title: Design and development of a 7.2 kV/200A hybrid circuit breaker based on 15 kV SiC emitter turn-off (ETO) thyristor

abstract: This paper deals with the design of a 7.2 kV, 200 A hybrid AC circuit breaker employing a 15 kV Silicon Carbide (SiC) Emitter Turn-off (ETO) thyristor device as the main semiconductor switch and presents the test results to verify the functions of both the high voltage and low voltage electronic components of the medium voltage hybrid circuit breaker. The hybrid circuit breaker consists a fast acting mechanical switch, a low voltage commutating switch in series with that mechanical switch, a single 15 kV SiC ETO Thyristor device, and a stack of MOVs. This paper focuses on the electronic parts and a companion paper would elucidate the principle and operation of the fast acting mechanical switch. To fulfill bidirectional interruption capability of the high voltage semiconductor device, the SiC ETO is equipped with a low cost silicon diode bridge; a snubber capacitor is equipped in parallel with this diode bridge to alleviate voltage stress of the ETO during turn-off transients. A compact low voltage commutating switch based on silicon MOSFETs has been built and tested up to the full current rating.


title: Social skills group training in high-functioning autism: A qualitative responder study

abstract: Systematic reviews show some evidence for the efficacy of group-based social skills group training in children and adolescents with autism spectrum disorder, but more rigorous research is needed to endorse generalizability. In addition, little is known about the perspectives of autistic individuals participating in social skills group training. Using a qualitative approach, the objective of this study was to examine experiences and opinions about social skills group training of children and adolescents with higher functioning autism spectrum disorder and their parents following participation in a manualized social skills group training (“KONTAKT”). Within an ongoing randomized controlled clinical trial (NCT01854346) and based on outcome data from the Social Responsiveness Scale, six high responders and five low-to-non-responders to social skills group training and one parent of each child (N = 22) were deep interviewed. Interestingly, both high responders and low-to-non-responders (and their parents) repo...


title: Software protection for dynamically-generated code

abstract: Process-level Virtual machines (PVMs) often play a crucial role in program protection. In particular, virtualization-based tools like VMProtect and CodeVirtualizer have been shown to provide desirable obfuscation properties (i.e., resistance to disassembly and code analysis). To be efficient, many tools cache frequently-executed code in a code cache. This code is run directly on hardware and consequently may be susceptible to unintended, malicious modification after it has been generated. To thwart such modifications, this work presents a novel methodology that imparts tamper detection at run time to PVM-protected applications. Our scheme centers around the run-time creation of a network of software knots (an instruction sequence that checksums portions of the code) to detect tamper. These knots are used to check the integrity of cached code, although our techniques could be applied to check any software-protection properties. Used in conjunction with established static techniques, our solution provides a mechanism for protecting PVM-generated code from modification. We have implemented a PVM system that automatically inserts code into an application to dynamically generate polymorphic software knots. Our experiments show that PVMs do indeed provide a suitable platform for extending guard protection, without the addition of high overheads to run-time performance and memory. Our evaluations demonstrate that these knots add less than 10% overhead while providing frequent integrity checks.


title: Understanding Global HIV Stigma and Discrimination: Are Contextual Factors Sufficiently Studied? (GAPRESEARCH)

abstract: Stigma and discrimination are among the greatest challenges that people living with human immunodeficiency virus (HIV) face, and both are known to negatively affect quality of life as well as treatment outcomes. We analyzed the growing research and current understanding of HIV-related stigma and contextual factors in HIV/AIDS (human Immunodeficiency virus/ acquired immunodeficiency syndrome) bibliography. A total of 5984 publications published from 1991 to 2017 were retrieved from the Web of Science database. The number of papers and their impacts have been considerably grown in recent years. Research landscapes related to stigma and discrimination include clinical, physical and mental health outcomes, risk behaviors of most-at-risk populations, and HIV-related services. We found a lack of empirical studies not only on social, cultural and economic contexts, but also on specific interventions for particular settings and sub-populations. This study highlights certain gaps and provides a basis for future studies and interventions on this critical issue given the changing drivers of HIV epidemics.


title: A cognitive-behavioral model of anxiety in social phobia

abstract: The current paper presents a model of the experience of anxiety in social/evaluative situations in people with social phobia. The model describes the manner in which people with social phobia perceive and process information related to potential evaluation and the way in which these processes differ between people high and low in social anxiety. It is argued that distortions and biases in the processing of social/evaluative information lead to heightened anxiety in social situations and, in turn, help to maintain social phobia. Potential etiological factors as well as treatment implications are also discussed.


title: Physical activity, exercise, and physical fitness : definitions and distinctions for health-related research

abstract: "Physical activity," "exercise," and "physical fitness" are terms that describe different concepts. However, they are often confused with one another, and the terms are sometimes used interchangeably. This paper proposes definitions to distinguish them. Physical activity is defined as any bodily movement produced by skeletal muscles that results in energy expenditure. The energy expenditure can be measured in kilocalories. Physical activity in daily life can be categorized into occupational, sports, conditioning, household, or other activities. Exercise is a subset of physical activity that is planned, structured, and repetitive and has as a final or an intermediate objective the improvement or maintenance of physical fitness. Physical fitness is a set of attributes that are either health- or skill-related. The degree to which people have these attributes can be measured with specific tests. These definitions are offered as an interpretational framework for comparing studies that relate physical activity, exercise, and physical fitness to health.


title: A Tutorial on Fingerprint Recognition 1

abstract: This tutorial introduces fingerprint recognition systems and their main components: sensing, feature extraction and matching. The basic technologies are surveyed and some state-of-the-art algorithms are discussed. Due to the extent of this topic it is not possible to provide here all the details and to cover a number of interesting issues such as classification, indexing and multi-modal systems. Interested readers can find in [21] a complete and comprehensive guide to fingerprint recognition.


title: Attendance management system (AMS): Comparison of two different approaches

abstract: Two different approaches of implementing Fast track analysis for attendance management system has been designed and analyzed. The first design is applying data access through card reader and the data is being saved in the memory card. All the data which are representing Student ID are processed by attendance management system for monitoring the attendance student performance. This is done via off-line approach. While the second approach is applying same data access through RFID via a control unit for validating the Student ID. Once the ID is recognized, the control unit will send the ID through Bluetooth to Smart-Phone. An App such as MagnetCode is installed to receive the student ID and save it in Excel format. The two approaches have pro and con in-term of costing and processing speed. However after thorough comparison study have been conducted the 2nd approach has more advantages compared to 1st approach. Therefore the main aim to verily which of the two approaches would be more practical for the lecturer to use in the class is finally revealed and summarized.


title: A Lattice-Based Privacy Aware Access Control Model

abstract: As the amount of data being collected by service providers increases, privacy concerns increase for the data owners that must provide private data to get services. Legislative acts require enterprises protect the privacy of their customers and privacy policy frameworks such as P3P assist enterprises in demonstrating their privacy policies to customers (i.e. publishing privacy policy on websites). Unfortunately, defining these standards does not guarantee that the privacy policies are actually enforced since privacy is not central to conventional access control models. Furthermore, a privacy-preserving model should consider the privacy preferences of both data provider and data collector. This paper presents a Lattice-based Privacy Aware Access Control (LPAAC) Model. The key contribution is providing a privacy preserving model that enforces privacy policies and facilitates customization of privacy agreements and preferences of both data providers and organizations that collect data.


title: Creating personalized systems that people can scrutinize and control: Drivers, principles and experience

abstract: Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, a user model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related to privacy, invisibility of personalization, errors in user models, wasted user models, and the broad issue of enabling people to control their user models and associated personalization. We offer scrutable user models as a foundation for tackling these problems. This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.


title: Garbage collecting the Internet: a survey of distributed garbage collection

abstract: Internet programming languages such as Java present new challenges to garbage-collection design. The spectrum of garbage-collection schema for linked structures distributed over a network are reviewed here. Distributed garbage collectors are classified first because they evolved from single-address-space collectors. This taxonomy is used as a framework to explore distribution issues: locality of action, communication overhead and indeterministic communication latency.


