title: Multi-domain WLAN load balancing in WLAN/WPAN interference environments

abstract: The proliferation of wireless local area network (WLAN) deployments in enterprises, public areas, and homes will likely cause frequent geographical coverage overlap among multiple networks. A recent growing interest is the coordination among WLAN providers for efficient network management over a large coverage area. While radio resource management for a single WLAN has been studied extensively, little research work addresses resource management over multiple domains. When multiple WLANs co-locate in a small geographic vicinity, the lack of cooperative multi-domain resource management can cause significant performance degradation due to inter-domain interference. Unbalanced loads among multiple networks can incur congestion in a few WLANs while foregoing unused excess resources in others. Moreover, since WLANs often operate in unlicensed frequency bands shared by various public and private networks, they must cope with the dynamic RF environment involving a diverse set of interference sources. This paper introduces a new cooperative load balancing framework for multi-domain WLANs operating in an interference environment. A third-party-based architecture is proposed to facilitate fair radio resource allocation among multiple networks. The proposed third-party agent collects observed network state information from different WLANs and optimizes resource usage. Under the proposed scheme, resource utilization and co-channel interference can be adaptively balanced across the entire integrated system. The impact of other co-located interference sources in the operational environment are taken into account in the optimization process. The proposed load balancing framework for interference environments is a complement to the interference mitigation mechanisms operated at the PHY/MAC layer. Simulation results show that the proposed multi-domain load balancing scheme outperforms other schemes which do not consider interdomain interference or environmental interference.


title: A bidirectional current-fed resonant push-pull converter for low voltage, high current applications

abstract: In this paper, a bidirectional current-fed resonant push-pull converter is proposed for energy storage applications where high voltage conversion ratio, high power and low current ripple are needed. In this study, theoretical waveforms of bidirectional RPP are shown, analytical analysis is provided and a modulation method for reverse power flow operation is proposed. Finally, theoretical results are verified with a 5kW prototype. The main contribution of this paper is modifying previously presented resonant push-pull topology and its modulation principle to provide bidirectional power flow.


title: Learning robust failure response for autonomous vision based flight

abstract: The ability of autonomous mobile robots to react to and recover from potential failures of on-board systems is an important area of ongoing robotics research. With increasing emphasis on robust systems and long-term autonomy, mobile robots must be able to respond safely and intelligently to dangerous situations. Recent developments in computer vision have made autonomous vision based navigation possible. However, vision systems are known to be imperfect and prone to failure due to variable lighting, terrain changes, and other environmental variables. We describe a system for learning simple failure recovery maneuvers based on experience. This involves both recognizing when the vision system is prone to failure, and associating failures with appropriate responses that will most likely help the robot recover. We implement this system on an autonomous quadrotor and demonstrate that behaviors learned with our system are effective in recovering from situational perception failure, thereby improving reliability in cluttered and uncertain environments.


title: How valuable is medical social media data? Content analysis of the medical web

abstract: It is still an open question where to search for complying a specific information need due to the large amount and diversity of information available. In this paper, a content analysis of health-related information provided in the Web is performed to get an overview on the medical content available. In particular, the content of medical Question & Answer Portals, medical weblogs, medical reviews and Wikis is compared. For this purpose, medical concepts are extracted from the text material with existing extraction technology. Based on these concepts, the content of the different knowledge resources is compared. Since medical weblogs describe experiences as well as information, it is of large interest to be able to distinguish between informative and affective posts. For this reason, a method to classify blogs based on their information content is presented, which exploits high-level features describing the medical and affective content of blog posts. The results show that there are substantial differences in the content of various health-related Web resources. Weblogs and answer portals mainly deal with diseases and medications. The Wiki and the encyclopedia provide more information on anatomy and procedures. While patients and nurses describe personal aspects of their life, doctors aim to present health-related information in their blog posts. The knowledge on content differences and information content can be exploited by search engines to improve ranking, search and to direct users to appropriate knowledge sources.


title: Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

abstract: Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate 'ideal' binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for 'karaoke' type applications.


title: Relationship of the zygomatic facial nerve to the retaining ligaments of the face: the Sub-SMAS danger zone.

abstract: BACKGROUND ::: The transition zone between cheek superficial musculoaponeurotic system (SMAS) and malar SMAS is difficult to raise because of proximity of zygomatic nerve branches. The authors attempted to clarify the three-dimensional anatomy of the retaining ligaments in relation to nerve branches in this area. ::: ::: ::: METHODS ::: Facial dissection was performed on 22 cadaver hemifaces. The zygomatic and masseteric retaining ligaments and the zygomatic and buccal facial branches in the area of dissection were identified. Ninety-five percent confidence regions for the locations of the zygomaticus major origin and the main zygomatic retaining ligament and upper masseteric retaining ligament were created. ::: ::: ::: RESULTS ::: The distribution, density, and size of the retaining ligaments varied. The main zygomatic and upper masseteric retaining ligaments were located at a mean distance of 44.91 ± 9.72 mm and 46.35 ± 8.34 mm from the tragus. An upper zygomatic branch passed between the main zygomatic and the upper masseteric retaining ligaments and was always located deep (4.07 ± 1.29 mm) in the sub-SMAS plane of dissection and passed deep under the upper third of the zygomaticus major muscle. An inferior zygomatic branch passed inferior to the upper masseteric retaining ligament or penetrated its inferior margin (54 percent of cases) and was located more superficially (1.41 ± 0.95 mm), becoming visible just distal to the ligament. ::: ::: ::: CONCLUSIONS ::: Despite anatomical variation, the main zygomatic and upper masseteric retaining ligaments create a safe passage in between, through which a zygomatic facial branch passes deep. The area of danger is immediately inferomedial to the upper masseteric retaining ligament, where a zygomatic branch becomes superficial and vulnerable.


title: Collective privacy management in social networks

abstract: Social Networking is one of the major technological phenomena of the Web 2.0, with hundreds of millions of people participating. Social networks enable a form of self expression for users, and help them to socialize and share content with other users. In spite of the fact that content sharing represents one of the prominent features of existing Social Network sites, Social Networks yet do not support any mechanism for collaborative management of privacy settings for shared content. In this paper, we model the problem of collaborative enforcement of privacy policies on shared data by using game theory. In particular, we propose a solution that offers automated ways to share images based on an extended notion of content ownership. Building upon the Clarke-Tax mechanism, we describe a simple mechanism that promotes truthfulness, and that rewards users who promote co-ownership. We integrate our design with inference techniques that free the users from the burden of manually selecting privacy preferences for each picture. To the best of our knowledge this is the first time such a protection mechanism for Social Networking has been proposed. In the paper, we also show a proof-of-concept application, which we implemented in the context of Facebook, one of today's most popular social networks. We show that supporting these type of solutions is not also feasible, but can be implemented through a minimal increase in overhead to end-users.


title: Fuzzy target tracking control of autonomous mobile robots by using infrared sensors

abstract: The theme of this paper is to design a real-time fuzzy target tracking control scheme for autonomous mobile robots by using infrared sensors. At first two mobile robots are setup in the target tracking problem, where one is the target mobile robot with infrared transmitters and the other one is the tracker mobile robot with infrared receivers and reflective sensors. The former is designed to drive in a specific trajectory. The latter is designed to track the target mobile robot. Then we address the design of the fuzzy target tracking control unit, which consists of a behavior network and a gate network. The behavior network possesses the fuzzy wall following control (FWFC) mode, fuzzy target tracking control (FTTC) mode, and two fixed control modes to deal with different situations in real applications. Both the FWFC and FTTC are realized by the fuzzy sliding-mode control scheme. A gate network is used to address the fusion of measurements of two infrared sensors and is developed to recognize which situation is belonged to and which action should be executed. Moreover, the target tracking control with obstacle avoidance is also investigated in this paper. Both computer simulations and real-time implementation experiments of autonomous target tracking control demonstrate the effectiveness and feasibility of the proposed control schemes.


