title: A novel framework for optimal sizing of hybrid stand-alone renewable energy system: A gray wolf optimizer

abstract: Hybrid photovoltaic (PV), wind turbine (WT) diesel generator (DG) systems with battery bank have been suggested as a green and reliable power system for electrification of rural locations. There is a continues enhance in usage of green system and successively sizing optimization is the important problem for having the most cost-effective system. This study evaluates the performance of various heuristic algorithms for sizing optimization of a PV/WT//DG/battery hybrid system to continuously satisfy the electrical consumption of ten residual building located in khalkhal, Ardabil, Iran and the same block located in Rafsanjan, Kerman, Iran to have the lowest total annual cost. For this purpose, the mathematical model of each of system components are expressed and a fitness function is introduced based on the total annual cost. As well as in the problem of optimization, the maximum admissible loss of power supply probability (LPSP) is considered to have a dependable system, and three famous evolutionary algorithms, namely, particle swarm optimization (PSO), genetic algorithm(GA), salp swarm algorithm (SSA) and gray wolf optimizer algorithm (GWO), are used for the system and the results are compared in terms of the minimum total annual cost. So we can conclude that the results obtained by GWO shows that the PV/WT/DG/battery system is more economically than those of the other algorithms.


title: Does fragile co-adaptation occur in small datasets?

abstract: Recent study using AlexNet architecture and ImageNet dataset has shown that the transferability of each layer in Convolutional Neural Network (CNN) can be quantified. One interesting finding from this study is that performance degradation when performing transfer learning without finetuning is not only caused by the specificity of the features, but also due to fragile co-adapted neurons between neighboring layers. This raises a question whether this phenomenon also occurs on smaller datasets and simpler network architectures. Using CIFAR-10 and three different CNN architectures, we reported that we have not seen this effect. The drop in performance is solely due to the specificity of the features learned for the source task.


title: Home security system based on PIC18F452 microcontroller

abstract: We used PIC18F452 microcontroller for hardware and software implementation of our home security system design. Based on PIC18F452, our system can monitor doors and windows of a house and can set alarm and warming signal to a nearest police station if anybody tries to break in. This security system also provides the functionality to identify the residents ID card to get access to the house without turning on the warning signal and alarm. Also, the security system provides a status that is not monitoring the door and windows since there is some possibility that the host do not want the system always checks the status of their house.


title: Statistical Parametric Speech Synthesis

abstract: This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.


title: Integrative Neuromuscular Training in Youth Athletes. Part II

abstract: ABSTRACTTHE SECOND PART OF THIS REVIEW PROVIDES A FLEXIBLE APPROACH TO INTEGRATIVE NEUROMUSCULAR TRAINING (INT) WITH THE GOALS TO IMPROVE INJURY RESILIENCE AND TO ENHANCE SPORT AND MOTOR PERFORMANCE ABILITIES IN YOUTH POPULATIONS. THE PROPOSED MODEL OF INT IN THIS MANUSCRIPT PRESENTS 6 ESSENTIAL COM


title: Secure and Privacy-Aware Data Dissemination for Cloud-Based Applications

abstract: In this paper we propose a data dissemination platform that supports data security and different privacy levels even when the platform and the data are hosted by untrusted infrastructures. The proposed system aims at enabling an application ecosystem that uses off-the-shelf trusted platforms (in this case, Intel SGX), so that users may allow or disallow third parties to access the live data stream with a specific sensitivity-level. Moreover, this approach does not require users to manage the encryption keys directly. Our experiments show that such an approach is indeed practical for medium scale systems, where participants disseminate small volumes of data at a time, such as in smart grids and IoT environments.


title: DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers With Different Defuzzification Methods

abstract: A fuzzy control system to control the position of a DC motor. The motor was modelled and converted to a subsystem in Simulink. First, a crisp proportional-derivative (PD) controller was designed and tuned using a Simulink block instead of conventional tuning methods such as hand-tuning or Ziegler-Nichols frequency response method. Then a fuzzy proportional-derivative (FPD) controller was designed and system responses of FPDs with different defuzzification methods were investigated. A disturbance signal was also applied to the input of the control system. FPD controller succeeded to reject the disturbance signal without further tuning of the parameters whereby crisp PD controller failed The purpose of this project is to control the position of DC Motor by using Fuzzy Logic Controller (FLC) with MATLAB application. The scope includes the simulation and modelling of DC motor, fuzzy controller and conventional PID controller as benchmark to the performance of fuzzy system. The position control is an adaptation of Closed Circuit Television (CCTV) system. Fuzzy Logic control can play important role because knowledge based design rules can be easily implemented in the system with unknown structure and it is going to be popular since the control design strategy is simple and practical. This make FLC an alternative method to the conventional PID control method used in nonlinear industrial system. The results obtained from FLC are compared with PID control for the dynamic response of the closed loop system. Parameters such as peak position in degree, settling time in second and maximum overshoot in percent will be part of the simulation result..


title: 20 $\mu$ A to 100 mA DC–DC Converter With 2.8-4.2 V Battery Supply for Portable Applications in 45 nm CMOS

abstract: A DC-DC buck converter capable of handling loads from 20 μA to 100 mA and operating off a 2.8-4.2 V battery is implemented in a 45 nm CMOS process. In order to handle high battery voltages in this deeply scaled technology, multiple transistors are stacked in the power train. Switched-Capacitor DC-DC converters are used for internal rail generation for stacking and supplies for control circuits. An I-C DAC pulse width modulator with sleep mode control is proposed which is both area and power-efficient as compared with previously published pulse width modulator schemes. Both pulse frequency modulation (PFM) and pulse width modulation (PWM) modes of control are employed for the wide load range. The converter achieves a peak efficiency of 75% at 20 μA, 87.4% at 12 mA in PFM, and 87.2% at 53 mA in PWM.


DETECTION OF LEUKEMIA IN HUMAN BLOOD SAMPLE BASED ON MICROSCOPIC


title: Validated Assessment Scales for the Upper Face

abstract: BACKGROUND ::: Age-related upper face changes such as wrinkles, lines, volume loss, and anatomic alterations may affect quality of life and psychological well-being. The development of globally accepted tools to assess these changes objectively is an essential contribution to aesthetic research and routine clinical medicine. ::: ::: ::: OBJECTIVE ::: To establish the reliability of several upper face scales for clinical research and practice: forehead lines, glabellar lines, crow's feet (at rest and dynamic expression), sex-specific brow positioning, and summary scores of forehead and crow's feet areas and of the entire upper face unit. ::: ::: ::: METHODS AND MATERIALS ::: Four 5-point photonumerical rating scales were developed to assess glabellar lines and sex-specific brow positioning. Twelve experts rated identical upper face photographs of 50 subjects in two separate rating cycles using all eight scales. Responses of raters were analyzed to assess intra- and interrater reliability. ::: ::: ::: RESULTS ::: Interrater reliability was substantial for all upper face scales, aesthetic areas, and the upper face score except for the brow positioning scales. Intrarater reliability was high for all scales and resulting scores. ::: ::: ::: CONCLUSION ::: Except for brow positioning, the upper face rating scales are reliable tools for valid and reproducible assessment of the aging process.


title: An optimized axial flux variable reluctance resolver with concentric windings

abstract: Radial flux variable reluctance resolvers (RFVR2s) are widely used in variety of applications. However, they suffer from an intrinsic error which is called static eccentricity (SE). Although most of the errors in the resolvers can be suppressed using electronic methods in resolver to digital (R/D) converters, static misalignment is an exception. In this paper an axial flux configuration is proposed for variable reluctance (VR) resolvers with concentric windings. Then the proposed structure is optimized to achieve the minimum sensitivity of accuracy against SE. The performance of the presented structure, so called axial flux variable reluctance resolver (AFVR2), is validated using 3-D time stepping finite element method and compared with that of RFVR2. Results show the robustness of the optimized structure against static misalignment.


title: DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks

abstract: Depression is a prevailing issue and is an increasing problem in many people's lives. Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting in high demand for detecting depression in advance automatically. This paper tackles the challenging problem of forecasting severely depressed moods based on self-reported histories. Despite the large amount of research on understanding individual moods including depression, anxiety, and stress based on behavioral logs collected by pervasive computing devices such as smartphones, forecasting depressed moods is still an open question. This paper develops a recurrent neural network algorithm that incorporates categorical embedding layers for forecasting depression. We collected large-scale records from 2,382 self-declared depressed people to conduct the experiment. Experimental results show that our method forecast the severely depressed mood of a user based on self-reported histories, with higher accuracy than SVM. The results also showed that the long-term historical information of a user improves the accuracy of forecasting depressed mood.


