title: Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

abstract: This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports. The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing, segmentation, feature extraction, dimensionality reduction and classification. For each step, we present the main techniques utilized, their advantages and drawbacks, performance metrics and usage examples. We also discuss the research challenges, such as user behavior and technical limitations, as well as the remaining open research questions.


Virt u al So un d Source Posi t io n i n g U si n g Vec t or Base Amplitude


title: Maturity and change in personality: Developmental trends of temperament and character in adulthood

abstract: We studied the developmental trends of temperament and character in a longitudinal population-based sample of Finnish men and women aged 20‐45 years using the Temperament and Character Inventory model of personality. Personality was assessed in 1997, 2001, and 2007 (n ¼ 2,104, 2,095, and 2,056, respectively). Mean-level changes demonstrated qualitatively distinct developmental patterns for character (self-directedness, cooperativeness, and selftranscendence) and temperament (novelty seeking, harm avoidance, reward dependence, and persistence). Character developed toward greater maturity, although self-transcendence decreased with age. However, self-transcendence was the strongest predictor of overall personality change. Cohort effects indicatedlowerlevelofself-transcendenceandhigherlevelof self-directednessandcooperativenessinyoungerbirthcohorts.Regardingtemperament,novelty seeking decreased and persistence increased slightly with age. Both high novelty seeking and high persistence predicted overall personality change. These findings suggest that temperament and character traits follow different kinds of developmental trajectories. The degree and direction of personality change in adulthood is a central topic in understanding human psychological development. Several personality theories have emphasized how adult personality tends to develop toward higher levels of psychological maturity (Caspi, Roberts, & Shiner, 2005; Hopwood et al., 2011; Lucas & Donnellan, 2011; McAdams & Olson, 2010; Roberts, Wood, & Caspi, 2008; Soto, John, Gosling, & Potter, 2011). Maturity is a somewhat ambiguous concept that attempts to describe directions and end states of personality development that are psychologically healthier and more fulfilling than lower levels of psychological maturity. Developmental theories offer two different views on maturity (Caspi et al., 2005; Helson & Wink, 1987; Hogan & Roberts, 2004; Staudinger & Kunzmann, 2005). The perspective of personal growth concentrates on concepts such as insight, integrity, and wisdom. Personal growth is seen as positive development that enables the individual to actualize his or her full potential as a person. The functional perspective, in contrast, equates maturity with adjustment to the society, that is, being a productive contributor to the society and being respected and liked by other people. Maturity and Psychological Well-Being


title: Data Governance Framework for Big Data Implementation with a Case of Korea

abstract: Big Data governance requires a data governance that can satisfy the needs for corporate governance, IT governance, and ITA/EA. While the existing data governance focuses on the processing of structured data, Big Data governance needs to be established in consideration of a broad sense of Big Data services including unstructured data. To achieve the goals of Big Data, strategies need to be established together with goals that are aligned with the vision and objective of an organization. In addition to the preparation of the IT infrastructure, a proper preparation of the components is required to effectively implement the strategy for Big Data services. We propose the Big Data Governance Framework in this paper. The Big Data governance framework presents criteria different from existing criteria at the data quality level. It focuses on timely, reliable, meaningful, and sufficient data services, focusing on what data attributes should be achieved based on the data attributes of Big Data services. In addition to the quality level of Big Data, the personal information protection strategy and the data disclosure/accountability strategy are also needed to achieve goals and to prevent problems. This paper performed case analysis based on the Big Data Governance Framework with the National Pension Service of South Korea. Big Data services in the public sector are an inevitable choice to improve the quality of people's life. Big Data governance and its framework are the essential components for the realization of Big Data service.


title: Effects of 24-week Tai Chi exercise on the knee and ankle proprioception of older women

abstract: This study examined the effects of regular Tai Chi (TC) exercise on the kinaesthesia of the knee and ankle joints of older women. A total of 43 women aged 55–68 years participated in this study. In a 24-week study period, the TC group (n = 22) underwent an organized TC exercise, whereas the control group (n = 21) maintained a sedentary lifestyle. Customized instruments were used to measure the threshold for the detection of the passive motion of the knee and ankle joints. After 24 weeks, the TC group showed a significantly smaller threshold for the detection of passive motion of knee extension (31.4%, p = 0.009), knee flexion (27.0%, p = 0.044), and ankle dorsal flexion (28.9%, p = 0.014) than the control group. Other comparisons showed no significant differences. The 24-week TC exercise benefited the lower-limb kinaesthesia of the knee joint flexion and extension and ankle dorsal flexion.


title: Knowledge Building: Theory, Pedagogy, and Technology

abstract: A radiological system, wherein x-ray images (radiation images) are produced and are converted, through a video chain, for display as a video image, the system having a modulation transfer function, has the capability of influencing the modulation transfer function dependent on ambient light which is present at the display. To that end, a control unit is provided which provides two-dimensional control of the modulation transfer function dependent on illumination which is present in the environment of the display. The control is effected by means of a two-dimensional spatial frequency filter.


title: TCP/IP Enhancements for satellite Networks

abstract: With the emerging market for high-mobility remote access broadband services, satellite networks are becoming increasingly popular. Although the ubiquitous TCP/IP protocol is widely used to provide reliable data delivery in terrestrial networks, it faces many challenges in satellite environments. These stem from the inherent features of satellite channels, such as large delays, increased error rates, and bandwidth asymmetry. To address these concerns, a variety of solutions have been proposed. These include direct TCP enhancements to better tune the TCP/IP stack and additional selective acknowledgment mechanisms. Other proposals use advanced interworking to reduce sensitivity to various channel features. Examples include multiple TCP sessions, link-layer interworking, and ACK control schemes. Improving onboard satellite features (buffer management, flow control) can also provide benefits for TCP/IP transport. The objective of this article is to present the various solutions and discuss their possible trade-offs. Overall, there exists a rich set of alternatives to meet the challenges in this important arena.


title: A simple and efficient template matching algorithm

abstract: We propose a general framework for object tracking in video images. It consists of low-order parametric models for the image motion of a target region. These models are used to predict the movement and to track the target. The difference of intensity between the pixels belonging to the current region and the pixels of the selected target (learnt during an off-line stage) allows a straightforward prediction of the region position in the current image. The proposed algorithm allows to track in real time (less than 10 ms) any planar textured target under homographic motions. This algorithm is very simple (a few lines of code) and very efficient (less than 10 ms on a 150 MHz hardware).


title: Lung Nodule Detection Using Fuzzy Clustering and Support Vector Machines S.Sivakumar #1, Dr.C.Chandrasekar *2

abstract: Lung cancer is the primary cause of tumor deaths for both sexes in most countries. Lung nodule, an abnormality which leads to lung cancer is detected by various medical imaging techniques like X-ray, Computerized Tomography (CT), etc. Detection of lung nodules is a challenging task since the nodules are commonly attached to the blood vessels. Many studies have shown that early diagnosis is the most efficient way to cure this disease. This paper aims to develop an efficient lung nodule detection scheme by performing nodule segmentation through fuzzy based clustering models; classification by using a machine learning technique called Support Vector Machine (SVM). This methodology uses three different types of kernels among these RBF kernel gives better class performance. Keywords-Lung cancer, Image segmentation, FCM, WPFCM, Classification, Support Vector Machines


title: Hot-hardening: getting more out of your security settings

abstract: Applying optimized security settings to applications is a difficult and laborious task. Especially in cloud computing, where virtual servers with various pre-installed software packages are leased, selecting optimized security settings is very difficult. In particular, optimized security settings are not identical in every setup. They depend on characteristics of the setup, on the ways an application is used or on other applications running on the same system. Configuring optimized settings given these interdependencies is a complex and time-consuming task. In this work, we present an autonomous agent which improves security settings of applications which run in virtual servers. The agent retrieves custom-made security settings for a target application by investigating its specific setup, it tests and transparently changes settings via introspection techniques unbeknownst from the perspective of the virtual server. During setting selection, the application's operation is not disturbed nor any user interaction is needed. Since optimal settings can change over time or they can change depending on different tasks the application handles, the agent can continuously adapt settings as well as improve them periodically. We call this approach hot-hardening and present results of an implementation that can hot-harden popular networking applications such as Apache2 and OpenSSH.


