title: Optimal Data Fusion in Multiple Sensor Detection Systems

abstract: There is an increasing interest in employing multiple sensors for surveillance and communications. Some of the motivating factors are reliability, survivability, increase in the number of targets under consideration, and increase in required coverage. Tenney and Sandell have recently treated the Bayesian detection problem with distributed sensors. They did not consider the design of data fusion algorithms. We present an optimum data fusion structure given the detectors. Individual decisions are weighted according to the reliability of the detector and then a threshold comparison is performed to obtain the global decision.


title: Discrete math with programming: better together

abstract: This paper proposes a Discrete Mathatics course that is integrated with programming. The course consists of a sequence of Math modules with coordinated programming projects. Advantages of this approach are presented, and a methodology for developing the course is shown. A sample list of Math modules and brief project descriptions are included.


title: Effects of rest frames on cybersickness and oscillatory brain activity

abstract: Cybersickness is an undesirable side effect, which occurs when people experience in a virtual environment (VE). A number of studies have tried to find ways to control cybersickness and some of them have demonstrated that presenting rest frames in VE might be one promising method to reduce cybersickness. This study investigates the effects of rest frames on cybersickness and the cybersickness-related brain activities. Participants (n=22) were exposed to a roller coaster simulator in a VE, both under a rest frame condition and a nonrest frame condition, in counter-balanced order while undergoing EEG recordings. Participants who experienced less cybersickness in the rest frame condition showed characteristic oscillations in different EEG frequency bands compared to those of in the nonrest frame condition. Based on the level of cybersickness and oscillatory EEG changes, we suggest that rest frames may reduce or delay the onset of cybersickness by alleviating users' attention or perception load.


title: Visual Fatigue Prediction for Stereoscopic Image

abstract: In this letter, we propose a visual fatigue prediction metric which can replace subjective evaluation for stereoscopic images. It detects stereoscopic impairments caused by inappropriate shooting parameters or camera misalignment which induces excessive horizontal and vertical disparities. Pearson's correlation was measured between the proposed metrics and the subjective results by using k-fold cross-validation, acquiring ranges of 78-87% with sparse feature and 74-85% with dense feature.


title: A renewed approach to serious games for cyber security

abstract: We are living in a world which is continually evolving and where modern conflicts have moved to the cyber domain. In its 2010 Strategic Concept, NATO affirmed its engagement to reinforce the defence and deterrence of its state members. In this light, it has been suggested that the gamification of training and education for cyber security will be beneficial. Although serious games have demonstrated pedagogic effectiveness in this field, they have only been used in a limited number of contexts, revealing some limitations. Thus, it is argued that serious games could be used in informal contexts while achieving similar pedagogic results. It is also argued that the use of such a serious game could potentially reach a larger audience than existing serious games, while complying with national cyber strategies. To this end, a framework for designing serious games which are aimed at raising an awareness of cyber security to those with little or no knowledge of the subject is presented. The framework, based upon existing frameworks and methodologies, is also accompanied with a set of cyber security skills, itself based upon content extracted from government sponsored awareness campaigns, and a method of integrating these skills into the framework. Finally, future research will be conducted to refine the framework and to improve the set of cyber security related skills in order to suit a larger range of players. A proof of concept will also be designed in order to collect empirical data and to validate the effectiveness of the framework.


title: A SiGe BiCMOS Concurrent K/V Dual-Band 16-Way Power Divider and Combiner

abstract: A new dual-band 16-way power divider and combiner on a 0.18- ${\mu }\text{m}$ SiGe BiCMOS process that works concurrently over 18–26 GHz (K-band) and 57–64 GHz (V-band) is presented. The 16-way K/V dual-band power divider integrates a two-way K/V dual-band Wilkinson-based power divider with a high-pass filter and multiple broad-band two-way lumped-element and transmission-line Wilkinson power dividers. The two-way dual-band power divider is designed by employing a slow-wave transmission line and two shunt-connected series LC resonators in each arm, leading to miniaturization and low loss with dual-band transmission in K- and V-band, decent return losses, and good isolation of larger than 20 dB over the dual-band. The developed 16-way K/V dual-band power divider possesses good performance over the dual-band. Specifically, it achieves measured insertion losses of 18.4 and 21 dB at 24 and 60 GHz, respectively, with good return losses. Furthermore, it also exhibits measured isolation larger than 20.1 and 17.7 dB between any two output ports at 24 and 60 GHz, respectively.


title: Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda

abstract: We combine data from a field survey with transaction log data from a mobile phone operator to provide new insight into daily patterns of mobile phone use in Rwanda. The analysis is divided into three parts. First, we present a statistical comparison of the general Rwandan population to the population of mobile phone owners in Rwanda. We find that phone owners are considerably wealthier, better educated, and more predominantly male than the general population. Second, we analyze patterns of phone use and access, based on self-reported survey data. We note statistically significant differences by gender; for instance, women are more likely to use shared phones than men. Third, we perform a quantitative analysis of calling patterns and social network structure using mobile operator billing logs. By these measures, the differences between men and women are more modest, but we observe vast differences in utilization between the relatively rich and the relatively poor. Taken together, the evidence in this paper suggests that phones are disproportionately owned and used by the privileged strata of Rwandan society.


title: Complications and Management of Polymethylmethacrylate (PMMA) Injections to the Midface

abstract: BACKGROUND ::: Polymethylmethacrylate (PMMA) has been used as an injectable filler to treat hollows and reduce rhytids. PMMA injections have been associated with several side effects, however, the literature is scarce on periorbital complications and their treatments. ::: ::: ::: OBJECTIVES ::: The purpose of this study is to report a series of complications after periorbital PMMA injections to the midface and to describe their management. ::: ::: ::: METHODS ::: Retrospective chart review, including photography and histopathology when available. ::: ::: ::: RESULTS ::: The authors identified 11 cases of complications of PMMA injections to the midface. Patient ages ranged from 36 to 62 years (mean, 47 years; median, 44 years). Two (18%) were males and 9 (82%) were females. Adverse effects began between 2 to 24 months after injection (mean, 7.2 months; median, 6 months). All patients had edema, erythema, and contour irregularity. Seven (64%) patients had nodules, 4 (36%) had yellow, xanthomatous skin changes, and 2 (18%) had eyelid malposition. Histopathology demonstrated a giant cell inflammation in 5 of 6 cases. Corticosteroid injection was tried in 6 cases but was associated with minimal clinical improvement. Surgical debulking of the implanted material was performed in 9 (82%) cases and was effective in improving edema, erythema, and nodularity. ::: ::: ::: CONCLUSIONS ::: PMMA injection to the midface may be associated with chronic inflammation, fibrotic nodules, yellowing of the skin, and eyelid malposition. Intralesional corticosteroid injections yielded minimal or no improvement; surgical debulking achieved favorable results. LEVEL OF EVIDENCE 4: Therapeutic.


Classifying Human Walking Patterns using Accelerometer Data from Smartphone


title: A novel method for stemmer generation based on hidden markov models

abstract: In this paper, we present a method based on Hidden Markov Models (HMMs) to generate statistical stemmers. Using a list of words as training set, the method estimates the HMM parameters which are used to calculate the most probable stem for an arbitrary word. Stemming is performed by computing the most probable path, through the HMM states, corresponding to the input word. Linguistic knowledge or a training set of manually stemmed words are not required. We describe the method and the results of the experiments carried out using standard test collections for five different languages.


Evolving the Theory of Waste Management – Implications to waste minimization


title: A 16-element W-band phased array transceiver chipset with flip-chip PCB integrated antennas for multi-gigabit data links

abstract: This paper describes the design and implementation of a W-band phased array system with integrated PCB antennas capable of multi-gigabit spectrally-efficient wireless communication. The chipset is manufactured in a 0.18µm SiGe BiCMOS technology with f T /f MAX of 240/270GHz and is flip-chipped onto an organic PCB with integrated antenna arrays. Each chip is equipped with 16-transmit/4-receive or 16-receive/4-transmit calibrated phase shifter elements, direct up- and down-converters plus a half-rate phase locked loop. Each transceiver IC operates from 1.5V and 2.5V supplies and consumes 5.5W and 4.5W in transmit and receive mode respectively. The transmitter EIRP is 34dBm in each polarization. A 4.8Gb/s QPSK wireless link in each polarization is demonstrated at a distance of 20-meters.


title: Performance measurement for brain–computer or brain–machine interfaces: a tutorial

abstract: Objective. Brain–computer interfaces (BCIs) have the potential to be valuable clinical tools. However, the varied nature of BCIs, combined with the large number of laboratories participating in BCI research, makes uniform performance reporting difficult. To address this situation, we present a tutorial on performance measurement in BCI research. Approach. A workshop on this topic was held at the 2013 International BCI Meeting at Asilomar Conference Center in Pacific Grove, California. This paper contains the consensus opinion of the workshop members, refined through discussion in the following months and the input of authors who were unable to attend the workshop. Main results. Checklists for methods reporting were developed for both discrete and continuous BCIs. Relevant metrics are reviewed for different types of BCI research, with notes on their use to encourage uniform application between laboratories. Significance. Graduate students and other researchers new to BCI research may find this tutorial a helpful introduction to performance measurement in the field.