title: Automated nucleus and cytoplasm segmentation of overlapping cervical cells

abstract: In this paper we describe an algorithm for accurately segmenting the individual cytoplasm and nuclei from a clump of overlapping cervical cells. Current methods cannot undertake such a complete segmentation due to the challenges involved in delineating cells with severe overlap and poor contrast. Our approach initially performs a scene segmentation to highlight both free-lying cells, cell clumps and their nuclei. Then cell segmentation is performed using a joint level set optimization on all detected nuclei and cytoplasm pairs. This optimisation is constrained by the length and area of each cell, a prior on cell shape, the amount of cell overlap and the expected gray values within the overlapping regions. We present quantitative nuclei detection and cell segmentation results on a database of synthetically overlapped cell images constructed from real images of free-lying cervical cells. We also perform a qualitative assessment of complete fields of view containing multiple cells and cell clumps.


title: Thermal Cycling Reliability of Chip Resistor Lead Free Solder Joints

abstract: In this study, we have examined the thermal cycling reliability of several lead free chip resistor solder joint configurations. Five sizes of resistors (2512, 1206, 0805, 0603, 0402), 2 temperature ranges (−40 to 125°C and −40 to 150°C), and five different solder types have been examined. The solders include the normal SnAgCu alloy recommended by earlier studies (95.5Sn-3.8Ag-0.7Cu), and several variations that include small percentages of Bismuth and Indium to enhance fatigue resistance. Results have been compared to data for standard 63Sn-37Pb joints.Copyright © 2003 by ASME


title: Classification performance using gated recurrent unit recurrent neural network on energy disaggregation

abstract: Energy disaggregation or NILM is the best solution to reduce our consumption of electricity. Many algorithms in machine learning are applied to this field. However, the classification results from those algorithms are not as well as expected. In this paper, we propose a new approach to construct a classifier for energy disaggregation with deep learning field. We apply Gated Recurrent Unit (GRU) based on Recurrent Neural Network (RNN) to train our model using UK DALE dataset on this field. Besides, we compare our approach to original RNN on energy disaggregation. By applying GRU RRN, we achieve accuracy and F-measure for energy disaggregation with the ranges [89%–98%] and [81%–98%] respectively. Through these results of the experiment, we confirm that the deep learning approach is really effective for NILM.


title: A Compact Gate Control and Voltage-Balancing Circuit for Series-Connected SiC MOSFETs and Its Application in a DC Breaker

abstract: This paper presents a novel compact circuit combining function of gate control and voltage balancing for series-connected silicon carbide (SiC) metal–oxide–semiconductor field-effect transistor (MOSFET). Two series-connected SiC MOSFETs with the proposed circuit only require a single standard gate driver to achieve the gate control and voltage balancing during both steady-state and switching transition. Moreover, the proposed circuit is only composed of ten passive components. Therefore, the proposed circuit provides a low-cost and highly reliable method to increase the blocking voltage of the SiC MOSFET. The operation principles of the proposed circuit are theoretically analyzed. In addition, the high-blocking-voltage device is not only required in switching-mode power supply (SMPS) but also in dc-breaker applications. The proposed circuit is then modified to make it suitable to the dc-breaker applications. The simulation and experimental results validate the effectiveness and superiority of the proposed circuit in both SMPS and dc-breaker applications.


title: Robust Predictive Feedback Control for Constrained Systems

abstract: A new method for the design of predictive controllers for SISO systems is presented. The proposed technique allows uncertainties and constraints to be concluded in the design of the control law. The goal is to design, at each sample instant, a predictive feedback control law that minimizes a performance measure and guarantees of constraints are satisfied for a set of models that describes the system to be controlled. The predictive controller consists of a finite horizon parametric-optimization problem with an additional constraint over the manipulated variable behavior. This is an end-constraint based approach that ensures the exponential stability of the closed-loop system. The inclusion of this additional constraint, in the on-line optimization algorithm, enables robust stability properties to be demonstrated for the closed- loop system. This is the case even though constraints and disturbances are present. Finally, simulation results are presented using a nonlinear continuous stirred tank reactor model.


title: Cross layer attacks on GSM mobile networks using software defined radios

abstract: The ubiquitous adoption of cellular technologies, such as, Long Term Evolution (LTE) and Wide-band Code Division Multiple Access (WCDMA) has not diminished the impact of Global System for Mobile communication (GSM) technologies. Despite 20 years of deployment, GSM still poses significant security threats to its users due to adversaries exploiting protocol vulnerabilities. In this paper, we present an attack which leverages cross-layer information from network or data link layers to craft an attack vector targeting the physical layer. The cross-layer attack provides attacker sufficient knowledge to specifically target cells, control channels, and mobile stations (MS) with minimal investment of communication and energy resources. We have designed and implemented an experimental testbed, which comprises of, software defined radios (SDR) (USRP and gnuradio), open source GSM channel sniffer (gr-gsm), and distributed processing engine (Apache Spark). The experimental testbed will also facilitate cloning a base transceiver station (BTS) which will benefit from availability of cross-layer information to create customized attack vectors. The cross-layer attack capability on our experimental testbed provides a cost effective scheme to achieve desired benefits with optimal usage of communication and energy resources. Experimental results shows that the testbed can be used to successfully attack multiple mobile stations with minimal usage of power resources.


title: A Spelling Correction Model for End-to-end Speech Recognition

abstract: Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance degradation especially on rare words. While there have been a variety of work that look at incorporating an external LM trained on text-only data into the end-to-end framework, none of them have taken into account the characteristic error distribution made by the model. In this paper, we propose a novel approach to utilizing text-only data, by training a spelling correction (SC) model to explicitly correct those errors. On the LibriSpeech dataset, we demonstrate that the proposed model results in an 18.6% relative improvement in WER over the baseline model when directly correcting top ASR hypothesis, and a 29.0% relative improvement when further rescoring an expanded n-best list using an external LM.


title: Detecting mobile malware threats to homeland security through static analysis

abstract: Recent years have seen the significant increase in the popularity of smartphones. This popularity has been accompanied with an equally alarming rise in mobile malware. Recently released mobile malware targeting Android devices have been found to specifically focus on root exploits to obtain root-level access and execute instructions from a remote server. Thus, this kind of mobile malware presents a significant threat to Homeland Security. This is possible because smartphones can serve as zombie devices which are then controlled by hackers' via a C&C server. In this paper, we discuss the defining characteristics inherent in mobile malware and show mobile attack scenarios which are feasible against Homeland Security. We also propose a static analysis tool, DroidAnalyzer, which identifies potential vulnerabilities of Android apps and the presence of root exploits. Then, we analyze various mobile malware samples and targeting apps such as banking, flight tracking and booking, home&office monitoring apps to examine potential vulnerabilities by applying DroidAnalyzer.


title: Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition

abstract: Currently, there is an increasing number of patients that are treated in-home, mainly in countries such as Japan, USA and Europe. As well as this, the number of elderly people has increased significantly in the last 15 years and these people are often treated in-home and at times enter into a critical situation that may require help (e.g. when facing an accident, or becoming depressed). Advances in ubiquitous computing and the Internet of Things (IoT) have provided efficient and cheap equipments that include wireless communication and cameras, such as smartphones or embedded devices like Raspberry Pi. Embedded computing enables the deployment of Health Smart Homes (HSH) that can enhance in-home medical treatment. The use of camera and image processing on IoT is still an application that has not been fully explored in the literature, especially in the context of HSH. Although use of images has been widely exploited to address issues such as safety and surveillance in the house, they have been little employed to assist patients and/or elderly people as part of the home-care systems. In our view, these images can help nurses or caregivers to assist patients in need of timely help, and the implementation of this application can be extremely easy and cheap when aided by IoT technologies. This article discusses the use of patient images and emotional detection to assist patients and elderly people within an in-home healthcare context. We also discuss the existing literature and show that most of the studies in this area do not make use of images for the purpose of monitoring patients. In addition, there are few studies that take into account the patient's emotional state, which is crucial for them to be able to recover from a disease. Finally, we outline our prototype which runs on multiple computing platforms and show results that demonstrate the feasibility of our approach.