title: PROS AND CONS OF CONNECTIVISM AS A LEARNING THEORY Dr. Mehmet ŞAHİN*

abstract: It is a clear fact that nowadays knowledge is growing faster and with the spreading of information and communication technology, the dream of network learning has become a reality, at least technically, and now a vast amount of spontaneous knowledge exchange is possible. Younger and older learners need to generate new ideas and new products that are to be innovative. In this context, this study aims to explore the nature of Connectivism (Siemens, 2004) using available literature as a traditional qualitative method. The second issue is the advantages and disadvantages of Connectivism as it is concieved by the educationalists. For this, a focus group discussion was used to obtain data. The data obtained formed the following categories: Shortness of traditional theories, the tools of Connectivism, digital literacy, flexible learning time and ecenomc competetion, learning to learn, media psychology, need for expertise, dependence on electricity and available sources. Since half of what is known today was not known 10 years ago, there should be more researches about the use, benefits and drawbacks of Connectivism in the cotext of formal and informal lerning.


title: Integrating magnetics for on-chip power: Challenges and opportunities

abstract: Integration of efficient power converters requires a technology for efficient, high-power on-chip inductors. The state of the art is reviewed, and possible future developments are discussed for both air-core and magnetic-core inductors. Performance limits and scaling are analyzed. General design possibilities are outlined and different approaches are compared. Magnetic materials are reviewed, and nano-granular composite materials are highlighted as an attractive option.


title: The Controversy Surrounding The Man Who Would Be Queen: A Case History of the Politics of Science, Identity, and Sex in the Internet Age

abstract: In 2003, psychology professor and sex researcher J. Michael Bailey published a book entitled The Man Who Would Be Queen: The Science of Gender-Bending and Transsexualism. The book’s portrayal of male-to-female (MTF) transsexualism, based on a theory developed by sexologist Ray Blanchard, outraged some transgender activists. They believed the book to be typical of much of the biomedical literature on transsexuality—oppressive in both tone and claims, insulting to their senses of self, and damaging to their public identities. Some saw the book as especially dangerous because it claimed to be based on rigorous science, was published by an imprint of the National Academy of Sciences, and argued that MTF sex changes are motivated primarily by erotic interests and not by the problem of having the gender identity common to one sex in the body of the other. Dissatisfied with the option of merely criticizing the book, a small number of transwomen (particularly Lynn Conway, Andrea James, and Deirdre McCloskey) worked to try to ruin Bailey. Using published and unpublished sources as well as original interviews, this essay traces the history of the backlash against Bailey and his book. It also provides a thorough exegesis of the book’s treatment of transsexuality and includes a comprehensive investigation of the merit of the charges made against Bailey that he had behaved unethically, immorally, and illegally in the production of his book. The essay closes with an epilogue that explores what has happened since 2003 to the central ideas and major players in the controversy.


title: Power laws, Pareto distributions and Zipf’s law

abstract: When the probability of measuring a particular value of some quantity varies inversely as a power of that value, the quantity is said to follow a power law, also known variously as Zipf's law or the Pareto distribution. Power laws appear widely in physics, biology, earth and planetary sciences, economics and finance, computer science, demography and the social sciences. For instance, the distributions of the sizes of cities, earthquakes, solar flares, moon craters, wars and people's personal fortunes all appear to follow power laws. The origin of power-law behaviour has been a topic of debate in the scientific community for more than a century. Here we review some of the empirical evidence for the existence of power-law forms and the theories proposed to explain them.


title: Relational Inference for Wikification

abstract: Wikification, commonly referred to as Disambiguation to Wikipedia (D2W), is the task of identifying concepts and entities in text and disambiguating them into the most specific corresponding Wikipedia pages. Previous approaches to D2W focused on the use of local and global statistics over the given text, Wikipedia articles and its link structures, to evaluate context compatibility among a list of probable candidates. However, these methods fail (often, embarrassingly), when some level of text understanding is needed to support Wikification. In this paper we introduce a novel approach to Wikification by incorporating, along with statistical methods, richer relational analysis of the text. We provide an extensible, efficient and modular Integer Linear Programming (ILP) formulation of Wikification that incorporates the entity-relation inference problem, and show that the ability to identify relations in text helps both candidate generation and ranking Wikipedia titles considerably. Our results show significant improvements in both Wikification and the TAC Entity Linking task.


title: A 1 V 2.5 μW fully-differential ASK demodulator with 12.5 pJ/bit FOM for ultra-low power biomedical applications

abstract: Wireless transceivers that receive data and power are important circuit blocks in implantable biomedical devices. In such wirelessly powered devices, supply and common-mode variations increase the error rate of the received data. In this paper we propose a fully-differential amplitude-shift-keying (ASK) demodulator for suppressing the effect of such undesired common mode variations on the received data. The proposed demodulator consists of a fully-differential envelope detector (ED) and programmable gain amplifier (PGA) that operates in the sub-threshold region, and a differential comparator that consumes only dynamic current. The proposed system is implemented in a 0.18 μm CMOS process and consumes 2.5 μΑ from a 1 V power supply. At a data rate of 200 kbps, this leads to an FOM of 12.5 pJ/bit.


title: www.palgrave-journals.com/ejis A framework for the ex-ante evaluation of ERP software

abstract: It has been widely reported that a large number of ERP implementations fail to meet expectations. This is indicative, firstly, of the magnitude of the problems involved in ERP systems implementation and, secondly, of the importance of the ex-ante evaluation and selection process of ERP software. This paper argues that ERP evaluation should extend its scope beyond operational improvements arising from the ERP software/product per se to the strategic impact of ERP on the competitive position of the organisation. Due to the complexity of ERP software, the intangible nature of both costs and benefits, which evolve over time, and the organisational, technological and behavioural impact of ERP, a broad perspective of the ERP systems evaluation process is needed. The evaluation has to be both quantitative and qualitative and requires an estimation of the perceived costs and benefits throughout the life-cycle of ERP systems. The paper concludes by providing a framework of the key issues involved in the selection process of ERP software and the associated costs and benefits.


title: A generalized approach for historical mock-up acquisition and data modelling: Towards historically enriched 3D city models

abstract: Museums are filled with hidden secrets. One of those secrets lies behind historical mock-ups whose signification goes far behind a simple representation of a city. We face the challenge of designing, storing and showing knowledge related to these mock-ups in order to explain their historical value. Over the last few years, several mock-up digitalisation projects have been realised. Two of them, Nantes 1900 and Virtual Leodium, propose innovative approaches that present a lot of similarities. This paper presents a framework to go one step further by analysing their data modelling processes and extracting what could be a generalized approach to build a numerical mock-up and the knowledge database associated. Geometry modelling and knowledge modelling influence each other and are conducted in a parallel process. Our generalized approach describes a global overview of what can be a data modelling process. Our next goal is obviously to apply this global approach on other historical mock-up, but we also think about applying it to other 3D objects that need to embed semantic data, and approaching historically enriched 3D city models.


title: Insider Threat Identification by Process Analysis

abstract: The insider threat is one of the most pernicious in computer security. Traditional approaches typically instrument systems with decoys or intrusion detection mechanisms to detect individuals who abuse their privileges (the quintessential "insider"). Such an attack requires that these agents have access to resources or data in order to corrupt or disclose them. In this work, we examine the application of process modeling and subsequent analyses to the insider problem. With process modeling, we first describe how a process works in formal terms. We then look at the agents who are carrying out particular tasks, perform different analyses to determine how the process can be compromised, and suggest countermeasures that can be incorporated into the process model to improve its resistance to insider attack.


title: Query-Directed Probing LSH for Cosine Similarity

abstract: Locality-sensitive hashing (LSH) considered as an efficient algorithm for large-scale similarity search has become increasingly popular. Recently, many of its variants have been applied widely in high-dimensional similarity search. To overcome the drawback of requirement for a large number of hash tables, researchers proposed the famous Multi-Probe LSH (MP-LSH). It has been used to improve the utilization of hash tables. There are two major probing sequences mentioned in MP-LSH, i.e., Step-Wise Probing (SWP) sequence and Query-Directed Probing (QDP) sequence. It is verified that QDP sequence is better than SWP sequence in number of probes and query time. However, the proposed QDP sequence is based on the E2LSH. It means that the method is only adopted for Euclidean distance. For cosine similarity, SWP sequence is still the only feasible method to perform Multi-Probe LSH. This paper proposes an approach based on QDP sequence for cosine similarity search. Moreover, we give a set of complete theories and the corresponding proof for our method. Several experiments are performed on two types of open data sets. The experiments demonstrate our algorithm requires a small amount of probes and less time to achieve a high query quality than SWP sequence for cosine similarity.


title: Vortex Methods: Theory and Practice

abstract: The book Vortex Methods: Theory and Practice presents a comprehensive account of the numerical technique for solving fluid flow problems. It provides a very nice balance between the theoretical development and analysis of the various techniques and their practical implementation. In fact, the presentation of the rigorous mathematical analysis of these methods instills confidence in their implementation. The book goes into some detail on the more recent developments that attempt to account for viscous effects, in particular the presence of viscous boundary layers in some flows of interest. The presentation is very readable, with most points illustrated with well-chosen examples, some quite sophisticated. It is a very worthy reference book that should appeal to a large body of readers, from those interested in the mathematical analysis of the methods to practitioners of computational fluid dynamics. The use of the book as a text is compromised by its lack of exercises for students, but it could form the basis of a graduate special topics course. Juan Lopez


title: Why Do Parents Become Involved? Research Findings and Implications.

abstract: Abstract A decade ago, Hoover‐Dempsey and Sandler offered a model of the parental involvement process that focused on understanding why parents become involved in their children’s education and how their involvement influences student outcomes. Since then, we and others have conducted conceptual and empirical work to enhance understanding of processes examined in the model. In this article (companion to Walker and colleagues’ article about scale development on the model in this issue), we review recent work on constructs central to the model’s initial question: Why do parents become involved in children’s education? Based on this review, we offer suggestions for (1) research that may deepen understanding of parents’ motivations for involvement and (2) school and family practices that may strengthen the incidence and effectiveness of parental involvement across varied school communities.