abstract: Finger vein recognition is a newly developed and promising biometrics technology. To facilitate evaluation in this area and study state-of-the-art performance of the finger vein recognition algorithms, we organized The ICB-2015 Competition on Finger Vein Recognition (ICFVR2015). This competition is held on a general recognition algorithm evaluation platform called RATE, with 3 data sets collected from volunteers and actual usage. 7 algorithms were finally submitted, with the best EER achieving 0.375%. This paper will first introduce the organization of the competition and RATE, then describe data sets and test protocols, and finally present results of the competition.


title: Conceptual integration and metaphor: An event-related potential study

abstract: Event-related brain potentials (ERPs) were recorded from 18 normal adults as they read sentences that ended with words used literally, metaphorically, or in an intermediateliteral mapping condition. In the latter condition, the literal sense of the word was used in a way that prompted readers to map conceptual structure from a different domain. ERPs measured from 300 to 500 msec after the onset of the sentence-final words differed as a function of metaphoricity: Literal endings elicited the smallest N400, metaphors the largest N400, whereas literal mappings elicited an N400 of intermediate amplitude. Metaphoric endings also elicited a larger posterior positivity than did either literal or literal mapping words. Consistent with conceptual blending theory, the results suggest that the demands of conceptual integration affect the difficulty of both literal and metaphorical language.


title: Induction motors with changeable pole windings in the ratio 1:4

abstract: The paper presents a new three phase 4 to 16 pole-changing winding for an induction motor with 36 stator slots and 6 terminals. The principle of design is explained and the space harmonics are calculated for both of polarities. In order to obtain a higher fundamental winding factor and lower space harmonic content for 16-pole winding, a degree of non-uniformity was accepted for the slot filling factor. The winding is developed partially in single layer and partially in double layers and contains two types of coils wound by different wire diameter. An experimental motor with the new winding was built on a 6-pole base motor in the IEC frame 132 having the rated power of 4 kW for 4-poles (double star) and 1 kW (single star) for 16-poles. The test results have shown the validity of the design. The manufactured motor was modeled using 2D FEM field-circuit method in order to simulate the operation characteristics in the both speeds.


Self-regulated Learning Strategies and Achievement in an Introduction to Information Systems Course


title: Test Case Selection and Prioritization Using Multiple Criteria

abstract: Regression testing activities such as test case selection and test case prioritization are ordinarily based on the criteria which focused around code coverage, code modifications and test execution costs. The approach mainly based on the multiple criteria of code coverage which performs efficient selection of test case. The method mainly aims to maximize the coverage size by executing the test cases effectively. The selected test cases are then prioritized based on the priority which depends on the code coverage to achieve the desired results.


title: Implementation of Total Quality Management in Higher Education

abstract: In this paper it has been tried to bring out a clear status of higher education and emergent needs to enhance the quality of higher education. Globalization of higher educational services has become an area of key focus for many countries in post WTO scenario. In order to fuel the socio-economic development of the country, higher education is playing a more active role in our country and this requires a paradigm shift in terms of governance and service delivery. Higher education institutions must become more innovative leading to quality institutions of knowledge production and dissemination. Realizing the importance of higher education, a lot of innovative experiments are being done to improve the performance of this sector. Application of TQM concepts is one of such measures, which will go a long way in revolutionizing the higher education system. The paper attempts to theoretically conceptualize TQM in higher education.


title: Correctly Rounded Arbitrary-Precision Floating-Point Summation

abstract: We present a fast algorithm together with its low-level implementation of correctly rounded arbitrary-precision floating-point summation. The arithmetic is the one used by the GNU MPFR library: radix 2; no subnormals; each variable (each input and the output) has its own precision. We also describe how the implementation is tested.


TEXT-TO-SPEECH SYNTHESIS FOR WELSH AND WELSH ENGLISH


title: Design of narrow wall slotted waveguide antenna for X-band application

abstract: An antenna is one of the prime important parts in the radar system to transmit and receive the electromagnetic signal. In order a coastal radar is able to detect an object at the sea and minimize sea clutter, it is needed to have horizontal polarization signal, and therefore a narrow wall slotted waveguide antenna is designed. This paper describes the design and fabrication of a narrow wall slotted waveguide antenna with 12 slots. The objective of this antenna design is to operate in X-band with center frequency at 9.4 GHz, VSWR ≤ 1.5, bandwidth ≥ 100MHz. There are many studies which discuss narrow wall slotted waveguide antenna by using the quadratic equation to obtain a slot angle from each slot. However only one the quadratic equation can result in center frequency at 9.4 GHz. To validate the result of the antenna designed, fabrication and measurement were conducted. From the measurement results, the antenna produces 145 MHz bandwidth with VSWR ≤ 1.5 and the center frequency is 9.35 GHz at −27.098 dB minimum return loss. While the antenna simulation results generated 159 MHz with VSWR ≤ 1.5 and the center frequency is 9.4 GHz at −22.076 dB minimum return loss. The simulation and measurement results show similar results.


title: Detecting requirements defects with NLP patterns: an industrial experience in the railway domain

abstract: In the railway safety-critical domain requirements documents have to abide to strict quality criteria. Rule-based natural language processing (NLP) techniques have been developed to automatically identify quality defects in natural language requirements. However, the literature is lacking empirical studies on the application of these techniques in industrial settings. Our goal is to investigate to which extent NLP can be practically applied to detect defects in the requirements documents of a railway signalling manufacturer. To address this goal, we first identified a set of typical defects classes, and, for each class, an engineer of the company implemented a set of defect-detection patterns by means of the GATE tool for text processing. After a preliminary analysis, we applied the patterns to a large set of 1866 requirements previously annotated for defects. The output of the patterns was further inspected by two domain experts to check the false positive cases. Additional discard-patterns were defined to automatically remove these cases. Finally, SREE, a tool that searches for typically ambiguous terms, was applied to the requirements. The experiments show that SREE and our patterns may play complementary roles in the detection of requirements defects. This is one of the first works in which defect detection NLP techniques are applied on a very large set of industrial requirements annotated by domain experts. We contribute with a comparison between traditional manual techniques used in industry for requirements analysis, and analysis performed with NLP. Our experience shows that several discrepancies can be observed between the two approaches. The analysis of the discrepancies offers hints to improve the capabilities of NLP techniques with company specific solutions, and suggests that also company practices need to be modified to effectively exploit NLP tools.


title: Model Driven Development with Interactive Use Cases and UML Models

abstract: Large scale enterprise software systems are inherently complex and hard to maintain. To deal with this complexity, current mainstream software engineering practices aim at raising the level of abstraction to visual models described in OMG’s UML modeling language. Current UML tools, however, produce static design diagrams for documentation which quickly become out-ofsync with the software. To address this issue, modeldriven development approaches focus on software automation with generators that translate models into code. Unfortunately, automated code generation tends to emphasize “replacement” rather than “evolution”, and as a result, don’t integrate well with existing legacy code. This research investigates an alternative approach to model driven development using dynamic models developed interactively with existing code. We believe that such an approach provides better support for maintaining and evolving software by keeping models more in sync with code and focusing on code integration with models rather than model driven code generation.


title: Review of Existing Analysis Tools for SELinux Security Policies: Challenges and a Proposed Solution

abstract: Access control policy management is an increasingly hard problem from both the security point of view and the verification point of view. SELinux is a Linux Security Module (LSM) implementing a mandatory access control mechanism. SELinux integrates user identity, roles, and type security attributes for stating rules in security policies. As SELinux policies are developed and maintained by security administrators, they often become quite complex, and it is important to carefully analyze them in order to have high assurance of their correctness. There are many existing analysis tools for modeling and analyzing SELinux policies with the goal of answering specific safety and functionality questions. In this paper, we identify and highlight current gaps in these existing tools for SELinux policy analysis, and propose new tools and technologies with the potential to lead to significant improvements. The proposed solution includes adopting a certified access control policy language such as ACCPL (A Certified Access Core Policy Language). ACCPL comes with formal proofs of important properties, and our proposed solution includes adopting it to facilitate various analyses and proof of reasonability properties. ACCPL is general, and our goal is to design a certified domain-specific policy language based on it, specialized to our task.


title: Bayesian Checking for Topic Models

abstract: Real document collections do not fit the independence assumptions asserted by most statistical topic models, but how badly do they violate them? We present a Bayesian method for measuring how well a topic model fits a corpus. Our approach is based on posterior predictive checking, a method for diagnosing Bayesian models in user-defined ways. Our method can identify where a topic model fits the data, where it falls short, and in which directions it might be improved.