FPGA Based Non Uniform Illumination Correction in Image Processing Applications


title: Creativity, the Turing Test, and the (Better) Lovelace Test

abstract: The Turing Test (TT) is claimed by many to be a way to test for the presence, in computers, of such ``deep'' phenomena as thought and consciousness. Unfortunately, attempts to build computational systems able to pass TT (or at least restricted versions of this test) have devolved into shallow symbol manipulation designed to, by hook or by crook, trick. The human creators of such systems know all too well that they have merely tried to fool those people who interact with their systems into believing that these systems really have minds. And the problem is fundamental: the structure of the TT is such as to cultivate tricksters. A better test is one that insists on a certain restrictive epistemic relation between an artificial agent (or system) A, its output o, and the human architect H of A – a relation which, roughly speaking, obtains when H cannot account for how A produced o. We call this test the ``Lovelace Test'' in honor of Lady Lovelace, who believed that only when computers originate things should they be believed to have minds.


Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence ℓ2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning


title: Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos

abstract: A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions. The task of video retrieval using natural language descriptions seeks to address this through rich, unconstrained supervision about complex activities. However, while this formulation offers hope of leveraging underlying compositional structure in activity descriptions, existing approaches typically do not explicitly model compositional reasoning. In this work, we introduce an approach for explicitly and dynamically reasoning about compositional natural language descriptions of activity in videos. We take a modular neural network approach that, given a natural language query, extracts the semantic structure to assemble a compositional neural network layout and corresponding network modules. We show that this approach is able to achieve state-of-the-art results on the DiDeMo video retrieval dataset.


title: Context Refinement for Object Detection

abstract: Current two-stage object detectors, which consists of a region proposal stage and a refinement stage, may produce unreliable results due to ill-localized proposed regions. To address this problem, we propose a context refinement algorithm that explores rich contextual information to better refine each proposed region. In particular, we first identify neighboring regions that may contain useful contexts and then perform refinement based on the extracted and unified contextual information. In practice, our method effectively improves the quality of the final detection results as well as region proposals. Empirical studies show that context refinement yields substantial and consistent improvements over different baseline detectors. Moreover, the proposed algorithm brings around 3% performance gain on PASCAL VOC benchmark and around 6% gain on MS COCO benchmark respectively.


title: PSANet: Point-wise Spatial Attention Network for Scene Parsing

abstract: We notice information flow in convolutional neural networks is restricted inside local neighborhood regions due to the physical design of convolutional filters, which limits the overall understanding of complex scenes. In this paper, we propose the point-wise spatial attention network (PSANet) to relax the local neighborhood constraint. Each position on the feature map is connected to all the other ones through a self-adaptively learned attention mask. Moreover, information propagation in bi-direction for scene parsing is enabled. Information at other positions can be collected to help the prediction of the current position and vice versa, information at the current position can be distributed to assist the prediction of other ones. Our proposed approach achieves top performance on various competitive scene parsing datasets, including ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating its effectiveness and generality.


title: On-line flatness measurement of large steel plates using moiré topography

abstract: The quality demands for steel plate and strip products are increasing, and flatness and dimensional measurements performed on these products must fulfill quality standards, and above all the needs of the clients. Many quality assurance methods exist with regard to flatness and dimensional measurements in the steel industry. Manual gauging methods are often still used, but they are costly in terms of time and labour. Automated noncontact optical measurement systems are becoming more common due to their benefits over traditional methods. In this thesis a projection moire topography is applied for automatic measurement of the flatness of large steel plates and a fully automated system applicable to varying surface properties of large steel plates is implemented in a hostile industrial environment. The specifications, design and performance of the system are presented. The measurement area of the system is 3.5 metres x 1.0 metre (width x length), and it achieves a specified depth resolution of 0.3 mm. The system operates in real time on the production line at a speed of 1 m/s. Experiences gained with the system and the different requirements applying to another measurement site then led to the invention of a novel multipurpose flatness measurement system, the design, laboratory prototype and performance of which are also described in this thesis. The usability and competitiveness of the novel system have been increased by adding 2D dimensional measurements such as edges, widths, lengths etc., and even surface quality inspection, to the same unit. The depth resolution of the laboratory system is better than 0.6 mm.


APISan: Sanitizing {API} Usages through Semantic Cross-Checking


Learning local and global contexts using a convolutional recurrent network model for relation classification in biomedical text.


title: Optimal design of a miniature quad tilt rotor UAV

abstract: This paper describes the design procedure of a convertible miniature (mini and micro) quad tilt rotor unmanned air vehicle (UAV), which has about 2 meters of wing span, one hour of mission time and 5 kilograms of total weight. The aircraft is driven by four brushless direct current motors, and the structure of it completely made of composite materials. When the wing and tail of the aircraft are dismounted, it operates as a quad-rotor with tilting rotors. The aircraft is planned to carry a gimbal camera weighing about 700 grams. The primary operation areas of the aircraft are intelligence, surveillance and reconnaissance missions in an operational radius of 15 km. The aircraft is capable of changing its flight modes between horizontal and vertical flights as required by the mission profile.


title: Aérgia: exploiting packet latency slack in on-chip networks

abstract: Traditional Network-on-Chips (NoCs) employ simple arbitration strategies, such as round-robin or oldest-first, to decide which packets should be prioritized in the network. This is counter-intuitive since different packets can have very different effects on system performance due to, e.g., different level of memory-level parallelism (MLP) of applications. Certain packets may be performance-critical because they cause the processor to stall, whereas others may be delayed for a number of cycles with no effect on application-level performance as their latencies are hidden by other outstanding packets'latencies. In this paper, we define slack as a key measure that characterizes the relative importance of a packet. Specifically, the slack of a packet is the number of cycles the packet can be delayed in the network with no effect on execution time. This paper proposes new router prioritization policies that exploit the available slack of interfering packets in order to accelerate performance-critical packets and thus improve overall system performance. When two packets interfere with each other in a router, the packet with the lower slack value is prioritized. We describe mechanisms to estimate slack, prevent starvation, and combine slack-based prioritization with other recently proposed application-aware prioritization mechanisms. We evaluate slack-based prioritization policies on a 64-core CMP with an 8x8 mesh NoC using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 21.0% over the commonlyused round-robin policy. Averaged over 56 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 10.3%, while also reducing application-level unfairness by 30.8%.


title: Color-defective vision and computer graphics displays

abstract: A color space defined by the fundamental spectral sensitivity functions of the human visual system is used to assist in the design of computer graphics displays for color-deficient users. The functions are derived in terms of the CIE standard observer color-matching functions. The Farnsworth-Munsell 100-hue test, a widely used color vision test administered using physical color samples, is then implemented on a digitally controlled color television monitor. The flexibility of this computer graphics medium is then used to extend the Farnsworth-Munsell test in a way that improves the specificity of the diagnoses rendered by the test. The issue of how the world appears to color-deficient observers is addressed, and a full-color image is modified to represent a color-defective view of the scene. Specific guidelines are offered for the design of computer graphics displays that will accommodate almost all color-deficient users. >


title: Fatigue characteristics of McKibben artificial muscle actuators

abstract: The McKibben artificial muscle is a pneumatic actuator whose properties include a very high force to weight ratio. This characteristic makes it very attractive for a wide range of applications such as mobile robots and prosthetic appliances for the disabled. Typical applications often require a significant number of repeated contractions and extensions or cycles of the actuator. This repeated action leads to fatigue and failure of the actuator, yielding a life span that is often shorter than its more common robotic counterparts such as electric motors or pneumatic cylinders. In this paper, we develop a model that predicts the maximum number of life cycles of the actuator based on available uniaxial tensile properties of the actuator's inner bladder. Experimental results, which validate the model, reveal McKibben actuators fabricated with natural latex rubber bladders have a fatigue limit 24 times greater than actuators fabricated with synthetic silicone rubber at large contraction ratios.


title: LCC Approaches to Knowledge Base Population at TAC 2010

abstract: The Knowledge Base Population (KBP) track at the Text Analysis Conference 2010 marks the second year of this important information extractionevaluation. Thispaperdescribesthe design and implementation of LCC’s systems which participated in the tasks of Entity Linking, Slot Filling, and the new task of Surprise Slot Filling. For the entitylinkingtask, ourtop score was achieved through a robust context modeling approach which incorporates topical evidence. For slot filling, we used the output of the entity linking system together with a combination of different types of relation extractors. For surprise slot filling, our customizableextractionsystem was extremely useful due to the time sensitive nature of the task.