title: Using trust for collaborative filtering in eCommerce

abstract: Personalization and customization have been shown to be an indispensable function in today's eCommerce businesses and highly applauded by their customers. Collaborative filtering is one of the two major techniques commonly employed by today's recommender systems and has found its way into the recommendation of many diversified types of products. However, collaborative filtering technique suffers from sparsity and cold start problems. The recent emergence of Web 2.0 offers an opportunity to remedy these problems by incorporating the trust relationships explicitly expressed by the users, as evident by some recent research. Previous work in using trust for making recommendation mainly focuses on inferring trust weights for unspecified trust relations. In this paper, we model the problem of using trust for recommendation as a linear program. We then describe two heuristics that leverages trust to estimate the ratings of unseen products by a given user. Finally we develop various strategies of giving continuous trust weights by considering the contextual information pertaining to trust statements and examine their impact on recommendation accuracy using the empirical data collected from Epinion.com. The experimental results show that assigning continuous trust weights using some of the proposed strategies yields higher recommendation accuracy when compared to the baseline approach that gives Boolean trust values.


title: Characterisation and modeling of mismatch in MOS transistors for precision analog design

abstract: A characterization methodology is presented that accurately predicts the mismatch in drain current over a wide operating range using a minimum set of measured data. The physical causes of mismatch are discussed in detail for both p- and n-channel devices. Statistical methods are used to develop analytical models that relate the mismatch to the device dimensions. It is shown that these models are valid for small-geometry devices only. Extensive experimental data from a 3-/spl mu/m CMOS process are used to verify the models. The application of the transistor matching studies to the design of a high-performance digital-to-analog converter (DAC) is discussed. A circuit design methodology is presented that highlights the close interaction between the circuit yield and the matching accuracy of devices. It has been possible to achieve a circuit yield of greater than 97% as a result of the knowledge generated regarding the matching behavior of transistors and due to the systematic design approach.


title: Massive MIMO: survey and future research topics

abstract: Massive multiple-input multiple-output technology has been considered a breakthrough in wireless communication systems. It consists of equipping a base station with a large number of antennas to serve many active users in the same time-frequency block. Among its underlying advantages is the possibility to focus transmitted signal energy into very short-range areas, which will provide huge improvements in terms of system capacity. However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few. This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research.


title: Feedase : the new generation of feed enzymes to optimise complete nutrient availability in diets : feed science

abstract: Feed is the major component of live production input costs in poultry integration, and it generally accounts for up to 70% of total poultry production costs. Energy, protein and phosphorus (P) represent the highest cost inputs in feed formulation. By improving global nutrient digestibility, the new generation of feed enzymes will revolutionise the way diets are reformulated, with feed enzymes enhancing the sustainability of poultry protein production, thereby increasing flexibility in least cost formulations by nutritionists.


title: A survey on moving object detection for wide area motion imagery

abstract: Wide Area Motion Imagery (WAMI) enables the surveillance of tens of square kilometers with one airborne sensor Each image can contain thousands of moving objects. Applications such as driver behavior analysis or traffic monitoring require precise multiple object tracking that is dependent on initial detections. However, low object resolution, dense traffic, and imprecise image alignment lead to split, merged, and missing detections. No systematic evaluation of moving object detection exists so far although many approaches have been presented in the literature. This paper provides a detailed overview of existing methods for moving object detection in WAMI data. Also we propose a novel combination of short-term background subtraction and suppression of image alignment errors by pixel neighborhood consideration. In total, eleven methods are systematically evaluated using more than 160,000 ground truth detections of the WPAFB 2009 dataset. Best performance with respect to precision and recall is achieved by the proposed one.


title: Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model

abstract: In this paper we introduce a novel single-document summarization method based on a hidden semi-Markov model. This model can naturally model single-document summarization as the optimization problem of selecting the best sequence from among the sentences in the input doc- ument under the given objective function and knapsack constraint. This advantage makes it possible for sentence selection to take the coherence of the summary into account. In addition our model can also incorporate sentence compression into the summarization process. To demon- strate the effectiveness of our method, we conduct an experimental evaluation with a large-scale corpus consisting of 12,748 pairs of a document and its reference. The results show that our method significantly outperforms the competitive baselines in terms of ROUGE evaluation, and the linguistic quality of summaries is also improved. Our method successfully mimicked the reference summaries, about 20 percent of the summaries generated by our method were com- pletely identical to their references. Moreover, we show that large-scale training samples are quite effective for training a summarizer.


title: Learning part-of-speech taggers with inter-annotator agreement loss

abstract: In natural language processing (NLP) annotation projects, we use inter-annotator agreement measures and annotation guidelines to ensure consistent annotations. However, annotation guidelines often make linguistically debatable and even somewhat arbitrary decisions, and interannotator agreement is often less than perfect. While annotation projects usually specify how to deal with linguistically debatable phenomena, annotator disagreements typically still stem from these “hard” cases. This indicates that some errors are more debatable than others. In this paper, we use small samples of doublyannotated part-of-speech (POS) data for Twitter to estimate annotation reliability and show how those metrics of likely interannotator agreement can be implemented in the loss functions of POS taggers. We find that these cost-sensitive algorithms perform better across annotation projects and, more surprisingly, even on data annotated according to the same guidelines. Finally, we show that POS tagging models sensitive to inter-annotator agreement perform better on the downstream task of chunking.


title: Neural signature of fictive learning signals in a sequential investment task

abstract: .¶ ‡ § Reinforcement learning models now provide principled guides for a wide range of reward learning experiments in animals and humans. One key learning (error) signal in these models is experiential and reports ongoing temporal differences between expected and experienced reward. However, these same abstract learning models also accommodate the existence of another class of learning signal that takes the form of a fictive error encoding ongoing differences between experienced returns and returns that ‘‘could-have-been-experienced’’ if decisions had been different. These observations suggest the hypothesis that, for all real-world learning tasks, one should expect the presence of both experiential and fictive learning signals. Motivated by this possibility, we used a sequential investment game and fMRI to probe ongoing brain responses to both experiential and fictive learning signals generated throughout the game. Using a large cohort of subjects (n 54), we report that fictive learning signals strongly predict changes in subjects’ investment behavior and correlate with fMRI signals measured in dopaminoceptive structures known to be involved in valuation and choice. counterfactual signals decision-making neuroeconomics reinforcement learning


title: The nature of feedback: how different types of peer feedback affect writing performance

abstract: Although providing feedback is commonly practiced in education, there is no general agreement regarding what type of feedback is most helpful and why it is helpful. This study examined the relationship between various types of feedback, potential internal mediators, and the likelihood of implementing feedback. Five main predictions were developed from the feedback literature in writing, specifically regarding feedback features (summarization, identifying problems, providing solutions, localization, explanations, scope, praise, and mitigating language) as they relate to potential causal mediators of problem or solution understanding and problem or solution agreement, leading to the final outcome of feedback implementation. To empirically test the proposed feedback model, 1,073 feedback segments from writing assessed by peers was analyzed. Feedback was collected using SWoRD, an online peer review system. Each segment was coded for each of the feedback features, implementation, agreement, and understanding. The correlations between the feedback features, levels of mediating variables, and implementation rates revealed several significant relationships. Understanding was the only significant mediator of implementation. Several feedback features were associated with understanding: including solutions, a summary of the performance, and the location of the problem were associated with increased understanding; and explanations of problems were associated with decreased understanding. Implications of these results are discussed.


title: Summarization of personal photologs using multidimensional content and context

abstract: In this paper, we propose a framework for generation of representative subset summaries from large personal photo collections. These summaries will help in effective sharing and browsing of the personal photos. We define three salient properties: quality, diversity and coverage that an informative summary should satisfy. We propose methods to compute these properties using multidimensional content and context data. The objective of summarization is modeled as an optimization of these properties, given the size constraints. We also propose metrics which will evaluate the photo summaries based on their representation of the larger corpus and the ability to satisfy user's information needs. We use a dataset of 40K personal photos collected by crawling photo sharing and storage sites of sixteen users. Our experiments show that the summarization algorithm works better than the baseline algorithms.


title: First-person hyper-lapse videos

abstract: We present a method for converting first-person videos, for example, captured with a helmet camera during activities such as rock climbing or bicycling, into hyper-lapse videos, i.e., time-lapse videos with a smoothly moving camera. At high speed-up rates, simple frame sub-sampling coupled with existing video stabilization methods does not work, because the erratic camera shake present in first-person videos is amplified by the speed-up. Our algorithm first reconstructs the 3D input camera path as well as dense, per-frame proxy geometries. We then optimize a novel camera path for the output video that passes near the input cameras while ensuring that the virtual camera looks in directions that can be rendered well from the input. Finally, we generate the novel smoothed, time-lapse video by rendering, stitching, and blending appropriately selected source frames for each output frame. We present a number of results for challenging videos that cannot be processed using traditional techniques.


