is designed as a combination of the Sugeno fuzzy model and neural network. ANFIS of five layers with four fuzzy rules is used to acquire a high preci- sion of locating Vmax with few adaptation epochs. The fuzzy logic controller (FLC) utilizes the ANFIS output voltage to track the MPP. The MPPT con- troller is designed to acquire high eciency with low fluctuation.


title: Toward a Maturity Model Development Process for Information Systems (MMDePSI)

abstract: Maturity models are significant tools to ensure continuous improvement of systems and activities. They allow selfassessment and provide a means benchmark of these activities in relation to best practices. This article proposes the MMDePSI process for the development of new models of maturity in information systems. It begins by presenting the existing methods and approaches. Then he describes the MMDePSI process and the corresponding meta-model. Before concluding and presenting prospects of this work, it evaluates the proposed process.


title: CREST: Convolutional Residual Learning for Visual Tracking

abstract: Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual tracking. They only need a small set of training samples from the initial frame to generate an appearance model. However, existing DCFs learn the filters separately from feature extraction, and update these filters using a moving average operation with an empirical weight. These DCF trackers hardly benefit from the end-to-end training. In this paper, we propose the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network. Our method integrates feature extraction, response map generation as well as model update into the neural networks for an end-to-end training. To reduce model degradation during online update, we apply residual learning to take appearance changes into account. Extensive experiments on the benchmark datasets demonstrate that our CREST tracker performs favorably against state-of-the-art trackers.


Less is More: Active Learning with Support Vector Machines


Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning


title: How to Recommend?: User Trust Factors in Movie Recommender Systems

abstract: How much trust a user places in a recommender is crucial to the uptake of the recommendations. Although prior work established various factors that build and sustain user trust, their comparative impact has not been studied in depth. This paper presents the results of a crowdsourced study examining the impact of various recommendation interfaces and content selection strategies on user trust. It evaluates the subjective ranking of nine key factors of trust grouped into three dimensions and examines the differences observed with respect to users' personality traits.


title: System Call Redirection: A Practical Approach to Meeting Real-World Virtual Machine Introspection Needs

abstract: Existing VMI techniques have high overhead, and require customized introspection programs/tools for different guest OS versions - lack of generality. In this paper, we present Shadow Context, a system for close-to-real time manual-effort-free VMI. Shadow Context can meet several important real-world VMI needs which existing VMI techniques cannot. Compared to other automatic introspection tool generation techniques, Shadow Contexthas two merits: (1) Its overhead is significantly less. It achieves close-to-real time VMI. (2) It significantly improves the practical usefulness of introspection tools by allowing one introspection program to inspect a variety of guest OS versions. These merits are achieved via a new concept called "Shadow Context" which allows the guest OSessystem call code to be reused inside a "shadowed" portion of the context of the out-of-guest inspection program. Besides, Shadow Context is secure enough to defend against a variety of real world attacks. Shadow Context is designed, implemented and systematically evaluated. Experimental results show that the performance overhead is about 75%with a median initialization time of 0.117 milliseconds.


A package for Survival Analysis in S


title: Metasynthesis: An Original Method to Synthesize Qualitative Literature in Psychiatry

abstract: Background: Metasynthesis — the systematic review and integration of findings from qualitative studies — is an emerging technique in medical research that can use many different methods. Nevertheless, the method must be appropriate to the specific scientific field in which it is used. The objective is to describe the steps of a metasynthesis method adapted from Thematic Synthesis and phenomenology to fit the particularities of psychiatric research. Method: We detail each step of the method used in a metasynthesis published in 2015 on adolescent and young adults suicidal behaviors. We provide clarifications in several methodological points using the latest literature on metasyntheses. The method is described in six steps: define the research question and the inclusion criteria; select the studies; assess their quality; extract and present the formal data; analyze the data; and express the synthesis. Conclusions: Metasyntheses offer an appropriate balance between an objective framework, a rigorously scientific approach to data analysis and the necessary contribution of the researcher's subjectivity in the construction of the final work. They propose a third level of comprehension and interpretation that brings original insights, improve the global understanding in psychiatry and propose immediate therapeutic implications. They should be included in the psychiatric common research toolkit, to become better recognized by clinicians and mental health professionals.