Automatic Nepali Number Plate Recognition with Support Vector Machines


title: Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach

abstract: Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users’ preferences often change over time, leading to studies on time-dependent recommender systems. However, most existing approaches that deal with time information remain primitive. In this paper, we extend existing methods and propose a hidden semi-Markov model to track the change of users’ interests. Particularly, this model allows for capturing the different durations of user stays in a (latent) interest state, which can better model the heterogeneity of user interests and focuses. We derive an expectation maximization algorithm to estimate the parameters of the framework and predict users’ actions. Experiments on three real-world datasets show that our model significantly outperforms the state-of-the-art time-dependent and static benchmark methods. Further analyses of the experiment results indicate that the performance improvement is related to the heterogeneity of state durations and the drift of user interests in the dataset.


title: Genetic algorithms and neural networks: optimizing connections and connectivity

abstract: Abstract Genetic algorithms are a robust adaptive optimization method based on biological principles. A population of strings representing possible problem solutions is maintained. Search proceeds by recombining strings in the population. The theoretical foundations of genetic algorithms are based on the notion that selective reproduction and recombination of binary strings changes the sampling rate of hyperplanes in the search space so as to reflect the average fitness of strings that reside in any particular hyperplane. Thus, genetic algorithms need not search along the contours of the function being optimized and tend not to become trapped in local minima. This paper is an overview of several different experiments applying genetic algorithms to neural network problems. These problems include 1. (1) optimizing the weighted connections in feed-forward neural networks using both binary and real-valued representations, and 2. (2) using a genetic algorithm to discover novel architectures in the form of connectivity patterns for neural networks that learn using error propagation. Future applications in neural network optimization in which genetic algorithm can perhaps play a significant role are also presented.


title: Embedded Streaming Deep Neural Networks Accelerator With Applications

abstract: Deep convolutional neural networks (DCNNs) have become a very powerful tool in visual perception. DCNNs have applications in autonomous robots, security systems, mobile phones, and automobiles, where high throughput of the feedforward evaluation phase and power efficiency are important. Because of this increased usage, many field-programmable gate array (FPGA)-based accelerators have been proposed. In this paper, we present an optimized streaming method for DCNNs’ hardware accelerator on an embedded platform. The streaming method acts as a compiler, transforming a high-level representation of DCNNs into operation codes to execute applications in a hardware accelerator. The proposed method utilizes maximum computational resources available based on a novel-scheduled routing topology that combines data reuse and data concatenation. It is tested with a hardware accelerator implemented on the Xilinx Kintex-7 XC7K325T FPGA. The system fully explores weight-level and node-level parallelizations of DCNNs and achieves a peak performance of 247 G-ops while consuming less than 4 W of power. We test our system with applications on object classification and object detection in real-world scenarios. Our results indicate high-performance efficiency, outperforming all other presented platforms while running these applications.


title: Common Topics And Coherent Situations: Interpreting Ellipsis In The Context Of Discourse Inference

abstract: It is claimed that a variety of facts concerning ellipsis, event reference, and interclausal coherence can be explained by two features of the linguistic form in question: (1) whether the form leaves behind an empty constituent in the syntax, and (2) whether the form is anaphoric in the semantics. It is proposed that these features interact with one of two types of discourse inference, namely Common Topic inference and Coherent Situation inference. The differing ways in which these types of inference utilize syntactic and semantic representations predicts phenomena for which it is otherwise difficult to account.


title: Solution synthesis of metal oxides for electrochemical energy storage applications

abstract: This article provides an overview of solution-based methods for the controllable synthesis of metal oxides and their applications for electrochemical energy storage. Typical solution synthesis strategies are summarized and the detailed chemical reactions are elaborated for several common nanostructured transition metal oxides and their composites. The merits and demerits of these synthesis methods and some important considerations are discussed in association with their electrochemical performance. We also propose the basic guideline for designing advanced nanostructure electrode materials, and the future research trend in the development of high power and energy density electrochemical energy storage devices.


title: Photobook: Content-based manipulation of image databases

abstract: We describe the Photobook system, which is a set of interactive tools for browsing and searching images and image sequences. These query tools differ from those used in standard image databases in that they make direct use of the image content rather than relying on text annotations. Direct search on image content is made possible by use of semantics-preserving image compression, which reduces images to a small set of perceptually-significant coefficients. We discuss three types of Photobook descriptions in detail: one that allows search based on appearance, one that uses 2-D shape, and a third that allows search based on textural properties. These image content descriptions can be combined with each other and with text-based descriptions to provide a sophisticated browsing and search capability. In this paper we demonstrate Photobook on databases containing images of people, video keyframes, hand tools, fish, texture swatches, and 3-D medical data.


title: Insider Threat Behavior Factors: A Comparison of Theory with Reported Incidents

abstract: Almost all organizations and sectors are currently faced with the problem of insider threats to vital computer assets. Internal incidents can cause more than just financial losses, the costs can also include loss of clients and damage to an organization's reputation. Substantial academic research investigating internal threats has been conducted. This paper examines a number of theoretical models drawn from academic literature to identify a set of factors that are thought to be behavior factors associated with insider threats. These factors are then critiqued using empirical evidence from reported incidents, resulting in insights into areas where the theoretical perspectives of academic literature are both supported and unsupported by actual case evidence. The paper concludes with recommendations for future research directions for academic researchers.


title: Randomised controlled trial of early frenotomy in breastfed infants with mild–moderate tongue-tie

abstract: Trial design A randomised, parallel group, pragmatic trial. ::: ::: Setting A large UK maternity hospital. ::: ::: Participants Term infants <2 weeks old with a mild or moderate degree of tongue-tie, and their mothers who were having difficulties breastfeeding. ::: ::: Objectives To determine if immediate frenotomy was better than standard breastfeeding support. ::: ::: Interventions Participants were randomised to an early frenotomy intervention group or a ‘standard care’ comparison group. ::: ::: Outcomes Primary outcome was breastfeeding at 5 days, with secondary outcomes of breastfeeding self-efficacy and pain on feeding. Final assessment was at 8 weeks; 20 also had qualitative interviews. Researchers assessing outcomes, but not participants, were blinded to group assignment. ::: ::: Results 107 infants were randomised, 55 to the intervention group and 52 to the comparison group. Five-day outcome measures were available for 53 (96%) of the intervention group and 52 (100%) of the comparison group, and intention-to-treat analysis showed no difference in the primary outcome— L atch, A udible swallowing, nipple T ype, C omfort, H old score. Frenotomy did improve the tongue-tie and increased maternal breastfeeding self-efficacy. At 5 days, there was a 15.5% increase in bottle feeding in the comparison group compared with a 7.5% increase in the intervention group. ::: ::: After the 5-day clinic, 44 of the comparison group had requested a frenotomy; by 8 weeks only 6 (12%) were breastfeeding without a frenotomy. At 8 weeks, there were no differences between groups in the breastfeeding measures or in the infant weight. No adverse events were observed. ::: ::: Conclusions Early frenotomy did not result in an objective improvement in breastfeeding but was associated with improved self-efficacy. The majority in the comparison arm opted for the intervention after 5 days.


title: Bayesian Saliency via Low and Mid Level Cues

abstract: Visual saliency detection is a challenging problem in computer vision, but one of great importance and numerous applications. In this paper, we propose a novel model for bottom-up saliency within the Bayesian framework by exploiting low and mid level cues. In contrast to most existing methods that operate directly on low level cues, we propose an algorithm in which a coarse saliency region is first obtained via a convex hull of interest points. We also analyze the saliency information with mid level visual cues via superpixels. We present a Laplacian sparse subspace clustering method to group superpixels with local features, and analyze the results with respect to the coarse saliency region to compute the prior saliency map. We use the low level visual cues based on the convex hull to compute the observation likelihood, thereby facilitating inference of Bayesian saliency at each pixel. Extensive experiments on a large data set show that our Bayesian saliency model performs favorably against the state-of-the-art algorithms.


title: K-nearest correlated neighbor classification for Indian sign language gesture recognition using feature fusion

abstract: A sign language recognition system is an attempt to bring the speech and the hearing impaired community closer to more regular and convenient forms of communication. Thus, this system requires to recognize the gestures from a sign language and convert them to a form easily understood by the hearing. The model that has been proposed in this paper recognizes static images of the signed alphabets in the Indian Sign Language. Unlike the alphabets in other sign languages like the American Sign Language and the Chinese Sign language, the ISL alphabet are both single-handed and double-handed. Hence, to make recognition easier the model first categorizes them as single-handed or double-handed. For both categories two kinds of features, namely HOG and SIFT, are extracted for a set of training images and are combined in a single matrix. After which, HOG and SIFT features for the input test image are combined with the HOG and SIFT feature matrices of the training set. Correlation is computed for these matrices and is fed to a K-Nearest Neighbor Classifier to obtain the resultant classification of the test image.