title: The Nest Architecture of the Ant Odontomachus brunneus

abstract: The architecture of the subterranean nests of the ant Odontomachus brunneus (Patton) (Hymenoptera: Formicidae) was studied by means of casts with dental plaster or molten metal. The entombed ants were later recovered by dissolution of plaster casts in hot running water. O. brunneus excavates simple nests, each consisting of a single, vertical shaft connecting more or less horizontal, simple chambers. Nests contained between 11 and 177 workers, from 2 to 17 chambers, and 28 to 340 cm(2) of chamber floor space and reached a maximum depth of 18 to 184 cm. All components of nest size increased simultaneously during nest enlargement, number of chambers, mean chamber size, and nest depth, making the nest shape (proportions) relatively size-independent. Regardless of nest size, all nests had approximately 2 cm(2) of chamber floor space per worker. Chambers were closer together near the top and the bottom of the nest than in the middle, and total chamber area was greater near the bottom. Colonies occasionally incorporated cavities made by other animals into their nests.


title: An FPGA Logic Cell and Carry Chain Configurable as a 6:2 or 7:2 Compressor

abstract: To improve FPGA performance for arithmetic circuits that are dominated by multi-input addition operations, an FPGA logic block is proposed that can be configured as a 6:2 or 7:2 compressor. Compressors have been used successfully in the past to realize parallel multipliers in VLSI technology; however, the peculiar structure of FPGA logic blocks, coupled with the high cost of the routing network relative to ASIC technology, renders compressors ineffective when mapped onto the general logic of an FPGA. On the other hand, current FPGA logic cells have already been enhanced with carry chains to improve arithmetic functionality, for example, to realize fast ternary carry-propagate addition. The contribution of this article is a new FPGA logic cell that is specialized to help realize efficient compressor trees on FPGAs. The new FPGA logic cell has two variants that can respectively be configured as a 6:2 or a 7:2 compressor using additional carry chains that, coupled with lookup tables, provide the necessary functionality. Experiments show that the use of these modified logic cells significantly reduces the delay of compressor trees synthesized on FPGAs compared to state-of-the-art synthesis techniques, with a moderate increase in area and power consumption.


title: HAIL: a high-availability and integrity layer for cloud storage

abstract: We introduce HAIL (High-Availability and Integrity Layer), a distributed cryptographic system that allows a set of servers to prove to a client that a stored file is intact and retrievable. HAIL strengthens, formally unifies, and streamlines distinct approaches from the cryptographic and distributed-systems communities. Proofs in HAIL are efficiently computable by servers and highly compact---typically tens or hundreds of bytes, irrespective of file size. HAIL cryptographically verifies and reactively reallocates file shares. It is robust against an active, mobile adversary, i.e., one that may progressively corrupt the full set of servers. We propose a strong, formal adversarial model for HAIL, and rigorous analysis and parameter choices. We show how HAIL improves on the security and efficiency of existing tools, like Proofs of Retrievability (PORs) deployed on individual servers. We also report on a prototype implementation.


title: Feminist HCI: taking stock and outlining an agenda for design

abstract: Feminism is a natural ally to interaction design, due to its central commitments to issues such as agency, fulfillment, identity, equity, empowerment, and social justice. In this paper, I summarize the state of the art of feminism in HCI and propose ways to build on existing successes to more robustly integrate feminism into interaction design research and practice. I explore the productive role of feminism in analogous fields, such as industrial design, architecture, and game design. I introduce examples of feminist interaction design already in the field. Finally, I propose a set of femi-nist interaction design qualities intended to support design and evaluation processes directly as they unfold.


title: Characterizing cloud computing hardware reliability

abstract: Modern day datacenters host hundreds of thousands of servers that coordinate tasks in order to deliver highly available cloud computing services. These servers consist of multiple hard disks, memory modules, network cards, processors etc., each of which while carefully engineered are capable of failing. While the probability of seeing any such failure in the lifetime (typically 3-5 years in industry) of a server can be somewhat small, these numbers get magnified across all devices hosted in a datacenter. At such a large scale, hardware component failure is the norm rather than an exception. Hardware failure can lead to a degradation in performance to end-users and can result in losses to the business. A sound understanding of the numbers as well as the causes behind these failures helps improve operational experience by not only allowing us to be better equipped to tolerate failures but also to bring down the hardware cost through engineering, directly leading to a saving for the company. To the best of our knowledge, this paper is the first attempt to study server failures and hardware repairs for large datacenters. We present a detailed analysis of failure characteristics as well as a preliminary analysis on failure predictors. We hope that the results presented in this paper will serve as motivation to foster further research in this area.


title: The Generalized C Index for Internal Fuzzy Cluster Validity

abstract: The C index is an internal cluster validity index that was introduced in 1970 as a way to define and identify a “best” crisp partition on n objects represented by either unlabeled feature vectors or dissimilarity matrix data. This index is often one of the better performers among the plethora of internal indices available for this task. This paper develops a soft generalization of the C index that can be used to evaluate sets of candidate partitions found by either fuzzy or probabilistic clustering algorithms. We define four generalizations based on relational transformations of the soft partition and, then, compare their performance to eight other popular internal fuzzy cluster indices using two methods of comparison (internal “best- c ” and internal/external (I/E) “best match”), six synthetic datasets, and six real-world labeled datasets. Our main conclusion is that the sum-min generalization is the second best performer in the best-c tests and the best performer in the I/E tests on small data.


title: Upper-limb robot-assisted therapy in rehabilitation of acute stroke patients: Focused review and results of new randomized controlled trial

abstract: The successful motor rehabilitation of stroke patients requires early intensive and task-specific therapy. A recent Cochrane Review, although based on a limited number of randomized controlled trials (RCTs), showed that early robotic training of the upper limb (i.e., during acute or subacute phase) can enhance motor learning and improve functional abilities more than chronic-phase training. In this article, a new subacute-phase RCT with the Neuro-Rehabilitation-roBot (NeReBot) is presented. While in our first study we used the NeReBot in addition to conventional therapy, in this new trial we used the same device in substitution of standard proximal upper-limb rehabilitation. With this protocol, robot patients achieved similar reductions in motor impairment and enhancements in paretic upper-limb function to those gained by patients in a control group. By analyzing these results and those of previous studies, we hypothesize a new robotic protocol for acute and subacute stroke patients based on both treatment modalities (in addition and in substitution).


title: Review of the Principal Indicators and Data Science Techniques Used for the Detection of Financial Fraud and Money Laundering

abstract: Financial fraud and money laundering represent an important concern both nationally and worldwide due to the huge amounts of monetary losses they imply. Besides human inspection, Data Science (DS) has proved so far to be a useful tool in order to fight these activities by automatic means. Nevertheless, this approach to the problem is still in early stages, thus yielding plenty of field to be explored. This document discusses some of the principal variables/indicators and DS techniques that have been used in the recent years for detecting and describing fraudulent operations, to serve as a guide for future work in the field.


title: Named Entity Recognition with Word Embeddings and Wikipedia Categories for a Low-Resource Language

abstract: In this article, we propose a word embedding--based named entity recognition (NER) approach. NER is commonly approached as a sequence labeling task with the application of methods such as conditional random field (CRF). However, for low-resource languages without the presence of sufficiently large training data, methods such as CRF do not perform well. In our work, we make use of the proximity of the vector embeddings of words to approach the NER problem. The hypothesis is that word vectors belonging to the same name category, such as a person’s name, occur in close vicinity in the abstract vector space of the embedded words. Assuming that this clustering hypothesis is true, we apply a standard classification approach on the vectors of words to learn a decision boundary between the NER classes. Our NER experiments are conducted on a morphologically rich and low-resource language, namely Bengali. Our approach significantly outperforms standard baseline CRF approaches that use cluster labels of word embeddings and gazetteers constructed from Wikipedia. Further, we propose an unsupervised approach (that uses an automatically created named entity (NE) gazetteer from Wikipedia in the absence of training data). For a low-resource language, the word vectors obtained from Wikipedia are not sufficient to train a classifier. As a result, we propose to make use of the distance measure between the vector embeddings of words to expand the set of Wikipedia training examples with additional NEs extracted from a monolingual corpus that yield significant improvement in the unsupervised NER performance. In fact, our expansion method performs better than the traditional CRF-based (supervised) approach (i.e., F-score of 65.4% vs. 64.2%). Finally, we compare our proposed approach to the official submission for the IJCNLP-2008 Bengali NER shared task and achieve an overall improvement of F-score 11.26% with respect to the best official system.