title: Compassion fade and the challenge of environmental conservation

abstract: Compassion shown towards victims often decreases as the number of individuals in need of aid increases, identifiability of the victims decreases, and the proportion of victims helped shrinks. Such ``compassion fade'' may hamper individual-level and collective responses to pressing large-scale crises. To date, research on compassion fade has focused on humanitarian challenges; thus, it remains unknown whether and to what extent compassion fade emerges when victims are non-human others. Here we show that compassion fade occurs in the environmental domain, but only among non-environmentalists. These findings suggest that compassion fade may challenge our collective ability and willingness to confront the major environmental problems we face, including climate change. The observed moderation effect of environmental identity further indicates that compassion fade may present a significant psychological barrier to building broad public support for addressing these problems. Our results highlight the importance of bringing findings from the field of judgment and decision making to bear on pressing societal issues.


Superpixel Benchmark and Comparison


Study of Timing Values in EAP Authenticated Wireless Hosts


Peripheral retinal changes in acute retinal necrosis imaged by ultra widefield scanning laser ophthalmoscopy


title: Decentralised reinforcement learning for energy-efficient scheduling in wireless sensor networks

abstract: We present a self-organising reinforcement learning (RL) approach for scheduling the wake-up cycles of nodes in a wireless sensor network. The approach is fully decentralised, and allows sensor nodes to schedule their active periods based only on their interactions with neighbouring nodes. Compared to standard scheduling mechanisms such as SMAC, the benefits of the proposed approach are twofold. First, the nodes do not need to synchronise explicitly, since synchronisation is achieved by the successful exchange of data messages in the data collection process. Second, the learning process allows nodes competing for the radio channel to desynchronise in such a way that radio interferences and therefore packet collisions are significantly reduced. This results in shorter communication schedules, allowing to not only reduce energy consumption by reducing the wake-up cycles of sensor nodes, but also to decrease the data retrieval latency. We implement this RL approach in the OMNET++ sensor network simulator, and illustrate how sensor nodes arranged in line, mesh and grid topologies autonomously uncover schedules that favour the successful delivery of messages along a routing tree while avoiding interferences.


title: Computers are social actors

abstract: This paper presents a new experimental paradigm for the study of human-computer interaction, Five experiments provide evidence that individuals’ interactions with computers are fundamentally social. The studies show that social responses to computers are not the result of conscious beliefs that computers are human or human-like. Moreover, such behaviors do not result from users’ ignorance or from psychological or social dysfunctions, nor from a belief that subjects are interacting with programmers. Rather, social responses to computers are commonplace and easy to generate. The results reported here present numerous and unprecedented hypotheses, unexpected implications for design, new approaches to usability testing, and direct methods for verii3cation.


title: An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments

abstract: Smart energy in buildings is an important research area of Internet of Things (IoT). As important parts of the smart grids, the energy efficiency of buildings is vital for the environment and global sustainability. Using a LEED-gold-certificated green office building, we built a unique IoT experimental testbed for our energy efficiency and building intelligence research. We first monitor and collect 1-year-long building energy usage data and then systematically evaluate and analyze them. The results show that due to the centralized and static building controls, the actual running of green buildings may not be energy efficient even though they may be “green” by design. Inspired by “energy proportional computing” in modern computers, we propose an IoT framework with smart location-based automated and networked energy control, which uses smartphone platform and cloud-computing technologies to enable multiscale energy proportionality including building-, user-, and organizational-level energy proportionality. We further build a proof-of-concept IoT network and control system prototype and carried out real-world experiments, which demonstrate the effectiveness of the proposed solution. We envision that the broad application of the proposed solution has not only led to significant economic benefits in term of energy saving, improving home/office network intelligence, but also bought in a huge social implication in terms of global sustainability.


The importance of rehabilitation concerning upper extremity amputees: A Systematic review


title: Improving Physical Layer Security of NOMA Networks by Using Opportunistic Scheduling

abstract: In this paper, we study how to improve physical layer security capability of multiple near users and multiple far users non-orthogonal multiple access (NOMA) networks. To this end, we propose an opportunistic user scheduling scheme, named the best-secure-near-user best-secure-far-user (BSNBSF) scheme. The BSNBSF aims to select the best near-far user pair, whose data transmission is the most robust against the interception of an eavesdropper. In order to facilitate the performance analysis of the proposed user scheduling scheme in terms of secrecy outage probability (SOP), we derive an exact closed-form expression and a tight approximate closed-form expression for the SOP of the selected near and far users, respectively. Numerical results show that the BSNBSF scheme significantly improves the secrecy outage performance NOMA networks compared to that of the random near user and random far user selection scheme. Additionally, discussions on the complicate convex characteristic of the total SOP with respect to the power allocation coefficients and the impact the number of near and/or far users are provided.


