title: A novel anomaly detection algorithm for sensor data under uncertainty

abstract: It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications.


title: Hate speech detection in the Indonesian language: A dataset and preliminary study

abstract: The objective of our work is to detect hate speech in the Indonesian language. As far as we know, the research on this subject is still very rare. The only research we found has created a dataset for hate speech against religion, but the quality of this dataset is inadequate. Our research aimed to create a new dataset that covers hate speech in general, including hatred for religion, race, ethnicity, and gender. In addition, we also conducted a preliminary study using machine learning approach. Machine learning so far is the most frequently used approach in classifying text. We compared the performance of several features and machine learning algorithms for hate speech detection. Features that extracted were word n-gram with n=l and n=2, character n-gram with n=3 and n=4, and negative sentiment. The classification was performed using Naive Bayes, Support Vector Machine, Bayesian Logistic Regression, and Random Forest Decision Tree. An F-measure of 93.5% was achieved when using word n-gram feature with Random Forest Decision Tree algorithm. Results also show that word n-gram feature outperformed character n-gram.


title: Fundamental biomechanics of the spine—What we have learned in the past 25 years and future directions

abstract: Since the publication of the 2nd edition of White and Panjabi׳s textbook, Clinical Biomechanics of the Spine in 1990, there has been considerable research on the biomechanics of the spine. The focus of this manuscript will be to review what we have learned in regards to the fundamentals of spine biomechanics. Topics addressed include the whole spine, the functional spinal unit, and the individual components of the spine (e.g. vertebra, intervertebral disc, spinal ligaments). In these broad categories, our understanding in 1990 is reviewed and the important knowledge or understanding gained through the subsequent 25 years of research is highlighted. Areas where our knowledge is lacking helps to identify promising topics for future research. In this manuscript, as in the White and Panjabi textbook, the emphasis is on experimental research using human material, either in vivo or in vitro. The insights gained from mathematical models and animal experimentation are included where other data are not available. This review is intended to celebrate the substantial gains that have been made in the field over these past 25 years and also to identify future research directions.


title: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach

abstract: With the rapidly growing location-based social networks (LBSNs), personalized geo-social recommendation becomes an important feature for LBSNs. Personalized geo-social recommendation not only helps users explore new places but also makes LBSNs more prevalent to users. In LBSNs, aside from user preference and social influence, geographical influence has also been intensively exploited in the process of location recommendation based on the fact that geographical proximity significantly affects users' check-in behaviors. Although geographical influence on users should be personalized, current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we propose a new framework called iGSLR to exploit personalized social and geographical influence on location recommendation. iGSLR uses a kernel density estimation approach to personalize the geographical influence on users' check-in behaviors as individual distributions rather than a universal distribution for all users. Furthermore, user preference, social influence, and personalized geographical influence are integrated into a unified geo-social recommendation framework. We conduct a comprehensive performance evaluation for iGSLR using two large-scale real data sets collected from Foursquare and Gowalla which are two of the most popular LBSNs. Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques.


title: RevARM: A Platform-Agnostic ARM Binary Rewriter for Security Applications

abstract: ARM is the leading processor architecture in the emerging mobile and embedded market. Unfortunately, there has been a myriad of security issues on both mobile and embedded systems. While many countermeasures of such security issues have been proposed in recent years, a majority of applications still cannot be patched or protected due to run-time and space overhead constraints and the unavailability of source code. More importantly, the rapidly evolving mobile and embedded market makes any platform-specific solution ineffective. In this paper, we propose RevARM, a binary rewriting technique capable of instrumenting ARM-based binaries without limitation on the target platform. Unlike many previous binary instrumentation tools that are designed to instrument binaries based on x86, RevARM must resolve a number of new, ARM-specific binary rewriting challenges. Moreover, RevARM is able to handle stripped binaries, requires no symbolic/semantic information, and supports Mach-O binaries, overcoming the limitations of existing approaches. Finally, we demonstrate the capabilities of RevARM in solving real-world security challenges. Our evaluation results across a variety of platforms, including popular mobile and embedded systems, show that RevARM is highly effective in instrumenting ARM binaries with an average of 3.2% run-time and 1.3% space overhead.


title: A resonant MOSFET gate driver with efficient energy recovery

abstract: High frequency pulse-width modulation (PWM) converters generally suffer from excessive gate drive loss. This paper presents a resonant gate drive circuit that features efficient energy recovery at both charging and discharging transitions. Following a brief introduction of metal oxide semiconductor field effect transistor (MOSFET) gate drive loss, this paper discusses the gate drive requirements for high frequency PWM applications and common shortcomings of existing resonant gate drive techniques. To overcome the apparent disparity, a new resonant MOSFET gate drive circuit is then presented. The new circuit produces low gate drive loss, fast switching speed, clamped gate voltages, immunity to false trigger and has no limitation on the duty cycle. Experimental results further verify its functionality.


title: Stimulus contrast modulates functional connectivity in visual cortex

abstract: Neurons in visual cortex are linked by an extensive network of lateral connections. To study the effect of these connections on neural responses, we recorded spikes and local field potentials (LFPs) from multi-electrode arrays that were implanted in monkey and cat primary visual cortex. Spikes at each location generated outward traveling LFP waves. When the visual stimulus was absent or had low contrast, these LFP waves had large amplitudes and traveled over long distances. Their effect was strong: LFP traces at any site could be predicted by the superposition of waves that were evoked by spiking in a approximately 1.5-mm radius. As stimulus contrast increased, both the magnitude and the distance traveled by the waves progressively decreased. We conclude that the relative weight of feedforward and lateral inputs in visual cortex is not fixed, but rather depends on stimulus contrast. Lateral connections dominate at low contrast, when spatial integration of signals is perhaps most beneficial.


title: Object-wise 3D Gaze Mapping in Physical Workspace

abstract: Understanding the intention of other people is a fundamental social skill in human communication. Eye behavior is an important, yet implicit communication cue. In this work, we focus on enabling people to see the users' gaze associated with objects in the 3D space, namely, we present users the history of gaze linked to real 3D objects. Our 3D gaze visualization system automatically segments objects in the workspace and projects user's gaze trajectory onto the objects in 3D for visualizing user's intention. By combining automated object segmentation and head tracking via the first-person video from a wearable eye tracker, our system can visualize user's gaze behavior more intuitively and efficiently compared to 2D based methods and 3D methods with manual annotation. We performed an evaluation of the system to measure the accuracy of object-wise gaze mapping. In the evaluation, the system achieved 94% accuracy of gaze mapping onto 40, 30, 20, 10-centimeter cubes. We also conducted a case study of through a case study where the user looks at food products, we showed that our system was able to predict products that the user is interested in.


title: An image schema language

abstract: Abstract : This paper introduces ISL, a language for representing and manipulating image schemas. ISL supports the representation of symbolic as well as quantitative dynamic properties of objects and relationships. We have encoded a number of the image schemas commonly covered in the cognitive linguistics literature and tested them in three domains: patterns in chess, tactics in military scenarios, and behavior in a simple robot arm simulation. This paper discusses the design of the language and demonstrates its representational capabilities with examples from these domains.


title: Cloud-assisted body area networks: state-of-the-art and future challenges

abstract: Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.


title: Design of a Wideband Planar Printed Quasi-Yagi Antenna Using Stepped Connection Structure

abstract: Although conventional Yagi antenna has the advantage of unidirectional radiation patterns, it is not suitable for wideband applications due to its drawback of narrow bandwidth. In this communication, a compact wideband planar printed quasi-Yagi antenna is presented. The proposed quasi-Yagi antenna consists of a microstrip line to slotline transition structure, a driver dipole, and a parasitic strip element. The driver dipole is connected to the slotline through a coplanar stripline (CPS). The proposed antenna uses a stepped connection structure between the CPS and the slotline to improve the impedance matching. Two apertures are symmetrically etched in the ground plane to improve the unidirectional radiation characteristics. Simulation and experimental results show that the unidirectional radiation patterns of the proposed antenna are good. A 92.2% measured bandwidth with ${\rm S} _{11} from 3.8 to 10.3 GHz is achieved. A moderate gain, which is better than 4 dBi, is also obtained.


title: Simplified Optimal Trajectory Control (SOTC) for LLC Resonant Converters

abstract: In this paper, a simplified optimal trajectory control (SOTC) for the LLC resonant converter is proposed. During the steady state, a linear compensator such as proportional-integral or proportional-integral-derivative (PID) is used, controlling the switching frequency fs to eliminate the steady-state error. However, during load transients, the SOTC method takes over, immediately changing the pulse widths of the gate-driving signals. Using the state-plane analysis, the pulse widths are estimated, letting the state variables track the optimal trajectory locus in the minimum period of time. The proposed solution is implemented in a digital controller, and experimental results show that while the digital logic requirement is very small, the performance improvement is significant.