title: Semantic Interoperability on the Web

abstract: XML will have a profound impact on the way data is exchanged on the Internet. An important feature of thislanguage is the separation of content from presentation, which makes it easier to select and/or reformat the data.However, due to the likelihood of numerous industry and domain specific DTDs, those who wish to integrateinformation will still be faced with the problem of semantic interoperability. In this paper we discuss why thisproblem is not solved by XML, and then discuss why the Resource Description Framework is only a partial solution.We then present the SHOE language, which we feel has many of the features necessary to enable a semantic web,and describe an existing set of tools that make it easy to use the language.Jeff Heflin, University of MarylandJames Hendler, University of Maryland


title: Two-Stage Deep Learning for Noisy-Reverberant Speech Enhancement

abstract: In real-world situations, speech reaching our ears is commonly corrupted by both room reverberation and background noise. These distortions are detrimental to speech intelligibility and quality, and also pose a serious problem to many speech-related applications, including automatic speech and speaker recognition. In order to deal with the combined effects of noise and reverberation, we propose a two-stage strategy to enhance corrupted speech, where denoising and dereverberation are conducted sequentially using deep neural networks. In addition, we design a new objective function that incorporates clean phase during model training to better estimate spectral magnitudes, which would in turn yield better phase estimates when combined with iterative phase reconstruction. The two-stage model is then jointly trained to optimize the proposed objective function. Systematic evaluations and comparisons show that the proposed algorithm improves objective metrics of speech intelligibility and quality substantially, and significantly outperforms previous one-stage enhancement systems.


title: LIN validation frame-work: a novel approach

abstract: Automotive semiconductor industry is passing through an era where it is pushing the complex mechanical interconnects to be replaced by the mechatronical system. To simplify the wiring design and to handle the communication process effectively between various systems in automobiles, LIN (Local Interconnect Network) bus protocol has been designed. It is widely used in numerous applications like door locks, mirrors, rain sensors, powertrain, central ECU, etc even at very high temperature.


title: Control and Understanding in Malware and Legitimate Software

abstract: This paper presents a study examining mental models of malware and regular software, in search of deep misunderstandings about malware and software which can be used in the design of new software and educational material. The study involved both a questionnaire, and two diagramming exercises. We decided to use a diagramming exercise because it is an effective medium for expressing spatial information which is important to mental models, and can get lost in verbal reports. Ours is the first study to examine mental models of malware using this technique. For the diagramming tasks, participants were asked to draw their understanding of how a word processor and malware work, respectively. Several key patterns emerged. General knowledge about malware, shown in the questionnaire responses was reasonable, but the deeper understanding of how malware functions, shown in the drawings, was concerning. Participants showed lesser knowledge of malware compared to regular software, and they seemed to regard malware as a fundamentally different kind of entity than regular software. They made black-and-white distinctions between malware and regular software in terms of whether the software is helpful or harmful, who the software serves, and who controls it. We discuss how these findings relate to decision-making online, and suggest that it might be beneficial to increase support for the control users have over their software. We speculate this might better equip users to make safe decisions surrounding software, thereby decreasing the effectiveness of malware.


title: A Half-Bridge LLC Resonant Converter Adopting Boost PWM Control Scheme for Hold-Up State Operation

abstract: This paper presents a half-bridge LLC resonant converter having a boost pulse width modulation (PWM) converter characteristic for hold-up state operation. The proposed converter is based on a half-bridge LLC resonant converter structure and a single auxiliary switch is added at the primary side. The converter has two different operational characteristics. It shows the same operational characteristic with the conventional LLC resonant converters during nominal state, which is frequency modulation (FM) method. However, when ac line lost and the converter enters into the hold-up time state, which requires wide voltage gain changes, the control method of the proposed converter is changed to the PWM method using the auxiliary switch. Since the proposed converter compensates wide voltage gain variation with PWM method of the auxiliary switch rather than adopting the FM method of main switches, the frequency variation range for the LLC resonant converter is highly reduced in the proposed converter. Therefore, the transformer in the proposed converter can be designed at the optimal operating point and it results in decreased conduction loss of the magnetizing inductor current. Furthermore, the maximum voltage gain of the proposed converter is easily increased by extending the duty ratio of the auxiliary switch. It helps to decrease the link capacitance. To verify the effectiveness of the proposed circuit, operational principle will be explained and experimental results will be presented with following specification. 100 kHz of switching frequency, 250-400 V of input voltage range, 250 V of output voltage, and 75 W output power.


title: Adoption and Use of Social Media in Small and Medium-Sized Enterprises

abstract: In recent years, social media have been increasingly adopted in enterprises. Enterprises use social media as an additional way to get in contact with their customers and support internal communication and collaboration. However, little research is devoted to the adoption and internal usage of social media in small and medium-sized enterprises (SMEs), which are of high social and economic importance. The purpose of this paper is to examine the adoption, usage, and benefits of social media in SMEs as well as potential concerns that may prevent a wider adoption of social media in SMEs. Therefore, a survey of decision-makers in German SMEs was conducted. Findings based on 190 responses indicate that SMEs started to use internal social media (e.g., wikis, blogs) in order to support collaboration among employees and to improve knowledge management. However, SMEs still face problems to manage adoption and to identify relevant business values. Based on our results, we derive several implications for SMEs, in particular how to overcome the obstacles to a wider adoption of social media.


title: Physiological determinants of Yo-Yo intermittent recovery tests in male soccer players

abstract: The physiological determinants of performance in two Yo-Yo intermittent recovery tests (Yo-YoIR1 and Yo-YoIR2) were examined in 25 professional (n = 13) and amateur (n = 12) soccer players. The aims of the study were (1) to examine the differences in physiological responses to Yo-YoIR1 and Yo-YoIR2, (2) to determine the relationship between the aerobic and physiological responses to standardized high-intensity intermittent exercise (HIT) and Yo-Yo performance, and (3) to investigate the differences between professional and amateur players in performance and responses to these tests. All players performed six tests: two versions of the Yo-Yo tests, a test for the determination of maximum oxygen uptake (\( {\dot{\text{V}}}{\text{O}}_{{2\,{ \max }}} \)), a double test to determine \( {\dot{\text{V}}}{\text{O}}_{2} \) kinetics and a HIT evaluation during which several physiological responses were measured. The anaerobic contribution was greatest during Yo-YoIR2. \( {\dot{\text{V}}}{\text{O}}_{{2\,{ \max }}} \) was strongly correlated with Yo-YoIR1 (r = 0.74) but only moderately related to Yo-YoIR2 (r = 0.47). The time constant (τ) of \( {\dot{\text{V}}}{\text{O}}_{2} \) kinetics was largely related to both Yo-Yo tests (Yo-YoIR1: r = 0.60 and Yo-YoIR2: r = 0.65). The relationships between physiological variables measured during HIT (blood La−, H+, HCO3− and the rate of La− accumulation) and Yo-Yo performance (in both versions) were very large (r > 0.70). The physiological responses to HIT and the τ of the \( {\dot{\text{V}}}{\text{O}}_{2} \) kinetics were significantly different between professional and amateur soccer players, whilst \( {\dot{\text{V}}}{\text{O}}_{{2\,{ \max }}} \) was not significantly different between the two groups. In conclusion, \( {\dot{\text{V}}}{\text{O}}_{{2\,{ \max }}} \) is more important for Yo-YoIR1 performance, whilst τ of the \( {\dot{\text{V}}}{\text{O}}_{2} \) kinetics and the ability to maintain acid–base balance are important physiological factors for both Yo-Yo tests.