RFID in the Retail Supply Chain


title: Image-Based Pointing and Tracking for Inertially Stabilized Airborne Camera Platform

abstract: This paper describes a novel image-based pointing-tracking feedback control scheme for an inertially stabilized double-gimbal airborne camera platform combined with a computer vision system. The key idea is to enhance the intuitive decoupled controller structure with measurements of the camera inertial angular rate around its optical axis. The resulting controller can also compensate for the apparent translation between the camera and the observed object, but then the velocity of this mutual translation must be measured or estimated. Even though the proposed controller is more robust against longer sampling periods of the computer-vision system then the decoupled controller, a sketch of a simple compensation of this delay is also given. Numerical simulations are accompanied by laboratory experiments with a real benchmark system.


title: 3D cell nuclei segmentation based on gradient flow tracking

abstract: BackgroundReliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding.ResultsBoth qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.ConclusionThe proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.


title: Design and analysis of microstrip antenna array in E & H plane for high gain applications

abstract: Linear patch arrays can be very useful where space is the matter of concern in one direction. Like planar array, linear arrays are also capable of providing high gain and directivity with beam scanning. The paper focuses on air borne applications. Proposed linear array are suitable due to its conformability and meeting the constraint of space for such applications. The design presented in this paper proposes to increase the gain and as well as bandwidth by keeping acceptable values of VSWR and return loss.


title: An energy-efficient wire-based storage and retrieval system

abstract: Conventional automated storage and retrieval systems (AS/RS) are widely used to realize an efficient infrastructure for large warehouses. Nevertheless, they require a lot of energy when performing the storage of goods in high racks. This paper presents a new AS/RS type based on a wire robot technology which is very lightweight and therefore capable for huge energy savings compared to the conventional systems. While wire robots usually create the required wire forces only by electrical motors, the application of counterweights and springs offers additional energy saving potential which is simulated and discussed. Since the computation of the wire forces is usually computationally demanding, the paper also proposes a new real-time-capable and simple method to solve this problem.


title: Control of Prosthetic Device Using Support Vector Machine Signal Classification Technique

abstract: An appropriate classification of the surface myoelectric signals (MES) allows people with disabilities to control assistive prosthetic devices. The performance of these pattern recognition methods significantly affects the accuracy and smoothness of the target movements. We designed an intelligent Support Vector Machine (SVM) classifier to incorporate potential variations in electrode placement, thus achieving high accuracy for predictive control. MES from seven locations of the forearm were recorded over six different sessions. Despite meticulous attempt to keep the recording locations consistent between trials, slight shifts may still occur affecting the classification performance. We hypothesize that the machine learning algorithm is able to compensate for these variations. The recorded data was first processed using Discrete Wavelet Transform over 9 frequency bands. As a result, a 63-dimension embedding of the wavelet coefficients were used as the training data for the SVM classifiers. For each session of recordings, a new classifier was trained using only the data sets from the previous sessions. The new classifier was then tested with the data obtained in the current session. The performance of the classifier was evaluated by calculating the sensitivity and specificity. The result indicated that after a critical number of recording sessions, the classifier accuracy starts to reach a plateau, meaning that inclusions of new training data will not significant improve the performance of the classifier. It was observed that the effect of electrode placement variations was reduced and that the classification accuracy of >89% can be obtained.


title: A digitally corrected bandgap voltage reference with a 3σ temperature coefficient of 3.8 ppm/K

abstract: Bangap voltage references (BGRs) are widely used in today's circuits as references with a low temperature coefficient. Especially measurement circuits and metering applications demand a very low temperature coefficient to maintain the desired precision over the entire temperature range. Today's BGR designs use analog circuits to correct for the effects which lead to a temperature drift. In this paper a bandgap reference voltage which uses a digital correction technique is presented. The proposed design includes a temperature sensor to measure the current chip temperature and a bandgap reference which is controllable by a 3-bit digital input. The input to the bandgap block is calculated using a digital correction algorithm. The proposed design was implemented in a 0.35 μm CMOS process and occupies 0.437 mm2. After calibration, a 3σ temperature coefficient of 3.8 ppm/K is achieved over a temperature range from −40°C to 100 °C. With the proposed design, high performance measurements over a large temperature range have become possible. The digital design allows for an easy adaptation to various needs and temperature coefficients.


