title: A Novel Method of Feature Selection based on SVM

abstract: A novel method of feature selection combined with sample selection is proposed to select discriminant features in this paper. Based on support vector machine trained on training set, the samples excluding the misclassified samples and support vector samples are used to select informative features during the procedure of recursive feature selection. The feature selection method is applied to seven datasets, and the classification results of the selected discriminant features show that the method is effective and reliable for selecting features with high classification information.


title: Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

abstract: Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional neural networks proved to be a boon for the image classification and recognition tasks, specifically for food recognition because of the wide variety of food items. In this paper, we report experiments on food/non-food classification and food recognition using a GoogLeNet model based on deep convolutional neural network. The experiments were conducted on two image datasets created by our own, where the images were collected from existing image datasets, social media, and imaging devices such as smart phone and wearable cameras. Experimental results show a high accuracy of 99.2% on the food/non-food classification and 83.6% on the food category recognition.


title: Opinion Overlapping memory replay during sleep builds cognitive schemata

abstract: Sleep enhances integration across multiple stimuli, abstraction of general rules, insight into hidden solutions and false memory formation. Newly learned information is better assimilated if compatible with an existing cognitive framework or schema. This article proposes a mechanism by which the reactivation of newly learned memories during sleep could actively underpin both schema formation and the addition of new knowledge to existing schemata. Under this model, the overlapping replay of related memories selectively strengthens shared elements. Repeated reactivation of memories in different combinations progressively builds schematic representations of the relationships between stimuli. We argue that this selective strengthening forms the basis of cognitive abstraction, and explain how it facilitates insight and false memory formation.


title: IR real-time analyses for PV system monitoring by digital image processing techniques

abstract: PV modules as main component of PV system might be subjected to various internal or external stresses hence monitoring and maintenance are a crucial issue to assure PV module lifetime and appropriate energy performance. Current experimental research proposes an applicable approach of digital image processing technique in PV module inspection by thermography assessment. For this purpose, an algorithm was designed to perform IR images analysis. The monitoring was performed in order to detect defects and failures due to particular events on the PV systems by thermography analysis. The investigation was carried out by mounted IR thermo-camera (Flir A35) on a light Unmanned Aerial System (UAS) in Solar Tech Laboratory. The captured IR-images have been processed by proposed algorithm to determine specific defect and degradation percentage. The results have proven that digital image processing technique is very useful and reliable for IR-image analysis to identify possible defects during the PV module inspection procedure.


title: WHEN THEORY MEETS PRACTICE: THE VALUE AND LIMITATIONS OF THE CONCEPT OF PROJECTIVE IDENTIFICATION

abstract: Before the corporate entity of A T & T was broken up so that hungry predators could sweep up the fragments and take over the telecommunications industry, it was affectionately known as “Ma Bell.” I recall as a youngster hearing its radio advertisement that told us, over and over, “we’re all connected.” And indeed we are all connected. No one exists in isolation. We start out in life developing inside another person, to whom we are connected by an umbilical cord as our lifeline. And that is only the beginning. When we emerge into the world, in a physically helpless but mentally active, secondarily altricial state, we continue to be connected to a mothering caregiver who among other things is indeed our Ma Bell. We are in continual and, in a sense, continuous communication with her as we use our mindbrain—the felicitous term employed by Damasio (2010) and Panksepp (2012)—to learn how to affectively regulate ourselves and


title: Learning to Win by Reading Manuals in a Monte-Carlo Framework

abstract: This paper presents a novel approach for leveraging automatically extracted textual knowledge to improve the performance of control applications such as games. Our ultimate goal is to enrich a stochastic player with high-level guidance expressed in text. Our model jointly learns to identify text that is relevant to a given game state in addition to learning game strategies guided by the selected text. Our method operates in the Monte-Carlo search framework, and learns both text analysis and game strategies based only on environment feedback. We apply our approach to the complex strategy game Civilization II using the official game manual as the text guide. Our results show that a linguistically-informed game-playing agent significantly outperforms its language-unaware counterpart, yielding a 27% absolute improvement and winning over 78% of games when playing against the built-in AI of Civilization II.