title: Uploading and Replicating Internet of Things (IoT) Data on Distributed Cloud Storage

abstract: The Internet of Things (IoT) phenomenon is creatinga world of billions of connected devices generating enormousamounts of data items. In particular, for purposes of highavailability and disaster recovery, replication of this data on cloudstorage needs to be implemented efficiently. To that end, in thispaper, we investigate the combined problem of uploading IoTdata from a set of sensor gateways and efficient replication ofthis data on distributed cloud storage. While other efforts doexist in this space, either they do not consider replication incontext of small size and high number of data items, which isinherent nature of IoT data or they concentrate on the access timeafter replication. Our solution assumes the existence of multipledistributed cloud data centers, called mini-Clouds, among whichdata can be replicated. We model our problem comprehensivelybased on various parameters such as effective bandwidth of theIoT network, available number and size of data items at eachmini-Cloud, and we present our problem as a collection of varioussub-problems based on subsets of these parameters. We provethat the exact solution to the problem is intractable, and wepresent a number of heuristic strategies to solve it. Our resultsshow that the performance of any heuristic is bounded by theread and write latency of mini-Clouds and the best we can do isoften 12 times the worst we can do for a given number of dataitems to be uploaded and replicated from the gateways to themini-Clouds in test setup.


title: Robot Dynamics And Control

abstract: From the Publisher: ::: This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. Provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. Includes many worked examples, examples illustrating all aspects of the theory, and problems.


title: Image partitioning into convex polygons

abstract: The over-segmentation of images into atomic regions has become a standard and powerful tool in Vision. Traditional superpixel methods, that operate at the pixel level, cannot directly capture the geometric information disseminated into the images. We propose an alternative to these methods by operating at the level of geometric shapes. Our algorithm partitions images into convex polygons. It presents several interesting properties in terms of geometric guarantees, region compactness and scalability. The overall strategy consists in building a Voronoi diagram that conforms to preliminarily detected line-segments, before homogenizing the partition by spatial point process distributed over the image gradient. Our method is particularly adapted to images with strong geometric signatures, typically man-made objects and environments. We show the potential of our approach with experiments on large-scale images and comparisons with state-of-the-art superpixel methods.


title: DBMiner: A System for Mining Knowledge in Large Relational Databases

abstract: A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporating several interesting data mining techniques, including attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive data mining environment with good performance.


title: Automotive E/E-architecture enhancements by usage of ethernet TSN

abstract: A huge upheaval emerges from the transition to autonomous vehicles in the domain of road vehicles, ongoing with a change in the vehicle architecture. Many sensors and Electronic Control Units are added to the current vehicle architecture and further safety requirements like reliability become even more necessary. In this paper we present a potential evolution of the Electrical/Electronic-Architecture, including a Zone Architecture, to enable future functionality. We reveal the impact on the communication network concerning these architectures and present a potential communication technology to facilitate such architectures.


title: Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.


title: The promising future of microalgae: current status, challenges, and optimization of a sustainable and renewable industry for biofuels, feed, and other products

abstract: Microalgae have recently attracted considerable interest worldwide, due to their extensive application potential in the renewable energy, biopharmaceutical, and nutraceutical industries. Microalgae are renewable, sustainable, and economical sources of biofuels, bioactive medicinal products, and food ingredients. Several microalgae species have been investigated for their potential as value-added products with remarkable pharmacological and biological qualities. As biofuels, they are a perfect substitute to liquid fossil fuels with respect to cost, renewability, and environmental concerns. Microalgae have a significant ability to convert atmospheric CO2 to useful products such as carbohydrates, lipids, and other bioactive metabolites. Although microalgae are feasible sources for bioenergy and biopharmaceuticals in general, some limitations and challenges remain, which must be overcome to upgrade the technology from pilot-phase to industrial level. The most challenging and crucial issues are enhancing microalgae growth rate and product synthesis, dewatering algae culture for biomass production, pretreating biomass, and optimizing the fermentation process in case of algal bioethanol production. The present review describes the advantages of microalgae for the production of biofuels and various bioactive compounds and discusses culturing parameters.


title: Neural Probabilistic Language Models

abstract: A central goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on several methods to speed-up both training and probability computation, as well as comparative experiments to evaluate the improvements brought by these techniques. We finally describe the incorporation of this new language model into a state-of-the-art speech recognizer of conversational speech.


title: Understanding usage intention in innovative mobile app service: Comparison between millennial and mature consumers

abstract: This study sought to analyze the impact of consumer technological and fashion innovativeness on usage intention to use a fashion image search app. Based on the Technology Acceptance Model (TAM), which was proven quite robust, the study introduced an additional variable of playfulness to verify the relationship between variables related to technological innovativeness and fashion innovativeness. Consumers with a high degree of technological innovativeness tended to find the fashion app easy to use, derived enjoyment from using it, and adopted it if the app is useful. However, those with a high degree of fashion innovativeness adopted the fashion service simply because it was fun to use rather than beneficial or useful. In addition, this study analyzed the differences in acceptance with respect to cohort group, particularly regarding millennial consumers compared to their mature counterparts. The results of this research improved understanding of the generational differences in accepting innovative marketing technology. We study consumers with high technological innovativeness.They adopt app services if they find it easy to use and useful and they enjoy it.We study consumers with high fashion innovativeness.They adopt fashion service because it is fun to use rather than useful.We analyze the differences in acceptance between millennials and mature consumers.


LSTM Shift-Reduce CCG Parsing


title: A Flexible Low-Power 130 nm CMOS Read-Out Circuit With Tunable Sensitivity for Commercial Robotic Resistive Pressure Sensors

abstract: This paper presents a flexible and low-power Read-Out Circuit (ROC) with tunable sensitivity, designed to interface a wide range of commercial resistive pressure sensors for robotic applications. The ROC provides contact detection, monitoring small capacitance variations at low pressure ( $428\times 159~\mu \text{m}^{2}$ ) has a power consumption of 27.2 $\mu \text{W}$ , for $V_{\mathrm {DD}}~1$ V. Using digital control inputs, the ROC allows a wide range tuning of measurement sensitivity (6.7–46.4 mbar/LSB) and adjustable acquisition time (226.6–461.7 $\mu \text{s}$ and 648–890 $\mu \text{s}$ , for contact detection and pressure evaluation, respectively). The read-out time of $\sim 1$ ms is compatible with human response after touch.


title: Intel® IBIST, the full vision realized

abstract: Third generation Intel® IBIST (IBIST) is the first full featured edition of what was originally envisioned in 1999. The objective was to create a standard infrastructure for validating, debugging, and testing high speed IOs (Input/Output) which could be supported by a common software toolset. This vision was realized in 2009 on Intel products. The IBIST methodology has become a standard at Intel. Today, IBIST is utilized from the very beginning of the product verification including initial power-on silicon debug. It a staple throughout the back-end product validation process and is also utilized in end-customer validation and high volume testing. Intel's platform Reliability, Availability, Serviceability(collectively referred to as RAS) architecture exploits the technology on a number of fronts as well. The content of this paper includes an overview of the problems which mandated this paradigm shift away from the historical IO testing methodologies, an IBIST architectural overview, and the key application spaces addressed by this technology.


title: Strength matters: Self-presentation to the strongest audience rather than lowest common denominator when faced with multiple audiences in social network sites

abstract: On social network sites (e.g. Facebook), individuals self-present to multiple audiences simultaneously 24źh a day. Prior research has inferred this results in a lowest common denominator effect (LCDE) whereby people constrain their online presentation to the standards of their strictest audience. However, this existing work neglects to address differences in the 'value' (social/economic) of the audience. Through the lens of self-presentation theory, we argue that it is not the strictest audience that constrains behavior but the strongest (i.e. that which has the highest score for standards and value combined). We call this the strongest audience effect (SAE). The aim of this research is to examine and contrast the LCDE and SAE. A survey of young Facebook users (nź=ź379) provides support for the SAE when compared to LCDE, with the strength of the strongest audience predicting behavioral constraint and also social anxiety. Additional insights are generated into which audiences are perceived as the strongest. This study contributes a novel and more holistic lens to understand self-presentation in the presence of multiple audiences in social network sites. Examines 'strongest audience effect' vs. 'lowest-common denominator effect'.Audience 'value' not solely 'standards' shape self-presentation on Facebook.Newly theorized 'strongest audience effect' superior predictor of self-presentation.Social anxiety predicted by audience 'strength' rather than 'strictness'.Employers considered the strongest audience followed by guardians and partners.


title: FPGA-Based Spectrum Analyzer with High Area Efficiency by Goertzel Algorithm

abstract: FFT algorithm is the popular software design for spectrum analyzer, but doesnpsilat work well for parallel hardware system due to complex calculation and huge memory requirement. Observing the key components of a spectrum analyzer are the intensities for respective frequencies, we propose a Goertzel algorithm to directly extract the intensity factors for respective frequency components in the input signal. Goertzel algorithm dispenses with the memory for z-1 and z-2 processing, and only needs two multipliers and three adders for real number calculation. In this paper, we present the spectrum extraction algorithm and implement a spectrum extractor with high speed and low area consumption in a FPGA (field programmable gate array) chip. It proves the feasibility of implementing a handheld concurrent multi-channel real-time spectrum analysis IP into a low gate counts and low power consumption CPLD (complex programmable logic device) chip.