Detection and Tracking of Moving Objects Using 2 . 5 D Motion Grids


title: Elastic Transformation of the Image Pixel Grid for Similarity Based Face Identification

abstract: Nonlinear transformation of one image plane relative to another by spatially constrained elastic matching of two pixel grids is proposed as a technique of measuring image similarity for the purpose of featureless face identification. The elastic matching algorithm is devised as a combination of two dynamic programming procedures applied independently to each row and then to each column of the pixel grid. In contrast to the commonly adopted method of measuring face image similarity based on the dynamic link architecture, the proposed method is non-iterative and it avoids image segmentation. Most importantly the method provides the linear computational complexity with respect to the number of pixels without application of parallel computers.


title: On-line EM Algorithm for the Normalized Gaussian Network

abstract: A normalized gaussian network (NGnet) (Moody & Darken, 1989) is a network of local linear regression units. The model softly partitions the input space by normalized gaussian functions, and each local unit linearly approximates the output within the partition. In this article, we propose a new on-line EMalgorithm for the NGnet, which is derived from the batch EMalgorithm (Xu, Jordan, &Hinton 1995), by introducing a discount factor. We show that the on-line EM algorithm is equivalent to the batch EM algorithm if a specific scheduling of the discount factor is employed. In addition, we show that the on-line EM algorithm can be considered as a stochastic approximation method to find the maximum likelihood estimator. A new regularization method is proposed in order to deal with a singular input distribution. In order to manage dynamic environments, where the input-output distribution of data changes over time, unit manipulation mechanisms such as unit production, unit deletion, and unit division are also introduced based on probabilistic interpretation. Experimental results show that our approach is suitable for function approximation problems in dynamic environments. We also apply our on-line EM algorithm to robot dynamics problems and compare our algorithm with the mixtures-of-experts family.


title: Practical Signal Recovery from Random Projections

abstract: Can we recover a signal f 2 R N from a small number of linear measurements? A series of recent papers developed a collection of results showing that it is surprisingly possible to reconstruct certain types of signals accurately from limited measurements. In a nutshell, suppose that f is compressible in the sense that it is well-approximated by a linear combination of M vectors taken from a known basis . Then not knowing anything in advance about the signal, f can (very nearly) be recovered from about M logN generic nonadaptive measurements only. The recovery procedure is concrete and consists in solving a simple convex optimization program. In this paper, we show that these ideas are of practical significance. Inspired by theoretical developments, we propose a series of practical recovery procedures and test them on a series of signals and images which are known to be well approximated in wavelet bases. We demonstrate empirically that it is possible to recover an object from about 3M ‐5M projections onto generically chosen vectors with the same accuracy as the ideal M -term wavelet approximation. We briefly discuss possible implications in the areas of data compression and medical imaging.


title: Cytoreductive surgery (SRC) and hyperthermic intraperitoneal chemotherapy (HIPEC) for treatment of peritoneal carcinomatosis: Our initial experience and technical details.

abstract: OBJECTIVE ::: The aim of this study is to present our initial experience in peritoneal carcinomatosis treatment and the technical details of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) in the light of current literature. ::: ::: ::: MATERIAL AND METHODS ::: Data of 27 consecutive patients who were treated with CRS and HIPEC for peritoneal carcinomatosis in Medical Park Samsun Hospital, between November 2012 and September 2014 were retrospectively reviewed. Treatment indication and management were evaluated at the multidisciplinary oncology council. All patients underwent CRS and HIPEC with the aim of complete cytoreduction. Patients with unresectable disease and/or palliative surgery were excluded from analysis. Perioperative complications were classified according to Clavien-Dindo classification, and HIPEC-related side effects were identified using National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) criteria. Demographic, clinical and histopathological data of the patients were analyzed. ::: ::: ::: RESULTS ::: The mean age was 54 (32-72). Nineteen patients were female. The origin of peritoneal carcinomatosis was colorectal cancer in 12 patients, ovarian cancer in 12 patients, gastric cancer in 2 patients and pseudomyxoma peritonei in 1 patient. The mean Peritoneal Carcinomatosis Index was 12 (3-32), with a mean operative time of 420 (300-660) minutes. Perioperative morbidity, HIPEC-related toxicity and perioperative mortality were observed in eight (30%), one (3.7%) and four patients (14.8%), respectively. During a mean follow up of 13 (1-22) months, overall and disease-free survival rates were 95.8% and 82.6%, respectively. Two patients with colorectal cancer (after 9 and 12 months) and one patient with ovarian cancer (after 11 months) had intra-abdominal recurrence. One patient with ovarian cancer had liver metastases 13 months after surgery, and underwent resection of segments 6-7. The remaining patients are being followed-up without any recurrence. ::: ::: ::: CONCLUSION ::: Cytoreductive surgery and HIPEC have favorable results in the treatment of patients with peritoneal carcinomatosis. Compatible with the literature, surgical outcomes of the presented series are encouraging for this treatment modality that have been recently popularized in our country. Careful perioperative evaluation, proper patient selection and multidisciplinary approach are essential for success in curative treatment of peritoneal carcinomatosis.


title: New High Dose Pulsed Hyaluronidase Protocol for Hyaluronic Acid Filler Vascular Adverse Events

abstract: &NA; The purpose of this article is to update the changes to the author's protocols used to manage acute filler related vascular events from those previously published in this journal. For lack of a better term, this new protocol has been called the High Dose Pulsed Hyaluronidase (HDPH) protocol for vascular embolic events with hyaluronic acid (HA) fillers. The initial protocol used involved many different modalities of treatment. The current protocol is exceedingly simple and involves solely the use of hyaluronidase in repeated high doses. Despite the simplicity of the treatment, it has proven itself to be very successful over the past two years of clinical use. There has been no partial or complete skin loss associated with this protocol since its implementation if the protocol was implemented within 2 days of the ischemic event onset. The protocol involves diagnosis and repeated administration of relatively high doses hyaluronidase (HYAL) into the ischemic tissue repeated hourly until resolution (as detected clinically through capillary refill, skin color, and absence of pain). The dosage of HYAL varies as the amount of ischemic tissue, consistent with the new underlying hypothesis that we must flood the occluded vessels with a sufficient concentration of HYAL for a sufficient period of time in order to dissolve the HA obstruction to the point where the products of hydrolysis can pass through the capillary beds. Although vascular embolic events are rare, it is important to note that the face has higher risk and lower risk areas for filler treatment, but there are no “zero risk” areas with respect to filler treatments. Even with good anatomic knowledge and correct technique, there is still some nonzero risk of vascular embolic events (including highly skilled, experienced injectors). However, with careful low pressure, low volume injection technique, and adequate preparation for treatment of acute vascular events, the risk is quite manageable and the vast majority of adverse events are very treatable with an excellent prognosis, with a few exceptions. This new protocol offers excellent results, but requires further research to determine optimal parameters for various HA fillers.


title: Deep learning networks for stock market analysis and prediction

abstract: Deep learning networks are applied to stock market analysis and prediction.A comprehensive analysis with different data representation methods is offered.Five-minute intraday data from the Korean KOSPI stock market is used.The network applied to residuals of autoregressive model improves prediction.Covariance estimation for market structure analysis is improved with the network. We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using high-frequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methodsprincipal component analysis, autoencoder, and the restricted Boltzmann machineon the networks overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction.


title: Twitter-driven YouTube Views: Beyond Individual Influencers

abstract: This paper proposes a novel method to predict increases in YouTube viewcount driven from the Twitter social network. Specifically, we aim to predict two types of viewcount increases: a sudden increase in viewcount (named as Jump), and the viewcount shortly after the upload of a new video (named as Early). Experiments on hundreds of thousands of videos and millions of tweets show that Twitter-derived features alone can predict whether a video will be in the top 5% for Early popularity with 0.7 Precision@100. Furthermore, our results reveal that while individual influence is indeed important for predicting how Twitter drives YouTube views, it is a diversity of interest from the most active to the least active Twitter users mentioning a video (measured by the variation in their total activity) that is most informative for both Jump and Early prediction. In summary, by going beyond features that quantify individual influence and additionally leveraging collective features of activity variation, we are able to obtain an effective cross-network predictor of Twitter-driven YouTube views.


title: Preparation of carbon nanotubes composite sheet using electrophoretic deposition process

abstract: Since the discovery by Ijima [1] in 1991, carbon nanotubes have attracted tremendous attention. Interest in such a material arise from the fact that the mechanical, chemical, electrical, optical, magnetic, and electroand magneto-optical properties of these nanotubes are different from their bulk properties, attributed largely to the quantum confinement effects [2–11]. For instance, theoretical predictions have suggested that the Young’s modulus of carbon nanotubes could be as high as 1– 5 TPa [12, 13] and the theoretical tensile strength as high as 200 GPa [14]. Various experimental techniques [15–19] conducted on the mechanical properties of carbon nanotubes also showed very promising results, albeit still much smaller than theoretical prediction. Furthermore, it is widely accepted that the issue of heat dissipation in miniature devices becomes increasingly important as the size of the device reduces. Therefore, carbon nanotubes may play an important role in improving the performance and stability of nanosized devices because of their high thermal conductivity. In spite of the vast potential of nanoscale materials, some inherent problems have hindered significantly, in some case prohibitively, further progress. First, it is extremely difficult to manipulate the sample for any tests due to its sheer size. To conduct a measurement on an individual nanotube, researchers have to select a suitable tube, which is extremely difficult and may result in the situation that the selected tube is very likely not a typical one, and this may be one of the reasons why similar researches usually lead to different results. Next, although many theoretical predictions have indicated unique properties of the nano materials, the traditional test methods and equipment are not designed for them so that it is extremely difficult to say the least to get direct experimental evidence and reliable data to validate the predictions. As a result, we are still largely in a primary stage to probe and investigate the properties, and have proceeded even more slowly for practical applications. Obviously, by assembling the nanotubes into a macroscale object, we can then conduct all the conventional tests at samples of manageable dimensions to examine the material properties, which in turn will lay a solid foundation of experimental evidence to facilitate further studies of all related issues. More importantly, a new bulk material made of nanotubes may offer many attractive applications of its own at the conventional


title: The Persona Effect: How Substantial Is It?