title: DBpedia: A Multilingual Cross-domain Knowledge Base

abstract: The DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world knowledge. The knowledge base contains textual descriptions (titles and abstracts) of concepts in up to 97 languages. It also contains structured knowledge that has been extracted from the infobox systems of Wikipedias in 15 different languages and is mapped onto a single consistent ontology by a community effort. The knowledge base can be queried using the SPARQL query language and all its data sets are freely available for download. In this paper, we describe the general DBpedia knowledge base and as well as the DBpedia data sets that specifically aim at supporting computational linguistics tasks. These task include Entity Linking, Word Sense Disambiguation, Question Answering, Slot Filling and Relationship Extraction. These use cases are outlined, pointing at added value that the structured data of DBpedia provides.


title: Practical privacy: the SuLQ framework

abstract: We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is Σ ieS f(d i ), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise -- much less than the sampling error -- provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].


title: Content Based Image Retrieval: Survey

abstract: The requirement for development of CBIR is enhanced due to tremendous growth in volume of images as well as the widespread application in multiple fields. Texture, color, shape and spatial layout are the underlying traits to represent and index the images. These peculiar features of images are extracted and implemented for a similarity check among images. The problem of content based image retrieval is based on generation of peculiar query. For relevant images that meet their information need, an automated search is initiated by drawing a sketch or with the submission of image having similar features. Similarity between extracted features can be measured by using different algorithms. The use of relevance feedback as a post retrieval step enhances the optimization of the process. The necessity to explore the ever growing volume of image and video is motivating the development of efficient CBIR algorithms. Different algorithms and models for the retrieval of images have been explored over the last twenty years. In this paper an analysis of visual contents of image is done with respect to features related to low level after extracting from image that are color, texture and shape. Here most popular algorithms of feature extraction and relevance feedback that try to bridge extracted low level features and features with high level semantics gap from image are discussed. A brief overview of an algorithm has been presented which is based on fuzzy logic and is used for the selection of peculiar features.


title: Deep Convolutional Neural Networks for Pairwise Causality

abstract: Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning. As has been shown before, the direction of pairwise causal relations can, under certain conditions, be inferred from observational data via standard gradient-boosted classifiers (GBC) using carefully engineered statistical features. In this paper we apply deep convolutional neural networks (CNNs) to this problem by plotting attribute pairs as 2-D scatter plots that are fed to the CNN as images. We evaluate our approach on the 'Cause- Effect Pairs' NIPS 2013 Data Challenge. We observe that a weighted ensemble of CNN with the earlier GBC approach yields significant improvement. Further, we observe that when less training data is available, our approach performs better than the GBC based approach suggesting that CNN models pre-trained to determine the direction of pairwise causal direction could have wider applicability in causal discovery and enabling what-if or counterfactual analysis.


title: A Software Gamification Model for Cross-Cultural Software Development Teams

abstract: Gamification is the use of game design elements in non-game context platforms such as services and marketing to motivate people to participate in planned activities to increase engagement and loyalty to achieve goals. Gamification has been applied to academic fields including software engineering in recent years. Some studies show that gamification can motivate engineers in Software Engineering (SE) if applied appropriately. However, most gamification implementations are lacking well-defined frameworks or models. This paper develops a software gamification model (SGM) with a well-defined framework that provides a robust process for implementing gamification for SE. This model also contains elements from applied psychology, Social Software Engineering (SSE), and the Capability Maturity Model (CMM). Having those elements ensures that the model has a solid foundation in gamification and SE disciplines. The model is further tailored for cross-cultural software development teams (CCSDT). CCSDT have been popular in recent years due to the fact that software development has become a global business. However, as more and more CCSDT are formed, many challenges and issues have been raised in those cross-cultural environments stemming from miscommunication, misunderstanding, cultural differences and conflicts. This paper uses the SGM to help resolve the issues.


title: TOM-Net: Learning Transparent Object Matting from a Single Image

abstract: This paper addresses the problem of transparent object matting. Existing image matting approaches for transparent objects often require tedious capturing procedures and long processing time, which limit their practical use. In this paper, we first formulate transparent object matting as a refractive flow estimation problem. We then propose a deep learning framework, called TOM-Net, for learning the refractive flow. Our framework comprises two parts, namely a multi-scale encoder-decoder network for producing a coarse prediction, and a residual network for refinement. At test time, TOM-Net takes a single image as input, and outputs a matte (consisting of an object mask, an attenuation mask and a refractive flow field) in a fast feed-forward pass. As no off-the-shelf dataset is available for transparent object matting, we create a large-scale synthetic dataset consisting of 178K images of transparent objects rendered in front of images sampled from the Microsoft COCO dataset. We also collect a real dataset consisting of 876 samples using 14 transparent objects and 60 background images. Promising experimental results have been achieved on both synthetic and real data, which clearly demonstrate the effectiveness of our approach.