title: A detailed DVB-S2 receiver implementation: FPGA prototyping and preliminary ASIC resource estimation

abstract: Design and implementation of signal processing and synchronization algorithms for digital receivers are challenging tasks, especially concerning the verification phase that must cover as many functional tests as possible. This paper discloses the entire internal architecture of the receive chain of the ETSI DVB-S2 digital satellite communication standard and the methodology used for implementing it. It covers architectural, algorithm, and RTL design, together with laboratory setup, FPGA prototyping and VLSI resource estimation in 65nm CMOS. The result section demonstrates that our approach is able to synchronize and demodulate an 8-PSK DVB-S2 compliant signal, corrupted by all the impairments expected in a digital receiver.


title: DNN-based Speech Synthesis for Indian Languages from ASCII text

abstract: Text-to-Speech synthesis in Indian languages has a seen lot of progress over the decade partly due to the annual Blizzard challenges. These systems assume the text to be written in Devanagari or Dravidian scripts which are nearly phonemic orthography scripts. However, the most common form of computer interaction among Indians is ASCII written transliterated text. Such text is generally noisy with many variations in spelling for the same word. In this paper we evaluate three approaches to synthesize speech from such noisy ASCII text: a naive Uni-Grapheme approach, a Multi-Grapheme approach, and a supervised Grapheme-to-Phoneme (G2P) approach. These methods first convert the ASCII text to a phonetic script, and then learn a Deep Neural Network to synthesize speech from that. We train and test our models on Blizzard Challenge datasets that were transliterated to ASCII using crowdsourcing. Our experiments on Hindi, Tamil and Telugu demonstrate that our models generate speech of competetive quality from ASCII text compared to the speech synthesized from the native scripts. All the accompanying transliterated datasets are released for public access.


title: Quantitative analysis of fitness and genetic interactions in yeast on a genome scale

abstract: Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.


An Investigation of Emotion Dynamics and Kalman Filtering for Speech-Based Emotion Prediction.


ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars


title: Structural detection of android malware using embedded call graphs

abstract: The number of malicious applications targeting the Android system has literally exploded in recent years. While the security community, well aware of this fact, has proposed several methods for detection of Android malware, most of these are based on permission and API usage or the identification of expert features. Unfortunately, many of these approaches are susceptible to instruction level obfuscation techniques. Previous research on classic desktop malware has shown that some high level characteristics of the code, such as function call graphs, can be used to find similarities between samples while being more robust against certain obfuscation strategies. However, the identification of similarities in graphs is a non-trivial problem whose complexity hinders the use of these features for malware detection. In this paper, we explore how recent developments in machine learning classification of graphs can be efficiently applied to this problem. We propose a method for malware detection based on efficient embeddings of function call graphs with an explicit feature map inspired by a linear-time graph kernel. In an evaluation with 12,158 malware samples our method, purely based on structural features, outperforms several related approaches and detects 89% of the malware with few false alarms, while also allowing to pin-point malicious code structures within Android applications.


title: PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning

abstract: We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling-based path planning with reinforcement learning (RL) agents. The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of the large-scale topology, while the sampling-based planners provide an approximate map of the space of possible configurations of the robot from which collision-free trajectories feasible for the RL agents can be identified. The same RL agents are used to control the robot under the direction of the planning, enabling long-range navigation. We use the Probabilistic Roadmaps (PRMs) for the sampling-based planner. The RL agents are constructed using feature-based and deep neural net policies in continuous state and action spaces. We evaluate PRM-RL on two navigation tasks with non-trivial robot dynamics: end-to-end differential drive indoor navigation in office environments, and aerial cargo delivery in urban environments with load displacement constraints. These evaluations included both simulated environments and on-robot tests. Our results show improvement in navigation task completion over both RL agents on their own and traditional sampling-based planners. In the indoor navigation task, PRM-RL successfully completes up to 215 meters long trajectories under noisy sensor conditions, and the aerial cargo delivery completes flights over 1000 meters without violating the task constraints in an environment 63 million times larger than used in training.


title: Fast shape-based road sign detection for a driver assistance system

abstract: A new method is presented for detecting triangular, square and octagonal road signs efficiently and robustly. The method uses the symmetric nature of these shapes, together with the pattern of edge orientations exhibited by equiangular polygons with a known number of sides, to establish possible shape centroid locations in the image. This approach is invariant to in-plane rotation and returns the location and size of the shape detected. Results on still images show a detection rate of over 95%. The method is efficient enough for real-time applications, such as on-board-vehicle sign detection.


title: A Method for Off-line Persian Signature Identification and Verification Using DWT

abstract: Signature verification and Identification has great importance for authentication purpose. Persian signatures are different from other signature types because people usually do not use text in it and they draw a shape as their signature, therefore, a different approach should be considered to process such signatures. In this paper, a method for off-line Persian signature identification and verification is proposed that is based on Image Registration, DWT (Discrete Wavelet Transform) and Image Fusion. Training signatures of each person are registered to overcome shift and scale problem. To extract features, at first, DWT is used to access details of signature; then several registered instances of each person signatures are fused together to generate reference pattern of person's signatures. In the classification phase, Euclidean distance between the test image and each pattern is used in different sub-bands. Experimental results confirmed the effectiveness of the proposed method. However, the proposed method has been tested on Persian signature database but we believe it can be extended for other languages.


title: Surviving or flourishing? Integrating business resilience and sustainability

abstract: Purpose – Businesses are always seeking resilient strategies so they can weather unpredictable competitive environments. One source of unpredictability is the unsustainability of commerce's environmental, economic or social impacts and the limitations this places on businesses. Another is poor resilience causing erroneous and unexpected outputs. Companies prospering long-term must have both resilience and sustainability, existing in a symbiotic state. The purpose of this paper is to explore the two concepts and their relationship, their combined benefits and propose an approach for supporting decision makers to proactively build both characteristics. Design/methodology/approach – The paper looks at businesses as complex adaptive systems, how their resilience and sustainability can be defined and how these might be exhibited. It then explores how they can be combined in practice. Findings – The two qualities are related but have different purposes, moreover resilience has two major forms related to timesca...


title: Software-defined optical networks (SDONs): a survey

abstract: This paper gives an overview of software-defined optical networks (SDONs). It explains the general concepts on software-defined networks (SDNs), their relationship with network function virtualization, and also about OpenFlow, which is a pioneer protocol for SDNs. It then explains the benefits and challenges of extending SDNs to multilayer optical networks, including flexible grid and elastic optical networks, and how it compares to generalized multi-protocol label switching for implementing a unified control plane. An overview on the industry and research efforts on SDON standardization and implementation is given next, to bring the reader up to speed with the current state of the art in this field. Finally, the paper outlines the benefits achieved by SDONs for network operators, and also some of the important and relevant research problems that need to be addressed.


title: Active Noise Control in Headsets by Using a Low-Cost Microcontroller

abstract: Conventional passive headsets often perform well in reducing high-frequency noise but perform badly to cancel the low-frequency noise. Accordingly, academic researches and companies have developed active noise-reducing headsets to cancel annoying noise at such lower frequencies using expensive digital signal processors (DSPs). This paper develops a modified feedback active noise control (ANC)algorithm to design an active noise-reducing headset with a low-cost microcontroller unit (MCU), offering a cheaper solution to noise reduction than existing audio headsets. The ways to save the computing load of a least mean square (LMS) algorithm by a microcontroller and to compensate for the output limit of speakers in a headset are also provided. Experimental results show that the proposed approach removes disturbing noise very well and outperforms the commercial headset.


title: Fault tolerant three-phase AC motor drive topologies: a comparison of features, cost, and limitations

abstract: This paper compares the many fault tolerant three-phase ac motor drive topologies that have been proposed to provide output capacity for the inverter faults of switch short or open-circuits, phase-leg short-circuits, and single-phase open-circuits. Also included is a review of the respective control methods for fault tolerant inverters including two-phase and unipolar control methods. The output voltage and current space in terms of dq components is identified for each topology and fault. These quantities are then used to normalize the power capacity of each system during a fault to a standard inverter during normal operation. A silicon overrating cost factor is adopted as a metric to compare the relative switching device costs of the topologies compared to a standard three-phase inverter.


title: Big Five factors of personality and replicated predictions of behavior.

abstract: Measures of the Big Five factors of personality were used to predict a variety of criterion variables thought to represent behaviors of some social and cultural significance (e.g., alcohol consumption, grade point average). Analyses focused on replicated predictions across 2 independent samples of participants (Ns = 276 and 142) with 3 different measures of the Big Five (the NEO Five-Factor Inventory, the Revised NEO Personality Inventory, and the Five-Factor Nonverbal Personality Questionnaire, the latter an experimental nonverbal personality inventory). The results indicated substantial consistency in behavior predictions across the different Big Five assessments. The data are interpreted as supporting both the construct validity of the personality measures used and the role of the Big Five factors as determinants of certain complex behaviors.