abstract: Personification of interface agents has been speculated to have several advantages, such as a positive effect on agent credibility and on the perception of learning experience. However, important questions less often addressed so far are what effect personification has on more objective measures, such as comprehension and recall, and furthermore, under what circumstances this effect (if any) occurs. We performed an empirical study with adult participants to examine the effect of the Ppp Persona not only on subjective but also on objective measures. In addition, we tested it both with technical and non-technical domain information. The results of the study indicate that the data from the subjective measures support the so called persona effect for the technical information but not for non-technical information. With regard to the objective measures, however, neither a positive nor a negative effect could be found. Implications for software development are discussed.


title: Punching shear strength of steel fibre reinforced concrete slabs

abstract: The ultimate strength of reinforced concrete slabs is frequently governed by the punching shear capacity, which may be increased with addition of traditional fitments such as reinforcing steel, headed studs or shear heads. In addition to these traditional methods of strengthening against punching, steel fibre reinforcement has proved to be an effective and viable alternative. The addition of fibres into the concrete improves not only the shear behaviour but also the deformation capacity of reinforced concrete slabs. This paper presents a mechanical model for predicting the punching strength and behaviour of concrete slabs reinforced with steel fibres as well as conventional reinforcement. The proposed model is validated against a wide number of available experimental data and its accuracy is verified. On this basis, a simple design equation for the punching shear capacity of steel fibre reinforced concrete (SFRC) slabs is proposed.


title: Modeling consumer loan default prediction using ensemble neural networks

abstract: In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network. The neural networks are trained using real world credit application cases from a German bank datasets which has 1000 cases; each case with 24 numerical attributes; upon, which the decision is based. The aim of this paper was to compare between the resulting models produced from using different training algorithms, scaled conjugate gradient backpropagation, Levenberg-Marquardt algorithm, One-step secant backpropagation (SCG, LM and OSS) and an ensemble of SCG, LM and OSS. Empirical results indicate that training algorithms improve the design of a loan default prediction model and ensemble model works better than the individual models.


title: A new method for violence detection in surveillance scenes

abstract: Violence detection is a hot topic for surveillance systems. However, it has not been studied as much as for action recognition. Existing vision-based methods mainly concentrate on violence detection and make little effort to determine the location of violence. In this paper, we propose a fast and robust framework for detecting and localizing violence in surveillance scenes. For this purpose, a Gaussian Model of Optical Flow (GMOF) is proposed to extract candidate violence regions, which are adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. Violence detection is then performed on each video volume constructed by densely sampling the candidate violence regions. To distinguish violent events from nonviolent events, we also propose a novel descriptor, named as Orientation Histogram of Optical Flow (OHOF), which are fed into a linear SVM for classification. Experimental results on several benchmark datasets have demonstrated the superiority of our proposed method over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.


title: Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space

abstract: We propose an approach to adjective-noun composition (AN) for corpus-based distributional semantics that, building on insights from theoretical linguistics, represents nouns as vectors and adjectives as data-induced (linear) functions (encoded as matrices) over nominal vectors. Our model significantly outperforms the rivals on the task of reconstructing AN vectors not seen in training. A small post-hoc analysis further suggests that, when the model-generated AN vector is not similar to the corpus-observed AN vector, this is due to anomalies in the latter. We show moreover that our approach provides two novel ways to represent adjective meanings, alternative to its representation via corpus-based co-occurrence vectors, both outperforming the latter in an adjective clustering task.


title: A dynamically coupled neural oscillator network for image segmentation

abstract: We propose a dynamically coupled neural oscillator network for image segmentation. Instead of pair-wise coupling, an ensemble of oscillators coupled in a local region is used for grouping. We introduce a set of neighborhoods to generate dynamical coupling structures associated with a specific oscillator. Based on the proximity and similarity principles, two grouping rules are proposed to explicitly consider the distinct cases of whether an oscillator is inside a homogeneous image region or near a boundary between different regions. The use of dynamical coupling makes our segmentation network robust to noise on an image, and unlike image processing algorithms no iterative operation is needed for noise removal. For fast computation, a segmentation algorithm is abstracted from the underlying oscillatory dynamics, and has been applied to synthetic and real images. Simulation results demonstrate the effectiveness of our oscillator network in image segmentation.


1033 FAULT-TOLERANT CONTROL SYSTEMS : THE 1997 SITUATION


title: A low profile LLC resonant converter using novel planar transformer

abstract: In this paper, a low profile LLC resonant converter with two planar transformers is proposed for a slim SMPS (Switching Mode Power Supply). Design procedures and voltage gain characteristics on the proposed planar transformer and converter are described in detail. Two planar transformers applied to LLC resonant converter are connected in series at primary and in parallel by the center-tap winding at secondary. Based on the theoretical analysis and simulation results of the voltage gain characteristics, a 300W LLC resonant converter for LED TV power module is designed and tested.


title: Brand in the hand: A cross-market investigation of consumer acceptance of mobile marketing

abstract: Given the great potential of developing marketing campaigns delivered via mobile devices and the evolution of near-field communication technologies, this study examines factors influencing consumers’ acceptance of untethered, or mobile, marketing across three influential markets: the United States, China, and Europe. We examine the extent to which the usefulness of mobile information/programs and individual characteristics—namely innovativeness, personal attachment, and risk avoidance—jointly influence attitudes toward mobile marketing, and how the latter influences consumers’ mobile marketing activity across three large and influential markets. We found perceived usefulness, consumer innovativeness, and personal attachment to directly influence attitudes toward mobile marketing in all three markets. In China and Europe, risk avoidance also negatively influences attitudes toward mobile marketing. Marketers seeking to build and maintain customer relationships via mobile platforms should view these individual characteristics as levers brands can push to amplify consumers’ acceptance of mobile marketing.


title: Exploring security vulnerabilities of unmanned aerial vehicles

abstract: We are currently observing a significant increase in the popularity of Unmanned Aerial Vehicles (UAVs), popularly also known by their generic term drones. This is not only the case for recreational UAVs, that one can acquire for a few hundred dollars, but also for more sophisticated ones, namely professional UAVs, whereby the cost can reach several thousands of dollars. These professional UAVs are known to be largely employed in sensitive missions such as monitoring of critical infrastructures and operations by the police force. Given these applications, and in contrast to what we have been seeing for the case of recreational UAVs, one might assume that professional UAVs are strongly resilient to security threats. In this demo we prove such an assumption wrong by presenting the security gaps of a professional UAV, which is used for critical operations by police forces around the world. We demonstrate how one can exploit the identified security vulnerabilities, perform a Man-in-the-Middle attack, and inject control commands to interact with the compromised UAV. In addition, we discuss appropriate countermeasures to help improving the security and resilience of professional UAVs.


title: NTT's Japanese-English Cross-Language Question Answering System

abstract: This paper describes NTT’s Japanese-English Cross-Language Question Answering System SAIQAJ/E. The system performed best among eight systems that participated in the Japanese-English subtask of the NTCIR Cross-Language Question Answering task. For cross-language document retrieval, we used a dictionary-based approach without word sense disambiguation. We used a synonym operator to represent translation alternatives. Our experiments show that the synonym operator improved the retrieval precision of our proximity-based document retrieval module. We also developed a web-based back-transliteration submodule for unknown katakana words.


title: Mechanical design and basic analysis of a modular robot with special climbing and manipulation functions

abstract: For applications in new fields such as agriculture, forestry and high-altitude work, a modular robot is developed for climbing posts, trees and trusses and manipulating objects. Inspired by the climbing motion of sloths, chimpanzees and inchworms, this new robot is designed to be anthropomorphic. It consists of six one-DOF joint modules in series and two special grippers at ends. The grippers are specially designed for various objects with different shapes and sizes. With the structure similar to a manipulator, the robot has both special climbing function and manipulation function. It is expected to perform a variety of tasks on posts, in trees and in trusses. Mechanical design and some basic analysis including the kinematics of the robot and force analysis of the grippers are presented in this paper.