title: Current Mirror Array: A Novel Circuit Topology for Combining Physical Unclonable Function and Machine Learning

abstract: Edge analytics support industrial Internet of Things by pushing some data processing capacity to the edge of the network instead of sending the streaming data captured by the sensor nodes directly to the cloud. It is advantageous to endow machine learners for data reduction with suitable security primitives for privacy protection in edge computing devices to conserve area and power consumption. In this paper, we propose a novel physical unclonable function (PUF) based on current mirror array (CMA) circuits that reuses the circuit implementation of a machine learner–the extreme learning machine (ELM), which is a randomized neural network. Seven different challenge activation and response readout schemes are proposed to realize different weak and strong PUF functions from within the same CMA array. ELM endowed with such reconfigurable challenge-response mechanism is more robust and adaptable to different authentication protocols and security functions. Measurement results on $0.35\mu m$ test chips demonstrate that the proposed strong PUF outperforms other state-of-the-art designs with smaller area/bit of $9\times 10^{-36} \mu m^{2}$ and lower native bit error rate (BER) of 0.16% with an added overhead of less than 2.5% power and 2.9% area over the native ELM implementation.


title: Mathematical Modeling and Solution of the Tomography Problem in Domains with Reflecting Obstacles

abstract: This work develops new numerical methods for the solution of the tomography problem in domains with reflecting obstacles. We compare the solution's performance for Lambertian reflection, for classical tomography with ubroken rays and for specular reflection. Our numerical method using Lambertian reflection improves the solution's accuracy by an order of magnitude compared to classical tomography with ubroken rays and for tomography in the presence of a specularly reflecting obstacle the numerical method improves the solution's accuracy approximately by a factor of three times. We present efficient new algorithms for the solution's software implementation and analyze the solution's performance and effectiveness.


title: The Youth Career Centre of Calgary: A Rationale for the Development of a Community-Based Centre for Meeting the Career Development Needs of Youth.

abstract: A Youth Career Centre in South Alberta has been proposed to fulfill the career development needs of youth. The center should be based on the tenets of career development theory that suggest that five basic processes are necessary to facilitate the career development of youth: initiation, exploration, decision making, preparation, and implementation. Results of a survey of community services for the career development of youth in Calgary showed no single service providing assistance in all basic career planning processes. The following gaps in the service network have been identified: lack of interagency collaboration; need for collaboration between business, schools, and agencies; need for services with mandates to help youth in specific age parameters; lack of consistency in program delivery; service delivery hindered by inadequate funding; service deliverers and educators overwhelmed by the enormous needs of youth in the city; services spread throughout the city and difficult for some youth to access them; career development with an occupational and not a life-planning and life-roles focus; over emphasis on skills development; inadequacy of the system's capacity to deal with dropouts; reactive rather than proactive services; and poorly marketed services. Interviews with Calgary practitioners regarding these gaps and needs resulted in overwhelming support for a centralized, specialized youth career center. The complexities of the problems suggest that services need to be multifaceted. (Contains 32 references.) (YLB) *********************************************************************** Aepkonu:Lions suppilea oy takt are tne best that can be mane from the original document. ***********************************************************************


title: Early prediction of scholar popularity

abstract: Prediction of scholar popularity has become an important research topic for a number of reasons. In this paper, we tackle the problem of predicting the popularity {\it trend} of scholars by concentrating on making predictions both as earlier and accurate as possible. In order to perform the prediction task, we first extract the popularity trends of scholars from a training set. To that end, we apply a time series clustering algorithm called K-Spectral Clustering (K-SC) to identify the popularity trends as cluster centroids. We then predict trends for scholars in a test set by solving a classification problem. Specifically, we first compute a set of measures for individual scholars based on the distance between earlier points in her particular popularity curve and the identified centroids. We then combine those distance measures with a set of academic features (e.g., number of publications, number of venues, etc) collected during the same monitoring period, and use them as input to a classification method. One aspect that distinguishes our method from other approaches is that the monitoring period, during which we gather information on each scholar popularity and academic features, is determined on a per scholar basis, as part of our approach. Using total citation count as measure of scientific popularity, we evaluate our solution on the popularity time series of more than 500,000 Computer Science scholars, gathered from Microsoft Azure Marketplace (https://datamarket.azure.com/dataset/mrc/microsoftacademic). The experimental results show that the our prediction method outperforms other alternative prediction methods. We also show how to apply our method jointly with regression models to improve the prediction of scholar popularity values (e.g., number of citations) at a given future time.