title: Verbal Fluency and Repetition Skills in Healthy Older Spanish-English Bilinguals

abstract: The influence of bilingualism on cognitive test performance in older adults has received limited attention in the neuropsychology literature. The aim of this study was to examine the impact of bilingualism on verbal fluency and repetition tests in older Hispanic bilinguals. Eighty-two right-handed participants (28 men and 54 women) with a mean age of 61.76 years (SD = 9.30; range = 50-84) and a mean educational level of 14.8 years (SD = 3.6; range 2-23) were selected. Forty-five of the participants were English monolinguals, 18 were Spanish monolinguals, and 19 were Spanish-English bilinguals. Verbal fluency was tested by electing a verbal description of a picture and by asking participants to generate words within phonemic and semantic categories. Repetition was tested using a sentence-repetition test. The bilinguals' test scores were compared to English monolinguals' and Spanish monolinguals' test scores. Results demonstrated equal performance of bilingual and monolingual participants in all tests excep...


title: PERSPECTIVES ON PSYCHOLOGICAL SCIENCE Development, Freedom, and Rising Happiness A Global Perspective (1981–2007)

abstract: Until recently, it was widely held that happiness fluctuates around set points, so that neither individuals nor societies can lastingly increase their happiness. Even though recent research showed ...


title: A Real Time Hybrid Pattern Matching Scheme for Stock Time Series

abstract: Pattern matching in stock time series is an active research area in data mining. We propose a new real-time hybrid pattern-matching algorithm in this paper. The algorithm is based on the Spearman's rank correlation, rule sets and sliding window. The concept of sliding windows enables patterns matching to be performed only based on subsequence of stock data which are freshly received. Therefore the proposed algorithm can be applied in real-time application and processing time can be reduced. Spearman's rank correlation coefficient is used to classify the preferred patterns effectively and efficiently first and use the rule sets to provide further ability for describing the query patterns so that is more effective, sensitive and constrainable in distinguishing individual patterns. Encouraging experiment is reported from the tests that the proposed scheme outperforms the other methods both effectively and efficiently, especially in differentiating the special preferred stock patterns or even distorted patterns.


title: Direct Inter-Process Communication (dIPC): Repurposing the CODOMs Architecture to Accelerate IPC

abstract: In current architectures, page tables are the fundamental mechanism that allows contemporary OSs to isolate user processes, binding each thread to a specific page table. A thread cannot therefore directly call another process's function or access its data; instead, the OS kernel provides data communication primitives and mediates process synchronization through inter-process communication (IPC) channels, which impede system performance. Alternatively, the recently proposed CODOMs architecture provides memory protection across software modules. Threads can cross module protection boundaries inside the same process using simple procedure calls, while preserving memory isolation. We present dIPC (for "direct IPC"), an OS extension that repurposes and extends the CODOMs architecture to allow threads to cross process boundaries. It maps processes into a shared address space, and eliminates the OS kernel from the critical path of inter-process communication. dIPC is 64.12× faster than local remote procedure calls (RPCs), and 8.87× faster than IPC in the L4 microkernel. We show that applying dIPC to a multi-tier OLTP web server improves performance by up to 5.12× (2.13× on average), and reaches over 94% of the ideal system efficiency.


title: Deep Automatic Portrait Matting

abstract: We propose an automatic image matting method for portrait images. This method does not need user interaction, which was however essential in most previous approaches. In order to accomplish this goal, a new end-to-end convolutional neural network (CNN) based framework is proposed taking the input of a portrait image. It outputs the matte result. Our method considers not only image semantic prediction but also pixel-level image matte optimization. A new portrait image dataset is constructed with our labeled matting ground truth. Our automatic method achieves comparable results with state-of-the-art methods that require specified foreground and background regions or pixels. Many applications are enabled given the automatic nature of our system.


title: The ESHRE/ESGE consensus on the classification of female genital tract congenital anomalies.

abstract: STUDY QUESTION ::: What classification system is more suitable for the accurate, clear, simple and related to the clinical management categorization of female genital anomalies? ::: ::: ::: SUMMARY ANSWER ::: The new ESHRE/ESGE classification system of female genital anomalies is presented. ::: ::: ::: WHAT IS KNOWN ALREADY ::: Congenital malformations of the female genital tract are common miscellaneous deviations from normal anatomy with health and reproductive consequences. Until now, three systems have been proposed for their categorization but all of them are associated with serious limitations. ::: ::: ::: STUDY DESIGN, SIZE AND DURATION ::: The European Society of Human Reproduction and Embryology (ESHRE) and the European Society for Gynaecological Endoscopy (ESGE) have established a common Working Group, under the name CONUTA (CONgenital UTerine Anomalies), with the goal of developing a new updated classification system. A scientific committee (SC) has been appointed to run the project, looking also for consensus within the scientists working in the field. ::: ::: ::: PARTICIPANTS/MATERIALS, SETTING, METHODS ::: The new system is designed and developed based on (i) scientific research through critical review of current proposals and preparation of an initial proposal for discussion between the experts, (ii) consensus measurement among the experts through the use of the DELPHI procedure and (iii) consensus development by the SC, taking into account the results of the DELPHI procedure and the comments of the experts. Almost 90 participants took part in the process of development of the ESHRE/ESGE classification system, contributing with their structured answers and comments. ::: ::: ::: MAIN RESULTS AND THE ROLE OF CHANCE ::: The ESHRE/ESGE classification system is based on anatomy. Anomalies are classified into the following main classes, expressing uterine anatomical deviations deriving from the same embryological origin: U0, normal uterus; U1, dysmorphic uterus; U2, septate uterus; U3, bicorporeal uterus; U4, hemi-uterus; U5, aplastic uterus; U6, for still unclassified cases. Main classes have been divided into sub-classes expressing anatomical varieties with clinical significance. Cervical and vaginal anomalies are classified independently into sub-classes having clinical significance. ::: ::: ::: LIMITATIONS, REASONS FOR CAUTION ::: The ESHRE/ESGE classification of female genital anomalies seems to fulfill the expectations and the needs of the experts in the field, but its clinical value needs to be proved in everyday practice. ::: ::: ::: WIDER IMPLICATIONS OF THE FINDINGS ::: The ESHRE/ESGE classification system of female genital anomalies could be used as a starting point for the development of guidelines for their diagnosis and treatment. ::: ::: ::: STUDY FUNDING/COMPETING INTEREST(S) ::: None.


title: Induction of human β-defensins and psoriasin in vulvovaginal human papillomavirus-associated lesions.

abstract: BACKGROUND ::: Infections with a low-risk type of human papillomavirus (HPV) may lead to genital warts. HPV targets the basal cell layer of epithelial cells. The first line of defense is the innate immune system, which provides nonspecific protection against a variety of pathogens. The antimicrobial peptides (AMPs) α- and β-defensins, cathelicidins, psoriasin, and RNase7 are central mediators. ::: ::: ::: METHODS ::: The expression of various α- and β-defensins, cathelicidin LL-37, psoriasin, and RNase7 was studied in biopsy samples from 35 patients with genital warts and 25 healthy women using quantitative real-time polymerase chain reaction and immunohistochemical analysis. ::: ::: ::: RESULTS ::: We found a significantly higher expression of the β-defensins hBD-1 (P = .03), hBD-2 (P < 0.01), and hBD-3 (P < .001), and psoriasin (P = .001) in condylomata acuminata, compared with normal controls. The RNA and protein levels of RNase7 did not differ between infected and uninfected samples (P = .55). The α-defensins HNP 1-3, HD5, and HD6 and the cathelicidin LL-37 were scarcely detectable in normal and infected tissue. ::: ::: ::: CONCLUSIONS ::: The differing expression of AMPs in HPV-infected, compared with noninfected, vulvovaginal biopsy samples suggests that these peptides are important in the local immune response. Curiously, hBD-1 shows a significant induction whereas RNase7 does not, which suggests differing regulation of AMPs over the course of bacterial and viral infections.


title: Quantum adiabatic machine learning

abstract: We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. We apply and illustrate this approach in detail to the problem of software verification and validation.


title: Mobility Enhanced RPL for Wireless Sensor Networks

abstract: In this paper, we investigate the problem of supporting mobility over RPL (IPv6 Routing Protocol for Low power and Lossy Networks) when applied to route traffic in Wireless Sensor Networks (WSNs). RPL is a routing protocol adapted for information routing with low power, low storage and processing sensor devices, in static topologies commonly found in WSNs, but which is not directly designed for mobile scenarios. Specifically, RPL actively decreases control traffic, at the price of lower reactivity to topology changes. In this paper, we propose to introduce some new mechanisms to the native RPL that reconcile decrease in control traffic and reactivity. They are based on an identification of mobile nodes, and furthermore they enhance RPL behavior in case of node mobility. Our approach will be, henceforth, called ME-RPL (Mobility Enhanced RPL).