title: Business-Driven IT Transformation at Royal Philips: Shedding Light on (Un)Rewarded Complexity

abstract: In 2013, Royal Philips was two years into a daunting transformation. Following declining financial performance, CEO Frans van Houten aimed to turn the Dutch icon into a “high-performing company” by 2017. This case study examines the challenges of the business-driven IT transformation at Royal Philips, a diversified technology company. The case discusses three crucial issues. First, the case reflects on Philips’ aim at creating value from combining locally relevant products and services while also leveraging its global scale and scope. Rewarded and unrewarded business complexity is analyzed. Second, the case identifies the need to design and align multiple elements of an enterprise (organizational, cultural, technical) to balance local responsiveness with global scale. Third, the case explains the role of IT (as an asset instead of a liability) in Philips’ transformation and discusses the new IT landscape with its digital platforms, and the new practices to create effective business-IT partnerships.


title: SICK through the SemEval glasses. Lesson learned from the evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment

abstract: This paper is an extended description of SemEval-2014 Task 1, the task on the evaluation of Compositional Distributional Semantics Models on full sentences. Systems participating in the task were presented with pairs of sentences and were evaluated on their ability to predict human judgments on (1) semantic relatedness and (2) entailment. Training and testing data were subsets of the SICK (Sentences Involving Compositional Knowledge) data set. SICK was developed with the aim of providing a proper benchmark to evaluate compositional semantic systems, though task participation was open to systems based on any approach. Taking advantage of the SemEval experience, in this paper we analyze the SICK data set, in order to evaluate the extent to which it meets its design goal and to shed light on the linguistic phenomena that are still challenging for state-of-the-art computational semantic systems. Qualitative and quantitative error analyses show that many systems are quite sensitive to changes in the proportion of sentence pair types, and degrade in the presence of additional lexico-syntactic complexities which do not affect human judgements. More compositional systems seem to perform better when the task proportions are changed, but the effect needs further confirmation.


title: Smart RDF Data Storage in Graph Databases

abstract: Graph Database Management Systems (GDBMs) provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In particular, the conversion of the persistent layer of an application from a RDF to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a RDF data store to a graph database by exploiting the ontology and the constraints of the source. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.


title: Improving load forecasting based on deep learning and K-shape clustering

abstract: One of the most crucial tasks for utility companies is load forecasting in order to plan future demand for generation capacity and infrastructure. Improving load forecasting accuracy over a short period is a challenging open problem due to the variety of factors that influence the load, and the volume of data that needs to be considered. This paper proposes a new approach for short term load forecasting using an effective new combination of clustering and deep learning methods, along with a new weighted aggregation mechanism. Our evaluation using smart meter data from a publicly available real-life dataset demonstrates the improved accuracy of our approach over existing methods.


title: Formation and morphological stability of polybutadiene rubber fibers prepared through combination of electrospinning and in-situ photo-crosslinking

abstract: Abstract Herein we report that fibers of polybutadiene rubber (BR) with high morphological stability were made through combination of electrospinning and in-situ photo-crosslinking. This study revealed that (1) the formation of electrospun BR fibers was correlated with the overlap/entanglement of BR macromolecules in the spinning solutions; for the formation of uniform fibers without beads and/or beaded fibers, the concentration of spinning solution had to be higher than the “critical chain entanglement concentration (Ce)”; and (2) the in-situ photo-crosslinking was an effective method to considerably improve the morphological stability of electrospun BR fibers; upon prolonged storage under ambient condition or even upon immersion in tetrahydrofuran (a good solvent for BR), the crosslinked BR fibers well retained their morphology. It is envisioned that electrospun rubber fibers could be utilized for the development of innovative composites with substantially improved toughness and/or impact strength.


title: Large deflection micromechanical scanning mirrors for linear scans and pattern generation

abstract: An electrostatically driven silicon micro scanning mirror (MSM) for one-dimensional (1-D) and two-dimensional (2-D) deflection of light is presented. A special configuration of the driving electrodes allows the use of small electrode gaps without restricting the deflection of the plate geometrically. In this paper, the starting of the oscillation and the operation of the scanner is discussed. Experimental results show that scan angles of up to 60/spl deg/ can be achieved at a driving voltage of only 20 V. The 2-D deflection of a laser beam is obtained by a gimbal mounting of the mirror plate. For the fabrication of the devices, SOI-wafers are used as the base material. The mechanical structures are defined by a deep silicon etch. For the electrical isolation of areas on the movable frame, polysilicon-filled trenches are used. The mechanical stability of the scanners is tested. The devices resist shocks of more than 1000 g and show no change of the resonance frequency even after long run tests of 7/spl times/10/sup 9/ periods.


title: Fall perception for elderly care: A fall detection algorithm in Smart Wristlet mHealth system

abstract: Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the “bottleneck” of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at today's socio-economic level.


title: Online wavelet based control of hybrid energy storage systems for smoothing wind farm output

abstract: An essential issue in the hybrid energy storage system (HESS) control for smoothing wind farm output is designing a controller that can decompose the frequency component of wind power quickly and allocate the power to the respective device in online processing. In this paper, a HESS consists of conventional battery-Li-Ion Battery and Ultra Capacitor (UC) is adopted to meet the electric grid technical requirements for smoothing wind power. We propose a HESS power allocation control consisting of two stages: online wavelet filter and a novel power allocation optimization algorithm. An online wavelet filtering method is established to minimize the phase lag in decomposing frequency components. The results show a significant reduction in UCs' utilization as compared with conventional method. The optimization algorithm also provides an optimal power allocation between storage devices with fast enough computation time to satisfy the need of online processing.


title: State-of-the-Art Medium Access Control (MAC) Protocols for Underwater Acoustic Networks: A Survey Based on a MAC Reference Model

abstract: Similar to radio-frequency-based wireless networks (RWNs) used in terrestrial environments, the medium access control (MAC) protocol is a key element for underwater acoustic networks (UWANs). However, due to peculiar features of underwater acoustic channels such as long propagation delay, very small channel capacity, low channel reliability, and high dynamics of channel quality, not only MAC protocols but also MAC design strategies originally developed for RWNs cannot work well in UWANs. A large number of UWAN MAC protocols have been investigated in the literature, and most of them have not been reviewed in the available surveys. To allow the reader to have an overview on the state-of-the-art UWAN MAC protocols, this paper reviews these proposals with an enhanced MAC reference model. This model decomposes an MAC protocol into a couple of components that make up a common structure for various MAC protocols so that they can be more easily understood. The major remaining issues and possible research directions are also discussed.


title: Smartphone Spectrometers

abstract: Smartphones are playing an increasing role in the sciences, owing to the ubiquitous proliferation of these devices, their relatively low cost, increasing processing power and their suitability for integrated data acquisition and processing in a 'lab in a phone' capacity. There is furthermore the potential to deploy these units as nodes within Internet of Things architectures, enabling massive networked data capture. Hitherto, considerable attention has been focused on imaging applications of these devices. However, within just the last few years, another possibility has emerged: to use smartphones as a means of capturing spectra, mostly by coupling various classes of fore-optics to these units with data capture achieved using the smartphone camera. These highly novel approaches have the potential to become widely adopted across a broad range of scientific e.g., biomedical, chemical and agricultural application areas. In this review, we detail the exciting recent development of smartphone spectrometer hardware, in addition to covering applications to which these units have been deployed, hitherto. The paper also points forward to the potentially highly influential impacts that such units could have on the sciences in the coming decades.


title: A review on the prevention of inflammatory periimplant diseases

abstract: Background: An impressive number of dental implants are inserted worldwide. Evolution in dental implants and simplification of surgical techniques allowed a significant increase in the number of dentists involved in implant surgery. Most of them are general dentists, are not always sufficiently formed and experienced, frequently use low-quality implants, do not adopt the proper patient selective criteria, do not adequately monitor and maintain the inserted implants, and do not report their own statistics to the dental community. Consequently, the incidence of inflammatory periimplant diseases (IPDs) has progressively increased to values significantly higher than those previously indicated by the scientific literature. Materials and Methods: Two main literature searches were undertaken in October 2018 in the PubMed Website database. Only articles written in English and published from 2008 onward were considered; “Clinical Trial,” “Meta analysis,” “Observational study,” “Review,” and “Validation study” were selected as article type filters. The following keywords were used in the searches: “Peri implantitis prevention” and “Dental implant failure prevention.”Results: Preventive measures are analyzed according to the different factors that can favor the occurrence of an infection. The factors are divided into (i) implant dependent, (ii) patient dependent, and (iii) surgeon dependent. Conclusions: Scientific and clinical data confirm that when materials are selected with care, patients are carefully evaluated for factors of risk and attitude to adhere to the necessary maintenance program, and operative protocols and maintenance programs are respected dental implants can be attractive and effective tools for oral rehabilitation. Nevertheless, dentists and patients should have greater awareness that in many cases the decision to utilize dental implants cannot be taken lightly.


title: Yelp Dataset Challenge: Review Rating Prediction

abstract: Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Naive Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.