title: Conservation cores: reducing the energy of mature computations

abstract: Growing transistor counts, limited power budgets, and the breakdown of voltage scaling are currently conspiring to create a utilization wall that limits the fraction of a chip that can run at full speed at one time. In this regime, specialized, energy-efficient processors can increase parallelism by reducing the per-computation power requirements and allowing more computations to execute under the same power budget. To pursue this goal, this paper introduces conservation cores. Conservation cores, or c-cores, are specialized processors that focus on reducing energy and energy-delay instead of increasing performance. This focus on energy makes c-cores an excellent match for many applications that would be poor candidates for hardware acceleration (e.g., irregular integer codes). We present a toolchain for automatically synthesizing c-cores from application source code and demonstrate that they can significantly reduce energy and energy-delay for a wide range of applications. The c-cores support patching, a form of targeted reconfigurability, that allows them to adapt to new versions of the software they target. Our results show that conservation cores can reduce energy consumption by up to 16.0x for functions and by up to 2.1x for whole applications, while patching can extend the useful lifetime of individual c-cores to match that of conventional processors.


title: Noncontact vital-sign radar sensor using metamaterial-based scanning leaky-wave antenna

abstract: In this paper, a metamaterial-based scanning leaky-wave antenna is developed, and then applied to the Doppler radar system for the noncontact vital-sign detection. With the benefit of the antenna beam scanning, the radar system can not only sense human subject, but also detect the vital signs within the specific scanning region. The Doppler radar module is designed at 5.8 GHz, and implemented by commercial integrated circuits and coplanar waveguide (CPW) passive components. Two scanning antennas are then connected with the transmitting and receiving ports of the module, respectively. In addition, since the main beam of the developed scanning antenna is controlled by the frequency, one can easily tune the frequency of the radar source from 5.1 to 6.5 GHz to perform the 59° spatial scanning. The measured respiration and heartbeat rates are in good agreement with the results acquired from the medical finger pulse sensor.


title: ATSGPN: adaptive threshold instance segmentation network in 3D point cloud

abstract: We introduce an adaptive threshold instance segmentation network in point cloud based on similarity group proposal network(SGPN), named adaptive threshold similarity group proposal network(ATSGPN). SGPN learns the feature of point cloud to process similarity matrix and clusters. In our experiments, we find that we cannot always get the proper threshold by heuristic method to divide the points although the similarity matrix is good enough. Based on this idea, we introduce the Threshold Map to learn segmentation threshold. We also improve the feature extraction using edge convolution(EdgeConv). The point cloud first passes EdgeConv to extract features and learns the similarity matrix in feature space. The semantic label of each point and the segmentation threshold can help to generate groups and then calculates confidence to evaluate the group quality and backpropagation. ATSGPN has higher accuracy on Stanford Large- Scale 3D Indoor Spaces Dataset (S3SID) and fewer steps than SGPN, and there are some experiments can be shown in the paper for its good performance.


GEOSTATISTICS : PAST , PRESENT , AND FUTURE


title: Face Alignment In-the-Wild: A Survey

abstract: Over the last two decades, face alignment or localizing fiducial facial points has received increasing attention owing to its comprehensive applications in automatic face analysis. However, such a task has proven extremely challenging in unconstrained environments due to many confounding factors, such as pose, occlusions, expression and illumination. While numerous techniques have been developed to address these challenges, this problem is still far away from being solved. In this survey, we present an up-to-date critical review of the existing literatures on face alignment, focusing on those methods addressing overall difficulties and challenges of this topic under uncontrolled conditions. Specifically, we categorize existing face alignment techniques, present detailed descriptions of the prominent algorithms within each category, and discuss their advantages and disadvantages. Furthermore, we organize special discussions on the practical aspects of face alignment in-the-wild, towards the development of a robust face alignment system. In addition, we show performance statistics of the state of the art, and conclude this paper with several promising directions for future research.