Prefix tree based n-best list re-scoring for recurrent neural network language model used in speech recognition system.


title: A Brief Survey of Deep Reinforcement Learning

abstract: Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.


title: Visual servoing of a wheeled mobile robot for intercepting a moving object

abstract: We present a vision-based scheme for driving a nonholonomic mobile robot to intercept a moving target. Our method relies on a two-level approach. On the lower level, the pan-tilt platform which carries the on-board camera is controlled so as to keep the target at the center of the image plane. On the higher level, the robot operates under the assumption that the camera system achieves perfect tracking. In particular, the relative position of the ball is retrieved from the pan/tilt angles through simple geometry, and used to compute a control law driving the robot to the target. Various possible choices are discussed for the high-level robot controller. The proposed visual interception method is validated through simulations as well as experiments on the mobile robot MagellanPro.


title: Photon Upmanship: Why Multiphoton Imaging Is More than a Gimmick

abstract: The unique niche that light microscopy occupies in biology is based on the ability to perform observations on living tissue at relatively high spatial resolution. This resolution is limited by the wavelength of light and does not rival that of electron microscopy, which is, however, a fundamentally nonvital form of observation. Other vital microscopies, such as MRI, can neither resolve subcellular structures nor provide the exquisite molecular selectivity that allows the detection of even single molecules in a background of billions of others.


title: Use of calcium hydroxylapatite (Radiesse®) for facial augmentation

abstract: Radiesse® (Bioform Inc, USA) is a sterile, latex-free, non-pyrogenic, semi-solid, cohesive subdermal, injectable implant, whose principal component is synthetic calcium hydroxylapatite, a biocompatible material with over 20 years of use in medicine. The semi-solid nature of the product is created by suspending calcium hydroxylapatite microspheres of 25–45 microns diameter in a gel carrier of carboxymethylcellulose. The product has FDA approval for esthetic facial augmentation in the US. Such approval includes the long-lasting correction of moderate to severe facial wrinkles and folds and the treatment of facial fat loss due to immunodeficiency virus infection. Diverse facial regions can be injected in order to ameliorate or enhance some features: glabellar lines, subdermal support of the brows, malar and buccal fat pads, tear troughs, nasolabial folds, nose, lips, perioral region, marionette lines, oral commisures and chin among others, as well as saucerized acne scars. Other medical indications include nipple projection for nipple areolar reconstruction, urinary incontinence, vesicoureteral reflux, vocal cord augmentation, and use as a radiographic tissue marker. The average lasting result is from 12 to 18 months. Radiesse can be considered an effective soft-tissue filler for overall longevity, biocompatibility, and low rate of side effects.


title: A Novel Cluster Selection Algorithm in the NIDS of Wireless Ad Hoc

abstract: The article presents a novel cluster selection algorithm from the point of the network security. The measure uses Kruskal algorithm to calculate the minimum spanning tree of the topological structure of wireless Ad Hoc networks and builds clustering on it which ensures the cost of communication among cluster nodes that keeps in a relatively low level. The algorithm for the NIDS (network intrusion detection system) of wireless Ad hoc provides a stable and effective support and enhances the survivability of the system. The result of simulation experiment shows that the measure has good performance.


title: LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text

abstract: Collaborative filtering (CF) is the key technique for recommender systems. Pure CF approaches exploit the user-item interaction data (e.g., clicks, likes, and views) only and suffer from the sparsity issue. Items are usually associated with content information such as unstructured text (e.g., abstracts of articles and reviews of products). CF can be extended to leverage text. In this paper, we develop a unified neural framework to exploit interaction data and content information seamlessly. The proposed framework, called LCMR, is based on memory networks and consists of local and centralized memories for exploiting content information and interaction data, respectively. By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction. On real-world datasets, LCMR shows better performance by comparing with various baselines in terms of the hit ratio and NDCG metrics. We further conduct analyses to understand how local and centralized memories work for the proposed framework.


Cross-lingual alignment transfer: a chicken-and-egg story?


title: High Voltage Ratio DC–DC Converter for Fuel-Cell Applications

abstract: Employing fuel cell (FC) as main source requires increasing and regulating its output voltage. In this paper, nonisolated dc-dc converter with high voltage ratio is proposed to interface between the FC and high-voltage dc bus. To take into account the low-voltage-high-density characteristics of power sources, a cascaded structure composed of two subconverters in cascade has been chosen and allows obtaining high voltage ratio. The choice of each subconverter is based on source requirements and its performances. Consequently, in this paper, a converter consisting of two-interleaved boost converter is chosen as first subconverter and a three-level boost converter is chosen as second subconverter. Control of the whole system is realized by energy trajectory planning based on flatness properties of the system. The design of trajectories is explained and allows respecting the fuel-cell constraints as main power source. To ensure correct design of the energy trajectories, a noninteger power-law function is used to model the static characteristic of the FC. This law allows investigating the effect of humidity and temperature on the dynamics of the proposed system. The control of both current and voltage balance across the output serial capacitors of the three-level boost converter is ensured by nonlinear controllers based on a new nonlinear model.


title: The cylindrical omnidirectional patch antenna

abstract: The need for omnidirectional antennas for wireless applications has increased considerably. The antennas are used in a variety of bands anywhere from 1.7 to 2.5 GHz, in different configurations which mainly differ in gain. The omnidirectionality is mostly obtained using back-to-back elements or simply using dipoles in different collinear-array configurations. The antenna proposed in this paper is a patch which was built in a cylindrical geometry rather than a planar one, and which generates an omnidirectional pattern in the H-plane.


title: Aspects Of Graphical Models Connected With Causality

abstract: ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY J udea Pearl Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles, CA 90024 judea@c5.ucla.edu Abstract This paper demonstrates the use of graphs as a mathematical tool for ex- pressing independenices, and as a formal language for communicating and pro- cessing causal information in statistical analysis. We show how complex infor- mation about external interventions can be organized and represented graphi- cally and, conversely, how the graphical representation can be used to facilitate quantitative predictions of the effects of interventions. We first review the Markovian account of causation and show that directed acyclic graphs (DAGS) offer an economical scheme for representing conditional independence assumptions and for deducing and displaying all the logical con- sequences of such assumptions. We then introduce the manipulative account of causation and show that any DAG deﬁnes a simple transformation which tells us how the probability distribution will change as a result of external in- terventions in the system. Using this transformation it is possible to quantify, from non—experimental data, the effects of external interventions and to specify conditions under which randomized experiments are not necessary. Finally, the paper offers a graphical interpretation for Rubin’s model of causal effects, and demonstrates its equivalence to the manipulative account of causation. We exemplify the tradeoffs between the two approaches by deriv- ing nonparametric bounds on treatment effects under conditions of imperfect compliance.


title: Autonomous staircase detection and stair climbing for a tracked mobile robot using fuzzy controller

abstract: Theoretical analysis and implementation of autonomous staircase detection and stair climbing algorithms on a novel rescue mobile robot are presented in this paper. The main goals are to find the staircase during navigation and to implement a fast, safe and smooth autonomous stair climbing algorithm. Silver is used here as the experimental platform. This tracked mobile robot is a tele-operative rescue mobile robot with great capabilities in climbing obstacles in destructed areas. Its performance has been demonstrated in rescue robot league of international RoboCup competitions. A fuzzy controller is applied to direct the robot during stair climbing. Controller inputs are generated by processing the range data from two LASER range finders which scan the environment one horizontally and the other vertically. The experimental results of stair detection algorithm and stair climbing controller are demonstrated at the end.


title: Motivation and Data Quality in a Citizen Science Game: A Design Science Evaluation

abstract: Citizen science is a form of social computation where members of the public are recruited to contribute to scientific investigations. Citizen-science projects often use web-based systems to support collaborative scientific activities. However, finding ways to attract participants and ensure the accuracy of the data they produce are key issues in making such systems successful. In this paper we describe the design and preliminary evaluation of a simple game that addresses these two concerns for the task of species identification.


title: Knowledge management approaches in managing agricultural indigenous and exogenous knowledge in Tanzania

abstract: Purpose – The purpose of this study is to assess the application of knowledge management (KM) models in managing and integrating indigenous and exogenous knowledge for improved farming activities in Tanzania, by examining the management of indigenous knowledge (IK), access and use of exogenous knowledge, the relevance of policies, legal framework, information and communication technologies (ICTs), and culture in KM practices in the communities.Design/methodology/approach – Semi‐structured interviews were used to collect qualitative and quantitative data from 181 farmers in six districts of Tanzania. Four IK policy makers were also interviewed.Findings – The study demonstrated that western‐based KM models should be applied cautiously in a developing world context. Both indigenous and exogenous knowledge was acquired and shared in different contexts. IK was shared within a local, small and spontaneous network, while exogenous knowledge was shared in a wide context, where formal sources of knowledge focused ...


title: Pooling Faces: Template Based Face Recognition with Pooled Face Images

abstract: We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.