title: Prior and contextual emotion of words in sentential context

abstract: A set of words labeled with their prior emotion is an obvious place to start on the automatic discovery of the emotion of a sentence, but it is clear that context must also be considered. It may be that no simple function of the labels on the individual words captures the overall emotion of the sentence; words are interrelated and they mutually influence their affect-related interpretation. It happens quite often that a word which invokes emotion appears in a neutral sentence, or that a sentence with no emotional word carries an emotion. This could also happen among different emotion classes. The goal of this work is to distinguish automatically between prior and contextual emotion, with a focus on exploring features important in this task. We present a set of features which enable us to take the contextual emotion of a word and the syntactic structure of the sentence into account to put sentences into emotion classes. The evaluation includes assessing the performance of different feature sets across multiple classification methods. We show the features and a promising learning method which significantly outperforms two reasonable baselines. We group our features by the similarity of their nature. That is why another facet of our evaluation is to consider each group of the features separately and investigate how well they contribute to the result. The experiments show that all features contribute to the result, but it is the combination of all the features that gives the best performance.


title: Gradient Energy Matching for Distributed Asynchronous Gradient Descent

abstract: Distributed asynchronous SGD has become widely used for deep learning in large-scale systems, but remains notorious for its instability when increasing the number of workers. In this work, we study the dynamics of distributed asynchronous SGD under the lens of Lagrangian mechanics. Using this description, we introduce the concept of energy to describe the optimization process and derive a sufficient condition ensuring its stability as long as the collective energy induced by the active workers remains below the energy of a target synchronous process. Making use of this criterion, we derive a stable distributed asynchronous optimization procedure, GEM, that estimates and maintains the energy of the asynchronous system below or equal to the energy of sequential SGD with momentum. Experimental results highlight the stability and speedup of GEM compared to existing schemes, even when scaling to one hundred asynchronous workers. Results also indicate better generalization compared to the targeted SGD with momentum.


title: Practice and forgetting effects on vocabulary memory: An activationbased model of the spacing effect

abstract: An experiment was performed to investigate the effects of practice and spacing on retention of Japanese–English vocabulary paired associates. The relative benefit of spacing increased with increased practice and with longer retention intervals. Data were fitted with an activation-based memory model, which proposes that each time an item is practiced it receives an increment of strength but that these increments decay as a power function of time. The rate of decay for each presentation depended on the activation at the time of the presentation. This mechanism limits long-term benefits from further practice at higher levels of activation and produces the spacing effect and its observed interactions with practice and retention interval. The model was compared with another model of the spacing effect (Raaijmakers, 2003) and was fit to some results from the literature on spacing and memory.


title: The Physical Layer of the IEEE 802.11p WAVE Communication Standard: The Specifications and Challenges

abstract: In the recent years, Vehicular Ad hoc Networks (VANETs) emerged with great interest due to their impact in reducing traffic jams and increasing safety as well as alternative emergency communication system in case of natural disasters, when there is lack of ordinary communication systems. IEEE 802.11p standard known as Wireless Access in Vehicular Environments (WAVE) is specially developed to adapt VANETs requirements and support intelligent transport systems (ITS). The performance of WAVE physical layer is one of the important factors that play a great role in the communication process. This paper presented an overview of the physical layer (PHY) of the IEEE 802.11p standard. The specifications, components, performance and challenges of the PHY layer are discussed and analyzed. Index Terms—VANET, WAVE, IEEE 802.11p, PHY, IEEE 802.11 frequency band, OFDM, preamble


title: Mechanisms of proinflammatory cytokine-induced biphasic NF-kappaB activation.

abstract: The transcription factor NF-kappaB regulates genes involved in innate and adaptive immune response, inflammation, apoptosis, and oncogenesis. Proinflammatory cytokines induce the activation of NF-kappaB in both transient and persistent phases. We investigated the mechanism for this biphasic NF-kappaB activation. Our results show that MEKK3 is essential in the regulation of rapid activation of NF-kappaB, whereas MEKK2 is important in controlling the delayed activation of NF-kappaB in response to stimulation with the cytokines TNF-alpha and IL-1alpha. MEKK3 is involved in the formation of the IkappaBalpha:NF-kappaB/IKK complex, whereas MEKK2 participates in assembling the IkappaBbeta:NF-kappaB/IKK complex; these two distinct complexes regulate the proinflammatory cytokine-induced biphasic NF-kappaB activation. Thus, our study reveals a novel mechanism in which different MAP3K and IkappaB isoforms are involved in specific complex formation with IKK and NF-kappaB for regulating the biphasic NF-kappaB activation. These findings provide further insight into the regulation of cytokine-induced specific and temporal gene expression.


title: Internet addiction among adolescents in Lebanon

abstract: Internet addiction has been a subject of research since 1996. In step with this trend, this study aims to illuminate Internet addiction in Lebanon based on research evidence. The timing of this study coincides with the growing rate of penetration of Internet service across the country and with current plans to improve this service. This study investigates Internet usage among adolescents, who are the most vulnerable to Internet addiction in the population. Permission to access schools to conduct this research was obtained from the Ministry of Education and Higher Education. A web-based questionnaire was completed by 833 intermediate and secondary students from public and private schools. The questionnaire included items on demographic information and Internet usage and an Arabic version of Young's Internet Addiction Test (YIAT). The results showed that 4.2% of the sample had significant problems. Furthermore, the more problematic Internet use (PIU) was, the less the Internet was used for information and research, and the more it was used for entertainment. PIU is driven by deficiency needs fuelled by the use of interactive applications. The more deficient the need, the greater the obsession with top-notch interactive applications, and the less frequent the usage of non-interactive applications.


title: LyricsRadar: A Lyrics Retrieval System Based on Latent Topics of Lyrics

abstract: This paper presents a lyrics retrieval system called LyricsRadar that enables users to interactively browse song lyrics by visualizing their topics. Since conventional lyrics retrieval systems are based on simple word search, those systems often fail to reflect user’s intention behind a query when a word given as a query can be used in different contexts. For example, the word“tears”can appear not only in sad songs (e.g., feel heartrending), but also in happy songs (e.g., weep for joy). To overcome this limitation, we propose to automatically analyze and visualize topics of lyrics by using a well-known text analysis method called latent Dirichlet allocation (LDA). This enables LyricsRadar to offer two types of topic visualization. One is the topic radar chart that visualizes the relative weights of five latent topics of each song on a pentagon-shaped chart. The other is radar-like arrangement of all songs in a two-dimensional space in which song lyrics having similar topics are arranged close to each other. The subjective experiments using 6,902 Japanese popular songs showed that our system can appropriately navigate users to lyrics of interests.


title: Designing two-echelon supply networks

abstract: A modern distribution network design model needs to deal with the trade-offs between a variety of factors, including (1) location and associated (fixed) operating cost of distribution centers (DCs), (2) total transportation costs, and (3) storage holding and replenishment costs at DCs and retail outlets. In addition, network design models should account for factors such as (4) stockouts, by setting appropriate levels of safety stocks, or (5) capacity concerns, which may affect operating costs in the form of congestion costs. The difficulty of making such trade-offs is compounded by the fact that even finding the optimal two-echelon inventory policy in a fixed and uncapacitated distribution network is already a hard problem. In this paper, we propose a generic modeling framework to address these issues that continues and extends a recent stream of research aimed at integrating insights from modern inventory theory into the supply chain network design domain. Our approach is flexible and general enough to incorporate a variety of important side constraints into the problem.


title: Automatic age estimation based on deep learning algorithm

abstract: Automatic age estimation has attracted much attention due to its potential applications. Most of the proposed approaches have mainly used low-level handcraft features to encode facial age related visual information and train an age estimation model. In this paper, we focus on age classification task in which face image is assigned to a label that represents an age range. We proposed a deep learning based framework for age classification task. In our proposed algorithm, Deep Convolutional Neural Networks (Deep ConvNets) are used to extract high-level complex age related visual features and predict age range of input face image. Due to lack of age labeled face images, we use the transfer learning strategy to train the Deep ConvNets. In addition, to describe the relationships between labels that compose an ordered sequence, we define a new loss function in the training process of age classification task. The experiments are conducted on a widely used age estimation dataset-Images of Groups of People. The experimental results demonstrate the excellent performance of our proposed algorithm against the state-of-the-art methods.