title: A linearity-enhanced wideband low-noise amplifier

abstract: Techniques are proposed to enhance linearity in a low-voltage wideband LNA for use in a multi-standard wideband receiver. To achieve high linearity over wide frequency range, two previous IMD 3 cancellation techniques are merged and modified to obtain IIP3 peaks at different frequencies, while minimizing component count. A self-biasing current reuse technique is developed to enhance low-voltage operation. Two LNAs are designed in 0.13 µm CMOS to demonstrate these techniques: the (Chebyshev, transformer) LNAs achieved (+10.6, +12.0) dBm IIP3 over (2.3–6.0, 2.0–5.3) GHz, (12.7, 12.4) dB gain, (4.8, 4.9) dB noise figure, while consuming 6.9mA from 1.2V.


title: A CNN for the identification of corresponding patches in SAR and optical imagery of urban scenes

abstract: In this paper we propose a convolutional neural network (CNN), which allows to identify corresponding patches of very high resolution (VHR) optical and SAR imagery of complex urban scenes. Instead of a siamese architecture as conventionally used in CNNs designed for image matching, we resort to a pseudo-siamese configuration with no interconnection between the two streams for SAR and optical imagery. The network is trained with automatically generated training data and does not resort to any hand-crafted features. First evaluations show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development to a generalized multi-sensor matching procedure.


title: Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization

abstract: We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of this fundamental problem is very limited. For example, it is not known whether the proximal stochastic gradient method with constant minibatch converges to a stationary point. To tackle this issue, we develop fast stochastic algorithms that provably converge to a stationary point for constant minibatches. Furthermore, using a variant of these algorithms, we obtain provably faster convergence than batch proximal gradient descent. Our results are based on the recent variance reduction techniques for convex optimization but with a novel analysis for handling nonconvex and nonsmooth functions. We also prove global linear convergence rate for an interesting subclass of nonsmooth nonconvex functions, which subsumes several recent works.


Pupillary Responses to Robotic and Human Emotions: The Uncanny Valley and Media Equation Confirmed


Epidemiological and Sociocultural Factors in the Etiology of Duodenal Ulcer


title: Securing cloud computing environment against DDoS attacks

abstract: Cloud computing is becoming one of the next IT industry buzz word. However, as cloud computing is still in its infancy, current adoption is associated with numerous challenges like security, performance, availability, etc. In cloud computing where infrastructure is shared by potentially millions of users, Distributed Denial of Service (DDoS) attacks have the potential to have much greater impact than against single tenanted architectures. This paper tested the efficiency of a cloud trace back model in dealing with DDoS attacks using back propagation neural network and finds that the model is useful in tackling Distributed Denial of Service attacks.


title: Mobile cloud computing

abstract: Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work. Highlights? Problems in mobile computing can be solved when combined with cloud computing. ? Three types of mobile clouds are; offloading to a remote server, to a local cloudlet, and sharing work in a mobile p2p network. ? Existing research on mobile cloud computing is presented as a taxonomy of issues. ? We identify issues in operational, end user, and service levels, and in security and context awareness.


title: Designing of android head system by applying facial muscle mechanism of humans

abstract: The facial system plays an important role in human-robot interaction. EveR-4 H33 is a head system for an android face controlled by thirty-three motors. It consists of three layers: a mechanical layer, an inner cover layer and an outer cover layer. Motors are attached under the skin and some motors are correlated with each other. Some expressions cannot be shown by moving just one motor. In addition, moving just one motor can cause damage to other motors or the skin. To solve these problems, a facial muscle control method that controls motors in a correlated manner is required. We designed a facial muscle control method and applied it to EveR-4 H33. We develop the actress robot EveR-4A by applying the EveR-4 H33 to the 24 degrees of freedom upper body and mannequin legs. EveR-4A shows various facial expressions with lip synchronization using our facial muscle control method.


title: Region-Based Segmentation versus Edge Detection

abstract: This paper, we will review the main approaches of partitioning an image into regions by using gray values in order to reach a correct interpretation of the image. We mainly compare the region-based segmentation with the boundary estimation using edge detection. Image segmentation is an important step for many image processing and computer vision algorithms while an edge can be described informally as the boundary between adjacent parts of an image. A formal definition is elusive, but edge detection is nonetheless a useful and ubiquitous image processing task. After comparing we have come to a conclusion that the edge detection has advantage of not necessarily needing closed boundaries and also its computation is based on difference. The region-segmentation in spite of improving multi-spectral images has the drawback of being applied only on closed boundaries. To reach the result of edge detection we have used the technique of performance metrics and Canny edge detection. We have applied Canny ground truth to acquire more features via displaying more details.


title: Coase's Penguin, or Linux and the Nature of the Firm

abstract: For decades our common understanding of the organization of economic production has been that individuals order their productive activities in one of two ways: either as employees in firms, following the directions of managers, or as individuals in markets, following price signals. This dichotomy was first identified in the early work of Ronald Coase and was developed most explicitly in the work of institutional economist Oliver Williamson. In this paper I explain why we are beginning to see the emergence of a new, third mode of production, in the digitally networked environment, a mode I call commons-based peer production. In the past three or four years, public attention has focused on a fifteen-yearold social-economic phenomenon in the software development world. This phenomenon, called free software or open source software, involves thousands or even tens of thousands of programmers contributing to large and small scale projects, where the central organizing principle is that the software remains free of most constraints on copying and use common to proprietary materials. No one “owns” the software in the traditional sense of being able to command how it is used or developed, or to control its disposition. The result has been the emergence of a vibrant, innovative and productive collaboration, whose participants are not organized in firms and do not choose their projects in response to price signals. This paper explains that while free software is highly visible, it is in fact only one example of a much broader social-economic phenomenon. I suggest that we are seeing the broad and deep emergence of a new, third mode of production in the ∗ Professor of Law, New York University School of Law. Research for this paper was partly supported by a grant from the Filomen D’Agostino and Max Greenberg Fund at NYU School of Law. I owe thanks to many for comments on this and earlier drafts, including: Bruce Ackerman, Ed Baker, Elazar Barkan, Dan Burk, Jamie Boyle, Niva Elkin Koren, Terry Fisher, Natalie Jeremijenko, Dan Kahan, Doug Lichtman, Tara Lemmy, Mark Nadel, Carol Rose, Bob Ellickson, Peggy Radin, Clay Shirky, Helen Nissenbaum, Jerry Mashaw, Eben Moglen, Larry Lessig, Chuck Sabel, Alan Schwartz, Richard Stallman, and Kenji Yoshino. I owe special thanks to Steve Snyder for his invaluable research assistance on the peer production enterprises described here. I have gotten many question about the “Coase’s Penguin” portion of the title. It turns out that the geek culture that easily recognizes “Coase” doesn’t’ recognize the “penguin,” and vice versa. So, “Coase” refers to Ronald Coase, who originated the transactions costs theory of the firm that provides the methodological template for the positive analysis of peer production that I offer here. The penguin refers to the fact that the Linux kernel development community has adopted the image of a paunchy penguin as its mascot/trademark. One result of this cross-cultural conversation is that I will occasionally explain in some detail concepts that are well known in one community but not in the other. 2 COASE’S PENGUIN V.04.3 AUGUST. 2002 2 digitally networked environment. I call this mode “commons-based peer production,” to distinguish it from the propertyand contract-based modes of firms and markets. Its central characteristic is that groups of individuals successfully collaborate on largescale projects following a diverse cluster of motivational drives and social signals, rather than either market prices or managerial commands. I explain why this mode has systematic advantages over markets and managerial hierarchies when the object of production is information or culture, and where the physical capital necessary for that production—computers and communications capabilities—is widely distributed instead of concentrated. In particular, this mode of production is better than firms and markets for two reasons. First, it is better at identifying and assigning human capital to information and cultural production processes. In this regard, peer production has an advantage in what I call “information opportunity cost.” That is, it loses less information about who the best person for a given job might be than either of the other two organizational modes. Second, there are substantial increasing returns, in terms of allocation efficiency, to allowing larger clusters of potential contributors to interact with large clusters of information resources in search of new projects and opportunities for collaboration. Removing property and contract as the organizing principles of collaboration substantially reduces transaction costs involved in allowing these large clusters of potential contributors to review and select which resources to work on, for which projects, and with which collaborators. This results in the potential for substantial allocation gains. The article concludes with an overview of how these models use a variety of technological and social strategies to overcome the collective action problems usually solved in managerial and market-based systems by property, contract, and managerial commands.


title: The Use of Control Charts in Health-Care and Public-Health Surveillance

abstract: There are many applications of control charts in health-care monitoring and in public-health surveillance. We introduce these applications to industrial practitioners and discuss some of the ideas that arise that may be applicable in industrial monitoring. The advantages and disadvantages of the charting methods proposed in the health-care and public-health areas are considered. Some additional contributions in the industrial statistical process control literature relevant to this area are given. There are many application and research opportunities available in the use of control charts for health-related monitoring.