title: IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties

abstract: In the context of Building Information Modelling, ontologies have been identified as interesting in achieving information interoperability. Regarding the construction and facility management domains, several IFC (Industry Foundation Classes) based ontologies have been developed, such as IfcOWL. In the context of ontology modelling, the constraint of optimizing the size of IFC STEP-based files can be leveraged. In this paper, we propose an adaptation of the IFC model into OWL which leverages from all modelling constraints required by the object-oriented structure of IFC schema. Therefore, we do not only present a syntactic but also a semantic adaptation of the IFC model. Our model takes into consideration the meaning of entities, relationships, properties and attributes defined by the IFC standard. Our approach presents several advantages compared to other initiatives such as the optimization of query execution time. Every advantage is defended by means of practical examples and benchmarks.


title: Principal Angles Between Subspaces and Their Tangents

abstract: Principal angles between subspaces (PABS) (also called canonical angles) serve as a classical tool in mathematics, statistics, and applications, e.g., data mining. Traditionally, PABS are introduced and used via their cosines. The tangents of PABS have attracted relatively less attention, but are important for analysis of convergence of subspace iterations for eigenvalue problems. We explicitly construct matrices, such that their singular values are equal to the tangents of PABS, using several approaches: orthonormal and non-orthonormal bases for subspaces, and orthogonal projectors.


Neural Inter face Technology for Rehabil itation : Exploiting and Promoting Neuroplastic ity


title: Detection of K-ras Mutations in Predicting Efficacy of Epidermal Growth Factor Receptor Tyrosine Kinase (EGFR-TK) Inhibitor in Patients with Metastatic Colorectal Cancer

abstract: Epidermal growth factor receptor tyrosine kinase (EGFR-TK) inhibitors are useful in treating different advanced human cancers; however, their clinical efficacy varies. This study detected K-ras mutations to predict the efficacy of EGFR-TK inhibitor cetuximab treatment on Chinese patients with metastatic colorectal cancer (mCRC). A total of 87 patients with metastatic colorectal cancer were treated with cetuximab for 2-16 months, in combination with chemotherapy between August 2008 and July 2012, and tissue samples were used to detect K-ras mutations. The data showed that K-ras mutation occurred in 27/87 (31%). The objective response rates and disease control rate in K-ras wild type and mutant patients were 42% (25/60) versus 11% (3/27) (p<0.05) and 60% (36/60) versus 26% (7/27) (p<0.05), respectively. Patients with the wild-type K-ras had significantly higher median survival times and progression-free survival, than patients with mutated K-ras (21 months versus 17 months, p=0.017; 10 months versus 6 months, p=0.6). These findings suggest that a high frequency of K-ras mutations occurs in Chinese mCRC patients and that K-ras mutation is required to select patients for eligibility for cetuximab therapy. Further prospective studies using a large sample size are needed to confirm these preliminary findings.


title: Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting

abstract: Hong Kong Observatory has been operating an in-house developed rainfall nowcasting system called “Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS)” to support rainstorm warning and rainfall nowcasting services. A crucial step in rainfall nowcasting is the tracking of radar echoes to generate motion fields for extrapolation of rainfall areas in the following few hours. SWIRLS adopted a correlation-based method in its first operational version in 1999, which was subsequently replaced by optical flow algorithm in 2010 and further enhanced in 2013. The latest optical flow algorithm employs a transformation function to enhance a selected range of reflectivity for feature tracking. It also adopts variational optical flow computation that takes advantage of the Horn–Schunck approach and the Lucas–Kanade method. This paper details the three radar echo tracking algorithms, examines their performances in several significant rainstorm cases and summaries verification results of multi-year performances. The limitations of the current approach are discussed. Developments underway along with future research areas are also presented.