Bag of Experts Architectures for Model Reuse in Conversational Language Understanding


title: Enhancing data utility in differential privacy via microaggregation-based $$k$$ k -anonymity

abstract: It is not uncommon in the data anonymization literature to oppose the "old" $$k$$ k -anonymity model to the "new" differential privacy model, which offers more robust privacy guarantees. Yet, it is often disregarded that the utility of the anonymized results provided by differential privacy is quite limited, due to the amount of noise that needs to be added to the output, or because utility can only be guaranteed for a restricted type of queries. This is in contrast with $$k$$ k -anonymity mechanisms, which make no assumptions on the uses of anonymized data while focusing on preserving data utility from a general perspective. In this paper, we show that a synergy between differential privacy and $$k$$ k -anonymity can be found: $$k$$ k -anonymity can help improving the utility of differentially private responses to arbitrary queries. We devote special attention to the utility improvement of differentially private published data sets. Specifically, we show that the amount of noise required to fulfill $$\varepsilon $$ ? -differential privacy can be reduced if noise is added to a $$k$$ k -anonymous version of the data set, where $$k$$ k -anonymity is reached through a specially designed microaggregation of all attributes. As a result of noise reduction, the general analytical utility of the anonymized output is increased. The theoretical benefits of our proposal are illustrated in a practical setting with an empirical evaluation on three data sets.


title: Tilt & touch: mobile phone for 3D interaction

abstract: Mobile phones are becoming de facto pervasive devices for people's daily use. This demonstration illustrates a new interaction, Tilt & Touch, to enable a smart phone to be a 3D controller. It exploits capacitive touchscreen and built-in MEMS motion sensors. When people want to navigate in a virtual reality environment on a large display, they can tilt the phone for viewpoint transforming, touch the phone screen for avatar moving, and pinch screen for viewing camera zooming. The virtual objects in the virtual reality environment can be rotated accordingly by tilting the phone.


title: Validation of a Standalone Smartphone Application for Measuring Heart Rate Using Imaging Photoplethysmography

abstract: BACKGROUND ::: The aim of this study was to determine the accuracy of a freely available smartphone application, Cardiio app (Cardiio, Inc., Cambridge, MA), to measure heart rate from the finger or face using imaging photoplethysmography, by comparing against an FDA-cleared pulse oximeter at rest, and after moderate to vigorous exercise. ::: ::: ::: METHODS ::: A total of 40 healthy adults participated in this study. Participants engaged in a period of rest, followed by 3 min of moderate to vigorous intensity cycling on a stationary bicycle. Heart rate measurements were obtained from both the finger and face of participants using the Cardiio app at rest, immediately after exercise, 1-2 min after exercise, and 2-3 min after exercise. Concurrent heart rate readings using an FDA-cleared finger pulse oximeter served as the reference measurement. ::: ::: ::: RESULTS ::: There was a very strong agreement between heart rate measurements obtained using the Cardiio app and the pulse oximeter, both at rest (r = 0.99 for finger, r = 0.97 for face) and after exercise (r = 0.99 for finger, r = 0.97 for face). At rest, the accuracy of the Cardiio app was ±1.58 beats per minute (bpm) (or ±2.27%) using the finger mode and ±2.28 bpm (or ±3.17%) for the face mode, compared to the pulse oximeter. After moderate to vigorous exercise, the accuracy of the Cardiio app was ±2.97 bpm (or ±2.79%) using the finger mode and ±5.31 bpm (or ±4.50%) for the face mode, compared to the pulse oximeter. ::: ::: ::: CONCLUSION ::: The Cardiio app provided accurate heart rate measurements from the finger and face, both at rest and after exercise.


title: Problems with the Concept of Video Game “Addiction”: Some Case Study Examples

abstract: This paper argues that the recent concerns about video game “addiction” have been based less on scientific facts and more upon media hysteria. By examining the literature, it will be demonstrated that the current criteria used for identifying this concept are both inappropriate and misleading. Furthermore, by presenting four case studies as examples it will be demonstrated how such claims of video game addiction can be inaccurately applied. It is concluded that the most likely reasons that people play video games excessively are due to either ineffective time management skills, or as a symptomatic response to other underlying problems that they are escaping from, rather than any inherent addictive properties of the actual games.