title: Learning abductive reasoning using random examples

abstract: We consider a new formulation of abduction in which degrees of "plausibility" of explanations, along with the rules of the domain, are learned from concrete examples (settings of attributes). Our version of abduction thus falls in the "learning to reason" framework of Khardon and Roth. Such approaches enable us to capture a natural notion of "plausibility" in a domain while avoiding the extremely difficult problem of specifying an explicit representation of what is "plausible." ::: ::: We specifically consider the question of which syntactic classes of formulas have efficient algorithms for abduction. We find that the class of k-DNF explanations can be found in polynomial time for any fixed k; but, we also find evidence that even weak versions of our abduction task are intractable for the usual class of conjunctions. This evidence is provided by a connection to the usual, inductive PAC-learning model proposed by Valiant. We also consider an exception-tolerant variant of abduction. We observe that it is possible for polynomial-time algorithms to tolerate a few adversarially chosen exceptions, again for the class of k-DNF explanations. All of the algorithms we study are particularly simple, and indeed are variants of a rule proposed by Mill.


title: An Update on Autoinflammatory Diseases

abstract: Autoinflammatory diseases area group of clinical conditions other than autoimmune diseases, characterized by recurrent inflammatory episodes. From apathogenetic point of view they are determined by a dys regulation of innate immunity, without involvement of specific immunity (auto reactive T cells and auto antibodies). Recently, the increased knowledge in the field of auto inflammation highlighted shared immune mechanisms in the pathogenesis of both classical monogenetic and multifactorial auto inflammatory diseases and a broad spectrum of chronic age-related inflammatory pathologies. The current increase in the prevalence of chronic inflammatory diseases makes this subject of topical interest. In the light of these considerations, we propose an update of auto inflammatory diseases and a new interpretation of auto inflammation with both theoretical and clinical implications.


title: Cryolipolysis for noninvasive body contouring: clinical efficacy and patient satisfaction

abstract: In recent years, a number of modalities have become available for the noninvasive reduction of adipose tissue, including cryolipolysis, radiofrequency, low-level laser, and high-intensity focused ultrasound. Each technology employs a different mechanism of action to cause apoptosis or necrosis of the targeted adipocytes. Among these technologies, cryolipolysis has not only been commercially available for the longest time, but has also been best researched including in vitro and animal models and randomized controlled clinical trials in humans. The principle behind cryolipolysis exploits the premise that adipocytes are more susceptible to cooling than other skin cells. The precise application of cold temperatures triggers apoptosis of the adipocytes, which invokes an inflammatory response and leads to slow digestion by surrounding macrophages. In clinical studies, cryolipolysis was shown to reduce subcutaneous fat at the treatment site by up to 25% after one treatment. Improvements were seen in 86% of treated subjects. At 73%, the patient satisfaction rate is higher than with other technologies used for noninvasive lipolysis. Cryolipolysis has been proven to be a very safe method for body contouring, and is accomplished with only minimal discomfort. Expected side effects are temporary erythema, bruising, and transient numbness that usually resolve within 14 days after treatment. With a prevalence of 0.1%, the most common complaint is late-onset pain, occurring 2 weeks post-procedure, which resolves without intervention. Although no procedure has been accepted as the gold standard for noninvasive body contouring as yet, cryolipolysis is considered to be both safe and efficient with a high patient satisfaction rate.


title: Control Architecture of LUCY, a Biped with Pneumatic Artificial Muscles

abstract: This paper describes the biped Lucy and it’s control architecture that will be used. Lucy is actuated by Pleated Pneumatic Artificial Muscles, which have a very high power to weight ratio and an inherent adaptable compliance. These characteristics will be used to let Lucy walk in a dynamically stable manner while exploiting the adaptable passive behaviour of these muscles. A quasi-static global control has been implemented while using adapted PID techniques for the local feedback joint control. These initial control techniques resulted in the first movements of Lucy. This paper will discuss a future control architecture of Lucy to induce faster and smoother motion. The proposed control scheme is a combination of a global trajectory planner and a local low-level joint controller. The trajectory planner generates motion patterns based on two specific concepts, being the use of objective locomotion parameters, and exploiting the natural upper body dynamics by manipulating the angular momentum equation. The low-level controller can be divided in four parts: a computed torque module, an inverse delta-p unit, a local PI controller and a bang-bang controller. In order to evaluate the proposed control structure a hybrid simulator was created. Both the pneumatics and mechanics are put together in this hybrid dynamic simulation.


title: Condition Monitoring of Railway Turnouts and Other Track Components Using Machine Vision

abstract: Individual railroad track maintenance standards and the Federal Railroad Administration (FRA) Track Safety Standards require periodic inspection of railway infrastructure to ensure safe and efficient operation. This inspection is a critical, but labor-intensive task that results in large annual operating expenditures and has limitations in speed, quality, objectivity, and scope. To improve the cost-effectiveness of the current inspection process, machine vision technology can be developed and used as a robust supplement to manual inspections. This paper focuses on the development and performance of machine vision algorithms designed to recognize turnout components, as well as the performance of algorithms designed to recognize and detect defects in other track components. In order to prioritize which components are the most critical for the safe operation of trains, a risk-based analysis of the FRA Accident Database was performed. Additionally, an overview of current technologies for track and turnout component condition assessment is presented. The machine vision system consists of a video acquisition system for recording digital images of track and customized algorithms to identify defects and symptomatic conditions within the images. A prototype machine vision system has been developed for automated inspection of rail anchors and cut spikes, as well as tie recognition. Experimental test results from the system have shown good reliability for recognizing ties, anchors, and cut spikes. This machine vision system, in conjunction with defect analysis and trending of historical data, will enhance the ability for longer-term predictive assessment of the health of the track system and its components.


title: WebChild: harvesting and organizing commonsense knowledge from the web

abstract: This paper presents a method for automatically constructing a large commonsense knowledge base, called WebChild, from Web contents. WebChild contains triples that connect nouns with adjectives via fine-grained relations like hasShape, hasTaste, evokesEmotion, etc. The arguments of these assertions, nouns and adjectives, are disambiguated by mapping them onto their proper WordNet senses. Our method is based on semi-supervised Label Propagation over graphs of noisy candidate assertions. We automatically derive seeds from WordNet and by pattern matching from Web text collections. The Label Propagation algorithm provides us with domain sets and range sets for 19 different relations, and with confidence-ranked assertions between WordNet senses. Large-scale experiments demonstrate the high accuracy (more than 80 percent) and coverage (more than four million fine grained disambiguated assertions) of WebChild.


title: Reaching for the clouds: contextually enhancing smartphones for energy efficiency

abstract: Energy efficiency has gradually become a compulsory need in mobile computing as the processing requirements for smartphones have increased exponentially. Moreover, the current demand is stretching beyond the extents of modern battery technology. In this sense, we introduce a novel collaboration solution for mobile devices based on a contextual search entitled Hybrid Contextual Cloud for Ubiquitous Platforms comprising of Smartphones (HYCCUPS). HYCCUPS takes advantage of the pervasive nature of smartphones and of current wireless communication technologies as to offer offloading execution of mobile applications in an opportunistic on-the-fly hybrid computing cloud. We design an adaptive contextual search algorithm for schedulling mobile code execution in smartphone communities based on predicting the availability and mobility of devices in the near vicinity. We emulate the HYCCUPS framework based on real user traces and we prove that it improves battery health, maximizes power save, minimizes overall execution time of mobile applications and it preserves or even enhances user experience.


title: Design of an airline coax radial power combiner with enhanced isolation

abstract: An X-band radial power combiner based on an airline coax with 0.15 dB measured insertion loss, 8 dB minimum port-to-port isolation, 33% bandwidth, and the ability to handle kilowatt power levels is presented in this work. The design can readily be scaled to arbitrary frequencies or any number of ports. The methods used to select the parameters and optimize the design are presented. The models are validated by a 9-port X-band proof-of-concept combiner.


title: Exercise Treatment for Major Depression : Maintenance of Therapeutic Benefit at 10 Months

abstract: OBJECTIVE: The purpose of this study was to assess the status of 156 adult volunteers with major depressive disorder (MDD) 6 months after completion of a study in which they were randomly assigned to a 4-month course of aerobic exercise, sertraline therapy, or a combination of exercise and sertraline. METHODS: The presence and severity of depression were assessed by clinical interview using the Diagnostic Interview Schedule and the Hamilton Rating Scale for Depression (HRSD) and by self-report using the Beck Depression Inventory. Assessments were performed at baseline, after 4 months of treatment, and 6 months after treatment was concluded (ie, after 10 months). RESULTS: After 4 months patients in all three groups exhibited significant improvement; the proportion of remitted participants (ie, those who no longer met diagnostic criteria for MDD and had an HRSD score <8) was comparable across the three treatment conditions. After 10 months, however, remitted subjects in the exercise group had significantly lower relapse rates (p = .01) than subjects in the medication group. Exercising on one's own during the follow-up period was associated with a reduced probability of depression diagnosis at the end of that period (odds ratio = 0.49, p = .0009). CONCLUSIONS: Among individuals with MDD, exercise therapy is feasible and is associated with significant therapeutic benefit, especially if exercise is continued over time.