title: The CoNLL-2013 Shared Task on Grammatical Error Correction

abstract: The CoNLL-2014 shared task was devoted to grammatical error correction of all error types. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results. Compared to the CoNLL2013 shared task, we have introduced the following changes in CoNLL-2014: (1) A participating system is expected to detect and correct grammatical errors of all types, instead of just the five error types in CoNLL-2013; (2) The evaluation metric was changed from F1 to F0.5, to emphasize precision over recall; and (3) We have two human annotators who independently annotated the test essays, compared to just one human annotator in CoNLL-2013.


title: Transfer of Asthma Management Responsibility from Parents to their School-Age Children

abstract: Abstract This study used grounded theory to explore the process of transfer of responsibility for asthma management from parents to their school-age children. Interviews were conducted with 11 mothers, 2 fathers, 1 grandmother, and their 14 children who were between 8 and 13 years old. Responses revealed that transfer of responsibility within families is a complex, uneven process that occurs over years and involves identifiable stages and transitions. The Theoretical Model for Parent-Child Transfer of Asthma Responsibility emerged from the data. The central concept underlying the process was controlling the situation. Specific cues stimulated changes in parent-child responsibility.


title: Comparable Study Of Modeling Units For End-To-End Mandarin Speech Recognition

abstract: End-To-End speech recognition have become increasingly popular in mandarin speech recognition and achieved delightful performance. Mandarin is a tonal language which is different from English and requires special treatment for the acoustic modeling units. There have been several different kinds of modeling units for mandarin such as phoneme, syllable and Chinese character. In this work, we explore two major end-to-end models: connectionist temporal classification (CTC) model and attention based encoder-decoder model for mandarin speech recognition. We compare the performance of three different scaled modeling units: context dependent phoneme(CDP), syllable with tone and Chinese character. We find that all types of modeling units can achieve approximate character error rate (CER) in CTC model and the performance of Chinese character attention model is better than syllable attention model. Furthermore, we find that Chinese character is a reasonable unit for mandarin speech recognition. On DidiCallcenter task, Chinese character attention model achieves a CER of 5.68% and CTC model gets a CER of 7.29%, on the other DidiReading task, CER are 4.89% and 5.79%, respectively. Moreover, attention model achieves a better performance than CTC model on both datasets.


title: Medical Internet of Things and Big Data in Healthcare

abstract: OBJECTIVES ::: A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track real-time health data when a patient self-administers a therapy. Because they have increased access to high-speed Internet and smartphones, many patients have started to use mobile applications (apps) to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (mIoT). This paper reviews mIoT and big data in healthcare fields. ::: ::: ::: METHODS ::: mIoT is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. ::: ::: ::: RESULTS ::: Wearables and mobile apps today support fitness, health education, symptom tracking, and collaborative disease management and care coordination. All those platform analytics can raise the relevancy of data interpretations, reducing the amount of time that end users spend piecing together data outputs. Insights gained from big data analysis will drive the digital disruption of the healthcare world, business processes and real-time decision-making. ::: ::: ::: CONCLUSIONS ::: A new category of "personalised preventative health coaches" (Digital Health Advisors) will emerge. These workers will possess the skills and the ability to interpret and understand health and well-being data. They will help their clients avoid chronic and diet-related illness, improve cognitive function, achieve improved mental health and achieve improved lifestyles overall. As the global population ages, such roles will become increasingly important.


title: A spatiotemporal communication protocol for wireless sensor networks

abstract: In this paper, we present a spatiotemporal communication protocol for sensor networks, called SPEED. SPEED is specifically tailored to be a localized algorithm with minimal control overhead. End-to-end soft real-time communication is achieved by maintaining a desired delivery speed across the sensor network through a novel combination of feedback control and nondeterministic geographic forwarding. SPEED is a highly efficient and scalable protocol for sensor networks where the resources of each node are scarce. Theoretical analysis, simulation experiments, and a real implementation on Berkeley motes are provided to validate the claims.


title: SNDlib 1.0–Survivable Network Design Library

abstract: This article describes the Survivable Network Design Library (SNDlib), a data library for fixed telecommunication network design available at http://sndlib. zib.de. In the current version 1.0, the library contains data related to 22 networks which, combined with a set of selected planning parameters, leads to 830 network design problem instances. In this article, we discuss the data concepts of SNDlib and describe a mathematical model for each design problem considered in the library. We also provide information on characteristic features and the origin of the SNDIib problem instances.


title: Identification of selected medicinal plant leaves using image features and ANN

abstract: Identification of proper medicinal plants is quite challenging and it is the time to protect medicinal plants since several plant species are becoming extinct. Leaves are the key components of a plant. Here we have proposed a method for the extraction of shape, color and texture features from leaf images and training an artificial neural network (ANN) classifier to identify the exact leaf class. The key issue lies in the selection of proper image input features to attain high efficiency with less computational complexity. We tested the accuracy of the network with different combination of image features. The test results on 63 leaf images reveals that this method gives 94.4% accuracy with a minimum of eight input features. This approach is more promising for leaf identification systems that have minimum input and demand less computation time. This work has been implemented using the image processing and neural network toolboxes in MATLAB.


Lecturer: Alistair Sinclair Based on scribe notes by:


title: Language Conflict in the Bilingual Brain

abstract: The large majority of humankind is more or less fluent in 2 or even more languages. This raises the fundamental question how the language network in the brain is organized such that the correct target language is selected at a particular occasion. Here we present behavioral and functional magnetic resonance imaging data showing that bilingual processing leads to language conflict in the bilingual brain even when the bilinguals’ task only required target language knowledge. This finding demonstrates that the bilingual brain cannot avoid language conflict, because words from the target and nontarget languages become automatically activated during reading. Importantly, stimulus-based language conflict was found in brain regions in the LIPC associated with phonological and semantic processing, whereas response-based language conflict was only found in the pre-supplementary motor area/anterior cingulate cortex when language conflict leads to response conflicts.


title: Graded Entailment for Compositional Distributional Semantics

abstract: The categorical compositional distributional model of natural language provides a conceptually motivated procedure to compute the meaning of sentences, given grammatical structure and the meanings of its words. This approach has outperformed other models in mainstream empirical language processing tasks. However, until recently it has lacked the crucial feature of lexical entailment -- as do other distributional models of meaning. ::: In this paper we solve the problem of entailment for categorical compositional distributional semantics. Taking advantage of the abstract categorical framework allows us to vary our choice of model. This enables the introduction of a notion of entailment, exploiting ideas from the categorical semantics of partial knowledge in quantum computation. ::: The new model of language uses density matrices, on which we introduce a novel robust graded order capturing the entailment strength between concepts. This graded measure emerges from a general framework for approximate entailment, induced by any commutative monoid. Quantum logic embeds in our graded order. ::: Our main theorem shows that entailment strength lifts compositionally to the sentence level, giving a lower bound on sentence entailment. We describe the essential properties of graded entailment such as continuity, and provide a procedure for calculating entailment strength.


title: GAIN: Missing Data Imputation using Generative Adversarial Nets

abstract: We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing components conditioned on what is actually observed, and outputs a completed vector. The discriminator (D) then takes a completed vector and attempts to determine which components were actually observed and which were imputed. To ensure that D forces G to learn the desired distribution, we provide D with some additional information in the form of a hint vector. The hint reveals to D partial information about the missingness of the original sample, which is used by D to focus its attention on the imputation quality of particular components. This hint ensures that G does in fact learn to generate according to the true data distribution. We tested our method on various datasets and found that GAIN significantly outperforms state-of-the-art imputation methods.


title: Evaluation parameters for computer-adaptive testing

abstract: With the proliferation of computers in test delivery today, adaptive testing has become quite popular, especially when examinees must be classified into two categories (pass/fail, master/nonmaster). Several well-established organisations have provided standards and guidelines for the design and evaluation of educational and psychological testing. The purpose of this paper was not to repeat the guidelines and standards that exist in the literature but to identify and discuss the main evaluation parameters for a computer-adaptive test (CAT). A number of parameters should be taken into account when evaluating CAT. Key parameters include utility, validity, reliability, satisfaction, usability, reporting, administration, security, and thoseassociated with adaptivity, item pool, and psychometric theory. These parameters are presented and discussed below and form a proposed evaluation model, Evaluation Model of Computer-Adaptive Testing.