title: Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks

abstract: Multilayered artificial neural networks have found widespread utility in classification and recognition applications. The scale and complexity of such networks together with the inadequacies of general purpose computing platforms have led to a significant interest in the development of efficient hardware implementations. In this work, we focus on designing energy-efficient on-chip storage for the synaptic weights, motivated primarily by the observation that the number of synapses is orders of magnitude larger than the number of neurons. Typical digital CMOS implementations of such large-scale networks are power hungry. In order to minimize the power consumption, the digital neurons could be operated reliably at scaled voltages by reducing the clock frequency. On the contrary, the on-chip synaptic storage designed using a conventional 6T SRAM is susceptible to bitcell failures at reduced voltages. However, the intrinsic error resiliency of neural networks to small synaptic weight perturbations enables us to scale the operating voltage of the 6T SRAM. Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200 mV from the nominal operating voltage (950 mV) for practically no loss (less than 0.5%) in accuracy (22 nm predictive technology). Scaling beyond that causes substantial performance degradation owing to increased probability of failures in the MSBs of the synaptic weights. We, therefore propose a significance driven hybrid 8T-6T SRAM, wherein the sensitive MSBs are stored in 8T bitcells that are robust at scaled voltages due to decoupled read and write paths. In an effort to further minimize the area penalty, we present a synaptic-sensitivity driven hybrid memory architecture consisting of multiple 8T-6T SRAM banks. Our circuit to system-level simulation framework shows that the proposed synaptic-sensitivity driven architecture provides a 30.91% reduction in the memory access power with a 10.41% area overhead, for less than 1% loss in the classification accuracy.


title: Determining the Camera Response from Images : What Is Knowable ?

abstract: An image acquired by a camera consists of measured intensity values which are related to scene radiance by a function called the camera response function. Knowledge of this response is necessary for computer vision algorithms which depend on scene radiance. One way the response has been determined is by establishing a mapping of intensity values between images taken with different exposures. We call this mapping the intensity mapping function. In this paper, we address two basic questions. What information from a pair of images taken at different exposures is needed to determine the intensity mapping function? Given this function, can the response of the camera and the exposures of the images be determined? We completely determine the ambiguities associated with the recovery of the response and the ratios of the exposures. We show all methods that have been used to recover the response break these ambiguities by making assumptions on the exposures or on the form of the response. We also show when the ratio of exposures can be recovered directly from the intensity mapping, without recovering the response. We show that the intensity mapping between images is determined solely by the intensity histograms of the images. We describe how this allows determination of the intensity mapping between images without registration. This makes it possible to determine the intensity mapping in sequences with some motion of both the camera and objects in the scene.


title: COBIT and its utilization: a framework from the literature

abstract: The control objectives for information and related technology (COBIT) is a "trusted" open standard that is being used increasingly by a diverse range of organizations throughout the world. COBIT is arguably the most appropriate control framework to help an organization ensure alignment between use of information technology (IT) and its business goals, as it places emphasis on the business need that is satisfied by each control objective by J. Colbert, and P. Bowen (1996). This paper reports on the use of a simple classification of the published literature on COBIT, to highlight some of the features of that literature. The appropriate alignment between use of IT and the business goals of a organization is fundamental to efficient and effective IT governance. IT governance "...is the structure of relationships and processes to develop, direct and control IS/IT resources in order to achieve the enterprise's goals". IT governance has been recognized as a critical success factor in the achievement of corporate success by deploying information through the application of technology by N. Korac-Kakabadse and A. Kakabadse (2001). The importance of IT governance can be appreciated in light of the Gartner Group's finding that large organizations spend over 50% of their capital investment on IT by C. Koch (2002). However, research has suggested that the contribution of IT governance varies in its effectiveness. IT control frameworks are designed to promote effective IT governance. Recent pressures, including the failure of organizations such as Enron, have led to an increased focus on corporate accountability. For example, the Sarbanes-Oxley Act of 2002 introduced legislation that imposed new governance requirements by G. Coppin (2003). These and other changes have resulted in a new corporate governance model with an increased emphasis on IT governance, which goes beyond the traditional focus of corporate governance on financial aspects by R. Roussey (2003).


title: Elastic antennas by metallised elastomers

abstract: This paper investigates the fundamental properties of metallised elastomers at microwave frequencies for microwave transmission line and wireless antenna applications. Stretchable or elastic antennas will significantly expand stretchable electronics applications both in the communication and biomedical sectors. Fundamental electromagnetic characterisations of elastic transmission line and antennas are evaluated using simulation software CST. A self compensating elastic antenna is presented.


title: Automated Assessment of Cognitive Health Using Smart Home Technologies

abstract: BACKGROUND ::: The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. ::: ::: ::: OBJECTIVE ::: This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. ::: ::: ::: METHODS ::: This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. ::: ::: ::: RESULTS ::: Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. ::: ::: ::: CONCLUSIONS ::: The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.