title: Personnel Economics: Hiring and Incentives

abstract: We survey the Personnel Economics literature, focusing on how firms establish, maintain, and end employment relationships and on how firms provide incentives to employees. This literature has been very successful in generating models and empirical work about incentive systems. Some of the unanswered questions in this area--for example, the empirical relevance of the risk/incentive tradeoff and the question of whether CEO pay arrangements reflect competitive markets and efficient contracting--are likely to be very difficult to answer due to measurement problems. The literature has been less successful at explaining how firms can find the right employees in the first place. Economists understand the broad economic forces--matching with costly search and bilateral asymmetric information--that firms face in trying to hire. But the main models in this area treat firms as simple black-box production functions. Less work has been done to understand how different firms approach the hiring problem, what determines the firm-level heterogeneity in hiring strategies, and whether these patterns conform to theory. We survey some literature in this area and suggest areas for further research.


title: Deep Neural Networks for Object Detection

abstract: Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.


title: E-learning services acceptance in higher educational institutes: A case study in Brunei

abstract: This research studies on the e-learning services acceptance in higher education institutions in Brunei Darussalam. This research has seven hypotheses, relating to independent, intermediary, and dependent variables. The independent variables include lecturer’s characteristics, design of learning contents, teaching materials, and playfulness; while the intermediary variables are perceived benefits and perceived ease of use. On the other hand, the dependent variable in this research is the intention to use e-learning. Multiple regression analysis were conducted to test the hypotheses proposed. Data analysis from this research has confirmed that the lecturer’s characteristics, teaching materials, perceived ease of use and the intention to use e-learning corresponds to the perceived benefits. Meanwhile, the design of learning content and the intention to use e-learning are positively related to the perceived ease of use and lastly, the variable playfulness is positively related to the intention to use e-learning.


title: All-hardware SIFT implementation for real-time VGA images feature extraction

abstract: This paper presents a real time hardware implementation of the scale invariant feature transform (SIFT) algorithm. To achieve real time requirements, pipeline structures have been widely exploited both in the keypoint extraction and in the descriptor generation stages. Simplifications to the original algorithm have been also applied to allow a simpler hardware implementation. The proposed architecture has been synthesized on a Xilinx Virtex 5 FPGA. It generates 3072 descriptor vectors for VGA images at 99 frames per second at a clock rate of 100 MHz.


Enabling Interoperability in the Internet of Things : A OSGi Semantic Information Broker Implementation


title: Causal Structure Learning and Inference: A Selective Review

abstract: AbstractIn this paper we give a review of recent causal inference methods. First, we discuss methods for causal structure learning from observational data when confounders are not present and have a close look at methods for exact identifiability. We then turn to methods which allow for a mix of observational and interventional data, where we also touch on active learning strategies. We also discuss methods which allow arbitrarily complex structures of hidden variables. Second, we present approaches for estimating the interventional distribution and causal effects given the (true or estimated) causal structure. We close with a note on available software and two examples on real data.


title: Sambot: A Self-Assembly Modular Robot System

abstract: The design and structure of a self-assembly modular robot (Sambot) are presented in this paper. Each module has its own autonomous mobility and can connect with other modules to form robotic structures with different manipulation abilities. Sambot has a versatile, robust, and flexible structure. The computing platform provided for each module is distributed and consists of a number of interlinked microcontrollers. The interaction and connectivity between different modules is achieved through infrared sensors and Zigbee wireless communication in discrete state and control area network bus communication in robotic configuration state. A new mechanical design is put forth to realize the autonomous motion and docking of Sambots. It is a challenge to integrate actuators, sensors, microprocessors, power units, and communication elements into a highly compact and flexible module with the overall size of 80 mm × 80 mm × 102 mm. The work describes represents a mature development in the area of self-assembly distributed robotics.


title: Trained Ternary Quantization

abstract: Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values. This method has very little accuracy degradation and can even improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet model is trained from scratch, which means it's as easy as to train normal full precision model. We highlight our trained quantization method that can learn both ternary values and ternary assignment. During inference, only ternary values (2-bit weights) and scaling factors are needed, therefore our models are nearly 16x smaller than full-precision models. Our ternary models can also be viewed as sparse binary weight networks, which can potentially be accelerated with custom circuit. Experiments on CIFAR-10 show that the ternary models obtained by trained quantization method outperform full-precision models of ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and outperforms previous ternary models by 3%.


title: Channel Adaptive One Hop Broadcasting for VANETs

abstract: One-hop broadcasting is the predominate form of network traffic in VANETs. Exchanging status information by broadcasting among the vehicles enhances vehicular active safety. Since there is no MAC layer broadcasting recovery for 802.11 based VANETs, efforts should be made towards more robust and effective transmission of such safety-related information. In this paper, a channel adaptive broadcasting method is proposed. It relies solely on channel condition information available at each vehicle by employing standard supported sequence number mechanisms. The proposed method is fully compatible with 802.11 and introduces no communication overhead. Simulation studies show that it outperforms standard broadcasting in term of reception rate and channel utilization.


title: Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects

abstract: Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.


title: Critical interlayer thickness for transient liquid phase bonding in the Cu–Sn system

abstract: The study focuses on the critical interlayer thickness for averting pore formation during transient liquid phase (TLP) bonding in the Cu–Sn system. Such pores are a consequence of the growth and subsequent contact of Cu6Sn5 intermetallic grains on the two surfaces to be bonded, prior to the formation of the transient liquid phase. A criterion for the critical interlayer thickness is developed, based on the heights of the largest intermetallic grains. Experiments are performed to ascertain the growth kinetics and the morphology of the intermetallic as a function of heating rate. On this basis, TLP bonding experiments are designed and implemented to demonstrate the transition from pore-containing to pore-free bonds as the interlayer thickness exceeds the predicted critical value. The benefits of high heating rates on both the critical interlayer thickness and the bonding time needed to achieve a targeted terminal microstructure are illustrated. � 2004 Published by Elsevier Ltd on behalf of Acta Materialia Inc.


title: Enhancing the security of caesar cipher substitution method using a randomized approach for more secure communication

abstract: cipher is an ancient, elementary method of encrypting plain text message into cipher text protecting it from adversaries. However, with the advent of powerful computers, there is a need for increasing the complexity of such techniques. This paper contributes in the area of classical cryptography by providing a modified and expanded version for Caesar cipher using knowledge of mathematics and computer science. To increase the strength of this classical encryption technique, the proposed modified algorithm uses the concepts of affine ciphers, transposition ciphers and randomized substitution techniques to create a cipher text which is nearly impossible to decode. It also increases the range of characters which Caesar cipher Algorithm can encrypt by including all ASCII and extended ASCII characters in addition to alphabets. A complex key generation technique which generates two keys from a single key is used to provide enhanced security. This paper aims to propose an enhanced version of Caesar cipher substitution technique which can overcome all the limitations faced by classical Caesar Cipher.


title: A 3-kW Wireless Power Transfer System for Sightseeing Car Supercapacitor Charge

abstract: Wireless power transfer (WPT) is the preferred charging method for electric vehicles (EVs) powered by battery and supercapacitor. In this paper, a novel WPT system with constant current charging capability for sightseeing car with supercapacitor storage is designed. First, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transfer and high efficiency. Compared with the traditional planar shape ferrite core coupler, the proposed magnetic coupler requires lesser ferrite material without degrading the performance of the WPT system. Second, the model of supercapacitor is applied to the WPT system and the relationship between equivalent load resistances of supercapacitor and charging time is analyzed in detail. Then, a Buck converter with proportional integral (PI) controller is implemented on the secondary side to maintain constant charging current for the variable load. Finally, the proposed design is verified by experiments. Constant charging current of 31.5 A across transfer distance of 15 cm is achieved. The peak transfer power and system efficiency are 2.86 kW and 88.05%, respectively.


title: MCS HOG Features and SVM Based Handwritten Digit Recognition System

abstract: Digit Recognition is an essential element of the process of scanning and converting documents into electronic format. In this work, a new Multiple-Cell Size (MCS) approach is being proposed for utilizing Histogram of Oriented Gradient (HOG) features and a Support Vector Machine (SVM) based classifier for efficient classification of Handwritten Digits. The HOG based technique is sensitive to the cell size selection used in the relevant feature extraction computations. Hence a new MCS approach has been used to perform HOG analysis and compute the HOG features. The system has been tested on the Benchmark MNIST Digit Database of handwritten digits and a classification accuracy of 99.36% has been achieved using an Independent Test set strategy. A Cross-Validation analysis of the classification system has also been performed using the 10-Fold Cross-Validation strategy and a 10-Fold classification accuracy of 99.26% has been obtained. The classification performance of the proposed system is superior to existing techniques using complex procedures since it has achieved at par or better results using simple operations in both the Feature Space and in the Classifier Space. The plots of the system’s Confusion Matrix and the Receiver Operating Characteristics (ROC) show evidence of the superior performance of the proposed new MCS HOG and SVM based digit classification system.


title: Review of ontology matching with background knowledge

abstract: The ontology matching process with background knowledge is more suitable to match heterogeneous ontologies, since background knowledge is used as a mediator or a reference to identify relation between two concepts being matched. This method is called indirect matching and the system is called indirect matching system. This paper reviews the motivation that described the urgency of ontology matching, the various background knowledge and their strengths, also indirect matching process. At the end we provide the comparison of indirect matching system. Based on the comparison, mapping repair function were added to improve the quality of mapping. The purpose of this paper is to help in guiding new practitioners get a general idea on the ontology matching field and to determine possible research lines.