title: An Investigation of Hostile and Alexithymic Characteristics in Breast Cancer Patients

abstract: Two measures of personality characteristics, the Personality Deviance Scale and the Toronto Alexithymia Scale, were administered to a total sample of 448 women and a subsample of 100 women, respective


title: Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English

abstract: The necessity of using a fixed-size word vocabulary in order to control the model complexity in state-of-the-art neural machine translation (NMT) systems is an important bottleneck on performance, especially for morphologically rich languages. Conventional methods that aim to overcome this problem by using sub-word or character-level representations solely rely on statistics and disregard the linguistic properties of words, which leads to interruptions in the word structure and causes semantic and syntactic losses. In this paper, we propose a new vocabulary reduction method for NMT, which can reduce the vocabulary of a given input corpus at any rate while also considering the morphological properties of the language. Our method is based on unsupervised morphology learning and can be, in principle, used for pre-processing any language pair. We also present an alternative word segmentation method based on supervised morphological analysis, which aids us in measuring the accuracy of our model. We evaluate our method in Turkish-to-English NMT task where the input language is morphologically rich and agglutinative. We analyze different representation methods in terms of translation accuracy as well as the semantic and syntactic properties of the generated output. Our method obtains a significant improvement of 2.3 BLEU points over the conventional vocabulary reduction technique, showing that it can provide better accuracy in open vocabulary translation of morphologically rich languages.


title: Ten years in the evolution of the internet ecosystem

abstract: Our goal is to understand the evolution of the Autonomous System (AS) ecosystem over the last decade. Instead of focusing on abstract topological properties, we classify ASes into a number of "species" depending on their function and business type. Further, we consider the semantics of inter-AS links, in terms of customer-provider versus peering relations. We find that the available historic datasets from RouteViews and RIPE are not sufficient to infer the evolution of peering links, and so we restrict our focus to customer-provider links. Our findings highlight some important trends in the evolution of the Internet over the last decade, and hint at what the Internet is heading towards. After an exponential increase phase until 2001, the Internet now grows linearly in terms of both ASes and inter-AS links. The growth is mostly due to enterprise networks and content/access providers at the periphery of the Internet. The average path length remains almost constant mostly due to the increasing multihoming degree of transit and content/access providers. In recent years, enterprise networks prefer to connect to small transit providers, while content/access providers connect equally to both large and small transit providers. The AS species differ significantly from each other with respect to their rewiring activity; content/access providers are the most active. A few large transit providers act as "attractors" or "repellers" of customers. For many providers, strong attractiveness precedes strong repulsiveness by 3-9 months. Finally, in terms of regional growth, we find that the AS ecosystem is now larger and more dynamic in Europe than in North America.


title: An overview of substrate noise reduction techniques

abstract: This paper provides an overview of the recent circuit level and physical level substrate noise reduction techniques. Several of these techniques are compared for their advantages and disadvantages in "system-on-chip" applications.


title: Automatic Essay Grading With Probabilistic Latent Semantic Analysis

abstract: Probabilistic Latent Semantic Analysis (PLSA) is an information retrieval technique proposed to improve the problems found in Latent Semantic Analysis (LSA). We have applied both LSA and PLSA in our system for grading essays written in Finnish, called Automatic Essay Assessor (AEA). We report the results comparing PLSA and LSA with three essay sets from various subjects. The methods were found to be almost equal in the accuracy measured by Spearman correlation between the grades given by the system and a human. Furthermore, we propose methods for improving the usage of PLSA in essay grading.


title: Learning robot tactile sensing of object for shape recognition using multi-fingered robot hands

abstract: Robots can deal with different kinds of challenges using tactile sensing arrays as a primary resource. This paper demonstrates the ability of robotic hands to recognize objects' shapes using only a flexible tactile sensor arrays attached to the robotic hand's surface without building the 3D models of objects. A telemanipulation module was developed to achieve a co-moving mechanism between the robotic hand and human hands so that the robotic hand can directly learn the best way to grasp objects from human hands without additional path planning process. Tactile array data were collected while the robotic hand was performing a reiterative grasping process. By extracting the proper features from the tactile sensor array data, the support vector machines (SVMs) were employed to perform object classification. From experiments, the proposed method can achieve 96.67% classification accuracy based on sensory data and SVMs.


title: Design of current mode instrumentation amplifier for portable biosignal acquisition system

abstract: This paper describes the design of current mode instrumentation amplifier (CMIA) for portable biosignal acquisition system. The CMIA topology is based on voltage mode operational amplifier (op amp) power supply current sensing technique. Op amp mismatch and precise current mirrors are two design challenges of this topology. A simulation analysis is conducted based on an implementation in a CMOS 0.35 µm technology. The CMIA consumes 20.22 µW under a 3 V DC power supply and has a continuous adjustable gain bandwidth product (GBW)-independent voltage gain via single resistor. The CMRR is higher than 120dB up to 1 Hz and more than 80 dB up to 100 Hz.