title: PZT thin films for microsensors and actuators: Where do we stand?

abstract: This paper reviews deposition, integration, and device fabrication of PbZr/sub x/Ti/sub 1-x/O/sub 3/ (PZT) films for applications in micro-electromechanical systems. An ultrasonic micromotor is described as an example. A summary of the published data on piezoelectric properties is given. The figures of merit for various applications are discussed. Some considerations and results on operation, reliability, and depolarization of PZT thin films are presented. The state of the art allows some preliminary conclusions.


title: Electrick: Low-Cost Touch Sensing Using Electric Field Tomography

abstract: Current touch input technologies are best suited for small and flat applications, such as smartphones, tablets and kiosks. In general, they are too expensive to scale to large surfaces, such as walls and furniture, and cannot provide input on objects having irregular and complex geometries, such as tools and toys. We introduce Electrick, a low-cost and versatile sensing technique that enables touch input on a wide variety of objects and surfaces, whether small or large, flat or irregular. This is achieved by using electric field tomography in concert with an electrically conductive material, which can be easily and cheaply added to objects and surfaces. We show that our technique is compatible with commonplace manufacturing methods, such as spray/brush coating, vacuum forming, and casting/molding enabling a wide range of possible uses and outputs. Our technique can also bring touch interactivity to rapidly fabricated objects, including those that are laser cut or 3D printed. Through a series of studies and illustrative example uses, we show that Electrick can enable new interactive opportunities on a diverse set of objects and surfaces that were previously static.


title: A simple histogram modification scheme for contrast enhancement

abstract: Histogram equalization (HE) is a widely used contrast enhancement (CE) method in image processing applications. The algorithm can be easily implemented; however, it tends to transform the average brightness of an image toward the middle of the gray scale. In addition, unpleasant artifacts often appear in the enhanced images. In order to overcome these drawbacks, various HE-based methods which aim at specific issues were proposed. Some of them might overlook the problems inherent in the implementations of histogram equalization and histogram specification (HS). This paper presents a simple histogram modification scheme to solve those problems according to the characteristic of implementation. Two boundary values of the support of histogram are found and set to corresponding values, respectively. The probability density function of an image is then recomputed and the updated mapping function is used to perform histogram equalization. Experimental results show that the proposed approach can effectively improve the quality of images enhanced by histogram equalization and specification methods, and even histogram redistribution methods such as gray-level grouping (GLG).


title: Self-perception of leadership styles and behaviour in primary health care

abstract: The concept of leadership has been studied in various disciplines and from different theoretical approaches. It is a dynamic concept that evolves over time. There are few studies in our field on managers’ self-perception of their leadership style. There are no pure styles, but one or another style is generally favoured to a greater or lesser degree. In the primary health care (PHC) setting, managers’ leadership style is defined as a set of attitudes, behaviours, beliefs and values. The objectives of this study were to describe and learn about the self-perception of behaviours and leadership styles among PHC managers; to determine the influence of the leadership style on job satisfaction, efficiency, and willingness to work in a team; and to determine the relationship between transformational and transactional styles according age, gender, profession, type of manager years of management experience, and the type of organization. To describe leadership styles as perceived by PHC managers, a cross sectional study was performed using an 82 items-self-administered Multifactor Leadership Questionnaire (MLQ). This questionnaire measures leadership styles, attitudes and behaviour of managers. The items are grouped into three first order variables (transformational, transactional and laissez-faire) and ten second order variables (which discriminate leader behaviours). Additionally, the questionnaire evaluates organizational consequences such as extra-effort, efficiency and satisfaction. One hundred forty responses from 258 managers of 133 PHC teams in the Barcelona Health Area (response rate: 54.26 %). Most participants were nurses (61.4 %), average age was 49 years and the gender predominantly female (75 %). Globally, managers assessed themselves as equally transactional and transformational leaders (average: 3.30 points). Grouped by profession, nurses (28.57 % of participants) showed a higher transactional leadership style, over transformational leadership style, compared to physicians (3.38 points, p < 0.003). Considering gender, men obtained the lowest results in transactional style (p < 0.015). Both transactional and transformational styles correlate with efficiency and job satisfaction (r = 0.724 and r = 0.710, respectively). PHC managers’ self-perception of their leadership style was transactional, focused on the maintenance of the status quo, although there was a trend in some scores towards the transformational style, mainly among nurse managers. Both styles correlate with satisfaction and willingness to strive to work better.