Nonparametric budgeted stochastic gradient descent


title: Variability in action: Contributions of a songbird cortical-basal ganglia circuit to vocal motor learning and control

abstract: Abstract Many motor behaviors, from walking to speaking, are acquired through experience, in particular, through trial-and-error learning. The acquisition and maintenance of such motor behaviors in a wide range of species, including humans, appear to depend on cortical-basal ganglia circuits. In this review, we discuss recent studies in songbirds that have been pivotal in informing our current understanding of motor learning and cortical-basal ganglia function. Songbirds are important ethological model systems for the study of motor learning because young songbirds naturally develop and refine their songs through trial-and-error learning. In addition, reinforcement mechanisms are hypothesized to be important for the maintenance and plasticity of structured adult song. Computational and experimental studies highlight the importance of vocal motor variability as the substrate upon which reinforcement mechanisms could operate to shape developing song and to maintain adult song. Recent studies in songbirds indicate that this vocal motor variability is actively generated and modulated by a highly specialized cortical-basal ganglia circuit evolved for a single behavior, song. We argue that these and other recent findings illustrate how the tight association between a specialized neural circuit and a natural behavior make songbirds a unique and powerful model in which to investigate the neural substrates of motor learning and plasticity.


title: Design of highly selective microstrip bandpass filters with a single pair of attenuation poles at finite frequencies

abstract: This paper presents the design of a class of highly selective microstrip bandpass filters that consist of microstrip open-loop resonators that exhibit a single pair of attenuation poles at finite frequencies. A practical design technique for this class of filters is introduced, including tables and formulas for accurate and fast filter synthesis. Two design examples of a six-pole filter with a fractional bandwidth of 7.331% at 955 MHz and an eight-pole filter with a fractional bandwidth of 10.359% at 985 MHz are described. Theoretical and experimental results are presented. The compact size and the excellent performance of this class of filters have been demonstrated.


title: A compact microstrip Rotman lens design

abstract: Rotman lenses have long served as analog beam forming networks (BFNs) to support linear scanning arrays. However, most lens designs have large electrical size which limits their uses in compact transceiver systems. In this paper, we aim to design a compact lens structure by folding the lens into two layers, and using coupling slots as transition. The proposed designed structure will reduce the overall dimensions of the conventional Rotman lens by 50%.


title: A Method of Aircraft Detection Using Fully Convolutional Network

abstract: Traditional methods on aircraft detection in remote sensing images rely on handcrafted design, which is difficult to detect and recognize the target in complex scenes and multiscale conditions. In this paper, we tackle these two problems by proposing a method for aircraft detection based on the fully convolutional neural network(FCNN). FCNN can obtain the location of the aircraft quickly and directly by minimizing a multi-task loss. Through data augmentation and transfer learning, the classification accuracy of FCN is much improved. In order to recognize small targets, we combine the resolution information with priori knowledge of aircraft to construct image pyramid structure on the test images. The experimental results show that the method with less parameters has higher accuracy and the model is simple to train.


A Novel Approach for Opinion Spam Detectionin E-Commerce


title: Observations of achievement and motivation in using cloud computing driven CAD: Comparison of college students with high school and vocational high school backgrounds

abstract: Cloud computing technology has matured as it has been integrated with every kind of digitalization processes. It offers numerous advantages for data and software sharing, and thus making the management of complex IT systems much simpler. For education in engineering, cloud computing even provides students with versatile and ubiquitous access to software commonly used in the field without having to step into an actual computer lab. Our study analyzed learning attitudes and academic performances induced by the utilization of resources driven by cloud computing technologies. Comparisons were made between college students with high school and vocational high school backgrounds. One hundred and thirty-two students who took the computer-aided designing (CAD) course participated in the study. Technology Acceptance Model (TAM) was used as the fundamental framework. Open-ended sets of questionnaires were designed to measure academic performance and causal attributions; the results indicated no significant differences in the cognitive domain between the two groups of students, though it is not so in both the psychomotor and the affective domains. College students with vocational high school background appeared to possess higher learning motivation in CAD applications.


title: Gender recognition from speech. Part I: Coarse analysis

abstract: The purpose of this research was to investigate the potential effectiveness of digital speech processing and pattern recognition techniques for the automatic recognition of gender from speech segments. In this paper ‘‘coarse’’ acoustic coefficients (autocorrelation, linear prediction, cepstrum, and reflection) were used to form test and reference templates for vowels, voiced fricatives, and unvoiced fricatives. The effects of different distance measures, filter orders, recognition schemes, and vowels and fricatives were comparatively assessed to determine their effectiveness for the task of gender recognition from speech segments. The results showed that most of the acoustic parameters worked well for gender recognition. A within‐gender and within‐subject averaging technique was important for generating appropriate test and reference templates. The Euclidean distance measure appeared to be the most robust as well as the simplest of the distance measures. The results from this study implied that the gender...


title: Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

abstract: Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process, and interpretative difficulties have often been criticized and hence limited its application in handling credit scoring problems. The objective of the proposed study is to explore the performance of credit scoring using two commonly discussed data mining techniques-classification and regression tree (CART) and multivariate adaptive regression splines (MARS). To demonstrate the effectiveness of credit scoring using CART and MARS, credit scoring tasks are performed on one bank credit card data set. As the results reveal, CART and MARS outperform traditional discriminant analysis, logistic regression, neural networks, and support vector machine (SVM) approaches in terms of credit scoring accuracy and hence provide efficient alternatives in implementing credit scoring tasks.


title: Privacy Preserving Clustering

abstract: The freedom and transparency of information flow on the Internet has heightened concerns of privacy. Given a set of data items, clustering algorithms group similar items together. Clustering has many applications, such as customer-behavior analysis, targeted marketing, forensics, and bioinformatics. In this paper, we present the design and analysis of a privacy-preserving κ-means clustering algorithm, where only the cluster means at the various steps of the algorithm are revealed to the participating parties. The crucial step in our privacy-preserving κ-means is privacy-preserving computation of cluster means. We present two protocols (one based on oblivious polynomial evaluation and the second based on homomorphic encryption) for privacy-preserving computation of cluster means. We have a JAVA implementation of our algorithm. Using our implementation, we have performed a thorough evaluation of our privacy-preserving clustering algorithm on three data sets. Our evaluation demonstrates that privacy-preserving clustering is feasible, i.e., our homomorphic-encryption based algorithm finished clustering a large data set in approximately 66 seconds.


title: A generalized normalized gradient descent algorithm

abstract: A generalized normalized gradient descent (GNGD) algorithm for linear finite-impulse response (FIR) adaptive filters is introduced. The GNGD represents an extension of the normalized least mean square (NLMS) algorithm by means of an additional gradient adaptive term in the denominator of the learning rate of NLMS. This way, GNGD adapts its learning rate according to the dynamics of the input signal, with the additional adaptive term compensating for the simplifications in the derivation of NLMS. The performance of GNGD is bounded from below by the performance of the NLMS, whereas it converges in environments where NLMS diverges. The GNGD is shown to be robust to significant variations of initial values of its parameters. Simulations in the prediction setting support the analysis.


title: Small-World Brain Networks Revisited

abstract: It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more b...


title: The Evolutionary Psychology of Facial Beauty

abstract: What makes a face attractive and why do we have the preferences we do? Emergence of preferences early in development and cross-cultural agreement on attractiveness challenge a long-held view that our preferences reflect arbitrary standards of beauty set by cultures. Averageness, symmetry, and sexual dimorphism are good candidates for biologically based standards of beauty. A critical review and meta-analyses indicate that all three are attractive in both male and female faces and across cultures. Theorists have proposed that face preferences may be adaptations for mate choice because attractive traits signal important aspects of mate quality, such as health. Others have argued that they may simply be by-products of the way brains process information. Although often presented as alternatives, I argue that both kinds of selection pressures may have shaped our perceptions of facial beauty.