Can Parenting Styles Affect the Children’s Development of Narcissism? A Systematic Review


title: Advanced magnetic resonance imaging of neurodegenerative diseases

abstract: Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.


title: Identification and application of Extract Class refactorings in object-oriented

abstract: Refactoring is recognized as an essential practice in the context of evolutionary and agile software development. Recognizing the importance of the practice, modern IDEs provide some support for low-level refactorings. A notable exception in the list of supported refactorings is the "Extract Class" refactoring, which is conceived to simplify large, complex, unwieldy and less cohesive classes.In this work, we describe a method and a tool, implemented as an Eclipse plugin, designed to fulfill exactly this need. Our method involves three steps: (a) recognition of Extract Class opportunities, (b) ranking of the identified opportunities in terms of the improvement each one is anticipated to bring about to the system design, and (c) fully automated application of the refactoring chosen by the developer. The first step relies on an agglomerative clustering algorithm, which identifies cohesive sets of class members within the system classes. The second step relies on the Entity Placement metric as a measure of design quality. Through a set of experiments we have shown that the tool is able to identify and extract new classes that developers recognize as "coherent concepts" and improve the design quality of the underlying system.


title: An Invitation to Imitation

abstract: Abstract : Imitation learning is the study of algorithms that attempt to improve performance by mimicking a teacher's decisions and behaviors. Such techniques promise to enable effective programming by demonstration to automate tasks, such as driving, that people can demonstrate but find difficult to hand program. This work represents a summary from a very personal perspective of research on computationally effective methods for learning to imitate behavior. I intend it to serve two audiences: to engage machine learning experts in the challenges of imitation learning and the interesting theoretical and practical distinctions with more familiar frameworks like statistical supervised learning theory; and equally, to make the frameworks and tools available for imitation learning more broadly appreciated by roboticists and experts in applied artificial intelligence.


title: Behavioral predictors of acute stress symptoms during intense military training

abstract: A better understanding of factors influencing human responses to acute stress is needed to enhance prevention and treatment of stress-related disorders. In the current study, the authors examined predictors of acute stress symptoms during intense military training in 35 men. In univariate and multivariate models, perceived stress, passive coping, and emotion-focused coping during daily living predicted acute stress symptoms in response to realistic survival training, whereas active coping and problem-focused coping did not. Baseline stress levels and coping styles, both of which may be modifiable, appear to play a fundamental role in the human response to acute uncontrollable stress. Additional research is needed to better elucidate the relative and interactive contributions of behavioral predictors of acute stress.


title: An overview on crime prediction methods

abstract: In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.


title: Class II malocclusion treated with miniscrew anchorage: comparison with traditional orthodontic mechanics outcomes.

abstract: INTRODUCTION ::: Anchorage control in patients with severe skeletal Class II malocclusion is a difficult problem in orthodontic treatment. In adults, treatment often requires premolar extractions and maximum anchorage. Recently, incisor retraction with miniscrew anchorage has become a new strategy for treating skeletal Class II patients. ::: ::: ::: METHODS ::: In this study, we compared treatment outcomes of patients with severe skeletal Class II malocclusion treated using miniscrew anchorage (n = 11) or traditional orthodontic mechanics of headgear and transpalatal arch (n = 11). Pretreatment and posttreatment lateral cephalograms were analyzed. ::: ::: ::: RESULTS ::: Both treatment methods, miniscrew anchorage or headgear, achieved acceptable results as indicated by the reduction of overjet and the improvement of facial profile. However, incisor retraction with miniscrew anchorage did not require patient cooperation to reinforce the anchorage and provided more significant improvement of the facial profile than traditional anchorage mechanics (headgear combined with transpalatal arch). ::: ::: ::: CONCLUSIONS ::: Orthodontic treatment with miniscrew anchorage is simpler and more useful than that with traditional anchorage mechanics for patients with Class II malocclusion.


title: Stability of treatment for anterior open-bite malocclusion: a meta-analysis.

abstract: INTRODUCTION ::: Anterior open-bite (AOB) treatment is considered challenging because of difficulties in determining and addressing etiologic factors and the potential for relapse in the vertical dimension after treatment. In this review, we compiled evidence on the long-term stability of the major therapeutic interventions for correcting AOB. Our objective was to review and compile evidence for the stability of surgical and nonsurgical therapies for AOB malocclusion. Our data sources were PubMed, EMBASE, Cochrane Library, limited gray literature search, and hand searching. ::: ::: ::: METHODS ::: A search was performed of the electronic health literature on the stability of AOB after treatment. Hand searching of major orthodontic journals and limited gray literature searching was also performed, and all pertinent abstracts were reviewed for inclusion. Full articles were retrieved for abstracts or titles that met the initial inclusion criteria or lacked sufficient detail for immediate exclusion. Studies accepted for analysis were reviewed and their relevant data retrieved for pooling. The long-term stability estimates were pooled into nonsurgical and surgical groups, and summary statistics were generated. ::: ::: ::: RESULTS ::: One hundred five abstracts met the initial search criteria, and 21 articles were included in final analyses. Rejected articles failed to exhibit follow-up times of 12 months or more, did not include measurements of overbite (OB), or did not meet inclusion criteria. All included articles were divided into a surgical group (SX) with a mean age of 23.3 years and a nonsurgical group (NSX) with a mean age of 16.4 years. All studies were case series. Random-effects statistical models were used to pool the mean OB measures before and after treatment and also at the long-term follow-up. The pretreatment adjusted means of OB were -2.8 mm for the SX and -2.5 mm for the NSX. AOB closures up to +1.6 mm (SX) and +1.4 mm (NSX) were achieved. Relapse in the SX group during the mean 3.5 years of follow-up reduced the OB to +1.3 mm; the NSX group relapsed to +0.8 mm in the mean 3.2 years of follow-up. Pooled results indicated reasonable stability of both the SX (82%) and NSX (75%) treatments of AOB measured by positive OB at 12 or more months after the treatment interventions. ::: ::: ::: CONCLUSIONS ::: In the included case series publications, success of both the SX and NSX treatments of AOB appeared to be greater than 75%. Because the SX and the NSX were examined in different studies and applied to different clinical populations, no direct assessment of comparative effectiveness was possible. The pooled results should be viewed with caution because of the lack of within-study control groups and the variability among studies.


title: Fatal impalement injury of the head due to a curtain rail

abstract: During a dispute, a boy was injured by a curtain rail which penetrated the skull in the left frontal region and led to severe brain injury. Despite intensive medical care, death occurred 10 days later due to malignant cerebral edema. With regard to the question whether the trauma resulted from throwing or pushing, some biomechanical aspects are discussed including individual morphologic findings as well as the kinetic energy required for penetrating the skull. According to the calculated data, we conclude that the velocity necessary for penetrating could be reached by throwing the curtain rail.


title: Joint optimization of bid and budget allocation in sponsored search

abstract: This paper is concerned with the joint allocation of bid price and campaign budget in sponsored search. In this application, an advertiser can create a number of campaigns and set a budget for each of them. In a campaign, he/she can further create several ad groups with bid keywords and bid prices. Data analysis shows that many advertisers are dealing with a very large number of campaigns, bid keywords, and bid prices at the same time, which poses a great challenge to the optimality of their campaign management. As a result, the budgets of some campaigns might be too low to achieve the desired performance goals while those of some other campaigns might be wasted; the bid prices for some keywords may be too low to win competitive auctions while those of some other keywords may be unnecessarily high. In this paper, we propose a novel algorithm to automatically address this issue. In particular, we model the problem as a constrained optimization problem, which maximizes the expected advertiser revenue subject to the constraints of the total budget of the advertiser and the ranges of bid price change. By solving this optimization problem, we can obtain an optimal budget allocation plan as well as an optimal bid price setting. Our simulation results based on the sponsored search log of a commercial search engine have shown that by employing the proposed method, we can effectively improve the performances of the advertisers while at the same time we also see an increase in the revenue of the search engine. In addition, the results indicate that this method is robust to the second-order effects caused by the bid fluctuations from other advertisers.