Acquisition, compression, and synthesis of bidirectional texture functions


title: A 1.8V 36-mW 11-bit 80MS/s pipelined ADC using capacitor and opamp sharing

abstract: A new capacitor and opamp sharing technique that enables a very efficient low power pipeline ADC design is proposed. A new method to cancel the effect of signal-dependent kick-back in the absence of sample and hold is also presented. Fabricated in a 0.18-mum CMOS process, the prototype 11-bit pipelined ADC occupies 2.2 mm2 of active die area and achieves 66.7 dB SFDR and 53.2 dB SNDR when a 1 MHz input signal is digitized at 80 MS/s. The SFDR and SNDR are unchanged for 50 MHz input signal. The prototype ADC consumes 36 mW at 1.8 V supply, of which analog portion consumes 24 mW.


PRACTICAL USES OF DIGITAL STORYTELLING


title: Analytical and Experimental Investigation of a Low Torque, Ultra-High Speed Drive System

abstract: New application areas are demanding the development of ultra-high speed electrical machines. Significant challenges exist to produce a test bench that is capable of withstanding operating speeds exceeding 500,000 rpm and measuring very low torque values of mNm. This paper describes a purpose built test bench that is able to characterize the operation of an ultra-high speed drive system. Furthermore, the calculation of electromagnetic losses, air friction and critical speeds is presented and a comparison of analytical and experimental results is given. The ultra-high speed machine has an efficiency of 95%, however, in the upper speed ranges the frictional losses become dominant.


title: Platelet-rich plasma: a review of biology and applications in plastic surgery.

abstract: Healing of hard and soft tissue is mediated by a complex array of intracellular and extracellular events that are regulated by signaling proteins, a process that is, at present, incompletely understood. What is certain, however, is that platelets play a prominent if not deciding role. Controlled animal studies of soft and hard tissues have suggested that the application of autogenous platelet-rich plasma can enhance wound healing. The clinical use of platelet-rich plasma for a wide variety of applications has been reported; however, many reports are anecdotal and few include controls to definitively determine the role of platelet-rich plasma. The authors describe platelet biology and its role in wound healing; the preparation, characterization, and use of platelet-rich plasma; and those applications in plastic surgery for which it may be useful.


title: State transition, balancing, station keeping, and yaw control for a dynamically stable single spherical wheel mobile robot

abstract: Unlike statically stable wheeled mobile robots, dynamically stable mobile robots can have higher centers of gravity, smaller bases of support and can be tall and thin resembling the shape of an adult human. This paper concerns the ballbot mobile robot, which balances dynamically on a single spherical wheel. The ballbot is omni-directional and can also rotate about its vertical axis (yaw motion). It uses a triad of legs to remain statically stable when powered off. This paper presents the evolved design with a four-motor inverse mouse-ball drive, yaw drive, leg drive, control system, and results including dynamic balancing, station keeping, yaw motion while balancing, and automatic transition between statically stable and dynamically stable states.


title: Ur/Web: A Simple Model for Programming the Web

abstract: The World Wide Web has evolved gradually from a document delivery platform to an architecture for distributed programming. This largely unplanned evolution is apparent in the set of interconnected languages and protocols that any Web application must manage. This paper presents Ur/Web, a domain-specific, statically typed functional programming language with a much simpler model for programming modern Web applications. Ur/Web's model is unified, where programs in a single programming language are compiled to other "Web standards" languages as needed; supports novel kinds of encapsulation of Web-specific state; and exposes simple concurrency, where programmers can reason about distributed, multithreaded applications via a mix of transactions and cooperative preemption. We give a tutorial introduction to the main features of Ur/Web and discuss the language implementation and the production Web applications that use it.


title: NFVnice: Dynamic Backpressure and Scheduling for NFV Service Chains

abstract: Managing Network Function (NF) service chains requires careful system resource management. We propose NFVnice, a user space NF scheduling and service chain management framework to provide fair, efficient and dynamic resource scheduling capabilities on Network Function Virtualization (NFV) platforms. The NFVnice framework monitors load on a service chain at high frequency (1000Hz) and employs backpressure to shed load early in the service chain, thereby preventing wasted work. Borrowing concepts such as rate proportional scheduling from hardware packet schedulers, CPU shares are computed by accounting for heterogeneous packet processing costs of NFs, I/O, and traffic arrival characteristics. By leveraging cgroups, a user space process scheduling abstraction exposed by the operating system, NFVnice is capable of controlling when network functions should be scheduled. NFVnice improves NF performance by complementing the capabilities of the OS scheduler but without requiring changes to the OS's scheduling mechanisms. Our controlled experiments show that NFVnice provides the appropriate rate-cost proportional fair share of CPU to NFs and significantly improves NF performance (throughput and loss) by reducing wasted work across an NF chain, compared to using the default OS scheduler. NFVnice achieves this even for heterogeneous NFs with vastly different computational costs and for heterogeneous workloads.


title: Design and evaluation of a wide-area event notification service

abstract: The components of a loosely coupled system are typically designed to operate by generating and responding to asynchronous events. An event notification service is an application-independent infrastructure that supports the construction of event-based systems, whereby generators of events publish event notifications to the infrastructure and consumers of events subscribe with the infrastructure to receive relevant notifications. The two primary services that should be provided to components by the infrastructure are notification selection (i. e., determining which notifications match which subscriptions) and notification delivery (i.e., routing matching notifications from publishers to subscribers). Numerous event notification services have been developed for local-area networks, generally based on a centralized server to select and deliver event notifications. Therefore, they suffer from an inherent inability to scale to wide-area networks, such as the Internet, where the number and physical distribution of the service's clients can quickly overwhelm a centralized solution. The critical challenge in the setting of a wide-area network is to maximize the expressiveness in the selection mechanism without sacrificing scalability in the delivery mechanism. This paper presents SIENA, an event notification service that we have designed and implemented to exhibit both expressiveness and scalability. We describe the service's interface to applications, the algorithms used by networks of servers to select and deliver event notifications, and the strategies used to optimize performance. We also present results of simulation studies that examine the scalability and performance of the service.


title: Learning to recognize reliable users and content in social media with coupled mutual reinforcement

abstract: Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data -- which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.


title: A Wideband Millimeter-Wave Circularly Polarized Antenna With 3-D Printed Polarizer

abstract: This paper presents a wideband circularly polarized millimeter-wave (mmw) antenna design. We introduce a novel 3-D-printed polarizer, which consists of several air and dielectric slabs to transform the polarization of the antenna radiation from linear to circular. The proposed polarizer is placed above a radiating aperture operating at the center frequency of 60 GHz. An electric field, ${E}$ , radiated from the aperture generates two components of electric fields, ${E} _{\mathrm {x}}$ and ${E} _{\mathrm {y}}$ . After passing through the polarizer, both ${E} _{\mathrm {x}}$ and ${E} _{\mathrm {y}}$ fields can be degenerated with an orthogonal phase difference which results in having a wide axial ratio bandwidth. The phase difference between ${E} _{\mathrm {x}}$ and ${E} _{\mathrm {y}}$ is determined by the incident angle $\phi $ , of the polarization of the electric field to the polarizer as well as the thickness, ${h}$ , of the dielectric slabs. With the help of the thickness of the polarizer, the directivity of the radiation pattern is increased so as to devote high-gain and wideband characteristics to the antenna. To verify our concept, an intensive parametric study and an experiment were carried out. Three antenna sources, including dipole, patch, and aperture antennas, were investigated with the proposed 3-D-printed polarizer. All measured results agree with the theoretical analysis. The proposed antenna with the polarizer achieves a wide impedance bandwidth of 50% from 45 to 75 GHz for the reflection coefficient less than or equal −10 dB, and yields an overlapped axial ratio bandwidth of 30% from 49 to 67 GHz for the axial ratio ≤ 3 dB. The maximum gain of the antenna reaches to 15 dBic. The proposed methodology of this design can apply to applications related to mmw wireless communication systems. The ultimate goal of this paper is to develop a wideband, high-gain, and low-cost antenna for the mmw frequency band.