title: Leveraging Value Locality in Optimizing NAND Flash-based SSDs

abstract: NAND flash-based solid-state drives (SSDs) are increasingly being deployed in storage systems at different levels such as buffer-caches and even secondary storage. However, the poor reliability and performance offered by these SSDs for write-intensive workloads continues to be their key shortcoming. Several solutions based on traditionally popular notions of temporal and spatial locality help reduce write traffic for SSDs. However, another form of locality - value locality - has remained completely unexplored. Value locality implies that certain data items (i.e., "values," not just logical addresses) are likely to be accessed preferentially. Given evidence for the presence of significant value locality in real-world workloads, we design CA-SSD which employs content-addressable storage (CAS) to exploit such locality. Our CA-SSD design employs enhancements primarily in the flash translation layer (FTL) with minimal additional hardware, suggesting its feasibility. Using three real-world workloads with content information, we devise statistical characterizations of two aspects of value locality - value popularity and temporal value locality - that form the foundation of CA-SSD. We observe that CA-SSD is able to reduce average response times by about 59-84% compared to traditional SSDs. Even for workloads with little or no value locality, CA-SSD continues to offer comparable performance to a traditional SSD. Our findings advocate adoption of CAS in SSDs, paving the way for a new generation of these devices.


title: Simulated Annealing Algorithm for 2D Image Compression

abstract: In this paper a new sign coding approximation method for the wavelet coefficients in a 2D image codec based on a simulated annealing metaheuristic is presented. The efficiency of the proposed algorithm versus a genetic algorithm using benchmarks of Kodak is compared and showing that the proposed sign prediction algorithm is efficient and provides a significant reduction of wavelet coefficients sign information in the final bit-stream. The results show that, by including sign coding capabilities to a nonembedded encoder, the sign compression gain is up to 17.35%, being the rate-distortion (R/D) performance improvement up to 0.25 dB.


title: Incorporating depth into both CNN and CRF for indoor semantic segmentation

abstract: To improve segmentation performance, a novel neural network architecture (termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional neural network (DFCN) with a depth-sensitive fully-connected conditional random field (DCRF). First, a DFCN architecture which fuses depth information into the early layers and applies dilated convolution for later contextual reasoning is designed. Then, a depth-sensitive fully-connected conditional random field (DCRF) is proposed and combined with the previous DFCN to refine the preliminary result. Comparative experiments show that the proposed DFCN-DCRF has the best performance compared with most state-of-the-art methods.


title: Towards macro- and micro-expression spotting in video using strain patterns

abstract: This paper presents a novel method for automatic spotting (temporal segmentation) of facial expressions in long videos comprising of continuous and changing expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field of each subjects face. Testing has been done on 2 datasets (which includes 100 macro-expressions) and promising results have been obtained. The method is robust to several common drawbacks found in automatic facial expression segmentation including moderate in-plane and out-of-plane motion. Additionally, the method has also been modified to work with videos containing micro-expressions. Micro-expressions are detected utilizing their smaller spatial and temporal extent. A subject's face is divided in to sub-regions (mouth, cheeks, forehead, and eyes) and facial strain is calculated for each of these regions. Strain patterns in individual regions are used to identify subtle changes which facilitate the detection of micro-expressions.


title: Bayesian Spam Detection

abstract: Spammers always find new ways to get spammy content to the public. Very commonly this is accomplished by using email, social media, or advertisements. According to a 2011 report by the Messaging Anti-Abuse Working Group roughly 90% of all emails in the United States are spam. This is why we will be taking a more detailed look at email spam. Spam filters have been getting better at detecting spam and removing it, but no method is able to block 100% of it. Because of this, many different methods of text classification have been developed, including a group of classifiers that use a Bayesian approach. The Bayesian approach to spam filtering was one of the earliest methods used to filter spam, and it remains relevant to this day. In this paper we will analyze 2 specific optimizations of Naive Bayes text classification and spam filtering, looking at the differences between them and how they have been used in practice. This paper will show that Bayesian filtering can be simply implemented for a reasonably accurate text classifier and that it can be modified to make a significant impact on the accuracy of the filter. A variety of applications will be explored as well.


title: Semantic Parsing for Single-Relation Question Answering

abstract: We develop a semantic parsing framework based on semantic similarity for open domain question answering (QA). We focus on single-relation questions and decompose each question into an entity mention and a relation pattern. Using convolutional neural network models, we measure the similarity of entity mentions with entities in the knowledge base (KB) and the similarity of relation patterns and relations in the KB. We score relational triples in the KB using these measures and select the top scoring relational triple to answer the question. When evaluated on an open-domain QA task, our method achieves higher precision across different recall points compared to the previous approach, and can improve F1 by 7 points.


title: Factorizing YAGO: scalable machine learning for linked data

abstract: Vast amounts of structured information have been published in the Semantic Web's Linked Open Data (LOD) cloud and their size is still growing rapidly. Yet, access to this information via reasoning and querying is sometimes difficult, due to LOD's size, partial data inconsistencies and inherent noisiness. Machine Learning offers an alternative approach to exploiting LOD's data with the advantages that Machine Learning algorithms are typically robust to both noise and data inconsistencies and are able to efficiently utilize non-deterministic dependencies in the data. From a Machine Learning point of view, LOD is challenging due to its relational nature and its scale. Here, we present an efficient approach to relational learning on LOD data, based on the factorization of a sparse tensor that scales to data consisting of millions of entities, hundreds of relations and billions of known facts. Furthermore, we show how ontological knowledge can be incorporated in the factorization to improve learning results and how computation can be distributed across multiple nodes. We demonstrate that our approach is able to factorize the YAGO~2 core ontology and globally predict statements for this large knowledge base using a single dual-core desktop computer. Furthermore, we show experimentally that our approach achieves good results in several relational learning tasks that are relevant to Linked Data. Once a factorization has been computed, our model is able to predict efficiently, and without any additional training, the likelihood of any of the 4.3 ⋅ 1014 possible triples in the YAGO~2 core ontology.


title: Assured Deletion in the Cloud: Requirements, Challenges and Future Directions

abstract: Inadvertent exposure of sensitive data is a major concern for potential cloud customers. Much focus has been on other data leakage vectors, such as side channel attacks, while issues of data disposal and assured deletion have not received enough attention to date. However, data that is not properly destroyed may lead to unintended disclosures, in turn, resulting in heavy financial penalties and reputational damage. In non-cloud contexts, issues of incomplete deletion are well understood. To the best of our knowledge, to date, there has been no systematic analysis of assured deletion challenges in public clouds. In this paper, we aim to address this gap by analysing assured deletion requirements for the cloud, identifying cloud features that pose a threat to assured deletion, and describing various assured deletion challenges. Based on this discussion, we identify future challenges for research in this area and propose an initial assured deletion architecture for cloud settings. Altogether, our work offers a systematization of requirements and challenges of assured deletion in the cloud, and a well-founded reference point for future research in developing new solutions to assured deletion.


Introduction to the Dirichlet Distribution and Related Processes


title: Differentially Private Data Release through Multidimensional Partitioning

abstract: Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release based on an interactive differential privacy interface. We propose two multidimensional partitioning strategies including a baseline cell-based partitioning and an innovative kd-tree based partitioning. In addition to providing formal proofs for differential privacy and usefulness guarantees for linear distributive queries, we also present a set of experimental results and demonstrate the feasibility and performance of our method.


title: RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection

abstract: Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS Forestto systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.


title: Energy-efficient signal acquisition in wireless sensor networks: a compressive sensing framework

abstract: The sampling rate of the sensors in wireless sensor networks (WSNs) determines the rate of its energy consumption, since most of the energy is used in sampling and transmission. To save the energy in WSNs and thus prolong the network lifetime, the authors present a novel approach based on the compressive sensing (CS) framework to monitor 1-D environmental information in WSNs. The proposed technique is based on CS theory to minimise the number of samples taken by sensor nodes. An innovative feature of the proposed approach is a new random sampling scheme that considers the causality of sampling, hardware limitations and the trade-off between the randomisation scheme and computational complexity. In addition, a sampling rate indicator feedback scheme is proposed to enable the sensor to adjust its sampling rate to maintain an acceptable reconstruction performance while minimising the number of samples. A significant reduction in the number of samples required to achieve acceptable reconstruction error is demonstrated using real data gathered by a WSN located in the Hessle Anchorage of the Humber Bridge.