title: Using Argumentative Zones for Extractive Summarization of Scientific Articles

abstract: Information structure, i.e the way speakers construct sentences to present new information in the context of old, can capture rich linguistic information about the discourse structure of scientific documents. Information structure has been found useful for important Natural Language Processing (NLP) tasks, such as information retrieval and extraction. Since scientific articles typically follow a certain discourse structure describing the prior work, problem being solved, methods used, and so forth, it could also be useful for summarization of these articles. In this work we focus on a scheme of information structure called Argumentative Zoning (AZ), and investigate whether its categories could support extractive text summarization in a scientific domain. We develop a summarization system that uses AZ categories (i) as features and (ii) in the final sentence selection process. We evaluate the system directly as well as using task-based evaluation. The results show that AZ can support both full document and customized summarization. We report a statistically significant improvement in summarization performance against a competitive baseline that uses journal section labels instead of AZ information. TITLE AND ABSTRACT IN MANDARIN


title: The importance of pay in employee motivation: Discrepancies between what people say and what they do

abstract: A majority of human resources professionals appear to believe that employees are likely to overreport the importance of pay in employee surveys. However, research suggests the opposite is actually true. We review evidence showing the discrepancies between what people say and do with respect to pay. We then discuss why pay is likely to be such an important general motivator, as well as a variety of reasons why managers might underestimate its importance. We note that pay is not equally important in all situations or to all individuals, and identify circumstances under which pay is likely to be more (or less) important to employees. We close with recommendations for implementing research findings with respect to pay and suggestions for evaluating pay systems. © 2004 Wiley Periodicals, Inc.


title: Embedded System for Biometric Identification

abstract: Biometrics refers to automatic identification of a person based on his or her physiological or behavioral characteristics which provide a reliable and secure user authentication for the increased security requirements of our personal information compared to traditional identification methods such as passwords and PINs (Jain et al., 2000). Organizations are looking to automate identity authentication systems to improve customer satisfaction and operating efficiency as well as to save critical resources due to the fact that identity fraud in welfare disbursements, credit card transactions, cellular phone calls, and ATM withdrawals totals over $6 billion each year (Jain et al., 1998). Furthermore, as people become more connected electronically, the ability to achieve a highly accurate automatic personal identification system is substantially more critical. Enormous change has occurred in the world of embedded systems driven by the advancement on the integrated circuit technology and the availability of open source. This has opened new challenges and development of advanced embedded system. This scenario is manifested in the appearance of sophisticated new products such as PDAs and cell phones and by the continual increase in the amount of resources that can be packed into a small form factor which requires significant high end skills and knowledge. More people are gearing up to acquire advanced skills and knowledge to keep abreast of the technologies to build advanced embedded system using available Single Board Computer (SBC) with 32 bit architectures.


title: A CMOS 3.3-8.4 GHz wide tuning range, low phase noise LC VCO

abstract: A novel inductor switching technique is used to design and implement a wideband LC voltage controlled oscillator (VCO) in 0.13µm CMOS. The VCO has a tuning range of 87.2% between 3.3 and 8.4 GHz with phase noise ranging from −122 to −117.2 dBc/Hz at 1MHz offset. The power varies between 6.5 and 15.4 mW over the tuning range. This results in a Power-Frequency-Tuning Normalized figure of merit (PFTN) between 6.6 and 10.2 dB which is one of the best reported to date.


title: Statistical mechanics of community detection.

abstract: Starting from a general ansatz, we show how community detection can be interpreted as finding the ground state of an infinite range spin glass. Our approach applies to weighted and directed networks alike. It contains the ad hoc introduced quality function from [J. Reichardt and S. Bornholdt, Phys. Rev. Lett. 93, 218701 (2004)] and the modularity Q as defined by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] as special cases. The community structure of the network is interpreted as the spin configuration that minimizes the energy of the spin glass with the spin states being the community indices. We elucidate the properties of the ground state configuration to give a concise definition of communities as cohesive subgroups in networks that is adaptive to the specific class of network under study. Further, we show how hierarchies and overlap in the community structure can be detected. Computationally efficient local update rules for optimization procedures to find the ground state are given. We show how the ansatz may be used to discover the community around a given node without detecting all communities in the full network and we give benchmarks for the performance of this extension. Finally, we give expectation values for the modularity of random graphs, which can be used in the assessment of statistical significance of community structure.


title: Learning Deep Representations of Appearance and Motion for Anomalous Event Detection

abstract: We present a novel unsupervised deep learning framework for anomalous event detection in complex video scenes. While most existing works merely use hand-crafted appearance and motion features, we propose Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations. To exploit the complementary information of both appearance and motion patterns, we introduce a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies. Specifically, stacked denoising autoencoders are proposed to separately learn both appearance and motion features as well as a joint representation (early fusion). Based on the learned representations, multiple one-class SVM models are used to predict the anomaly scores of each input, which are then integrated with a late fusion strategy for final anomaly detection. We evaluate the proposed method on two publicly available video surveillance datasets, showing competitive performance with respect to state of the art approaches.


title: An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization

abstract: This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.


title: Empirical study of the dynamic behavior of JavaScript objects

abstract: Despite the popularity of JavaScript for client-side web applications, there is a lack of effective software tools supporting JavaScript development and testing. The dynamic characteristics of JavaScript pose software engineering challenges such as program understanding and security. One important feature of JavaScript is that its objects support flexible mechanisms such as property changes at runtime and prototype-based inheritance, making it difficult to reason about object behavior. We have performed an empirical study on real JavaScript applications to understand the dynamic behavior of JavaScript objects. We present metrics to measure behavior of JavaScript objects during execution e.g., operations associated with an object, object size, and property type changes. We also investigated the behavioral patterns of observed objects to understand the coding or user interaction practices in JavaScript software. Copyright © 2015John Wiley & Sons, Ltd.


title: Crowd intelligence in AI 2.0 era

abstract: The Internet based cyber-physical world has profoundly changed the information environment for the development of artificial intelligence (AI), bringing a new wave of AI research and promoting it into the new era of AI 2.0. As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attracted much attention from both industry and research communities. Specifically, crowd intelligence provides a novel problem-solving paradigm through gathering the intelligence of crowds to address challenges. In particular, due to the rapid development of the sharing economy, crowd intelligence not only becomes a new approach to solving scientific challenges, but has also been integrated into all kinds of application scenarios in daily life, e.g., online-to-offline (O2O) application, real-time traffic monitoring, and logistics management. In this paper, we survey existing studies of crowd intelligence. First, we describe the concept of crowd intelligence, and explain its relationship to the existing related concepts, e.g., crowdsourcing and human computation. Then, we introduce four categories of representative crowd intelligence platforms. We summarize three core research problems and the state-of-the-art techniques of crowd intelligence. Finally, we discuss promising future research directions of crowd intelligence.


title: Quasi-Z source inverter with battery based PV power generation system

abstract: A new PV power generation topology, based on the quasi-Z source inverter (QZSI) with battery, is proposed in this paper. With a battery paralleled with one of the capacitors, this system can smooth the grid-injected/load power when the PV panel outputs a variable power with fluctuations. The battery can be charged or discharged without any extra circuit, because of the unique impedance network of QZSI. The operating principle and power flow of this system are analyzed. The closed-loop control strategy is used to achieve Maximum Power Point Tracking (MPPT) and keep the output voltage stable. Three operating modes of the proposed PV power generation system are simulated in the MATLAB/Simulink to verify its principle and theoretical analysis. A prototype is built in the lab, and the experimental results validate the whole system.


title: On the variation with flux and frequency of the core loss coefficients in electrical machines

abstract: A model of core losses, in which the hysteresis coefficients are variable with the frequency and induction (flux density) and the eddy-current and excess loss coefficients are variable only with the induction, is proposed. A procedure for identifying the model coefficients from multifrequency Epstein tests is described, and examples are provided for three typical grades of non-grain-oriented laminated steel suitable for electric motor manufacturing. Over a wide range of frequencies between 20-400 Hz and inductions from 0.05 to 2 T, the new model yielded much lower errors for the specific core losses than conventional models. The applicability of the model for electric machine analysis is also discussed, and examples from an interior permanent-magnet and an induction motor are included.