title: Risk personality traits of Internet addiction: A longitudinal study of Internet‐addicted Chinese university students

abstract: Introduction ::: As the world's fastest growing “addiction”, Internet addiction is still controversial. The present study aimed to examine the potential personality predictors of Internet addicts. ::: ::: Methods ::: Eight hundred and sixty-eight students were tested using the Eysenck Personality Questionnaire after they had just entered university. Two years later, 49 were found to be addicted to the Internet as defined by high Internet addiction test scores. Comparisons of means and logistic regression analysis were used to explore their relationship. ::: ::: Results ::: Students addicted to the Internet showed higher Neuroticism/Stability scores, higher Psychoticism/Socialization scores, and lower Lie scores than their normal peers before their addiction. Regression results showed that Internet addiction was accounted by three independent variables: Neuroticism/Stability, Psychoticism/Socialization, and Lie. ::: ::: Conclusion ::: These results suggest that the risk personality traits of Internet addiction include neuroticism, psychoticism, and immaturity.


title: Optimal Operator Replication and Placement for Distributed Stream Processing Systems

abstract: Exploiting on-the-fly computation, Data Stream Processing (DSP) applications are widely used to process unbounded streams of data and extract valuable information in a near real-time fashion. As such, they enable the development of new intelligent and pervasive services that can improve our everyday life. To keep up with the high volume of daily produced data, the operators that compose a DSP application can be replicated and placed on multiple, possibly distributed, computing nodes, so to process the incoming data flow in parallel. Moreover, to better exploit the abundance of diffused computational resources (e.g., Fog computing), recent trends investigate the possibility of decentralizing the DSP application placement. In this paper, we present and evaluate a general formulation of the optimal DSP replication and placement (ODRP) as an integer linear programming problem, which takes into account the heterogeneity of application requirements and infrastructural resources. We integrate ODRP as prototype scheduler in the Apache Storm DSP framework. By leveraging on the DEBS 2015 Grand Challenge as benchmark application, we show the benefits of a joint optimization of operator replication and placement and how ODRP can optimize different QoS metrics, namely response time, internode traffic, cost, availability, and a combination thereof.


title: Multidisciplinary Design Optimization through process integration in the AEC industry: Strategies and challenges

abstract: Abstract Recently Multidisciplinary Design Optimization (MDO) has emerged in the Architecture-Engineering-Construction (AEC) industry to assist designers in making the design process more efficient, by achieving more design alternatives in less time. Currently, MDO is developed with software tools that work together and automatically. However, the technical requisites to develop MDO using Process Integration and Design Optimization (PIDO) platforms are not clearly specified in the design optimization literature. There are many difficulties not covered by the literature: especially the tools' behavior, and the strategies to deal with PIDO. To determine the technical requirements, the tools' behavior, the challenges of interoperability and viable strategies, we reviewed the literature and tested five tools. This paper presents the main behavior of the tools we studied, and explains the challenges and strategies to develop MDO through PIDO. We observed three technical tool requisites: component interoperability, tool automation, and model parameterization capabilities. We detected low openness levels of the tool interfaces that did not always enable a full integration with PIDO or permit access to model properties. The scarcity of commands and the presence of pop-up menus impeded performing analyses automatically. Moreover, most of the tools did not allow parametric associations among components, compatibility among themselves or the addition of custom components. The strategies proposed focused on testing the tool interfaces, to validate that each computational process runs automatically, and to confirm that parametric relationships and components are possible. The tools tested were not specifically designed to include full capability to work with PIDO, therefore, enhancements would be needed to meet the three requisites: component interoperability, automation and parameterization. Technological, documentation and programming challenges also emerged when working with tools. We demonstrated that only certain tools can be used with a PIDO platform. However, there may be still other requisites for MDO using different methods that can become the focus of future work.


title: Squeezed Convolutional Variational AutoEncoder for unsupervised anomaly detection in edge device industrial Internet of Things

abstract: In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference times are reduced while similar levels of performance is maintained.


title: Exploring the potential of heterogeneous von neumann/dataflow execution models

abstract: General purpose processors (GPPs), from small inorder designs to many-issue out-of-order, incur large power overheads which must be addressed for future technology generations. Major sources of overhead include structures which dynamically extract the data-dependence graph or maintain precise state. Considering irregular workloads, current specialization approaches either heavily curtail performance, or provide simply too little benefit. Interestingly, well known explicit-dataflow architectures eliminate these overheads by directly executing the data-dependence graph and eschewing instruction-precise recoverability. However, even after decades of research, dataflow architectures have yet to come into prominence as a solution. We attribute this to a lack of effective control speculation and the latency overhead of explicit communication, which is crippling for certain codes. This paper makes the observation that if both out-of-order and explicit-dataflow were available in one processor, many types of GPP cores can benefit from dynamically switching during certain phases of an application's lifetime. Analysis reveals that an ideal explicit-dataflow engine could be profitable for more than half of instructions, providing significant performance and energy improvements. The challenge is to achieve these benefits without introducing excess hardware complexity. To this end, we propose the Specialization Engine for Explicit-Dataflow (SEED). Integrated with an inorder core, we see 1.67× performance and 1.65× energy benefits, with an Out-Of-Order (OOO) dual-issue core we see 1.33× and 1.70×, and with a quad-issue OOO, 1.14× and 1.54×.


Liquid Biopsy in the OMICS Era of Tumor Medicine


title: Conversational tagging in twitter

abstract: Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.


title: Quantitative analysis and fracture detection of pelvic bone X-ray images

abstract: Today bone fractures are very common in our country because of road accidents or through other injuries. The X-Ray images are the most common accessibility of peoples during the accidents. But the minute fracture detection in X-Ray image is not possible due to low resolution and quality of the original X-Ray image. The complexity of bone structure and the difference in visual characteristics of fracture by their location. So it is difficult to accurately detect and locate the fractures also determine the severity of the injury. The automatic detection of fractures in X-Ray images is a significant contribution for assisting the physicians in making faster and more accurate patient diagnostic decisions and treatment planning. In this paper, an automatic hierarchical algorithm for detecting bone fracture in X-Ray image is proposed. It uses the Gray level cooccurrence matrix for detecting the fracture. The results are promising, demonstrating that the proposed method is capable of automatically detecting both major and minor fractures accurately, and shows potential for clinical application. Statistical results also indicate the superiority of the proposed methods compared to other techniques. This paper examines the development of such a system, for the detection of long-bone fractures. This project fully employed MATLAB 7.8.0 (.r2009a) as the programming tool for loading image, image processing and user interface development. Results obtained demonstrate the performance of the pelvic bone fracture detection system with some limitations.


title: SLA Decomposition: Translating Service Level Objectives to System Level Thresholds

abstract: In today's complex and highly dynamic computing environments, systems/services have to be constantly adjusted to meet service level agreements (SLAs) and to improve resource utilization, thus reducing operating cost. Traditional design of such systems usually involves domain experts who implicitly translate service level objectives (SLOs) specified in SLAs to system-level thresholds in an ad-hoc manner. In this paper, we present an approach that combines performance modeling with performance profiling to create models that translate SLOs to lower-level resource requirements for each system involved in providing the service. Using these models, the process of creating an efficient design of a system/service can be automated, eliminating the involvement of domain experts. We demonstrate that our approach is practical and that it can be applied to different applications and software architectures. Our experiments show that for a typical 3-tier e-commerce application in a virtualized environment the SLAs can be met while improving CPU utilization up to 3 times.


title: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks

abstract: Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300–700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.


title: Trust and risk in consumer acceptance of e-services

abstract: Consumers' risk perception and trust are considered among the most important psychological states that influence online behavior. Despite the number of empirical studies that have explored the effects of trust and risk perceptions on consumer acceptance of e-services, the field remains fragmented and the posited research models are contradictory. To address this problem, we examined how trust and risk influence consumer acceptance of e-services through a meta-analysis of 67 studies, followed by tests of competing causal models. The findings confirm that trust and risk are important to e-services acceptance but that trust has a stronger effect size. We found that certain effect sizes were moderated by factors such as the consumer population under study, the type of e-service, and the object of trust under consideration. The data from the meta-analysis best supports the causal logic that positions trust as antecedent to risk perceptions. Risk partially mediates the effects of trust on acceptance.


title: Embracing digital innovation in incumbent firms: how volvo cars managed competing concerns

abstract: Past research provides instructive yet incomplete answers as to how incumbent firms can address competing concerns as they embrace digital innovation. In particular, it offers only partial explanations of why different concerns emerge, how they manifest, and how firms can manage them. In response, we present a longitudinal case study of Volvo Cars'connected car initiative. Combining extant literature with insights from the case, we argue that incumbent firms face four competing concerns—capability (existing versus requisite), focus (product versus process), collaboration (internal versus external), and governance (control versus flexibility)—and that these concerns are systemically interrelated. Firms must therefore manage these concerns cohesively by continuously balancing new opportunities and established practices.