title: Frame aggregation in fibre-wireless (FiWi) broadband access networks

abstract: MAC enhancements of emerging high-throughput WLANs are considered and a novel FiWi network architecture is introduced that integrates next-generation WLAN-based WMN and EPON in a pay-as-you-grow manner while providing backward compatibility with a legacy infrastructure and protecting previous investments. To investigate the performance of FiWi networks, the capacity of WMNs is evaluated through probabilistic analysis and verifying simulations. Advanced aggregation techniques are proposed and examined to improve FiWi network throughput-delay performance for voice, video, and data traffic under realistic wireless channel conditions.


title: Interaction of Human Ribosomal Protein S26 with Fragments of Its Own mRNA and Pre-mRNA in Nuclear Extract of HeLa Cells

abstract: As shown by nitrocellulose filtration assays with RNA fragments transcribed from various regions of the human ribosomal protein (rp) S26 gene, recombinant rpS26 binds to the first intron of the rpS26 pre-mRNA (apparent association constant (Ka) ∼ 5.0 · 107 M–1) and, to a lesser extent, to the rpS26 mRNA (Ka ∼ 2.0 · 107 M–1). The binding was specific, since human rpS19 had an order of magnitude lower Ka with the first intron and did not bind with the rpS26 mRNA. Immunoassays with specific antibodies showed that rpS26 contained in the nuclear extract of HeLa cells binds to the first intron of its pre-mRNA and, less efficiently, to its mRNA. In either case, RNA binding substantially increased in the presence of recombinant rpS26. Along with other (48K, 59K) nuclear proteins, rpS26 was assumed to form complexes, the functional role of which is storage of pre-mRNAs inactive in splicing.


title: A robust temperature trajectory following control system for space vehicle testing

abstract: The paper presents a new control scheme for high temperature trajectory following problems. In particular, as a study case, the thermal stress conditions occurring on parts of space vehicle during the re-entry phase is considered. The model of the thermal process, in the lumped parameter domain, is described by a quasi-linear system depending on a number of parameters which are uncertain and varying with the operating conditions. An ad hoc nonlinear trajectory following control system is proposed together with a robust performance analysis, which makes use of the recent concept of quadratic stability with an H/sub /spl infin// norm bound. The analysis technique allows one to evaluate the stability of the closed loop in the presence of time varying parameters and uncertainties, and the rejection of disturbances acting at the system input. An LMI based synthesis procedure is also proposed to increase the closed loop system performance. Numerical simulations are carried out to show the effectiveness of the control algorithm.


title: Plasmonic photoheating of gold nanorods in thermo-responsive chiral liquid crystals

abstract: We report on the thermo-optical properties of gold nanorods (GNRs) dispersed in a thermotropic cholesteric liquid crystal (CLC). We have characterized the CLC reflection band behavior for two different cell thicknesses under the influence of a suitable (resonant) pump beam. It turns out that for the 1.6 ?m thick cell there is a suppression of the CLC reflection band for both pure CLC and CLC/GNRs. For the 10 ?m thick cell, the presence of GNRs desensitizes the shift of the CLC reflection band to temperature. Suitable cell design enables one to 'turn off' the wavelength shift of the peak reflection, thereby turning the system into a pure amplitude measurement tool. This has implications where the probe wavelength is fixed at a common, single wavelength.


title: Impact and Sustainability of E-Government Services in Developing Countries : Lessons Learned from Tamil Nadu , India

abstract: We find that the presence of village Internet facilities, offering government to citizen services, is positively associated with the rate at which the villagers obtain some of these services. In a study of a rural Internet project in India, we identify a positive correlation for two such Internet services: obtaining birth certificates for children and applications for old age pensions. Both these government services are of considerable social and economic value to the citizens. Villagers report that the Internet based services saved them time, money, and effort compared with obtaining the services directly from the government office. We also find that these services can reduce corruption in the delivery of these services. After over one year of successful operation, however, the e-government program was not able to maintain the necessary level of local political and administrative support to remain institutionally viable. As government officers shifted from the region, or grew to find the program a threat, the e-government services faltered. We argue that this failure was due to a variety of Critical Failure Factors. We end with a simple sustainability failure model. In summary, we propose that the e-government program failed to be politically and institutionally sustainable due to people, management, cultural, and structural factors.


title: Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination

abstract: This paper investigates the problem of vision and inertial data fusion. A sensor assembling that is constituted by one monocular camera, three orthogonal accelerometers, and three orthogonal gyroscopes is considered. The first paper contribution is the analytical derivation of all the observable modes, i.e., all the physical quantities that can be determined by only using the information in the sensor data that are acquired during a short time interval. Specifically, the observable modes are the speed and attitude (roll and pitch angles), the absolute scale, and the biases that affect the inertial measurements. This holds even in the case when the camera only observes a single point feature. The analytical derivation of the aforementioned observable modes is based on a nonstandard observability analysis, which fully accounts for the system nonlinearities. The second contribution is the analytical derivation of closed-form solutions, which analytically express all the aforementioned observable modes in terms of the visual and inertial measurements that are collected during a very short time interval. This allows the introduction of a very simple and powerful new method that is able to simultaneously estimate all the observable modes with no need for any initialization or a priori knowledge. Both the observability analysis and the derivation of the closed-form solutions are carried out in several different contexts, including the case of biased and unbiased inertial measurements, the case of a single and multiple features, and in the presence and absence of gravity. In addition, in all these contexts, the minimum number of camera images that are necessary for the observability is derived. The performance of the proposed approach is evaluated via extensive Monte Carlo simulations and real experiments.


ZEUS: Analyzing Safety of Smart Contracts


title: Detecting Deceptive Opinion Spam Using Human Computation

abstract: Websites that encourage consumers to research, rate, and review products online have become an increasingly important factor in purchase decisions. This increased importance has been accompanied by a growth in deceptive opinion spam - fraudulent reviews written with the intent to sound authentic and mislead consumers. In this study, we pool deceptive reviews solicited through crowdsourcing with actual reviews obtained from product review websites. We then explore several human- and machine-based assessment methods to spot deceptive opinion spam in our pooled review set. We find that the combination of human-based assessment methods with easily-obtained statistical information generated from the review text outperforms detection methods using human assessors alone.


title: Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning

abstract: Recent model-free reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity. Such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the learning of continuous control tasks. Unfortunately, they rely on heuristics that limit usage of the dynamics model. We present model-based value expansion, which controls for uncertainty in the model by only allowing imagination to fixed depth. By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, we improve value estimation, which, in turn, reduces the sample complexity of learning.


IoTPOT: A Novel Honeypot for Revealing Current IoT Threats


title: Highly linear and sub 120 fs Ron × Coff 130 nm RF SOI technology targeting 5G carrier aggregation RF switches and FEM SOC

abstract: RF front end modules (FEMs) are currently realized using a variety of technologies. However, since integration drives wireless business in order to achieve the appropriate cost and form factor, CMOS Silicon-on-insulator (SOI) has emerged over the past few years as the dominant technology for RF switches in RF FEMs for cell phones and WiFi [1]. While current performances available on RF SOI technology are already exceeding what was feasible using GaAs one, new cellular system such as carrier aggregation require even more stringent performances (linearity, power handling, insertion loss, isolation). To address those new requirements, RF SOI technology has to be improved. In this paper, the performances results of the latest generations of RF SOI switch technologies from STMicroelectronics are reviewed and technology elements that contribute to improved performance are discussed. Future improvements are also proposed, paving the way for RF SOI technology able to address 5G RF switches challenges.


title: A First Step Towards Understanding Popularity in YouTube

abstract: Being popular in YouTube is becoming a fundamental way of promoting one's self, services or products. In this paper, we conduct an in depth study of fundamental properties of video popularity in YouTube. We collect and study arguably the largest dataset of YouTube videos, roughly 37 million, accounting for 25% of all YouTube videos. We analyze popularity in a comprehensive fashion by looking at properties and patterns in time and considering various popularity metrics. We further study the relationship of the popularity metrics and we find that four of them are highly correlated (viewcount, #comments, #ratings, #favorites) while the fifth one, the average rating, exhibits very little correlation with the other metrics. We also find a "magic number" in the average behavior of videos: for every 400 times a video is viewed, we have one of each of the following user actions: leaving a comment, rating the video and adding to one's favorite set.