title: Vector field based shape deformations

abstract: We present an approach to define shape deformations by constructing and interactively modifying C1 continuous time-dependent divergence-free vector fields. The deformation is obtained by a path line integration of the mesh vertices. This way, the deformation is volume-preserving, free of (local and global) self-intersections, feature preserving, smoothness preserving, and local. Different modeling metaphors support the approach which is able to modify the vector field on-the-fly according to the user input. The approach works at interactive frame rates for moderate mesh sizes, and the numerical integration preserves the volume with a high accuracy.


title: Using DevOps Principles to Continuously Monitor RDF Data Quality

abstract: One approach to continuously achieve a certain data quality level is to use an integration pipeline that continuously checks and monitors the quality of a data set according to defined metrics. This approach is inspired by Continuous Integration pipelines, that have been introduced in the area of software development and DevOps to perform continuous source code checks. By investigating in possible tools to use and discussing the specific requirements for RDF data sets, an integration pipeline is derived that joins current approaches of the areas of software-development and semantic-web as well as reuses existing tools. As these tools have not been built explicitly for CI usage, we evaluate their usability and propose possible workarounds and improvements. Furthermore, a real-world usage scenario is discussed, outlining the benefit of the usage of such a pipeline.


title: Distributed Kalman Filter with Embedded Consensus Filters

abstract: The problem of distributed Kalman filtering (DKF) for sensor networks is one of the most fundamental distributed estimation problems for scalable sensor fusion. This paper addresses the DKF problem by reducing it to two separate dynamic consensus problems in terms of weighted measurements and inverse-covariance matrices. These to data fusion problems are solved is a distributed way using low-pass and band-pass consensus filters. Consensus filters are distributed algorithms that allow calculation of average-consensus of time-varying signals. The stability properties of consensus filters is discussed in a companion CDC ’05 paper [24]. We show that a central Kalman filter for sensor networks can be decomposed into n micro-Kalman filters with inputs that are provided by two types of consensus filters. This network of micro-Kalman filters collectively are capable to provide an estimate of the state of the process (under observation) that is identical to the estimate obtained by a central Kalman filter given that all nodes agree on two central sums. Later, we demonstrate that our consensus filters can approximate these sums and that gives an approximate distributed Kalman filtering algorithm. A detailed account of the computational and communication architecture of the algorithm is provided. Simulation results are presented for a sensor network with 200 nodes and more than 1000 links.


title: Effect sizes in memory research

abstract: Effect sizes are omitted from many research articles and are rarely discussed. To help researchers evaluate effect sizes we collected values for the more commonly reported effect size measures (partial eta squared and d) from papers reporting memory research published in 2010. Cohen's small, medium, and large generic guideline values for d mapped neatly onto the observed distributions, but his values for partial eta squared were considerably lower than those observed in current memory research. We recommend interpreting effect sizes in the context of either domain-specific guideline values agreed for an area of research or the distribution of effect size estimates from published research in the domain. We provide cumulative frequency tables for both partial eta squared and d enabling authors to report and consider not only the absolute size of observed effects but also the percentage of reported effects that are larger or smaller than those observed.


title: Towards printable robotics: Origami-inspired planar fabrication of three-dimensional mechanisms

abstract: This work presents a technique which allows the application of 2-D fabrication methods to build 3-D robotic systems. The ability to print robots introduces a fast and low-cost fabrication method to modern, real-world robotic applications. To this end, we employ laser-engraved origami patterns to build a new class of robotic systems for mobility and manipulation. Origami is suitable for printable robotics as it uses only a flat sheet as the base structure for building complicated functional shapes, which can be utilized as robot bodies. An arbitrarily complex folding pattern can be used to yield an array of functionalities, in the form of actuated hinges or active spring elements. For actuation, we use compact NiTi coil actuators placed on the body to move parts of the structure on-demand. We demonstrate, as a proof-of-concept case study, the end-to-end fabrication and assembly of a simple mobile robot that can undergo worm-like peristaltic locomotion.


title: Unsupervised Learning of Distributions on Binary Vectors Using Two Layer Networks

abstract: We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model can be represented by a particular type of Boltzmann machine with a bipartite graph structure that we call the combination machine. This machine is closely related to the Harmonium model defined by Smolensky. In the first part of the paper we analyze properties of this distribution representation scheme. We show that arbitrary distributions of binary vectors can be approximated by the combination model. We show how the weight vectors in the model can be interpreted as high order correlation patterns among the input bits, and how the combination machine can be used as a mechanism for detecting these patterns. We compare the combination model with the mixture model and with principle component analysis. In the second part of the paper we present two algorithms for learning the combination model from examples. The first learning algorithm is the standard gradient ascent heuristic for computing maximum likelihood estimates for the parameters of the model. Here we give a closed form for this gradient that is significantly easier to compute than the corresponding gradient for the general Boltzmann machine. The second learning algorithm is a greedy method that creates the hidden units and computes their weights one at a time. This method is a variant of projection pursuit density estimation. In the third part of the paper we give experimental results for these learning methods on synthetic data and on natural data of handwritten digit images.


title: Hierarchical Clustering with Prior Knowledge

abstract: Hierarchical clustering is a class of algorithms that seeks to build a hierarchy of clusters. It has been the dominant approach to constructing embedded classification schemes since it outputs dendrograms, which capture the hierarchical relationship among members at all levels of granularity, simultaneously. Being greedy in the algorithmic sense, a hierarchical clustering partitions data at every step solely based on a similarity / dissimilarity measure. The clustering results oftentimes depend on not only the distribution of the underlying data, but also the choice of dissimilarity measure and the clustering algorithm. In this paper, we propose a method to incorporate prior domain knowledge about entity relationship into the hierarchical clustering. Specifically, we use a distance function in ultrametric space to encode the external ontological information. We show that popular linkage-based algorithms can faithfully recover the encoded structure. Similar to some regularized machine learning techniques, we add this distance as a penalty term to the original pairwise distance to regulate the final structure of the dendrogram. As a case study, we applied this method on real data in the building of a customer behavior based product taxonomy for an Amazon service, leveraging the information from a larger Amazon-wide browse structure. The method is useful when one wants to leverage the relational information from external sources, or the data used to generate the distance matrix is noisy and sparse. Our work falls in the category of semi-supervised or constrained clustering.


title: An Analysis of the Cloud Computing Security Problem

abstract: Cloud computing is a new computational paradigm that offers an innovative business model for organizations to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing, the model security is still questionable which impacts the cloud model adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. In this paper we introduce a detailed analysis of the cloud security problem. We investigated the problem from the cloud architecture perspective, the cloud offered characteristics perspective, the cloud stakeholders' perspective, and the cloud service delivery models perspective. Based on this analysis we derive a detailed specification of the cloud security problem and key features that should be covered by any proposed security solution.


title: Aligning images in the wild

abstract: Aligning image pairs with significant appearance change is a long standing computer vision challenge. Much of this problem stems from the local patch descriptors' instability to appearance variation. In this paper we suggest this instability is due less to descriptor corruption and more the difficulty in utilizing local information to canoni-cally define the orientation (scale and rotation) at which a patch's descriptor should be computed. We address this issue by jointly estimating correspondence and relative patch orientation, within a hierarchical algorithm that utilizes a smoothly varying parameterization of geometric transformations. By collectively estimating the correspondence and orientation of all the features, we can align and orient features that cannot be stably matched with only local information. At the price of smoothing over motion discontinuities (due to independent motion or parallax), this approach can align image pairs that display significant inter-image appearance variations.


title: Efficient local search heuristics for packing irregular shapes in two-dimensional heterogeneous bins

abstract: In this paper we proposed a local search heuristic and a genetic algorithm to solve the two-dimensional irregular multiple bin-size bin packing problem. The problem consists of placing a set of pieces represented as 2D polygons in rectangular bins with different dimensions such that the total area of bins used is minimized. Most packing algorithms available in the literature for 2D irregular bin packing consider single size bins only. However, for many industries the material can be supplied in a number of standard size sheets, for example, metal, foam, plastic and timber sheets. For this problem, the cut plans must decide the set of standard size stock sheets as well as which pieces to cut from each bin and how to arrange them in order to minimise waste material. Moreover, the literature constrains the orientation of pieces to a single or finite set of angles. This is often an artificial constraint that makes the solution space easier to navigate. In this paper we do not restrict the orientation of the pieces. We show that the local search heuristic and the genetic algorithm can address all of these decisions and obtain good solutions, with the local search performing better. We also discuss the affect of different groups of stock sheet sizes.