title: Rectifying transformations that minimize resampling effects

abstract: Image rectification is the process of warping a pair of stereo images in order to align the epipolar lines with the scan-lines of the images. Once a pair of images is rectified, stereo matching can be implemented in an efficient manner. Given the epipolar geometry, it is straightforward to define a rectifying transformation, however, many transformations will lead to unwanted image distortions. In this paper, we present a novel method for stereo rectification that determines the transformation that minimizes the effects of resampling that can impede stereo matching. The effects we seek to minimize are the loss of pixels due to under-sampling and the creation of new pixels due to over-sampling. To minimize these effects we parameterize the family of rectification transformations and solve for the one that minimizes the change in local area integrated over the area of the images.


ABSTRACT Affordable Infrared-Optical Pose-Tracking for Virtual and Augmented Reality


title: PolyRecs: Improving Page–View Rates Using Real-Time Data Analysis

abstract: In this paper, we outline our effort to enhance the page-view rates of e-content that online customers read on a popular portal in Greece. The portal, athensvoice.gr, provides continuous coverage on news, politics, science, the arts, and opinion columns and its customers generate approximately 6 million unique visits per month. Gains both in terms of advertisement and further e-content market penetration were the objectives of our effort which yielded the PolyRecs system, in production for more than a year now. In designing PolyRecs, we were primarily concerned with the use of pages in real-time and to this end, we elected to utilize five key criteria to achieve the aforementioned goals. We selected criteria for which we were able to obtain pertinent statistics without compromising performance and offered a real-time exploitation of the user page-views on the go. In addition, we were keen in realizing not only effective on-the-fly calculations of what might be interesting to the browsing individuals at specific points in time but also produce accurate results capable of improving the user-experience. The key factors exploited by PolyRecs entail features from both collaboration and content-based systems. Once operational, PolyRecs helped the news portal attain an average increase of 6.3% of the overall page-views in its traffic. To ascertain the PolyRecs utility, we provide a brief economic analysis in terms of measured performance indicators and identify the degree of contribution each of the key factors offers. Last but not least, we have developed PolyRecs as a domain-agnostic hybrid-recommendation system for we wanted it to successfully function regardless of the underlying data and/or content infrastructure.


title: 6D pose estimation of textureless shiny objects using random ferns for bin-picking

abstract: We address the problem of 6D pose estimation of a textureless and shiny object from single-view 2D images, for a bin-picking task. For a textureless object like a mechanical part, conventional visual feature matching usually fails due to the absence of rich texture features. Hierarchical template matching assumes that few templates can cover all object appearances. However, the appearance of a shiny object largely depends on its pose and illumination. Furthermore, in a bin-picking task, we must cope with partial occlusions, shadows, and inter-reflections.


title: Dynamic simulation of electric machines on FPGA boards

abstract: This paper presents the implementation of an induction machine dynamic simulation on a field-programmable gate array (FPGA) board. Using FPGAs as computational engines can lead to significant simulation speed gains when compared to a typical PC computer, especially when operations can be efficiently parallelized on the board. The textbook example of a free acceleration followed by a step load change is used to outline the basic steps of designing an explicit Runge-Kutta numerical ordinary differential equation (ODE) solver on the FPGA platform. The FPGA simulation results and speed improvement are validated versus a Matlab/Simulink simulation.


title: Characterization of dry biopotential electrodes

abstract: Driven by the increased interest in wearable long-term healthcare monitoring systems, varieties of dry electrodes are proposed based on different materials with different patterns and structures. Most of the studies reported in the literature focus on proposing new electrodes and comparing its performance with commercial electrodes. Few papers are about detailed comparison among different dry electrodes. In this paper, printed metal-plate electrodes, textile based electrodes, and spiked electrodes are for the first time evaluated and compared under the same experimental setup. The contact impedance and noise characterization are measured. The in-vivo electrocardiogram (ECG) measurement is applied to evaluate the overall performance of different electrodes. Textile electrodes and printed electrodes gain comparable high-quality ECG signals. The ECG signal obtained by spiked electrodes is noisier. However, a clear ECG envelope can be observed and the signal quality can be easily improved by backend signal processing. The features of each type of electrodes are analyzed and the suitable application scenario is addressed.