title: permission. Towards Practical Taint Tracking

abstract: This paper proposes several technical measures that significantly improve performance and largely limit kernel taint explosion in a XENand QEMU-based taint


title: BlinkToSCoAP: An end-to-end security framework for the Internet of Things

abstract: The emergence of Internet of Things and the availability of inexpensive sensor devices and platforms capable of wireless communications enable a wide range of applications such as intelligent home and building automation, mobile healthcare, smart logistics, distributed monitoring, smart grids, energy management, asset tracking to name a few. These devices are expected to employ Constrained Application Protocol for the integration of such applications with the Internet, which includes User Datagram Protocol binding with Datagram Transport Layer Security protocol to provide end-to-end security. This paper presents a framework called BlinkToSCoAP, obtained through the integration of three software libraries implementing lightweight versions of DTLS, CoAP and 6LoWPAN protocols over TinyOS. Furthermore, a detailed experimental campaign is presented that evaluates the performance of DTLS security blocks. The experiments analyze BlinkToSCoAP messages exchanged between two Zolertia Z1 devices, allowing evaluations in terms of memory footprint, energy consumption, latency and packet overhead. The results obtained indicate that securing CoAP with DTLS in Internet of Things is certainly feasible without incurring much overhead.


title: A Study on the Impact of Facebook Usage on Student’s Social Capital and Academic Performance.

abstract: Prior research has focused on the benefits of usage of social media such as Facebook in terms of the social capital gains they bring. Other research has studied the adverse impact of such usage on ...


title: Editorial: Current Concepts in Arthroplasty §

abstract: This hot topic issue of the Open Orthopaedics Journal deals with current concepts and topics in arthroplasty. With contributions from the orthopedic surgeons, researchers and epidemiologists, it features a few interesting articles. The articles focus on current concepts providing a comprehensive review of some topics or addressing some controversial areas in the field of arthroplasty. Arthroplasty is one of the commonest surgeries performed for the relief of refractory pain due to severe arthritis of various etiologies and improvement in functional ability and health-related quality of life of patients [1-3]. Although a variety of demographic and clinical factors impact the outcome of hip and knee arthroplasty [1, 4-9], in general the results are excellent. ::: ::: One of the articles reviews the epidemiology of knee and hip arthroplasty [Singh et al.]. In this systematic literature review, epidemiologic studies discussing the utilization rates of knee and hip arthroplasty and effect of important patient factors including age, gender and race/ethnicity are reviewed. With an aging population in the U.S. and other Western countries, the utilization rates of knee and hip arthroplasty are increasing. This can lead to a demand versus supply problem if the number of trained reconstructive orthopedic surgeons available fails to increase dramatically in the next few decades. The second article by Kalore et al. addresses a clinically, important and somewhat controversial area of diagnosis and management of infected Total Knee Arthroplasty (TKA). Infection is one of the most common underlying reasons for early TKA failure and has become the number one reason for revision TKA in the U.S [10]. A variety of new diagnostic methods including molecular studies, new diagnostic radiographic tools available in addition to the traditional evaluation with synovial fluid analysis, bone scan and acute phase reactants such as C-reactive protein and erythrocyte sedimentation rate. This article reviews the evidence supporting various diagnostic modalities and treatment approaches. Certainly more research in the coming years will help us with even a more systematic approach to diagnosis and treatment of infected arthroplasty. ::: ::: In the systematic review of biomarkers related to arthroplasty by Mertens et al., evidence is reviewed regarding the potential application of biomarkers to predict important outcomes after including osteolysis, aseptic loosening and infected arthroplasty. Thirty relevant studies were reviewed, with most relating to osteolysis or loosening. The study summarizes the current state-of-the-art in this field, which is clearly evolving. In another article by Dr. Sanchez-Sotelo, current state-of-the-art in shoulder arthroplasty is reviewed. The specific aspects of techniques, fixation and design, results, indications, outcomes and complications are discussed. Total shoulder arthroplasty, reverse shoulder arthroplasty and revision shoulder arthroplasty are discussed in detail with particular attention to underlying diagnoses and outcomes. Dr. Sanchez-Sotelo reviews state-of-the-art in total elbow arthroplasty in an accompanying article. This article provides a comprehensive overview of indications, outcomes, complications and issues related to technique and implant types. The two upper extremity reviews provide an up-to-date comprehensive review for practicing surgeons, residents and trainees. ::: ::: It has been a privilege for me to serve as guest editor for this hot topic issue. I have enjoyed working with my esteemed colleagues in putting together this issue and have learned a lot from them in the process. With gratitude to them for their valuable contributions, I hope that this hot issue will add knowledge to the area of arthroplasty and help practicing surgeons as well as researchers.


title: An Improved Location Estimation Method for Wifi Fingerprint- based Indoor Localization

abstract: The Accurate indoor localization is a challenging task due to the absence of GPS. Among numerous proposals, Wifi fingerprint-based localization is one of the most promising approach, since most buildings are nowadays equipped with Wifi access points for wireless network coverage. Due to the nature of Wifi access points in which any user can deploy and manage their own, fingerprints from some access points lead to estimation errors. Location estimation algorithms should consider these factors and be able to locate users with low error distance. Finding the nearest neighbor using Euclidean distance in signal space is most widely used method in estimating location. However, this paper shows that Euclidean distance is prone to error when unstable access points are present. Also, Euclidean distance does not differentiate strong signals and weak signals, which can also mislead location estimation. We propose a different way to determine the nearest neighbor, which penalizes signals from unstable access points, and signifies strong signals compared to weak signals. Experiments with real measurements show that the proposed algorithm reduces mean error distance by 57% and 90-percentile error distance by 64% compared to the Euclidean distance method.


title: Word lengths are optimized for efficient communication

abstract: We demonstrate a substantial improvement on one of the most celebrated empirical laws in the study of language, Zipf's 75-y-old theory that word length is primarily determined by frequency of use. In accord with rational theories of communication, we show across 10 languages that average information content is a much better predictor of word length than frequency. This indicates that human lexicons are efficiently structured for communication by taking into account interword statistical dependencies. Lexical systems result from an optimization of communicative pressures, coding meanings efficiently given the complex statistics of natural language use.


title: Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers

abstract: Software algorithms are changing how people work in an ever-growing number of fields, managing distributed human workers at a large scale. In these work settings, human jobs are assigned, optimized, and evaluated through algorithms and tracked data. We explore the impact of this algorithmic, data-driven management on human workers and work practices in the context of Uber and Lyft, new ridesharing services. Our findings from a qualitative study describe how drivers responded when algorithms assigned work, provided informational support, and evaluated their performance, and how drivers used online forums to socially make sense of the algorithm features. Implications and future work are discussed.


Neural network acoustic models for the DARPA RATS program.


title: Towards a Prescriptive Theory of Dynamic Capabilities: Connecting Strategic Choice, Learning, and Competition

abstract: The field of strategy has mounted an enormous effort to understand, define, predict, and measure how organizational capabilities shape competitive advantage. While the notion that capabilities influence strategy dates back to the work of Andrews (1971), attempts to formalize a “capabilities based” approach to strategy only began to take shape in the past twenty years. In particular, the publication of Teece and Pisano (1994), Teece, Pisano, and Shuen (1997), and Eisenhart and Martin (2000) works on “dynamic capabilities” triggered a flood of debate and discussion on the topic. Unfortunately, the literature on dynamic capabilities has become mired in endless debates about definitions and has engaged in an elusive search for properties that make organizations adaptable. This paper argues that the research program on dynamic capabilities needs to be reset around the fundamental strategic problem facing firms: how to identify and select capabilities that lead to competitive advantage. To this end, the paper develops a framework that attempts to connect firms’ capability search strategies with their strategies in product markets. It frames firms’ capability search strategies as choices among different types of capability enhancing investments. The key distinguishing feature of capabilities in this framework is their degree of fungibility: capabilities span a continuum ranging from highly general-purpose (e.g. quality management) to highly market-specific (e.g. knowing how to manufacture an airplane wing). To illustrate the potential of the framework to shed new light on traditional strategy questions, the paper applies the framework to explore some unexplained features of Penrosian diversification strategies. The paper concludes by suggesting a research agenda for dynamic capabilities.


title: Query-based Workload Forecasting for Self-Driving Database Management Systems

abstract: The first step towards an autonomous database management system (DBMS) is the ability to model the target application's workload. This is necessary to allow the system to anticipate future workload needs and select the proper optimizations in a timely manner. Previous forecasting techniques model the resource utilization of the queries. Such metrics, however, change whenever the physical design of the database and the hardware resources change, thereby rendering previous forecasting models useless. We present a robust forecasting framework called QueryBot 5000 that allows a DBMS to predict the expected arrival rate of queries in the future based on historical data. To better support highly dynamic environments, our approach uses the logical composition of queries in the workload rather than the amount of physical resources used for query execution. It provides multiple horizons (short- vs. long-term) with different aggregation intervals. We also present a clustering-based technique for reducing the total number of forecasting models to maintain. To evaluate our approach, we compare our forecasting models against other state-of-the-art models on three real-world database traces. We implemented our models in an external controller for PostgreSQL and MySQL and demonstrate their effectiveness in selecting indexes.