Using Information Retrieval to Evaluate Trustworthiness Assessment of Eshops


title: A Novel Method for Barcode Localization in Image Domain

abstract: Barcode localization is an essential step of the barcode reading process. For industrial environments, having high-resolution cameras and eventful scenarios, fast and reliable localization is crucial. Images acquired in those setups have limited parameters, however, they vary at each application. In earlier works we have already presented various barcode features to track for localization process. In this paper, we present a novel approach for fast barcode localization using a limited set of pixels in image domain.


title: Wideband Frequency Tunable Concentric Circular Microstrip Patch Antenna with Simultaneous Polarization Reconfiguration

abstract: i»?A wideband frequency tunable concentric circular microstrip patch antenna with simultaneous polarization reconfiguration is demonstrated by using four varactor diodes between a circular patch and a concentric ring patch around it. Both frequency tunability and polarization reconfigurability are controlled by a proposed feed network. Varying the capacitance value of the varactor diodes varies the operating frequency of the antenna from 1.17 to 1.58 GHz (around 30% tunability) with impedance matching better than S11 = S22 = -10 dB. Each set of the two varactor diodes provides vertical and horizontal linear polarizations excited using one of the feed ports at a time. Similarly, the right-handed circular polarization (RHCP) and left handed circular polarization (LHCP) radiation patterns are realized by employing all four varactor diodes and both feed ports with +90 degree time phase difference. The antenna is fabricated on a 120-mil-thick (3.05 mm) Arlon AD450 dielectric substrate (Er = 4.45, tan = 0.0035). The proposed feed network to control the excitation of ports for each of the polarizations is fabricated on 62-mil-thick (1.58 mm) FR-4 substrate material (Er = 4.4, tan = 0:02). A single-pole 4 through radio-frequency switch and two single-pole double through switches are used in this feed network for realizing the control circuitry. Simulated and measured impedance matching and radiation pattern results show good agreement with acceptable gain over the tunable frequency bands.


title: Central pattern generators and the control of rhythmic movements

abstract: Central pattern generators are neuronal circuits that when activated can produce rhythmic motor patterns such as walking, breathing, flying, and swimming in the absence of sensory or descending inputs that carry specific timing information. General principles of the organization of these circuits and their control by higher brain centers have come from the study of smaller circuits found in invertebrates. Recent work on vertebrates highlights the importance of neuromodulatory control pathways in enabling spinal cord and brain stem circuits to generate meaningful motor patterns. Because rhythmic motor patterns are easily quantified and studied, central pattern generators will provide important testing grounds for understanding the effects of numerous genetic mutations on behavior. Moreover, further understanding of the modulation of spinal cord circuitry used in rhythmic behaviors should facilitate the development of new treatments to enhance recovery after spinal cord damage.


title: Animal-assisted interventions for elderly patients affected by dementia or psychiatric disorders: a review.

abstract: OBJECTIVE ::: The aim of this literature review was to assess the effects of Animal-Assisted Interventions (AAI) on elderly patients with dementia or various psychiatric disorders. ::: ::: ::: METHODS ::: We conducted a comprehensive literature search using the online PubMed network of the US National Library of Medicine & National Institutes of Health, Embase, PsycINFO, with the purpose of investigating AAI effects on cognitive functions, mood, and behaviour. ::: ::: ::: RESULTS ::: A total of 18 articles on dementia and 5 on psychiatric disorders were included in the present review. AAI were found to have positive influences on demented patients by reducing degree of agitation and by improving degree and quality of social interaction. Few studies have assessed the effects of AAI on mood, and even fewer have assessed its consequences on cognitive functions. The results that are available indicate a positive effect on communication and coping ability, but none on cognitive performance. A substitute pet robot yielded encouraging results, but its use requires further investigation. The few studies conducted for elderly patients presenting a variety of psychiatric diagnoses produced controversial findings. ::: ::: ::: CONCLUSIONS ::: In spite of the encouraging results of AAI, much more research examining the issue of optimal AAI duration, frequency of sessions, and suitable target group is needed.


title: Self-Attentive Feature-Level Fusion for Multimodal Emotion Detection

abstract: Multimodal emotion recognition is the task of detecting emotions present in user-generated multimedia content. Such resources contain complementary information in multiple modalities. A stiff challenge often faced is the complexity associated with feature-level fusion of these heterogeneous modes. In this paper, we propose a new feature-level fusion method based on self-attention mechanism. We also compare it with traditional fusion methods such as concatenation, outer-product, etc. Analyzed using textual and speech (audio) modalities, our results suggest that the proposed fusion method outperforms others in the context of utterance-level emotion recognition in videos.


title: Deep Learning-Based Document Modeling for Personality Detection from Text

abstract: This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolutional neural network, with injection of the document-level Mairesse features, extracted directly from the text, into an inner layer. The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector. Filtering out emotionally neutral input sentences improved the performance. This method outperformed the state of the art for all five traits, and the implementation is freely available for research purposes.


title: High Switching Performance of 1700-V, 50-A SiC Power MOSFET Over Si IGBT/BiMOSFET for Advanced Power Conversion Applications

abstract: Due to wider band gap of silicon carbide (SiC) compared to silicon (Si), MOSFET made in SiC has considerably lower drift region resistance, which is a significant resistive component in high-voltage power devices. With low on-state resistance and its inherently low switching loss, SiC MOSFETs can offer much improved efficiency and compact size for the converter compared to those using Si devices. In this paper, we report switching performance of a new 1700-V, 50-A SiC MOSFET designed and developed by Cree, Inc. Hard-switching losses of the SiC MOSFETs with different circuit parameters and operating conditions are measured and compared with the 1700-V Si BiMOSFET and 1700-V Si IGBT, using same test set-up. Based on switching and conduction losses, the operating boundary of output power and switching frequency of these devices are found out in a dc–dc boost converter and compared. The switching $dv/dts$ and $di/dts$ of SiC MOSFET are captured and discussed in the perspective of converter design. To validate the continuous operation, three dc–dc boost converters using these devices, are designed and tested at 10 kW of power with 1 kV of output voltage and 10 kHz of switching frequency. 1700-V SiC Schottky diode is used as the blocking diode in each case. Corresponding converter efficiencies are evaluated and the junction temperature of each device is estimated. To demonstrate high switching frequency operation, the SiC MOSFET is switched upto 150 kHz within permissible junction temperature rise. A switch combination of the 1700-V SiC MOSFET and 1700-V SiC Schottky diode connected in series is also evaluated for zero voltage switching turn-ON behavior and compared with those of bipolar Si devices. Results show substantial power loss saving with the use of SiC MOSFET.


title: Bilateral teleoperation of an unmanned aerial vehicle for forest fire detection

abstract: In this paper, a bilateral aerial teleoperation system based on force feedback is proposed for monitoring and detecting the forest fires. First of all, an aerial teleoperation system is proposed to enforce the velocity synchronization of the master and the slave. Velocity synchronization is required to achieve motion tracking of the master and slave vehicles. A force reflection is provided to the operator to improve the perceptual sensitivity of the remote environment. In addition, a modified wave variables method is employed to deal with the time-varying delays in the aerial teleoperation system. Then, a forest fire detection algorithm utilizing both color and motion features is presented for detecting the forest fires. Finally, the experiments are conducted to validate the effectiveness of the proposed framework for forest fire detection.


title: Staging of brain pathology related to sporadic Parkinson’s disease

abstract: Abstract Sporadic Parkinson’s disease involves multiple neuronal systems and results from changes developing in a few susceptible types of nerve cells. Essential for neuropathological diagnosis are α-synuclein-immunopositive Lewy neurites and Lewy bodies. The pathological process targets specific induction sites: lesions initially occur in the dorsal motor nucleus of the glossopharyngeal and vagal nerves and anterior olfactory nucleus. Thereafter, less vulnerable nuclear grays and cortical areas gradually become affected. The disease process in the brain stem pursues an ascending course with little interindividual variation. The pathology in the anterior olfactory nucleus makes fewer incursions into related areas than that developing in the brain stem. Cortical involvement ensues, beginning with the anteromedial temporal mesocortex. From there, the neocortex succumbs, commencing with high order sensory association and prefrontal areas. First order sensory association/premotor areas and primary sensory/motor fields then follow suit. This study traces the course of the pathology in incidental and symptomatic Parkinson cases proposing a staging procedure based upon the readily recognizable topographical extent of the lesions.


title: Measuring the similarity of short texts by word similarity and tree kernels

abstract: A novel modeling method is presented in this paper to measure the similarity between short texts. We thought that the complete expression of a sentence or a short text, not only depends on the words, but also relies on the syntactic structure, thus the method takes word similarity feature and syntactic feature into account. The proposed method can be used in a variety of applications involving automatic document summarization, text knowledge representation and discovery. Experiment on two different data sets shows that the proposed method performs better than the measure proposed by Li et al.