Deep Convolutional Neural Networks for Brain Computer Interface using Motor Imagery


title: Which Penguin Is This? Attributing False Beliefs About Object Identity at 18 Months

abstract: Recent research has shown that infants as young as 13 months can attribute false beliefs to agents, suggesting that the psychological-reasoning subsystem necessary for attributing reality-incongruent informational states (Subsystem-2, SS2) is operational in infancy. The present research asked whether 18-month-olds’ false-belief reasoning extends to false beliefs about object identity. Infants watched events involving an agent and 2 toy penguins; 1 penguin could be disassembled (2-piece penguin) and 1 could not (1-piece penguin). Infants realized that outdated contextual information could lead the agent to falsely believe she was facing the 1-piece rather than the 2-piece penguin, suggesting that 18-month-olds can attribute false beliefs about the identity of objects and providing new evidence for SS2 reasoning in the 2nd year of life.


title: On the Evidence for Maturational Constraints in Second-Language Acquisition

abstract: Second-language (L2) acquisition is generally thought to be constrained by maturational factors that circumscribe a critical period for nativelike attainment. Consistent with the maturational view are age effects among learners who begin L2 acquisition prior to, but not after, closure of the putative critical period. Also favoring the maturational account is the scarcity of late L2 learners at asymptote who perform like natives, and weak effects of native language‐target language pairings. With Korean and Chinese learners of English, the experimental study of Johnson and Newport (1989) yielded just these types of evidence. Some subsequent studies do not support the critical period account of L2 acquisition constraints, however. Accordingly, we undertook a replication of Johnson and Newport (1989), using the exact methods and materials of the original experiment, and a sample of Spanish natives (n = 61). In keeping with recent research, L2 attainment negatively correlates with age of learning even if learning commences after the presumed end of the critical period. We also find modest evidence of nativelike attainment among late learners. Our data further suggest that the outcome of L2 acquisition may depend on L1‐L2 pairings and L2 use. © 2001 Academic Press


title: Differences in Context and Feedback Result in Different Trajectories and Adaptation Strategies in Reaching

abstract: Computational models of motor control have often explained the straightness of horizontal planar reaching movements as a consequence of optimal control. Departure from rectilinearity is thus regarded as sub-optimal. Here we examine if subjects may instead select to make curved trajectories following adaptation to force fields and visuomotor rotations. Separate subjects adapted to force fields with or without visual feedback of their hand trajectory and were retested after 24 hours. Following adaptation, comparable accuracies were achieved in two ways: with visual feedback, adapted trajectories in force fields were straight whereas without it, they remained curved. The results suggest that trajectory shape is not always straight, but is also influenced by the calibration of available feedback signals for the state estimation required by the task. In a follow-up experiment, where additional subjects learned a visuomotor rotation immediately after force field, the trajectories learned in force fields (straight or curved) were transferred when directions of the perturbations were similar but not when directions were opposing. This demonstrates a strong bias by prior experience to keep using a recently acquired control policy that continues to produce successful performance inspite of differences in tasks and feedback conditions. On relearning of force fields on the second day, facilitation by intervening visuomotor rotations occurred only when required motor adjustments and calibration of feedback signals were similar in both tasks. These results suggest that both the available feedback signals and prior history of learning influence the choice and maintenance of control policy during adaptations.


title: 1 Induction of Association Rules: Apriori Implementation

abstract: We describe an implementation of the well-known apriori algorithm for the induction of association rules [Agrawal et al. (1993), Agrawal et al. (1996)] that is based on the concept of a prefix tree. While the idea to use this type of data structure is not new, there are several ways to organize the nodes of such a tree, to encode the items, and to organize the transactions, which may be used in order to minimize the time needed to find the frequent itemsets as well as to reduce the amount of memory needed to store the counters. Consequently, our emphasis is less on concepts, but on implementation issues, which, however, can make a considerable difference in applications.