Manipulator Grasping and Pushing Operations


Modeling Commonsense Reasoning via Analogical Chaining: A Preliminary Report.


title: Automatic Text Categorization of Mathematical Word Problems

abstract: This paper describes a novel application of text categorization for mathematical word problems , namely Multiplicative Compare and Equal Group problems. The empirical results and analysis show that common text processing techniques such as stopword removal and stemming should be selectively used. It is highly beneficial not to remove stopwords and not to do stemming. Part of speech tagging should also be used to distinguish words in discriminative parts of speech from the non-discriminative parts of speech which not only fail to help but even mislead the categorization decision for mathematical word problems. An SVM classifier with these selectively used text processing techniques outperforms an SVM classifier with a default setting of text processing techniques (i.e. stopword removal and stemming). Furthermore, a probabilistic meta classifier is proposed to combine the weighted results of two SVM classifiers with different word problem representations generated by different text preprocessing techniques. The empirical results show that the probabilistic meta classifier further improves the categorization accuracy.


REORDERING TRIPLE PATTERNS OF SPARQL QUERIES USING ANT COLONY OPTIMIZATION Elem


title: A 3.6cm2 wirelessly-powered UWB SoC with −30.7dBm rectifier sensitivity and sub-10cm range resolution

abstract: This paper discusses the design of a battery-less wirelessly-powered UWB system-on-a-chip (SoC) tag for area-constrained localization applications. An antenna-rectifier co-design methodology optimizes sensitivity and increases range under tag area constraints. A low-voltage (0.8-V) UWB TX enables high rectifier sensitivity by reducing required rectifier output voltage. The 2.4GHz rectifier, power-management unit and 8GHz UWB TX are integrated in 65nm CMOS and the rectifier demonstrates state-of-the-art −30.7dBm sensitivity for 1V output with only 1.3cm2 antenna area, representing a 2.3× improvement in sensitivity over previously published work, at 2.6× higher frequency with 9× smaller antenna area. Measurements in an office corridor demonstrate 20m range with 36dBm TX EIRP. The 0.8-V 8GHz UWB TX consumes 64pJ/pulse at 28MHz pulse repetition rate and achieves 2.4GHz −10dB bandwidth. Wireless measurements demonstrate sub-10cm range resolution at range > 10m.


Exploring Game Space Using Survival Analysis


title: Mode of DNA binding with γ-butyrolactone receptor protein CprB from Streptomyces coelicolor revealed by site-specific fluorescence dynamics.

abstract: BACKGROUND ::: The γ-butyrolactone (GBL) binding transcription factors in Streptomyces species are known for their involvement in quorum sensing where they control the expression of various genes initiating secondary metabolic pathways. The structurally characterized member of this family CprB from Streptomyces coelicolor had earlier been demonstrated to bind a multitude of sequences containing a specific binding signature. Though structural breakthrough has been obtained for its complex with a consensus DNA sequence there is, however a dearth of information regarding the overall and site specific dynamics of protein-DNA interaction. ::: ::: ::: METHODS ::: To delineate the effect of CprB on the bound DNA, changes in motional dynamics of the fluorescent probe 2-aminopurine were monitored at three conserved base positions (5th, 12th and 23rd) for two DNA sequences: the consensus and the biologically relevant cognate element, on complex formation. ::: ::: ::: RESULTS ::: The changes in lifetime and generalized order parameter revealed a similarity in the binding pattern of the protein to both sequences with greater dynamic restriction at the end positions, 5th and 23rd, as compared to the middle 12th position. Also differences within this pattern demonstrated the influence of even small changes in sequence on protein interactions. ::: ::: ::: CONCLUSIONS ::: Here the study of motional dynamics was instrumental in establishing a structural footprint for the cognate DNA sequence and explaining the dynamics for the consensus DNA from structural correspondence. ::: ::: ::: GENERAL SIGNIFICANCE ::: Motional dynamics can be a powerful tool to efficiently study the mode of DNA binding to proteins that interact differentially with a plethora of DNA sequences, even in the absence of structural breakthrough.


title: An E-learning System Architecture based on Cloud Computing

abstract: In the recently years, there has been a big interest in ad hoc wireless network as they have tremendous military and commercial potential. An Ad hoc wireless network is composed of mobile computing devices that use having no fixed infrastructure of a multi-hop wireless network formed. So, the fact that limited resource could support the network of robust, simple framework and energy conserving etc. In this paper, we propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware. And the ontology clustering adopts a tree structure to enhance resilient against mobility and routing complexity. This proposed multicast routing protocol utilizes node locality to be improve the flexible connectivity and stable mobility on local discovery routing and flooding discovery routing. Also attempts to improve route recovery efficiency and reduce data transmissions of context-awareness. We also provide simulation results to validate the model complexity. We have developed that proposed an algorithm have design multi-hierarchy layered networks to simulate a desired system.


title: An 8$\,\times\,$ 8 Butler Matrix in 0.13-$\mu{\hbox {m}}$ CMOS for 5–6-GHz Multibeam Applications

abstract: This paper presents a miniature 5-6-GHz 8 × 8 Butler matrix in a 0.13-μm CMOS implementation. The 8 × 8 design results in an insertion loss of 3.5 dB at 5.5 GHz with a bandwidth of 5-6 GHz and no power consumption. The chip area is 2.5 × 1.9 mm2 including all pads. The 8 × 8 matrix is mounted on a Teflon board with eight antennas, and the measured patterns agree well with theory and show an isolation of >; 12 dB at 5-6 GHz. CMOS Butler matrices offer a simple and low-power alternative to replace eight-element phased-array systems for high gain transceivers. The applications areas are in high data-rate communications at 5-6 and at 57-66 GHz. They can also be excellent candidates for multiple-input-multiple-output systems.


title: Automatic vehicle perpendicular parking design using saturated control

abstract: This paper considers the perpendicular reverse parking problem of front wheel steering vehicles. Relationships between the widths of the parking aisle and the parking place, as well as the parameters and the starting position of the vehicle for planning a collision-free reverse perpendicular parking in one maneuver are presented. A nonlinear saturated (tanh-type) feedback steering controller for straight-line tracking is proposed and evaluated. It is demonstrated that the saturated controller, which is continuous, achieves quick steering can be successfully used in solving parking problems. Simulation results and first experimental tests confirm the effectiveness of the proposed control scheme.


title: THE VALUE OF BLOWING UP A BALLOON

abstract: Suboptimal breathing patterns and impairments of posture and trunk stability are often associated with musculoskeletal complaints such as low back pain. A therapeutic exercise that promotes optimal posture (diaphragm and lumbar spine position), and neuromuscular control of the deep abdominals, diaphragm, and pelvic floor (lumbar-pelvic stabilization) is desirable for utilization with patients who demonstrate suboptimal respiration and posture. This clinical suggestion presents a therapeutic exercise called the 90/90 bridge with ball and balloon. This exercise was designed to optimize breathing and enhance both posture and stability in order to improve function and/or decrease pain. Research and theory related to the technique are also discussed.


title: Tensor2Tensor for Neural Machine Translation

abstract: Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.


title: NPC three level inverter with dual DC bus for independent distributed generators. Neutral-point voltage balancing under the input power imbalance

abstract: This paper concerns the application of three-level neutral point clamped (NPC) voltage source inverter (VSI) to electrical energy conversion of the photovoltaic (PV) power plant. The grid-connected 3-phase NPC inverter is powered via the split DC-link composed of the capacitive divider (Fig. 1). It enables the usage of two DC-buses independently which can be supplied by the separated boost-type DC-DC converters. They are used to generate the maximal power value (MPP) from each string of PV modules. The main objective of this paper is the analysis of a DC-link voltage unbalance effect and development of dedicated balancing control algorithm based on the 7-segment space vector modulation method (SVM). The goal of proposed solution is proper operation of the three-phase NPC inverter under permanent input PV power unbalance (|ΔP dc | > 0). In the presented paper the maximal permissible level of input power unbalance was estimated which can be compensated by the use of developed NPC inverter modulation method as a function of instantaneous operation point (e.g. amplitude modulation index m aSVM ). The listed issues were verified via simulation research performed in Maltab\Simulink software as well as experimentally via the laboratory tests.


title: UNO: uniflying host and smart NIC offload for flexible packet processing

abstract: Increasingly, smart Network Interface Cards (sNICs) are being used in data centers to offload networking functions (NFs) from host processors thereby making these processors available for tenant applications. Modern sNICs have fully programmable, energy-efficient multi-core processors on which many packet processing functions, including a full-blown programmable switch, can run. However, having multiple switch instances deployed across the host hypervisor and the attached sNICs makes controlling them difficult and data plane operations more complex. This paper proposes a generalized SDN-controlled NF offload architecture called UNO. It can transparently offload dynamically selected host processors' packet processing functions to sNICs by using multiple switches in the host while keeping the data centerwide network control and management planes unmodified. UNO exposes a single virtual control plane to the SDN controller and hides dynamic NF offload behind a unified virtual management plane. This enables UNO to make optimal use of host's and sNIC's combined packet processing capabilities with local optimization based on locally observed traffic patterns and resource consumption, and without central controller involvement. Experimental results based on a real UNO prototype in realistic scenarios show promising results: it can save processing worth up to 8 CPU cores, reduce power usage by up to 2x, and reduce the control plane overhead by more than 50%.