title: 3-dimensional bioprinting for tissue engineering applications

abstract: The 3-dimensional (3D) printing technologies, referred to as additive manufacturing (AM) or rapid prototyping (RP), have acquired reputation over the past few years for art, architectural modeling, lightweight machines, and tissue engineering applications. Among these applications, tissue engineering field using 3D printing has attracted the attention from many researchers. 3D bioprinting has an advantage in the manufacture of a scaffold for tissue engineering applications, because of rapid-fabrication, high-precision, and customized-production, etc. In this review, we will introduce the principles and the current state of the 3D bioprinting methods. Focusing on some of studies that are being current application for biomedical and tissue engineering fields using printed 3D scaffolds.


title: Performance evaluation of V2V/V2I communications: The effect of midamble insertion

abstract: In vehicular communications, the channel estimation is critical to compensate signal distortion due to wireless channel in high mobility scenario. To improve the error performance of vehicular communications, we proposed the midamble-based channel estimation scheme [12]. Based on the proposed scheme, we investigate the effect of midamble insertion on the system performance. With various midamble insertion rates, the error performance for the system with different modulation scheme, packet size, and channel condition is explored. The effects of various factors on the error performance are also developed.


title: Translational science in action: Hostile attributional style and the development of aggressive behavior problems

abstract: A model of the development of hostile attributional style and its role in children's aggressive behavior is proposed, based on the translation of basic science in ethology, neuroscience, social psychology, personality psychology, and developmental psychology. Theory and findings from these domains are reviewed and synthesized in the proposed model, which posits that (a) aggressive behavior and hostile attributions are universal human characteristics, (b) socialization leads to the development of benign attributions, (c) individual differences in attributional style account for differences in aggressive behavior, and (d) interventions to change attributions have the potential to alter antisocial development. Challenges for future research are described.


title: ‘Hitting Highs at Rock Bottom’: LSD Treatment for Alcoholism, 1950–1970

abstract: In the 1950s, researchers in the Canadian province of Saskatchewan began treating alcoholics with d-lysergic acid diethylamide (LSD) and achieved significant rates of recovery. Psychiatrists, including Humphry Osmond who coined the term 'psychedelic' while working in Saskatchewan, believed that the successful treatment of alcoholism with biochemical means would scientifically prove that the condition was a disease and not the result of a weak or immoral character. Initial experiments demonstrated unprecedented rates of abstinence among alcoholics treated with LSD. The approach gained support from the provincial government, local chapters of Alcoholics Anonymous and the Bureau of Alcoholism, all of which collaborated in a public campaign that supported LSD treatments. Although Alcoholics Anonymous endorsed psychedelic therapy, the Addictions Research Foundation did not. The leading Canadian authority on addictions disputed the findings in Saskatchewan and challenged these advocates of psychedelic treatments to conduct trials with proper controls. Despite subsequent efforts to demonstrate that the success of psychedelic therapy relied on both medical and non-medical factors, the treatment failed to satisfy current medical methodology, embodied in controlled trials. By the late 1960s, LSD had become a popular recreational drug and gained media attention for its association with counter cultural youth, social disobedience and anti-authoritarian attitudes. All this served further to erode support for its clinical status.


title: Effects of diet on behaviour and cognition in children

abstract: Diet can affect cognitive ability and behaviour in children and adolescents. Nutrient composition and meal pattern can exert immediate or long-term, beneficial or adverse effects. Beneficial effects mainly result from the correction of poor nutritional status. For example, thiamin treatment reverses aggressiveness in thiamin-deficient adolescents. Deleterious behavioural effects have been suggested; for example, sucrose and additives were once suspected to induce hyperactivity, but these effects have not been confirmed by rigorous investigations. In spite of potent biological mechanisms that protect brain activity from disruption, some cognitive functions appear sensitive to short-term variations of fuel (glucose) availability in certain brain areas. A glucose load, for example, acutely facilitates mental performance, particularly on demanding, long-duration tasks. The mechanism of this often described effect is not entirely clear. One aspect of diet that has elicited much research in young people is the intake/omission of breakfast. This has obvious relevance to school performance. While effects are inconsistent in well-nourished children, breakfast omission deteriorates mental performance in malnourished children. Even intelligence scores can be improved by micronutrient supplementation in children and adolescents with very poor dietary status. Overall, the literature suggests that good regular dietary habits are the best way to ensure optimal mental and behavioural performance at all times. Then, it remains controversial whether additional benefit can be gained from acute dietary manipulations. In contrast, children and adolescents with poor nutritional status are exposed to alterations of mental and/or behavioural functions that can be corrected, to a certain extent, by dietary measures.


title: An Adversary-Centric Behavior Modeling of DDoS Attacks

abstract: Distributed Denial of Service (DDoS) attacks are some of the most persistent threats on the Internet today. The evolution of DDoS attacks calls for an in-depth analysis of those attacks. A better understanding of the attackers’ behavior can provide insights to unveil patterns and strategies utilized by attackers. The prior art on the attackers’ behavior analysis often falls in two aspects: it assumes that adversaries are static, and makes certain simplifying assumptions on their behavior, which often are not supported by real attack data. In this paper, we take a data-driven approach to designing and validating three DDoS attack models from temporal (e.g., attack magnitudes), spatial (e.g., attacker origin), and spatiotemporal (e.g., attack inter-launching time) perspectives. We design these models based on the analysis of traces consisting of more than 50,000 verified DDoS attacks from industrial mitigation operations. Each model is also validated by testing its effectiveness in accurately predicting future DDoS attacks. Comparisons against simple intuitive models further show that our models can more accurately capture the essential features of DDoS attacks.


title: Feature Selection for Maximizing the Area Under the ROC Curve

abstract: Feature selection is an important pre-processing step for solving classification problems. A good feature selection method may not only improve the performance of the final classifier, but also reduce the computational complexity of it. Traditionally, feature selection methods were developed to maximize the classification accuracy of a classifier. Recently, both theoretical and experimental studies revealed that a classifier with the highest accuracy might not be ideal in real-world problems. Instead, the Area Under the ROC Curve (AUC) has been suggested as the alternative metric, and many existing learning algorithms have been modified in order to seek the classifier with maximum AUC. However, little work was done to develop new feature selection methods to suit the requirement of AUC maximization. To fill this gap in the literature, we propose in this paper a novel algorithm, called AUC and Rank Correlation coefficient Optimization (ARCO) algorithm. ARCO adopts the general framework of a well-known method, namely minimal redundancy- maximal-relevance (mRMR) criterion, but defines the terms ”relevance” and ”redundancy” in totally different ways. Such a modification looks trivial from the perspective of algorithmic design. Nevertheless, experimental study on four gene expression data sets showed that feature subsets obtained by ARCO resulted in classifiers with significantly larger AUC than the feature subsets obtained by mRMR. Moreover, ARCO also outperformed the Feature Assessment by Sliding Thresholds algorithm, which was recently proposed for AUC maximization, and thus the efficacy of ARCO was validated.


title: Development of the Big Data Management System on National Virtual Power Plant

abstract: We are developing the Business Platform for demand side management in energy sector. This paper introduces the Business Platform and describes the development of the pilot platform and main components. In addition, main applications such as Automated Demand Response (AutoDR) and Monitoring Based Commissioning (MBCx) is introduced. One of main components of the platform is the Big Data Management System to save and analyze the energy big data. We constructed two big data cluster systems: one is a Hadoop cluster and the other is the one Spark installed. We also implemented software modules to collect and generate the energy big data. The data flow inside the platform is presented and implementation of Application Programing Interfaces (APIs) to access the big data is described. Furthermore, we developed various applications on top of the platform using the APIs. Among those, this paper presents the monitoring service.


title: Expressive ontology learning as neural machine translation

abstract: Abstract Automated ontology learning from unstructured textual sources has been proposed in literature as a way to support the difficult and time-consuming task of knowledge modeling for semantic applications. In this paper we propose a system, based on a neural network in the encoder–decoder configuration, to translate natural language definitions into Description Logics formulae through syntactic transformation. The model has been evaluated to assess its capacity to generalize over different syntactic structures, tolerate unknown words, and improve its performance by enriching the training set with new annotated examples. The results obtained in our evaluation show how approaching the ontology learning problem as a neural machine translation task can be a valid way to tackle long term expressive ontology learning challenges such as language variability, domain independence, and high engineering costs.


title: The future of parking: A survey on automated valet parking with an outlook on high density parking

abstract: In the near future, humans will be relieved from parking. Major improvements in autonomous driving allow the realization of automated valet parking (AVP). It enables the vehicle to drive to a parking spot and park itself. This paper presents a review of the intelligent vehicles literature on AVP. An overview and analysis of the core components of AVP such as the platforms, sensor setups, maps, localization, perception, environment model, and motion planning is provided. Leveraging the potential of AVP, high density parking (HDP) is reviewed as a future research direction with the capability to either reduce the necessary space for parking by up to 50 % or increase the capacity of future parking facilities. Finally, a synthesized view discussing the remaining challenges in automated valet parking and the technological requirements for high density parking is given.