title: Real-Time Gaze Estimation with Online Calibration

abstract: Gaze-tracking technology is highly valuable in many interactive and diagnostic applications. For many gaze estimation systems, calibration is an unavoidable procedure necessary to determine certain person-specific parameters, either explicitly or implicitly. Recently, several offline implicit calibration methods have been proposed to ease the calibration burden. However, the calibration procedure is still cumbersome, and gaze estimation accuracy needs further improvement. In this article, the authors present a novel 3D gaze estimation system with online calibration. The proposed system is based on a new 3D model-based gaze estimation method using a single consumer depth camera sensor (via Kinect). Unlike previous gaze estimation methods using explicit offline calibration with fixed number of calibration points or implicit calibration, their approach constantly improves person-specific eye parameters through online calibration, which enables the system to adapt gradually to a new user. The experimental results and the human-computer interaction (HCI) application show that the proposed system can work in real time with superior gaze estimation accuracy and minimal calibration burden.


title: TopicMF: simultaneously exploiting ratings and reviews for recommendation

abstract: Although users' preference is semantically reflected in the free-form review texts, this wealth of information was not fully exploited for learning recommender models. Specifically, almost all existing recommendation algorithms only exploit rating scores in order to find users' preference, but ignore the review texts accompanied with rating information. In this paper, we propose a novel matrix factorization model (called TopicMF) which simultaneously considers the ratings and accompanied review texts. Experimental results on 22 real-world datasets show the superiority of our model over the state-of-the-art models, demonstrating its effectiveness for recommendation tasks.


title: Captcha as graphical passwords-enhanced with video-based captcha for secure services

abstract: CAPTCHAs, also known as reverse Turing tests are real-time assessments that are commonly used by programs to tell humans and machines apart. This can be achieved by assigning and assessing or evaluating hard AI problems such that these problems could only be solved easily by human but not by machines. A new security approach based on hard AI problems and Captcha technology is known as Captcha as gRaphical Passwords (CaRP). This scheme can address many security problems such as dictionary attacks, online guessing attacks and shoulder-surfing attacks etc. In this paper, we present an enhanced security for the CaRP scheme i,e CaRP with motion-based Captcha. The motion can be done by using video. The movement of Captcha ensures high security over the normal CaRP scheme. Video-based captcha can provide a great challenge for humans in exploiting the remarkable perceptual abilities or skills of humans to unravel structure-from-motion. The scheme can resist attack caused due to moving object detection. Thus it can thwart many attacks, can enforce more security and it stipulates security along with usability to legitimate users in dealing with real time applications.


Graphical user interface ( GUI ) testing : Systematic mapping


FPGA Implementation of PV based Quasi Z-Source Cascaded Multilevel Inverter


title: Extracting Software Functional Requirements from Free Text Documents

abstract: The acquisition of requirements assets are important in software product line (SPL) engineering for it help enhancing the effectiveness of reuse. Traditional methods are heavily based on manual effort. This appears to be a barrier for many organizations which tend to launch a SPL. In this paper, we propose an approach to extract functional requirements by analyzing text-based software requirements specifications (SRSs). We analyze the linguistic characterization of SRSs. According to it we define extended functional requirements framework (EFRF) which consists of 10 semantic cases, then we generate converting rules. We introduce an NLP (Natural Language Process) approach to build EFRFs from documents based on the concept of EFRF and the converting rules. The extracted EFRFs are suitable for expression and modeling of functional requirements variability. We apply our method to an auto-marker software product line. The result shows the approach has high accuracy and efficiency, and the approach is readily scalable and extensible.


title: An analysis of social engineering principles in effective phishing

abstract: Phishing is a widespread practice and a lucrativebusiness. It is invasive and hard to stop: a company needsto worry about all emails that all employees receive, while anattacker only needs to have a response from a key person, e.g.,a finance or human resources' responsible, to cause a lot ofdamages. Some research has looked into what elements makephishing so successful. Many of these elements recall strategiesthat have been studied as principles of persuasion, scams andsocial engineering. This paper identifies, from the literature, theelements which reflect the effectiveness of phishing, and manuallyquantifies them within a phishing email sample. Most elementsrecognised as more effective in phishing commonly use persuasionprinciples such as authority and distraction. This insight couldlead to better automate the identification of phishing emails anddevise more appropriate countermeasures against them.


title: SAGE2: A new approach for data intensive collaboration using Scalable Resolution Shared Displays

abstract: Current web-based collaboration systems, such as Google Hangouts, WebEx, and Skype, primarily enable single users to work with remote collaborators through video conferencing and desktop mirroring. The original SAGE software, developed in 2004 and adopted at over one hundred international sites, was designed to enable groups to work in front of large shared displays in order to solve problems that required juxtaposing large volumes of information in ultra high-resolution. We have developed SAGE2, as a complete redesign and implementation of SAGE, using cloud-based and web browser technologies in order to enhance data intensive co-located and remote collaboration. This paper provides an overview of SAGE2's infrastructure, the technical design challenges, and the afforded benefits to data intensive collaboration. Lastly, we provide insight on how future collaborative applications can be developed to support large displays and demonstrate the power and flexibility that SAGE2 offers in collaborative scenarios through a series of use cases.


title: The Framing of Decisions and the Psychology of Choice

abstract: Explanations and predictions of people’s choices, in everyday life as well as in the social sciences, are often founded on the assumption of human rationality. The definition of rationality has been much debated, but there is general agreement that rational choices should satisfy some elementary requirements of consistency and coherence. In this chapter, we describe decision problems in which people systematically violate the requirements of consistency and coherence, and we trace these violations to the psychological principles that govern the perception of decision problems and the evaluation of options.


title: A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

abstract: The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver


title: Online Gaming, Internet Addiction, and Aggression in Chinese Male Students: The Mediating Role of Low Self-Control

abstract: Even though the General Aggression Model suggests that personality and situational variables can influence aggression, it is not clear if the model can be applied to Eastern cultures. The present study examined the mediating role of low self-control in the relationships between violent online gaming, Internet addiction, and aggression. We conducted a cross-sectional study using online self-report questionnaires to assess violent online game exposure, Internet addiction, low self-control, and aggression in 211 Chinese male students. We found that the majority of the 10 most popular games reported by the participants were violent. There were positive correlations between violent online game exposure, Internet addiction, low self-control, and aggression. Aggression was not only directly predicted by violent online game exposure and Internet addiction, but also mediated by low self-control. These findings support the development of educational programs and measures to cultivate self-control in college students and reduce aggression engendered by playing violent online games. Aggression and self-control, which is conducive to educational interventions, are related. The implications and limitations of this study are discussed.


title: A survey on engineering approaches for self-adaptive systems

abstract: The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation.


A hybrid factor analysis and probabilistic PCA-based system for dictionary learning and encoding for robust speaker recognition.


title: Mechanisms for multi-level marketing

abstract: Multi-level marketing is a marketing approach that motivates its participants to promote a certain product among their friends. The popularity of this approach increases due to the accessibility of modern social networks, however, it existed in one form or the other long before the Internet age began (the infamous Pyramid scheme that dates back at least a century is in fact a special case of multi-level marketing). This paper lays foundations for the study of reward mechanisms in multi-level marketing within social networks. We provide a set of desired properties for such mechanisms and show that they are uniquely satisfied by geometric reward mechanisms. The resilience of mechanisms to false-name manipulations is also considered; while geometric reward mechanisms fail against such manipulations, we exhibit other mechanisms which are false-name-proof.