title: Framework of Semantic Data Warehouse for heterogeneous and incomplete data

abstract: The demand to analyse and extract knowledge derived from the collaboration of various sources has motivated the construction of Semantic Data Warehouse (SDW). The data derived from various external source introduces challenges, because it has heterogeneous structural characteristics, syntax and semantics and gives the possibility of incomplete data sources to provide the data required for analysis. It is possible that the required data is not provided by the original data source, but there are other data that can be used to generate this data. This research proposes a framework for development semantic data warehouse that can handle incomplete data and the heterogeneity data problems (format, syntax, and structure) through the use of ontologies. The framework also provides tools to handle incomplete data source. The available tools function is to transform the relevant objects instances to an external ontology classes to generate the required data. Identification the multidimensional elements and the conceptual design of multidimensional schema uses a hybrid approach that combines requirement-driven and supply-driven. The construction of cube schema using QB4OLAP vocabularies generate multidimensional schema and RDF data cubes that can perform OLAP operations.


title: A review of high-power wireless power transfer

abstract: High-power wireless power transfer (HPWPT) is a key enabling technology for the adoption of battery electric vehicles (BEVs). As the technology has matured from year to year, discussions of standardization and many commercial partners have come to the area. Much research has been done in this area, and the realization of high-power charging has been furthered by the introduction of new techniques and devices. Here, current high-power WPT systems are reviewed and the passive elements, devices, subsystems, and techniques that have been developed to reach high-power levels are briefly detailed.


title: Towards open ontology learning and filtering

abstract: Open ontology learning is the process of extracting a domain ontology from a knowledge source in an unsupervised way. Due to its unsupervised nature, it requires filtering mechanisms to rate the importance and correctness of the extracted knowledge. This paper presents OntoCmaps, a domain-independent and open ontology learning tool that extracts deep semantic representations from corpora. OntoCmaps generates rich conceptual representations in the form of concept maps and proposes an innovative filtering mechanism based on metrics from graph theory. Our results show that using metrics such as Betweenness, PageRank, Hits and Degree centrality outperforms the results of standard text-based metrics (TF-IDF, term frequency) for concept identification. We propose voting schemes based on these metrics that provide a good performance in relationship identification, which again provides better results (in terms of precision and F-measure) than other traditional metrics such as frequency of co-occurrences. The approach is evaluated against a gold standard and is compared to the ontology learning tool Text2Onto. The OntoCmaps generated ontology is more expressive than Text2Onto ontology especially in conceptual relationships and leads to better results in terms of precision, recall and F-measure.


title: Ensemble methods with outliers for phonocardiogram classification.

abstract: OBJECTIVE ::: Heart sound classification and analysis play an important role in the early diagnosis and prevention of cardiovascular disease. To this end, this paper introduces a novel method for automatic classification of normal and abnormal heart sound recordings. ::: ::: ::: APPROACH ::: Signals are first preprocessed to extract a total of 131 features in the time, frequency, wavelet and statistical domains from the entire signal and from the timings of the states. Outlier signals are then detected and separated from those with a standard range using an interquartile range algorithm. After that, feature extreme values are given special consideration, and finally features are reduced to the most significant ones using a feature reduction technique. In the classification stage, the selected features either for standard or outlier signals are fed separately into an ensemble of 20 two-step classifiers for the classification task. The first step of the classifier is represented by a nested set of ensemble algorithms which was cross-validated on the training dataset provided by PhysioNet Challenge 2016, while the second one uses a voting rule of the class label. ::: ::: ::: MAIN RESULTS ::: The results show that this method is able to recognize heart sound recordings efficiently, achieving an overall score of 96.30% for standard signals and 90.18% for outlier signals on a cross-validated experiment using the available training data. ::: ::: ::: SIGNIFICANCE ::: The approach of our proposed method helped reduce overfitting and improved classification performance, achieving an overall score on the hidden test set of 80.1% (79.6% sensitivity and 80.6% specificity).


How to align IT with the changes using UML and according to BMM 1 ? Applying the “ Goal Driven Development ” Process on a case study using UML 2 and the BMM


SENSORLESS CONTROL OF BLDC MOTOR USING THIRD HARMONIC BACK


title: 60GHz high-gain low-noise amplifiers with a common-gate inductive feedback in 65nm CMOS

abstract: In this paper, a novel design technique of common-gate inductive feedback is presented for millimeter-wave low-noise amplifiers (LNAs). For this technique, by adopting a gate inductor at the common-gate transistor of the cascode stage, the gain of the LNA can be enhanced even under a wideband operation. Using a 65nm CMOS process, transmission-line-based and spiral-inductor-based LNAs are fabricated for demonstration. With a dc power consumption of 33.6 mW from a 1.2-V supply voltage, the transmission-line-based LNA exhibits a gain of 20.6 dB and a noise figure of 5.4 dB at 60 GHz while the 3dB bandwidth is 14.1 GHz. As for the spiral-inductor-based LNA, consuming a dc power of 28.8 mW from a 1.2-V supply voltage, the circuit shows a gain of 18.0 dB and a noise figure of 4.5 dB at 60 GHz while the 3dB bandwidth is 12.2 GHz.


title: A miniaturized, high frequency mechanical scanner for high speed atomic force microscope using suspension on dynamically determined points

abstract: One of the major limitations in the speed of the atomic force microscope (AFM) is the bandwidth of the mechanical scanning stage, especially in the vertical (z) direction. According to the design principles of "light and stiff" and "static determinacy," the bandwidth of the mechanical scanner is limited by the first eigenfrequency of the AFM head in case of tip scanning and by the sample stage in terms of sample scanning. Due to stringent requirements of the system, simply pushing the first eigenfrequency to an ever higher value has reached its limitation. We have developed a miniaturized, high speed AFM scanner in which the dynamics of the z-scanning stage are made insensitive to its surrounding dynamics via suspension of it on specific dynamically determined points. This resulted in a mechanical bandwidth as high as that of the z-actuator (50 kHz) while remaining insensitive to the dynamics of its base and surroundings. The scanner allows a practical z scan range of 2.1 μm. We have demonstrated the applicability of the scanner to the high speed scanning of nanostructures.


title: Mining system logs to learn error predictors: a case study of a telemetry system

abstract: Predicting system failures can be of great benefit to managers that get a better command over system performance. Data that systems generate in the form of logs is a valuable source of information to predict system reliability. As such, there is an increasing demand of tools to mine logs and provide accurate predictions. However, interpreting information in logs poses some challenges. This study discusses how to effectively mining sequences of logs and provide correct predictions. The approach integrates different machine learning techniques to control for data brittleness, provide accuracy of model selection and validation, and increase robustness of classification results. We apply the proposed approach to log sequences of 25 different applications of a software system for telemetry and performance of cars. On this system, we discuss the ability of three well-known support vector machines - multilayer perceptron, radial basis function and linear kernels - to fit and predict defective log sequences. Our results show that a good analysis strategy provides stable, accurate predictions. Such strategy must at least require high fitting ability of models used for prediction. We demonstrate that such models give excellent predictions both on individual applications - e.g., 1 % false positive rate, 94 % true positive rate, and 95 % precision - and across system applications - on average, 9 % false positive rate, 78 % true positive rate, and 95 % precision. We also show that these results are similarly achieved for different degree of sequence defectiveness. To show how good are our results, we compare them with recent studies in system log analysis. We finally provide some recommendations that we draw reflecting on our study.


title: Occlusal Pressure Redistribution with Single Implant Restorations

abstract: PURPOSE ::: To describe the occlusal pressure redistribution in dental arches following placement of single posterior implant-supported restorations in occlusion with natural dentitions. ::: ::: ::: MATERIALS AND METHODS ::: Fifteen patients who presented for successfully osseointegrated single posterior implant restorations participated in the study. Cement-retained, all-ceramic implant crowns were fabricated and delivered after adjustment for interproximal contacts, fit, and uniform occlusal contacts. Occlusal pressure distribution was recorded with a digital sensor that reported contact point forces as a percentage of total pressure applied to the sensor. Measurements were taken before and after restoration for comparison. The distribution of relative occlusal stress was analyzed for statistical significance by location. ::: ::: ::: RESULTS ::: The results indicated that placement of posterior implant-supported restorations increased the bite pressure in the sextants containing the restorations by an average of 4.18% (p = 0.019) of the maximum value registered (maximum bite pressure). The contralateral occlusal load was found to decrease by 2.9% (p = 0.047) of the maximum value registered; however, there was no significant difference among or between the tooth positions. ::: ::: ::: CONCLUSION ::: Single posterior implant-supported restorations significantly increased the percentage (%) of total occlusal pressure in the containing sextant and decreased percentage of total occlusal pressure in the contralateral sextant. When analyzing restorations by exact position, more posterior implant restorations decreased the occlusal pressure applied in anterior dentitions.