Deep Manifold Learning of Symmetric Positive Definite Matrices with Application to Face Recognition.


title: Recent advances in deep learning for speech research at Microsoft

abstract: Deep learning is becoming a mainstream technology for speech recognition at industrial scale. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. We organize this overview along the feature-domain and model-domain dimensions according to the conventional approach to analyzing speech systems. Selected experimental results, including speech recognition and related applications such as spoken dialogue and language modeling, are presented to demonstrate and analyze the strengths and weaknesses of the techniques described in the paper. Potential improvement of these techniques and future research directions are discussed.


title: A Component Analysis of Cognitive-Behavioral Therapy for Generalized Anxiety Disorder and the Role of Interpersonal Problems

abstract: 69 Clients with generalized anxiety disorder (GAD) diagnosed using the ADIS-R. Design: Participants were randomly assigned to (1) applied relaxation and self-control desensitization, (2) cognitive therapy, or (3) a combination of these methods. Results: Treatment resulted in significant improvement in anxiety and depression that was maintained for 2 yrs. The large majority no longer met diagnostic criteria; a minority sought further treatment during follow-up. No differences in outcome were found between conditions; review of the GAD therapy literature suggested that this may have been due to strong effects generated by each component condition. Repeated measures ANOVAs indicated significant pre-posttherapy improvement for the group as a whole on each of the eight octants of the IIP-C. Related t tests on pre-posttherapy scores within each condition found significant improvements after Sime’s Bonferroni correction only on the Cold/Distant scale for the CT condition, and on the Nonassertive scale for the SCD condition. Predictors: Expectancy did not correlate significantly with endstate at any assessment point. Credibility correlated significantly with posttherapy, 6-month, and 12-month, follow-up endstate, although the 12-month correlation was no longer significant once Sime’s Bonferroni correction was applied. Pretherapy and posttherapy IIP-C scores were also correlated with endstate measures from the posttherapy and follow-up assessments. Examination of pre-therapy IIP-C predictors in a subset of clients found that among the eight octants and the four assessment moments, only 6 of 32 correlations were significant and negative (1 at posttherapy, 4 at 6-month, and 1 at 12-month follow-up). Three of 6 pretherapy IIP-C correlations remained significant after Sime’s Bonferroni correction, each involving 6-month-endstate correlations with Domineering/Controlling, Intrusive/Needy, and Vindictive/Self-centered; greater interpersonal problems reported just before therapy were associated with poorer outcome. Examination of remaining interpersonal problem predictors at post-therapy found that among the eight IIP-C octants and the four assessment moments, 15 of 32 correlations (5 at posttherapy, 7 at 6-month, 1 at 12-month, and 2 at 24-month follow-up) were significant and negative. After Sime’s Bonferroni correction, 12 of these correlations remained significant. Five involved postendstate correlations with Vindictive/Self-centered; Intrusive/Needy; Domineering/Controlling; Overly accommodating; and Nonassertive. The other 7 involved 6-month-endstate correlations with Intrusive/Needy; Vindictive/Selfcentered; Domineering/Controlling; Nonassertive; Overly accommodating; Cold/Distant; and Self-sacrificing. The latter result suggests the possible utility of adding interpersonal treatment to cognitive-behavioral therapy to increase therapeutic effectiveness.


title: A Two-Step Parametric Method for Failure Prediction in Hard Disk Drives

abstract: Predicting the impending failure of hard disk drives (HDDs) is crucial for preventing essential data from losing. In this paper, a two-step parametric method was developed to predict the impending failure of HDDs using the aggregate of statistical models. This method deals with the problem of failure prediction in two steps: anomaly detection and failure prediction. First, Mahalanobis distance was used for aggregating all the monitored variables into one index, which was then transformed into Gaussian variables by Box-Cox transformation. By defining an appropriate threshold, anomalies in HDDs were detected as a result. Second, a sliding-window-based generalized likelihood ratio test was proposed to track the anomaly progression in an HDD. When the occurrence of anomalies in a time interval is found to be statistically significant, indicating the HDD is approaching failure. In this work, we also derived a new cost function to adjust the prediction rate. This is important in a way to balance the failure detection rate and false alarm rate as well as to provide an advanced warning of HDD failures to the users, whereby the users can back up their data in time. Then the developed method was applied on a synthetic data set showing its effectiveness on predicting failures. To demonstrate the practical usefulness, this method was also applied on a real-life HDD data set. The result shows that our method could achieve 68% failure detection rate with 0% false alarm rate. This is much better than the results achieved by the state-of-the-art methods, such as support vector machine and hidden Markov models.


title: The Influence of Rewards and Job Satisfaction on Employees in the Service Industry

abstract: The purpose of this study was to find out the relationship between employees’ rewards, and the dimension of their job satisfaction in the service sector. We hypothesized that rewards play a significant role in employee satisfaction, resulting in increased customer satisfaction and loyalty. Thus the role of job satisfcation and rewards was explored with samples from workers in two large Ghanian private organizations. The implications of job satisfcation on customer satisfaction and loyalty towards organzations were also invetsigated. A questionnaire was constructed, tested and administered to a total 110 subjects at two private organizations. Out of that 104 usable surveys were analyzed with microsoft excel to test the hypotheses of the study. Findings showed that rewards induced positive job satisfaction of employees. Additionally the findings also showed that job satisfaction of employees stimulated their loyalty to the organization. However, a very high level of employee dissatisfaction was recorded for employee pay and the amount work they do. The study result highlights the role of employees’ job satisfaction and the responsibilities of organizations in fostering good quality HRM practices in the service sectors, and offers an alternative pathway in employee satisfaction and performance. These results add to the evidence that HR practises influences business outcomes rather than the other way around.


title: Image Classification in Remote Sensing

abstract: One of the most important functions of remote sensing data is the production of Land Use and Land Cover maps and thus can be managed through a process called image classification. This paper looks into the following components related to the image classification process and procedures and image classification techniques and explains two common techniques K-means Classifier and Support Vector Machine (SVM). Keywords: Remote Sensing, Image Classification, K-means Classifier, Support Vector Machine


Low Cost Home Automation Using Offline Speech Recognition


Correlation between Automatic Evaluation Metric Scores , Post-Editing Speed , and Some Other Factors


title: A Theory of Social Comparison Processes

abstract: Hypothesis I: There exists, in the human organism, a drive to evaluate his opinions and his abilities. While opinions and abilities may, at first glance, seem to be quite different things, there is a close functional tie between them. They act together in the manner in which they affect behavior. A person’s cognition (his opinions and beliefs) about the situation in which he exists and his appraisals of what he is capable of doing (his evaluation of his abilities) will together have bearing on his behavior. The holding of incorrect opinions and/or inaccurate appraisals of one’s abilities can be punishing or even fatal in many situations. It is necessary, before we proceed, to clarify the distinction between opinions and evaluations of abilities since at first glance it may seem that one’s evaluation of one’s own ability is an opinion about it. Abilities are of course manifested only through performance which is assumed to depend upon the particular ability. The clarity of the manifestation or performance can vary from instances where there is no clear ordering criterion of the ability to instances where the performance which reflects the ability can be clearly ordered. In the former case, the evaluation of the ability does function like other opinions which are not directly testable in “objective reality’. For example, a person’s evaluation of his ability to write poetry will depend to a large extent on the opinions which others have of his ability to write poetry. In cases where the criterion is unambiguous and can be clearly ordered, this furnishes an objective reality for the evaluation of one’s ability so that it depends less on the opinions of other persons and depends more on actual comparison of one’s performance with the performance of others. Thus, if a person evaluates his running ability, he will do so by comparing his time to run some distance with the times that other persons have taken. In the following pages, when we talk about evaluating an ability, we shall mean specifically the evaluation of that ability in situations where the performance is unambiguous and is known. Most situations in real life will, of course, present situations which are a mixture of opinion and ability evaluation. In a previous article (7) the author posited the existence of a drive to determine whether or not one’s opinions were “correct”. We are here stating that this same drive also produces behavior in people oriented toward obtaining an accurate appraisal of their abilities. The behavioral implication of the existence of such a drive is that we would expect to observe behaviour on the part of persons which enables them to ascertain whether or not their opinions are correct and also behavior which enables them accurately to evaluate their abilities. It is consequently


title: Time Series Segmentation through Automatic Feature Learning

abstract: Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge extraction - whereby we map from observations to interpretable states and transitions - must be done at scale. As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence. These data can be used to build automatic labeling algorithms that produce labels as an expert would. Here, we refer to human-specified boundaries as breakpoints. Traditional changepoint detection methods only look for statistically-detectable boundaries that are defined as abrupt variations in the generative parameters of a data sequence. However, we observe that breakpoints occur on more subtle boundaries that are non-trivial to detect with these statistical methods. In this work, we propose a new unsupervised approach, based on deep learning, that outperforms existing techniques and learns the more subtle, breakpoint boundaries with a high accuracy. Through extensive experiments on various real-world data sets - including human-activity sensing data, speech signals, and electroencephalogram (EEG) activity traces - we demonstrate the effectiveness of our algorithm for practical applications. Furthermore, we show that our approach achieves significantly better performance than previous methods.