Scaling short read de novo DNA sequence assembly to gigabase genomes


title: When Things Matter : A Survey on Data-Centric Internet of Things

abstract: With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, but several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy and continuous. This paper reviews the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed.


title: Interpretation of Overtracing Freehand Sketching for Geometric Shapes

abstract: This paper presents a novel method for interpreting overtracing freehand sketch. The overtracing strokes are interpreted as sketch content and are used to generate 2D geometric primitives. The approach consists of four stages: stroke classification, strokes grouping and fitting, 2D tidy-up with endpoint clustering and parallelism correction, and in-context interpretation. Strokes are first classified into lines and curves by a linearity test. It is followed by an innovative strokes grouping process that handles lines and curves separately. The grouped strokes are fitted with 2D geometry and further tidied-up with endpoint clustering and parallelism correction. Finally, the in-context interpretation is applied to detect incorrect stroke interpretation based on geometry constraints and to suggest a most plausible correction based on the overall sketch context. The interpretation ensures sketched strokes to be interpreted into meaningful output. The interface overcomes the limitation where only a single line drawing can be sketched out as in most existing sketching programs, meanwhile is more intuitive to the user.


title: Automated Detection of Facial Expressions during Computer-Assisted Instruction in Individuals on the Autism Spectrum

abstract: It has been suggested that computer-assisted instruction (CAI) is a promising method for educating students on the autism spectrum. We aimed to determine whether automated recognition of facial expressions aided in predicting CAI engagement and learning performance. Seven youth with autism (mean age = 12.7, SD = 4.2) interacted with a CAI program, TeachTown Basics, for 15 consecutive sessions. Video recordings of the participants' faces were collected during these sessions and facial expressions from these videos were analyzed using CERT, an algorithm that automatically outputs intensity values for each facial action unit (AU). Using these data, we attempted to operationally define two engagement indices: (1) behavioral engagement, the proportion of time a participant had their face oriented to the computer screen; and (2) emotional engagement, the activation of AUs previously associated with CAI. Our results suggest that both indices strongly correlated with one another, but that emotional (not behavioral) engagement predicted test performance. CAI knowledge domain, participant sex, and developmental age also contributed to the prediction.


title: Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment

abstract: Accurate face alignment is a vital prerequisite step for most face perception tasks such as face recognition, facial expression analysis and non-realistic face re-rendering. It can be formulated as the nonlinear inference of the facial landmarks from the detected face region. Deep network seems a good choice to model the nonlinearity, but it is nontrivial to apply it directly. In this paper, instead of a straightforward application of deep network, we propose a Coarse-to-Fine Auto-encoder Networks (CFAN) approach, which cascades a few successive Stacked Auto-encoder Networks (SANs). Specifically, the first SAN predicts the landmarks quickly but accurately enough as a preliminary, by taking as input a low-resolution version of the detected face holistically. The following SANs then progressively refine the landmark by taking as input the local features extracted around the current landmarks (output of the previous SAN) with higher and higher resolution. Extensive experiments conducted on three challenging datasets demonstrate that our CFAN outperforms the state-of-the-art methods and performs in real-time(40+fps excluding face detection on a desktop).


title: A color based approach for eye blink detection in image sequences

abstract: The human eye gives a clue about the mental state in the context of various applications such as stress, confusion and drowsiness. In this work we present a new algorithm for eye blink detection, by employing color information of the surrounding eye region. Moreover it is capable to detect eye blinks as well as relative eye lid movement. The algorithm handles decent rotations of the head, as long the eye is reliably detectable. Our approach is efficient though quite simple, since no segmentation of discrete eye features like lids or iris are required. The results show good performance on non-acted, real Human-Computer-Interaction videos, where subjects are allowed to behave randomly. Further, we compare our results to state of art methods.


title: Machine Learning Based Missing Value Imputation Method for Clinical Datasets

abstract: Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the incomplete cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore the use of a machine learning technique as a missing value imputation method for incomplete cardiovascular data. Mean/mode imputation, fuzzy unordered rule induction algorithm imputation, decision tree imputation and other machine learning algorithms are used as missing value imputation and the final datasets are classified using decision tree, fuzzy unordered rule induction, KNN and K-Mean clustering. The experiment shows that final classifier performance is improved when the fuzzy unordered rule induction algorithm is used to predict missing attribute values for K-Mean clustering and in most cases, the machine learning techniques were found to perform better than the standard mean imputation technique.


title: Vision in natural and virtual environments

abstract: Our knowledge of the way that the visual system operates in everyday behavior has, until recently, been very limited. This information is critical not only for understanding visual function, but also for understanding the consequences of various kinds of visual impairment, and for the development of interfaces between human and artificial systems. The development of eye trackers that can be mounted on the head now allows monitoring of gaze without restricting the observer's movements. Observations of natural behavior have demonstrated the highly task-specific and directed nature of fixation patterns, and reveal considerable regularity between observers. Eye, head, and hand coordination also reveals much greater flexibility and task-specificity than previously supposed. Experimental examination of the issues raised by observations of natural behavior requires the development of complex virtual environments that can be manipulated by the experimenter at critical points during task performance. Experiments where we monitored gaze in a simulated driving environment demonstrate that visibility of task relevant information depends critically on active search initiated by the observer according to an internally generated schedule, and this schedule depends on learnt regularities in the environment. In another virtual environment where observers copied toy models we showed that regularities in the spatial structure are used by observers to control eye movement targeting. Other experiments in a virtual environment with haptic feedback show that even simple visual properties like size are not continuously available or processed automatically by the visual system, but are dynamically acquired and discarded according to the momentary task demands.


title: A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

abstract: A sliding window based strategy is employed for online and real-time ECG analysis.A novel algorithm, i.e. MMD, is proposed which uses Max-Min difference for robust QRS complex detection.It has better trade-off between speed and accuracy and shows impressive performances with great computational simplicity.Dynamic thresholding is proposed to deal with fluctuating average peak amplitudes in ECG signals.MMD is evaluated with five ECG databases for QRS detection and outperforms related work consistently. Background and objectivesDetection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. MethodsA lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. ResultsThe proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. ConclusionsIn comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal.


A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses


title: Single-Label Multi-Class Image Classification by Deep Logistic Regression

abstract: The objective learning formulation is essential for the success of convolutional neural networks. In this work, we analyse thoroughly the standard learning objective functions for multiclass classification CNNs: softmax regression (SR) for singlelabel scenario and logistic regression (LR) for multi-label scenario. Our analyses lead to an inspiration of exploiting LR for single-label classification learning, and then the disclosing of the negative class distraction problem in LR. To address this problem, we develop two novel LR based objective functions that not only generalise the conventional LR but importantly turn out to be competitive alternatives to SR in single label classification. Extensive comparative evaluations demonstrate the model learning advantages of the proposed LR functions over the commonly adopted SR in single-label coarse-grained object categorisation and cross-class fine-grained person instance identification tasks. We also show the performance superiority of our method on clothing attribute classification in comparison to the vanilla LR function. The code had been made publicly available.


title: A robust audio classification and segmentation method

abstract: In this paper, we present a robust algorithm for audio classification that is capable of segmenting and classifying an audio stream into speech, music, environment sound and silence. Audio classification is processed in two steps, which makes it suitable for different applications. The first step of the classification is speech and non-speech discrimination. In this step, a novel algorithm based on KNN and LSP VQ is presented. The second step further divides non-speech class into music, environment sounds and silence with a rule based classification scheme. Some new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. Our experiments in the context of video structure parsing have shown the algorithms produce very satisfactory results.


title: Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

abstract: This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most lethal skin cancer. The challenge was divided into 3 tasks: lesion segmentation, feature detection, and disease classification. Participation involved 593 registrations, 81 pre-submissions, 46 finalized submissions (including a 4-page manuscript), and approximately 50 attendees, making this the largest standardized and comparative study in this field to date. While the official challenge duration and ranking of participants has concluded, the dataset snapshots remain available for further research and development.


title: F.: SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery

abstract: In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or sampling-based methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.


title: HealthSense: classification of health-related sensor data through user-assisted machine learning

abstract: Remote patient monitoring generates much more data than healthcare professionals are able to manually interpret. Automated detection of events of interest is therefore critical so that these points in the data can be marked for later review. However, for some important chronic health conditions, such as pain and depression, automated detection is only partially achievable. To assist with this problem we developed HealthSense, a framework for real-time tagging of health-related sensor data. HealthSense transmits sensor data from the patient to a server for analysis via machine learning techniques. The system uses patient input to assist with classification of interesting events (e.g., pain or itching). Due to variations between patients, sensors, and condition types, we presume that our initial classification is imperfect and accommodate this by incorporating user feedback into the machine learning process. This is done by occasionally asking the patient whether they are experiencing the condition being monitored. Their response is used to confirm or reject the classification made by the server and continually improve the accuracy of the classifier's decisions on what data is of interest to the health-care provider.