title: Interpretable Clustering using Unsupervised Binary Trees

abstract: We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not share the same parent originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.


title: Applying the roofline model

abstract: The recently introduced roofline model plots the performance of executed code against its operational intensity (operations count divided by memory traffic). It also includes two platform-specific performance ceilings: the processor's peak performance and a ceiling derived from the memory bandwidth, which is relevant for code with low operational intensity. The model thus makes more precise the notions of memory- and compute-bound and, despite its simplicity, can provide an insightful visualization of bottlenecks. As such it can be valuable to guide manual code optimization as well as in education. Unfortunately, to date the model has been used almost exclusively with back-of-the-envelope calculations and not with measured data. In this paper we show how to produce roofline plots with measured data on recent generations of Intel platforms. We show how to accurately measure the necessary quantities for a given program using performance counters, including threaded and vectorized code, and for warm and cold cache scenarios. We explain the measurement approach, its validation, and discuss limitations. Finally, we show, to this extent for the first time, a set of roofline plots with measured data for common numerical functions on a variety of platforms and discuss their possible uses.


Whaling permits: Japan disregards whaling review again


title: Applying genetic algorithms to decision making in autonomic computing systems

abstract: Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve reconfiguration plans at run time in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, with the goal of minimizing costs while maximizing data reliability and network performance, even in the presence of link failures.


title: Virtual Reality Intervention for Older Women with Breast Cancer

abstract: This study examined the effects of a virtual reality distraction intervention on chemotherapy-related symptom distress levels in 16 women aged 50 and older. A cross-over design was used to answer the following research questions: (1) Is virtual reality an effective distraction intervention for reducing chemotherapy-related symptom distress levels in older women with breast cancer? (2) Does virtual reality have a lasting effect? Chemotherapy treatments are intensive and difficult to endure. One way to cope with chemotherapy-related symptom distress is through the use of distraction. For this study, a head-mounted display (Sony PC Glasstron PLM - S700) was used to display encompassing images and block competing stimuli during chemotherapy infusions. The Symptom Distress Scale (SDS), Revised Piper Fatigue Scale (PFS), and the State Anxiety Inventory (SAI) were used to measure symptom distress. For two matched chemotherapy treatments, one pre-test and two post-test measures were employed. Participants were ra...


title: Species of the Colletotrichum gloeosporioides complex associated with anthracnose diseases of Proteaceae

abstract: Anthracnose disease of Proteaceae has in the past chiefly been attributed to infections by C. acutatum, C. boninense and C. gloeosporioides. In the present study, a multi-locus phylogenetic analysis (ACT, CAL, CHS-1, GAPDH, GS, ITS, TUB2) revealed that strains of the C. gloeosporioides complex associated with Proteaceae belong to at least six species. These include C. alienum, C. aotearoa, C. kahawae (subsp. ciggaro), C. siamense, and two new taxa, C. proteae and C. grevilleae. The most economically important pathogen of Proteaceae seems to be C. alienum, and not C. gloeosporioides as previously reported. All taxa associated with Proteaceae are morphologically described on different media in culture, except strains of C. siamense, which proved to be sterile. Furthermore, C. populi is synonymised with C. aenigma.


title: Genetic Process Mining: A Basic Approach and Its Challenges

abstract: One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. To overcome these problems, we try to use genetic algorithms to mine process models. The non-trivial constructs are tackled by choosing an internal representation that supports them. The noise problem is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The definition of a good fitness measure is the most critical challenge in a genetic approach. This paper presents the current status of our research and the pros and cons of the fitness measure that we used so far. Experiments show that the fitness measure leads to the mining of process models that can reproduce all the behavior in the log, but these mined models may also allow for extra behavior. In short, the current version of the genetic algorithm can already be used to mine process models, but future research is necessary to always ensure that the mined models do not allow for extra behavior. Thus, this paper also discusses some ideas for future research that could ensure that the mined models will always only reflect the behavior in the log.


title: Predicting Students Performance in Educational Data Mining

abstract: Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. However measuring academic performance of students is challenging since students academic performance hinges on diverse factors. The interrelationship between variables and factors for predicting performance participate in complicated nonlinear ways. Traditional data mining and machine learning techniques may not be applied directly to these types of data and problems. In this study we develop a classification model to predict student performance using Deep Learning which automatically learns multiple levels of representation. We pre-train hidden layers of features layerwisely using an unsupervised learning algorithm sparse auto-encoder from unlabeled data, and then use supervised training for finetuning the parameters. We train model on a relatively large real world students dataset, and the experimental results show the effectiveness of the proposed method which can be applied into academic pre-warning mechanism.


title: A comparative analysis of machine learning techniques for botnet detection

abstract: Day by day more and more devices are getting connected to the Internet and with the advent of the Internet of Things, this rate has had an exponential growth. The lack of security in devices connected to the IoT is making them hot targets for cyber-criminals and strength of botnet attacks have increased drastically. Botnets are the technological backbones of multitudinous attacks including Distributed Denial of Service (DDoS), SPAM, identity theft and organizational spying. The 2016 Dyn cyber attack involved multiple DDoS attacks with an estimated throughput of 1.2 terabits per second; the attack is the largest DDoS attack on record. In this paper, we compare three different techniques for botnet detection with each having its unique use cases. The results of the detection methods were verified using ISCX Intrusion Detection Dataset and the CTU-13 Dataset.


title: Investigating the cross-linguistic potential of VerbNet-style classification

abstract: Verb classes which integrate a wide range of linguistic properties (Levin, 1993) have proved useful for natural language processing (NLP) applications. However, the real-world use of these classes has been limited because for most languages, no resources similar to VerbNet (Kipper-Schuler, 2005) are available. We apply a verb clustering approach developed for English to French - a language for which no such experiment has been conducted yet. Our investigation shows that not only the general methodology but also the best performing features are transferable between the languages, making it possible to learn useful VerbNet style classes for French automatically without language-specific tuning.


title: MEDUSA: MEtamorphic malware dynamic analysis usingsignature from API

abstract: Malware detection and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. The traditional signature based detection of malware fails for metamorphic malware which changes its code structurally while maintaining functionality at time of propagation. This category of malware is called metamorphic malware. In this paper we dynamically analyze the executables produced from various metamorphic generators through an emulator by tracing API calls. A signature is generated for an entire malware class (each class representing a family of viruses generated from one metamorphic generator) instead of for individual malware sample. We show that most of the metamorphic viruses of same family are detected by the same base signature. Once a base signature for a particular metamorphic generator is generated, all the metamorphic viruses created from that tool are easily detected by the proposed method. A Proximity Index between the various Metamorphic generators has been proposed to determine how similar two or more generators are.


title: Towards dynamic trot gait locomotion: Design, control, and experiments with Cheetah-cub, a compliant quadruped robot

abstract: We present the design of a novel compliant quadruped robot, called Cheetah-cub, and a series of locomotion experiments with fast trotting gaits. The robot's leg configuration is based on a spring-loaded, pantograph mechanism with multiple segments. A dedicated open-loop locomotion controller was derived and implemented. Experiments were run in simulation and in hardware on flat terrain and with a step down, demonstrating the robot's self-stabilizing properties. The robot reached a running trot with short flight phases with a maximum Froude number of FR = 1.30, or 6.9 body lengths per second. Morphological parameters such as the leg design also played a role. By adding distal in-series elasticity, self-stability and maximum robot speed improved. Our robot has several advantages, especially when compared with larger and stiffer quadruped robot designs. (1) It is, to the best of the authors' knowledge, the fastest of all quadruped robots below 30kg (in terms of Froude number and body lengths per second). (2) It shows self-stabilizing behavior over a large range of speeds with open-loop control. (3) It is lightweight, compact, and electrically powered. (4) It is cheap, easy to reproduce, robust, and safe to handle. This makes it an excellent tool for research of multi-segment legs in quadruped robots.


title: The unspoken revolution in software engineering

abstract: In this article the author describes the outsourcing aspects of software engineering. The author finds outsourcing so fascinating partly because it serves as a magnifier and revelator of just about everything in software engineering. The development of offshoring also raises a new challenge for those of us entrusted with educating future software professionals in the industrialized world.