Real-Time Object Detection for Unmanned Aerial Vehicles based on Cloud-based Convolutional Neural Networks


title: Fast and Efficient Pedestrian Detection via the Cascade Implementation of an Additive Kernel Support Vector Machine

abstract: For reliable driving assistance or automated driving, pedestrian detection must be robust and performed in real time. In pedestrian detection, a linear support vector machine (linSVM) is popularly used as a classifier but exhibits degraded performance due to the multipostures of pedestrians. Kernel SVM (KSVM) could be a better choice for pedestrian detection, but it has a disadvantage in that it requires too much more computation than linSVM. In this paper, the cascade implementation of the additive KSVM (AKSVM) is proposed for the application of pedestrian detection. AKSVM avoids kernel expansion by using lookup tables, and it is implemented in cascade form, thereby speeding up pedestrian detection. The cascade implementation is trained by a genetic algorithm such that the computation time is minimized, whereas the detection accuracy is maximized. In experiments, the proposed method is tested with the INRIA dataset. The experimental results indicate that the proposed method has better detection accuracy and reduced computation time compared with conventional methods.


title: Lift-the-Flap: Context Reasoning Using Object-Centered Graphs

abstract: Children benefit from lift-the-flap books by taking on an active role in guessing what is behind the flap based on the context. In this paper, we introduce lift-the-flap games for computational models. The task is to reason about the scene context and infer what the target behind the flap is in a natural image. Context reasoning is critical in many computer vision applications, such as object recognition and semantic segmentation. To tackle this problem, we propose an object-centered graph representing the scene configuration of the image where each node corresponds to a group of objects belonging to the same category. To infer the target's class label, we introduce an object-centered graph network model consisting of two sub-networks. The classification sub-network takes the complete graph as input and outputs a classification vector assigning the probability for each class. The reinforcement learning sub-network exploits the class label dependencies and learns the joint probability among objects in order to generate multiple reasonable answers for the missing target. To evaluate our model's performance, we carry out human behavioral experiments for lift-the-flap games as a benchmark. Our model makes reasonable inferences compared to humans, and significantly outperforms all the null models. We also demonstrate the usefulness of our object-centered graph network model in context-aware object recognition and target priming in visual search.


title: A semi-preemptive garbage collector for solid state drives

abstract: NAND flash memory is a preferred storage media for various platforms ranging from embedded systems to enterprise-scale systems. Flash devices do not have any mechanical moving parts and provide low-latency access. They also require less power compared to rotating media. Unlike hard disks, flash devices use out-of-update operations and they require a garbage collection (GC) process to reclaim invalid pages to create free blocks. This GC process is a major cause of performance degradation when running concurrently with other I/O operations as internal bandwidth is consumed to reclaim these invalid pages. The invocation of the GC process is generally governed by a low watermark on free blocks and other internal device metrics that different workloads meet at different intervals. This results in I/O performance that is highly dependent on workload characteristics. In this paper, we examine the GC process and propose a semi-preemptive GC scheme that can preempt on-going GC processing and service pending I/O requests in the queue. Moreover, we further enhance flash performance by pipelining internal GC operations and merge them with pending I/O requests whenever possible. Our experimental evaluation of this semi-preemptive GC sheme with realistic workloads demonstrate both improved performance and reduced performance variability. Write-dominant workloads show up to a 66.56% improvement in average response time with a 83.30% reduced variance in response time compared to the non-preemptive GC scheme.


A FLEXIBLE STREAM ARCHITECTURE FOR ASR USING ARTICULATORY FEATURES


title: A functional reference architecture for autonomous driving

abstract: ContextAs autonomous driving technology matures toward series production, it is necessary to take a deeper look at various aspects of electrical/electronic (E/E) architectures for autonomous driving.ObjectiveThis paper describes a functional reference architecture for autonomous driving, along with various considerations that influence such an architecture. The functionality is described at the logical level, without dependence on specific implementation technologies.MethodEngineering design has been used as the research method, which focuses on creating solutions intended for practical application. The architecture has been refined and applied over a 5 year period to the construction of prototype autonomous vehicles in three different categories, with both academic and industrial stakeholders.ResultsThe architectural components are divided into categories pertaining to (i) perception, (ii) decision and control, and (iii) vehicle platform manipulation. The architecture itself is divided into two layers comprising the vehicle platform and a cognitive driving intelligence. The distribution of components among the architectural layers considers two extremes: one where the vehicle platform is as "dumb" as possible, and the other, where the vehicle platform can be treated as an autonomous system with limited intelligence. We recommend a clean split between the driving intelligence and the vehicle platform. The architecture description includes identification of stakeholder concerns, which are grouped under the business and engineering categories. A comparison with similar architectures is also made, wherein we claim that the presence of explicit components for world modeling, semantic understanding, and vehicle platform abstraction seem unique to our architecture.ConclusionThe concluding discussion examines the influences of implementation technologies on functional architectures and how an architecture is affected when a human driver is replaced by a computer. The discussion also proposes that reduction and acceleration of testing, verification, and validation processes is the key to incorporating continuous deployment processes.


title: American sign language fingerspelling recognition with phonological feature-based tandem models

abstract: We study the recognition of fingerspelling sequences in American Sign Language from video using tandem-style models, in which the outputs of multilayer perceptron (MLP) classifiers are used as observations in a hidden Markov model (HMM)-based recognizer. We compare a baseline HMM-based recognizer, a tandem recognizer using MLP letter classifiers, and a tandem recognizer using MLP classifiers of phonological features. We present experiments on a database of fingerspelling videos. We find that the tandem approaches outperform an HMM-based baseline, and that phonological feature-based tandem models outperform letter-based tandem models.


title: Multi-Batch Experience Replay for Fast Convergence of Continuous Action Control

abstract: Policy gradient methods for direct policy optimization are widely considered to obtain optimal policies in continuous Markov decision process (MDP) environments. However, policy gradient methods require exponentially many samples as the dimension of the action space increases. Thus, off-policy learning with experience replay is proposed to enable the agent to learn by using samples of other policies. Generally, large replay memories are preferred to minimize the sample correlation but large replay memories can yield large bias or variance in importance-sampling-based off-policy learning. In this paper, we propose a multi batch experience replay scheme suitable for off-policy actor-critic-style policy gradient methods such as the proximal policy optimization (PPO) algorithm, which maintains the advantages of experience replay and accelerates learning without causing large bias. To demonstrate the superiority of the proposed method, we apply the proposed experience replay scheme to the PPO algorithm and various continuous control tasks. Numerical results show that our algorithm converges faster and closer to the global optimum than other policy gradient methods.


title: Cannabidiol in medicine: a review of its therapeutic potential in CNS disorders

abstract: Cannabidiol (CBD) is the main non-psychotropic component of the glandular hairs of Cannabis sativa. It displays a plethora of actions including anticonvulsive, sedative, hypnotic, antipsychotic, antiinflammatory and neuroprotective properties. However, it is well established that CBD produces its biological effects without exerting significant intrinsic activity upon cannabinoid receptors. For this reason, CBD lacks the unwanted psychotropic effects characteristic of marijuana derivatives, so representing one of the bioactive constituents of Cannabis sativa with the highest potential for therapeutic use. ::: ::: The present review reports the pharmacological profile of CBD and summarizes results from preclinical and clinical studies utilizing CBD, alone or in combination with other phytocannabinoids, for the treatment of a number of CNS disorders. Copyright © 2008 John Wiley & Sons, Ltd.


title: Biosignal-Based Spoken Communication: A Survey

abstract: Speech is a complex process involving a wide range of biosignals, including but not limited to acoustics. These biosignals—stemming from the articulators, the articulator muscle activities, the neural pathways, and the brain itself—can be used to circumvent limitations of conventional speech processing in particular, and to gain insights into the process of speech production in general. Research on biosignal-based speech processing is a wide and very active field at the intersection of various disciplines, ranging from engineering, computer science, electronics and machine learning to medicine, neuroscience, physiology, and psychology. Consequently, a variety of methods and approaches have been used to investigate the common goal of creating biosignal-based speech processing devices for communication applications in everyday situations and for speech rehabilitation, as well as gaining a deeper understanding of spoken communication. This paper gives an overview of the various modalities, research approaches, and objectives for biosignal-based spoken communication.