title: Image plane interaction techniques in 3D immersive environments

abstract: This paper presents a set of interaction techniques for use in headtracked immersive virtual environments. With these techniques, the user interacts with the 2D projections that 3D objects in the scene make on his image plane. The desktop analog is the use of a mouse to interact with objects in a 3D scene based on their projections on the monitor screen. Participants in an immersive environment can use the techniques we discuss for object selection, object manipulation, and user navigation in virtual environments. CR Categories and Subject Descriptors: 1.3.6 [Computer Graphics]: Methodology and Techniques - InteractionTechniques; 1.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism - VirtualReality. Additional Keywords: virtual worlds, virtual environments, navigation, selection, manipulation.


title: Robust heart rate from fitness videos

abstract: Remote photoplethysmography (rPPG) enables contactless heart-rate monitoring using a regular video camera. Objective: This paper aims to improve the rPPG technology targeting continuous heart-rate measurement during fitness exercises. The fundamental limitation of the existing (multi-wavelength) rPPG methods is that they can suppress at most n − 1 independent distortions by linearly combining n wavelength color channels. Their performance are highly restricted when more than n − 1 independent distortions appear in a measurement, as typically occurs in fitness applications with vigorous body motions. Approach: To mitigate this limitation, we propose an effective yet very simple method that algorithmically extends the number of possibly suppressed distortions without using more wavelengths. Our core idea is to increase the degrees-of-freedom of noise reduction by decomposing the n wavelength camera-signals into multiple orthogonal frequency bands and extracting the pulse-signal per band-basis. This processing, namely Sub-band rPPG (SB), can suppress different distortion-frequencies using independent combinations of color channels. Main results: A challenging fitness benchmark dataset is created, including 25 videos recorded from 7 healthy adult subjects (ages from 25 to 40 yrs; six male and one female) running on a treadmill in an indoor environment. Various practical challenges are simulated in the recordings, such as different skin-tones, light sources, illumination intensities, and exercising modes. The basic form of SB is benchmarked against a state-of-the-art method (POS) on the fitness dataset. Using non-biased parameter settings, the average signal-to-noise-ratio (SNR) for POS varies in [−4.18, −2.07] dB, for SB varies in [−1.08, 4.77] dB. The ANOVA test shows that the improvement of SB over POS is statistically significant for almost all settings (p-value <0.05). Significance: The results suggest that the proposed SB method considerably increases the robustness of heart-rate measurement in challenging fitness applications, and outperforms the state-of-the-art method.


title: Bounded Control for Double Integrator in Quadrotor Dynamics

abstract: We construct a trajectory tracking controller for a quadrotor system by finding a coordinate change which transforms the quadrotor's vector field into that of a thrust propelled system. In a thrust propelled system, the goal is to stabilize its position around the origin, while the system is actuated by a one dimensional acceleration/thrust along a direction vector, by a time-varying gravity, and by the angular acceleration of the direction vector. For this system, a solution has been proposed in a companion article, submitted to ECC 2016, based on the implicit knowledge of a bounded controller for a double integrator system, and on the implicit knowledge of a Lyapunov function that guarantees the origin is asymptotically stable for the double integrator controlled by the bounded controller. We present two alternative bounded controllers for a double integrator system, and corresponding Lyapunov functions.


title: Feasibility of a serverless distributed file system deployed on an existing set of desktop PCs

abstract: We consider an architecture for a serverless distributed file system that does not assume mutual trust among the client computers. The system provides security, availability, and reliability by distributing multiple encrypted replicas of each file among the client machines. To assess the feasibility of deploying this system on an existing desktop infrastructure, we measure and analyze a large set of client machines in a commercial environment. In particular, we measure and report results on disk usage and content; file activity; and machine uptimes, lifetimes, and loads. We conclude that the measured desktop infrastructure would passably support our proposed system, providing availability on the order of one unfilled file request per user per thousand days.


title: Soft Robotics: New Perspectives for Robot Bodyware and Control

abstract: The remarkable advances of robotics in the last fifty years, which represent an incredible wealth of knowledge, are based on the fundamental assumption that robots are chains of rigid links. The use of soft materials in robotics, driven not only by new scientific paradigms (biomimetics, morphological computation, and others), but also by many applications (biomedical, service, rescue robots, and many more), is going to overcome these basic assumptions and makes the well-known theories and techniques poorly applicable, opening new perspectives for robot design and control.The current examples of soft robots represent a variety of solutions for actuation, and control. Though very first steps, they have the potential for a radical technological change. Soft robotics is not just a new direction of technological development, but a novel approach to robotics, unhinging its fundamentals, with the potential to produce a new generation of robots, in the support of humans in our natural environments.


title: Accurate and scalable nearest neighbors in large networks based on effective importance

abstract: Nearest neighbor proximity search in large graphs is an important analysis primitive with a variety of applications in graph data from different domains. We propose a novel proximity measure for weighted graphs called Effective Importance which incorporates multiple paths between nodes and captures the inherent structural clusters within a network. We develop effective bounds on the EI value using a modified small subnetwork around a query node, enabling scalable exact nearest neighbor (NN) search at query time. Our NN search does not require heavy offline analysis or holistic knowledge of the graph, making our method suitable for very large dynamically changing networks or composite network overlays. We employ our NN search algorithm on social, information and biological networks and demonstrate the effectiveness and scalability of the approach. For million-node networks, our method retrieves the exact top 20 neighbors using less than $0.2%$ of the network edges in a fraction of a second on a conventional desktop machine. We also evaluate the effectiveness of our proximity measure and NN search for three applications, namely (i) finding good local clusters, (ii) network sparsification and (iii) prediction of node attributes in information networks. The EI measure and NN search method outperform recent counterparts from the literature in all applications.


title: Seeing Stars: Exploiting Class Relationships For Sentiment Categorization With Respect To Rating Scales

abstract: We address the rating-inference problem, wherein rather than simply decide whether a review is "thumbs up" or "thumbs down", as in previous sentiment analysis work, one must determine an author's evaluation with respect to a multi-point scale (e.g., one to five "stars"). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, "three stars" is intuitively closer to "four stars" than to "one star".We first evaluate human performance at the task. Then, we apply a meta-algorithm, based on a metric labeling formulation of the problem, that alters a given n-ary classifier's output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem.


title: Physiology-based model of cell viscoelasticity

abstract: The measured viscoelastic properties of biological tissues is the result of the passive and active response of the cells. We propose an evolution law of the remodeling process in the cytoskeleton which is able to mimic the viscous properties of biological cellular tissues. Our model is based on dynamical changes of the resting length. We show that under the small strain regime, the linear rheology models are recovered, with the relaxation time being replaced by the cell resistance to remodel. We implement the one-dimensional model into network systems of two and three dimensions, and show that the same conclusions may be drawn for those systems.


title: Feature extraction for the author name disambiguation problem in a bibliographic database

abstract: Author name disambiguation in bibliographic databases has been, and still is, a challenging research task due to the high uncertainty there is when matching a publication author with a concrete researcher. Common approaches normally either resort to clustering to group author's publications, or use a binary classifier to decide whether a given publication is written by a specific author. Both approaches benefit from authors publishing similar works (e.g. subject areas and venues), from the previous publication history of an author (the higher, the better), and validated publication-author associations for model creation. However, whenever such an algorithm is confronted with different works from an author, or an author without publication history, often it makes wrong identifications. In this paper, we describe a feature extraction method that aims to avoid the previous problems. Instead of generally characterizing an author, it selectively uses features that associate the author to a certain publication. We build a Random Forest model to assess the quality of our set of features. Its goal is to predict whether a given author is the true author of a certain publication. We use a bibliographic database named Authenticus with more than 250,000 validated author-publication associations to test model quality. Our model achieved a top result of 95.37+ accuracy in predicting matches and 91.92+ in a real test scenario. Furthermore, in the last case the model was able to correctly predict 61.86+ of the cases where authors had no previous publication history.