title: Fat is fashionable and fit: A comparative content analysis of Fatspiration and Health at Every Size® Instagram images

abstract: Abstract In step with the proliferation of Thinspiration and Fitspiration content disseminated in popular web-based media, the fat acceptance movement has garnered heightened visibility within mainstream culture via the burgeoning Fatosphere weblog community. The present study extended previous Fatosphere research by comparing the shared and distinct strategies used to represent and motivate a fat-accepting lifestyle among 400 images sourced from Fatspiration- and Health at Every Size ® -themed hashtags on Instagram. Images were systematically analyzed for the socio-demographic and body size attributes of the individuals portrayed alongside content reflecting dimensions of general fat acceptance, physical appearance pride, physical activity and health, fat shaming, and eating and weight loss-related themes. #fatspiration/#fatspo-tagged images more frequently promoted fat acceptance through fashion and beauty-related activism; #healthateverysize/#haes posts more often featured physically-active portrayals, holistic well-being, and weight stigma. Findings provide insight into the common and unique motivational factors and contradictory messages encountered in these fat-accepting social media communities.


title: The Significance of Drug�Drug and Drug�Food Interactions of Oral Anticoagulation

abstract: Vitamin K antagonists (VKAs) such as warfarin are the most commonly prescribed oral anticoagulants worldwide. However, factors affecting the pharmacokinetics of VKAs, such as food and drugs, can cause deviations from their narrow therapeutic window, increasing the bleeding or thrombosis risk and complicating their long-term use. The use of direct oral anticoagulants (DOACs) offers a safer and more convenient alternative to VKAs. However, it is important to be aware that plasma levels of DOACs are affected by drugs that alter the cell efflux transporter P-glycoprotein and/or cytochrome P450. In addition to these pharmacokinetic-based interactions, DOACs have the potential for pharmacodynamic interaction with antiplatelet agents and non-steroidal anti-inflammatory drugs. This is an important consideration in patient groups already at high risk of bleeding, such as patients with renal impairment.


title: Data-driven 3D visual pronunciation of Chinese IPA for language learning

abstract: In the framework of intelligent aided language learning, a real-time data-driven 3D visual pronunciation system of Chinese IPA is proposed. First, a high quality articulatory speech corpus including speech and 3D articulatory data of lips, tongue and jaw movements is collected through Electro-Magnetic Articulograph; second, the 3D articulatory modeling including shape design and motion synthesis is conducted. The articulatory shape is obtained by designing a precise 3D facial model including internal and external articulators. The articulatory motion synthesis is obtained combining parameterized model and anatomical model. The system can thus illustrate the slight differences among phonemes by synthesizing both internal and external articulatory movements. The perceptual evaluation shows the suitability of the system for instructing language learners to articulate.


title: Energy-Efficient Wireless Transmissions for Battery-Less Vehicle Tire Pressure Monitoring System

abstract: This paper presents an energy-efficient wireless transmission scheme for battery-less vehicle tire pressure monitoring system (TPMS). Our proposed transmission scheme includes a wake-up communication link with 125-kHz carrier frequency and a data communication link with 433-MHz carrier frequency. Considering the TPMS application requirements and the special nature of the vehicle environment, we derive the relevant circuit parameters. In order to verify the reliability of the wireless communication system under the derived circuit parameters, we design an in-tire data transmitter and wake-up receiver. The 125-kHz wake-up receiver adopts dual-channel to improve the communication reliability and logarithmic amplifiers to achieve ASK demodulation and dynamic range compression. The receiver is implemented in 0.35- $\mu \text{m}$ high voltage (HV) BCD process. Experiment results show that typical power consumption of the receiver is no more than $5~\mu \text{A}$ under 3.3 V supply voltage; the maximum data rate is 35 kb/s with 0.5 mVpp sensitivity. On the other hand, the data transmitter is implemented in 0.18- $\mu \text{m}$ MMRF process. Experiment results show that the typical power consumption is 7.3 mA under 1.8 V supply voltage, and the emission power is −10 dBm@433.92 MHz with a phase noise of −103 dBc/Hz@300 kHz. System level experiments demonstrate that the proposed wireless transmission scheme fulfills the vehicle TPMS requirements. The data transmitter and wake-up receiver can communicate with the commercial data receiver and wake-up transmitter in the range of 20 m, which meets the requirements of most vehicles.


title: A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation

abstract: Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages of the combination of convolutional neural networks and structured prediction. Our proposed convolutional neural network based model achieves strong performance and significantly outperforms the state-of-the-art for automatic retinal blood vessel segmentation on DRIVE dataset with 95.33% accuracy and 0.974 AUC score.


title: Maximum entropy inverse reinforcement learning

abstract: Recent research has shown the benefit of framing problems of imitation learning as solutions to Markov Decision Problems. This approach reduces learning to the problem of recovering a utility function that makes the behavior induced by a near-optimal policy closely mimic demonstrated behavior. In this work, we develop a probabilistic approach based on the principle of maximum entropy. Our approach provides a well-defined, globally normalized distribution over decision sequences, while providing the same performance guarantees as existing methods. ::: ::: We develop our technique in the context of modeling real-world navigation and driving behaviors where collected data is inherently noisy and imperfect. Our probabilistic approach enables modeling of route preferences as well as a powerful new approach to inferring destinations and routes based on partial trajectories.


title: SERIES: BASIC STATISTICS FOR BUSY CLINICIANS (IX) Research designs in clinical epidemiology

abstract: In clinical research, the epidemiological design conformsthe main part of the study plan and has an impact uponthe set of options to be applied in the rest of the researchprotocol, referring to the selection of the patients, collec-tion of the data and posterior analysis of the results. Thedesignconditionsthequalitylevelsofthescientiﬁcevidenceand the suitability of the recommendations for the adoptionof technologies or healthcare procedures in routine clinicalpractice.


title: Enhancing perceived enjoyment in social games through social and gaming factors

abstract: Purpose – Residing on social networking platforms, social games have unique characteristics distinguishing them from other digital games or online games. The purpose of this paper is to explore both social and gaming factors of social games and investigate their roles on enhancing perceived enjoyment. The authors also examine the relationships between perceived enjoyment, subject norm, perceived critical mass, intention to play, and actual behavior. Design/methodology/approach – This paper develops a research model including nine hypotheses. Using a survey questionnaire, empirical data were collected from 169 actual social game players. Structured equation modeling was used to test the proposed research models. Findings – Social identification, social interaction, and diversion significantly influence perceived enjoyment. Perceived enjoyment significantly influences the intention to play, which in turn significantly influences the actual behavior. Moreover, subject norm and perceived critical mass play di...


title: Efficient L-shape fitting for vehicle detection using laser scanners

abstract: The detection of surrounding vehicles is an essential task in autonomous driving, which has been drawing enormous attention recently. When using laser scanners, L-Shape fitting is a key step for model-based vehicle detection and tracking, which requires thorough investigation and comprehensive research. In this paper, we formulate the L-Shape fitting as an optimization problem. An efficient search based method is then proposed to find the optimal solution. Our method does not rely on laser scan sequence information and therefore supports convenient data fusion from multiple laser scanners; it is efficient and involves very few parameters for tuning; the approach is also flexible to suit various fitting demands with different fitting criteria. On-road experiments with production-grade laser scanners have demonstrated the effectiveness and robustness of our approach.


Egocentric Object Manipulation in Virtual Environments: Empirical Evaluation of Interaction Techniques


title: Single-Ended and Differential MRAMs Based on Spin Hall Effect: A Layout-Aware Design Perspective

abstract: Spin-based memories have shown an immense promise for on-chip memory applications due to the possibilities of introducing non-volatility in caches employing a CMOS compatible process. Non-volatility leads to zero-standby leakage. However, at the same time, exploration of energy-efficient read and write mechanisms is important to lower the overall energy-consumption of an MRAM cache. Magnetization control employing spin-hall effect is one of the most promising approaches to enhance the write energy efficiency. A separate path for the read which comprises of a magnetic tunnel junction (MTJ) offers the benefits of simultaneous optimization of the read and write operations. In fact, spin-hall effect also leads to the possibility of designing cells with differential sensing. However, the advantages of a differential read over a single-ended read in terms of higher read speed and better noise immunity comes at the cost of larger number of access transistors, which may translate to lower integration density. In this paper, we perform a comparative analysis of spin-hall effect (SHE) based MRAM cells with single-ended and differential read mechanisms in terms of the cell area, read performance and read stability. We perform a detailed layout analysis based on Fin FET technology to evaluate the impact of introducing differential sensing on cell area. Our analysis shows that when the layout area is determined by the pitch of the bit-line and source-line metal tracks, the differential cell shows 1.5X increase in the cell area compared to the single-ended SHE MRAM. The area increase is 2X if the read access transistor determines the layout footprint. However, the differential sensing offers the advantages of a#x007E;48% increase in the read performance along with 6% to 9% boost in the read stability compared to the single-ended SHE MRAM. At iso-area, the differential cell shows a#x007E;24% lower read time and 12% higher read disturb margin with a similar write performance and power. Our analysis also presents other layout-driven perspectives on the design of differential and single-ended SHE MRAMs.