title: Ether: malware analysis via hardware virtualization extensions

abstract: Malware has become the centerpiece of most security threats on the Internet. Malware analysis is an essential technology that extracts the runtime behavior of malware, and supplies signatures to detection systems and provides evidence for recovery and cleanup. The focal point in the malware analysis battle is how to detect versus how to hide a malware analyzer from malware during runtime. State-of-the-art analyzers reside in or emulate part of the guest operating system and its underlying hardware, making them easy to detect and evade. In this paper, we propose a transparent and external approach to malware analysis, which is motivated by the intuition that for a malware analyzer to be transparent, it must not induce any side-effects that are unconditionally detectable by malware. Our analyzer, Ether, is based on a novel application of hardware virtualization extensions such as Intel VT, and resides completely outside of the target OS environment. Thus, there are no in-guest software components vulnerable to detection, and there are no shortcomings that arise from incomplete or inaccurate system emulation. Our experiments are based on our study of obfuscation techniques used to create 25,000 recent malware samples. The results show that Ether remains transparent and defeats the obfuscation tools that evade existing approaches.


title: Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

abstract: This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.


title: Improved Radiation Characteristics of Small Antipodal Vivaldi Antenna for Microwave and Millimeter-Wave Imaging Applications

abstract: A small antipodal Vivaldi antenna with rectangular slits at sun-shaped configuration and a half elliptical-shaped dielectric lens is designed for microwave and millimeter-wave imaging applications. The slits are developed to extend low end of frequency band and increase antenna gain at lower frequencies, while the lens is applied to this antenna to feature high gain at higher frequencies, high front-to-back ratio, low sidelobe and cross-polarization levels, narrow half-power beamwidth, and modification on E-plane tilt of beam. A prototype of the antenna with small size of 30 × 55 × 0.508 mm3 is fabricated and employed as a part of microwave and millimeter-wave imaging system. Applicability of the proposed antenna for 3-D and 2-D images of targets such as rubber discs and metal rods at different depths inside construction materials including foam and plasterboard sheet-layered structures is demonstrated. High-range resolution images are achieved by applying synthetic aperture radar algorithm.


title: Sports Field Localization via Deep Structured Models

abstract: In this work, we propose a novel way of efficiently localizing a sports field from a single broadcast image of the game. Related work in this area relies on manually annotating a few key frames and extending the localization to similar images, or installing fixed specialized cameras in the stadium from which the layout of the field can be obtained. In contrast, we formulate this problem as a branch and bound inference in a Markov random field where an energy function is defined in terms of semantic cues such as the field surface, lines and circles obtained from a deep semantic segmentation network. Moreover, our approach is fully automatic and depends only on a single image from the broadcast video of the game. We demonstrate the effectiveness of our method by applying it to soccer and hockey.


Chapter 3 Large-Scale Image Geolocalization


title: Quantifying Test-Retest Reliability Using the Intraclass Correlation Coefficient and the SEM

abstract: Weir, J.P. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J. Strength Cond. Res. 19(1):231-240. 2005.—Reliability, the consistency of a test or measurement, is frequently quantified in the movement sciences literature. A common metric is the intraclass correla- tion coefficient (ICC). In addition, the SEM, which can be cal- culated from the ICC, is also frequently reported in reliability studies. However, there are several versions of the ICC, and con- fusion exists in the movement sciences regarding which ICC to use. Further, the utility of the SEM is not fully appreciated. In this review, the basics of classic reliability theory are addressed in the context of choosing and interpreting an ICC. The primary distinction between ICC equations is argued to be one concern- ing the inclusion (equations 2,1 and 2,k) or exclusion (equations 3,1 and 3,k) of systematic error in the denominator of the ICC equation. Inferential tests of mean differences, which are per- formed in the process of deriving the necessary variance com- ponents for the calculation of ICC values, are useful to deter- mine if systematic error is present. If so, the measurement schedule should be modified (removing trials where learning and/or fatigue effects are present) to remove systematic error, and ICC equations that only consider random error may be safe- ly used. The use of ICC values is discussed in the context of estimating the effects of measurement error on sample size, sta- tistical power, and correlation attenuation. Finally, calculation and application of the SEM are discussed. It is shown how the SEM and its variants can be used to construct confidence inter- vals for individual scores and to determine the minimal differ- ence needed to be exhibited for one to be confident that a true change in performance of an individual has occurred.