title: Development of a Psychoeducational Intervention for Community-Based Care Facilities to Substance Use Disorders in Mexico

abstract: In Mexico, there are over 2000 community-based care facilities (CRAMAAs), which use derivations of the 12-step program. Only 24% of the personnel working at these facilities have some kind of substance use disorder (SUD) care training, and manualized or evidence-based interventions for patient care are not used. The objective of this study was to describe the development of a psychoeducational intervention for SUD care at CRAMAAs. This development encompassed four stages: expert meetings, needs assessment, content development, and content validation. The intervention comprises 12 group sessions provided by a facilitator who does not require specific knowledge of SUD. Each session is structured according to five instructional events described by Gagne and to principles of adult education. The characteristics of this psychoeducational intervention allow to extend its use to public professional scenarios, whether residential or outpatient. The development of this kind of interventions reduces the gap between science and clinical practice in real treatment scenarios.


title: The application of graph theoretical analysis to complex networks in the brain

abstract: Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features. 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.


title: Polly- Polyhedral optimization in LLVM Tobias Grosser

abstract: Various powerful polyhedral techniques exist to optimize computation intensive programs effectively. Applying these techniques on any non-trivial program is still surprisingly difficult and often not as effective as expected. Most polyhedral tools are limited to a specific programming language. Even for this language, relevant code needs to match specific syntax that rarely appears in existing code. It is therefore hard or even impossible to process existing programs automatically. In addition, most tools target C or OpenCL code, which prevents effective communication with compiler internal optimizers. As a result target architecture specific optimizations are either little effective or not approached at all. In this paper we present Polly, a project to enable polyhedral optimizations in LLVM. Polly automatically detects and transforms relevant program parts in a language-independent and syntactically transparent way. Therefore, it supports programs written in most common programming languages and constructs like C++ iterators, goto based loops and pointer arithmetic. Internally it provides a state-of-the-art polyhedral library with full support for Z-polyhedra, advanced data dependency analysis and support for external optimizers. Polly includes integrated SIMD and OpenMP code generation. Through LLVM, machine code for CPUs and GPU accelerators, C source code and even hardware descriptions can be targeted.


title: 7nm FinFET standard cell layout characterization and power density prediction in near- and super-threshold voltage regimes

abstract: In this paper, we present a power density analysis for 7nm FinFET technology node, including both near-threshold and super-threshold operations. We first build a Liberty-formatted standard cell library by selecting the appropriate number of fins for the pull-up and pull-down networks of each logic cell. The layout of each cell then is characterized based on the lambda-based layout design rules for FinFET devices. Finally, the power density of the 7nm FinFET technology node is analyzed and compared with the state-of-the-art 45nm CMOS technology node for different circuits. Hspice results show that the power density of each 7nm FinFET circuit is at least 10 to 20 times larger than that of the same 45nm CMOS circuit in near- and super-threshold voltage regimes. Also the power densities of FinFET circuits are shown to be much higher than the limit of air cooling, which necessitates careful thermal management for the FinFET technology.


title: Quantum Bitcoin: An Anonymous and Distributed Currency Secured by the No-Cloning Theorem of Quantum Mechanics

abstract: The digital currency Bitcoin has had remarkable growth since it was first proposed in 2008. Its distributed nature allows currency transactions without a central authority by using cryptographic methods and a data structure called the blockchain. In this paper we use the no-cloning theorem of quantum mechanics to introduce Quantum Bitcoin, a Bitcoin-like currency that runs on a quantum computer. We show that our construction of quantum shards and two blockchains allows untrusted peers to mint quantum money without risking the integrity of the currency. The Quantum Bitcoin protocol has several advantages over classical Bitcoin, including immediate local verification of transactions. This is a major improvement since we no longer need the computationally intensive and time-consuming method Bitcoin uses to record all transactions in the blockchain. Instead, Quantum Bitcoin only records newly minted currency which drastically reduces the footprint and increases efficiency. We present formal security proofs for counterfeiting resistance and show that a quantum bitcoin can be re-used a large number of times before wearing out - just like ordinary coins and banknotes. Quantum Bitcoin is the first distributed quantum money system and we show that the lack of a paper trail implies full anonymity for the users. In addition, there are no transaction fees and the system can scale to any transaction volume.


title: Decreased dopamine brain reactivity in marijuana abusers is associated with negative emotionality and addiction severity

abstract: Moves to legalize marijuana highlight the urgency to investigate effects of chronic marijuana in the human brain. Here, we challenged 48 participants (24 controls and 24 marijuana abusers) with methylphenidate (MP), a drug that elevates extracellular dopamine (DA) as a surrogate for probing the reactivity of the brain to DA stimulation. We compared the subjective, cardiovascular, and brain DA responses (measured with PET and [11C]raclopride) to MP between controls and marijuana abusers. Although baseline (placebo) measures of striatal DA D2 receptor availability did not differ between groups, the marijuana abusers showed markedly blunted responses when challenged with MP. Specifically, compared with controls, marijuana abusers had significantly attenuated behavioral (“self-reports” for high, drug effects, anxiety, and restlessness), cardiovascular (pulse rate and diastolic blood pressure), and brain DA [reduced decreases in distribution volumes (DVs) of [11C]raclopride, although normal reductions in striatal nondisplaceable binding potential (BPND)] responses to MP. In ventral striatum (key brain reward region), MP-induced reductions in DVs and BPND (reflecting DA increases) were inversely correlated with scores of negative emotionality, which were significantly higher for marijuana abusers than controls. In marijuana abusers, DA responses in ventral striatum were also inversely correlated with addiction severity and craving. The attenuated responses to MP, including reduced decreases in striatal DVs, are consistent with decreased brain reactivity to the DA stimulation in marijuana abusers that might contribute to their negative emotionality (increased stress reactivity and irritability) and addictive behaviors.


title: Autonomic resource provisioning for cloud-based software

abstract: Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-the-practice with respect to auto-scaling involves specifying threshold-based rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner.


title: Voltage-Oriented Input–Output Linearization Controller as Maximum Power Point Tracking Technique for Photovoltaic Systems

abstract: This paper presents a robust input–output linearization controller as maximum power point tracking (MPPT) technique in a photovoltaic (PV) buck dc–dc converter with applications to dc microgrids, solar vehicles, or stand-alone systems. Due to the control structure proposed in this paper, the MPPT control system is able to track very fast irradiance changes. Meanwhile, the internal stability of the overall closed-loop system is guaranteed for different load scenarios. A sector condition is only required for the load current, which is satisfied for most of the current PV applications. In turn, this condition implies the robustness against oscillations in the dc bus voltage. Finally, the MPPT control system is validated through experimental results, where the closed-loop performance is evaluated under abrupt irradiance and set-point changes, parametric uncertainty, and dc bus load variations.


title: PULSE HEIGHT RESOLUTION OF HIGH GAIN FIRST DYNODE PHOTOMULTIPLIERS

abstract: The performance of a new photomultiplier with high‐gain GaP(Cs) first dynode is described. The photoelectron statistics are degraded by less than 5% as compared with 20% for conventional multipliers. The improved statistics make it possible, for the first time, to distinguish the emission of one, two, and up to five photoelectrons.


title: A wearable Malossi alphabet interface for deafblind people

abstract: Deafblind people have a severe degree of combined visual and auditory impairment resulting in problems with communication, (access to) information and mobility. Moreover, in order to interact with other people, most of them need the constant presence of a caregiver who plays the role of an interpreter with an external world organized for hearing and sighted people. As a result, they usually live behind an invisible wall of silence, in a unique and inexplicable condition of isolation. In this paper, we describe DB-HAND, an assistive hardware/software system that supports users to autonomously interact with the environment, to establish social relationships and to gain access to information sources without an assistant. DB-HAND consists of an input/output wearable peripheral (a glove equipped with sensors and actuators) that acts as a natural interface since it enables communication using a language that is easily learned by a deafblind: Malossi method. Interaction with DB-HAND is managed by a software environment, whose purpose is to translate text into sequences of tactile stimuli (and vice-versa), to execute commands and to deliver messages to other users. It also provides multi-modal feedback on several standard output devices to support interaction with the hearing and the sighted people.