title: Reverse engineering COBOL via formal methods

abstract: We describe methods and software tools which aid in reverse-engineering COBOL application programs back to specifications (and in validating them against specifications). The aim is to create object-based abstractions from the implementation to capture design and functionality. The central process which the tools support is ‘transformation from formalism to formalism’, first from COBOL to the intermediate language Uniform, then from Uniform to a functional description language, and then to the specification language Z. In the process, dataflow diagrams, entity-relationship diagrams and call-graphs, and other types of information, are extracted from the code.


title: Automatic Chinese food identification and quantity estimation

abstract: Computer-aided food identification and quantity estimation have caught more attention in recent years because of the growing concern of our health. The identification problem is usually defined as an image categorization or classification problem and several researches have been proposed. In this paper, we address the issues of feature descriptors in the food identification problem and introduce a preliminary approach for the quantity estimation using depth information. Sparse coding is utilized in the SIFT and Local binary pattern feature descriptors, and these features combined with gabor and color features are used to represent food items. A multi-label SVM classifier is trained for each feature, and these classifiers are combined with multi-class Adaboost algorithm. For evaluation, 50 categories of worldwide food are used, and each category contains 100 photographs from different sources, such as manually taken or from Internet web albums. An overall accuracy of 68.3% is achieved, and success at top-N candidates achieved 80.6%, 84.8%, and 90.9% accuracy accordingly when N equals 2, 3, and 5, thus making mobile application practical. The experimental results show that the proposed methods greatly improve the performance of original SIFT and LBP feature descriptors. On the other hand, for quantity estimation using depth information, a straight forward method is proposed for certain food, while transparent food ingredients such as pure water and cooked rice are temporarily excluded.


title: Poster abstract: anchor-free distributed localization in sensor networks

abstract: Physical location is an important attribute of a sensor’s data stream in a large number of sensor network applications. In addition, geographic information, for instance in the form of node coordinates in some common coordinate system, is a useful primitive in routing protocols such as geographic routing, information dissemination protocols such as directed diffusion using location attributes, and sensor query processing systems. We present a method to facilitate large-scale deployment of location-aware sensor networks. We show that large networks of location-aware sensors can be made cooperatively self-configuring, that is, that each sensor can run an algorithm locally, interacting only with neighboring nodes, such that after a number of iterations all sensors will have reached a consensus about their coordinates in some coordinate system. By doing this in an automated manner, large-scale sensor networks can eliminate the cumbersome and unscalable process of manually configuring sensor nodes with their location. In non-urban outdoor settings, nodes may obtain location information using an existing infrastructure such as GPS. However, GPS receivers may be too expensive, too large, too power-intensive for the desired application, or simply unavailable. One solution to this problem is an alternative location infrastructure such as Cricket that works in places that GPS does not. Another solution to these problems is to equip sensors with hardware capable of estimating distances to nearby nodes, and to have the sensors themselves selfconfigure into a consistent coordinate system.


Practical parallax occlusion mapping with approximate soft shadows for detailed surface rendering


title: Adaptive design methods in clinical trials – a review

abstract: In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.


title: Going back in Time: An Investigation of Social Media Re-finding

abstract: Social Media (SM) has become a valuable information source to many in diverse situations. In IR, research has focused on real-time aspects and as such little is known about how long SM content is of value to users, if and how often it is re-accessed, the strategies people employ to re-access and if difficulties are experienced while doing so. We present results from a 5 month-long naturalistic, log-based study of user interaction with Twitter, which suggest re-finding to be a regular activity and that Tweets can offer utility for longer than one might think. We shed light on re-finding strategies revealing that remembered people are used as a stepping stone to Tweets rather than searching for content directly. Bookmarking strategies reported in the literature are used infrequently as a means to re-access. Finally, we show that by using statistical modelling it is possible to predict if a Tweet has future utility and is likely to be re-found. Our findings have implications for the design of social media search systems and interfaces, in particular for Twitter, to better support users re-find previously seen content.


title: Beamforming: a versatile approach to spatial filtering

abstract: An overview of beamforming from a signal-processing perspective is provided, with an emphasis on recent research. Data-independent, statistically optimum, adaptive, and partially adaptive beamforming are discussed. Basic notation, terminology, and concepts are included. Several beamformer implementations are briefly described. >


title: Influence of lightning conditions on toxicity and genotoxicity of various PAH in the newt in vivo

abstract: Abstract We evaluated the influence of near-ultraviolet light (UVA) on the cytotoxicity and genotoxicity of 7 polycyclic aromatic hydrocarbons (PAH) in larvae of the amphibian Pleurodeles waltl . Benz[ a ]anthracene (BA), 7,12-benz[ a ]anthraquinone (BAQ) and anthracene (Ac) proved to be lethal at low doses (some ppb), and the following order of genotoxicity was observed: BA ≈ BAQ > DMBA > DMA (9,10-dimethylanthracene). Ac, AQ (9,10-anthraquinone) and DBA (dibenz[ a,h ]anthracene) were not found to be clastogenic. In the larvae reared in normal conditions (subdued natural daylight/darkness alternation) or in continuous darkness, the BA derivatives were shown to be more genotoxic than BA itself: DMBA > BAQ > BA; BA (≥ 187.5 ppb) slightly increased the level of micronuclei in circulating erythrocytes, while DMBA was strongly clastogenic, in line with their reported carcinogenicity. In other experiments, rearing media alone (i.e., water containing BA, BAQ or DMBA) were UVA-irradiated for 24 h, and then tested on larvae in the dark (‘IR-UV/dark’ conditions). Photodegradation of BA (50 and 100 ppb) gave rise to clastogenic products. By contrast, DMBA (12.5, 25 or 50 ppb) was destroyed by UVA, and we suggested that any potentially mutagenic photoproducts formed were not in sufficient amounts to yield a positive response in the newt micronucleus test.


title: An Infinite Restricted Boltzmann Machine

abstract: We present a mathematical construction for the restricted Boltzmann machine (RBM) that does not require specifying the number of hidden units. In fact, the hidden layer size is adaptive and can grow during training. This is obtained by first extending the RBM to be sensitive to the ordering of its hidden units. Then, with a carefully chosen definition of the energy function, we show that the limit of infinitely many hidden units is well defined. As with RBM, approximate maximum likelihood training can be performed, resulting in an algorithm that naturally and adaptively adds trained hidden units during learning. We empirically study the behavior of this infinite RBM, showing that its performance is competitive to that of the RBM, while not requiring the tuning of a hidden layer size.


title: A CNN based volumetric imaging method with single X-ray projection

abstract: The accurate volumetric imaging using breathing motion model and single X-ray projection has a significant meaning to the clinical treatment of moving tumors. However, it is of great difficulty to realize the real-time reconstruction of volumetric image with high accuracy. In this work, a volumetric imaging method from one X-ray projection utilizing convolutional neural network (CNN) is proposed. With the aid of principal component analysis (PCA)-based motion model, a CNN regression model with weighted object function is trained to estimate the volumetric image accurately. Due to the high parallelization of CNN, the computing efficiency of the proposed method is able to meet the real-time requirement of practical treatment (less than 0.05 seconds). A synthetic test using 4D Extended Cardiac-Torso (XCAT) Phantom is carried out which testify the effectiveness of our method.


title: The mechanisms for pattern completion and pattern separation in the hippocampus

abstract: The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 together with a large number of independent memories stored in CA3 include a sparse distributed representation which is enhanced by the graded firing rates of CA3 neurons, representations that are independent due to the randomizing effect of the mossy fibers, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction. Recall of information from CA3 is implemented by the entorhinal cortex perforant path synapses to CA3 cells, which in acting as a pattern associator allow some pattern generalization. Pattern separation is performed in the dentate granule cells using competitive learning to convert grid-like entorhinal cortex firing to place-like fields. Pattern separation in CA3, which is important for completion of any one of the stored patterns from a fragment, is provided for by the randomizing effect of the mossy fiber synapses to which neurogenesis may contribute, by the large number of dentate granule cells each with a sparse representation, and by the sparse independent representations in CA3. Recall to the neocortex is achieved by a reverse hierarchical series of pattern association networks implemented by the hippocampo-cortical backprojections, each one of which performs some pattern generalization, to retrieve a complete pattern of cortical firing in higher-order cortical areas.


title: ELECTROACTIVE FABRICS AND WEARABLE BIOMONITORING DEVICES

abstract: The implementation of truly wearable, instrumented garments which are capable of recording biomechanical variables is crucial in several fields of application, from multimedia to rehabilitatio n, from sport to artistic fields. In this paper we discuss wearable devices (a smart shirt, a leotard and a glove) which can read and record the vital signs and movements of a subject wearing the system. The sensing function of the garments is based on piezo -resistive fabric sensors, based on carbon -loaded rubbers (CLR) and different conductive materials.