title: Data Quality: Some Comments on the NASA Software Defect Datasets

abstract: Background--Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets. In recent years, there has been much interest in using machine learners to classify software modules into defect-prone and not defect-prone categories. The publicly available NASA datasets have been extensively used as part of this research. Objective--This short note investigates the extent to which published analyses based on the NASA defect datasets are meaningful and comparable. Method--We analyze the five studies published in the IEEE Transactions on Software Engineering since 2007 that have utilized these datasets and compare the two versions of the datasets currently in use. Results--We find important differences between the two versions of the datasets, implausible values in one dataset and generally insufficient detail documented on dataset preprocessing. Conclusions--It is recommended that researchers 1) indicate the provenance of the datasets they use, 2) report any preprocessing in sufficient detail to enable meaningful replication, and 3) invest effort in understanding the data prior to applying machine learners.


title: A comparative study of motivational differences for online shopping

abstract: Purchasing through online retailers, electronic markets, and virtual communities has its value. Often motivations to buy online differ from those in an offline environment and these motivations may be more incongruent for online shoppers dispersed globally. In this study, motives to purchase in an electronic environment in the context of two countries are studied. Two data sets of 538 respondents (304 in Singapore and 279 in China) were collected. Results show that there is a significant difference in motivations in China and Singapore buyers, and Singapore buyers have higher positive need gratifications. These findings indicate that Singapore buyers are more positively inclined toward online shopping, and they are likely to be more gratified when buying online than respondents from China. In country specific contexts, no motivational differences for male and female respondents are observed. However, gender is significant for the combined sample indicating that overall male and female populations have different motivations for online shopping. Online retailers could benefit from focusing on aspects that address perceptions of positive need-fulfillment on their e-commerce sites for the Singapore and Chinese market segments, while also differentiating websites for online markets by gender.


title: Machine Learning for Real-Time Single-Trial EEG Analysis: From Brain-Computer Interfacing to Mental State Monitoring

abstract: Abstract Machine learning methods are an excellent choice for compensating the high variability in EEG when analyzing single-trial data in real-time. This paper briefly reviews preprocessing and classification techniques for efficient EEG-based brain–computer interfacing (BCI) and mental state monitoring applications. More specifically, this paper gives an outline of the Berlin brain–computer interface (BBCI), which can be operated with minimal subject training. Also, spelling with the novel BBCI-based Hex-o-Spell text entry system, which gains communication speeds of 6–8 letters per minute, is discussed. Finally the results of a real-time arousal monitoring experiment are presented.


title: A modulo based LSB steganography method

abstract: Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover images. The Least Significant Bit (LSB) steganography that replaces the least significant bits of the host medium is a widely used technique with low computational complexity and high insertion capacity. Although it has good perceptual transparency, it is vulnerable to steganalysis which is based on histogram analysis. In all the existing schemes detection of a secret message in a cover image can be easily detected from the histogram analysis and statistical analysis. Therefore developing new LSB steganography algorithms against statistical and histogram analysis is the prime requirement.


title: Software defined radios for small satellites

abstract: Clusters, constellations, formations, or `swarms' of small satellites are fast becoming a way to perform scientific and technological missions more affordably. As objectives of these missions become more ambitious, there are still problems in increasing the number of communication windows, supporting multiple signals, and increasing data rates over reliable intersatellite and ground links to Earth. Also, there is a shortage of available frequencies in the 2 m and 70 cm bands due to rapid increase in the number of CubeSats orbiting the Earth - leading to further regulatory issues. Existing communication systems and radio signal processing Intellectual Property (IP) cores cannot fully address these challenges. One of the possible strategies to solve these issues is by equipping satellites with a Software Defined Radio (SDR). SDR is a key area to realise various software implementations which enable an adaptive and reconfigurable communication system without changing any hardware device or feature. This paper proposes a new SDR architecture which utilises a combination of Field Programmable Gate Array (FPGA) and field programmable Radio Frequency (RF) transceiver to solve back-end and front- end challenges and thereby enabling reception of multiple signals or satellites using single user equipment.


title: On the generalization of color texture-based face anti-spoofing

abstract: Abstract Despite the significant attention given to the problem of face spoofing, we still lack generalized presentation attack detection (PAD) methods performing robustly in practical face recognition systems. The existing face anti-spoofing techniques have indeed achieved impressive results when trained and evaluated on the same database (i.e. intra-test protocols). Cross-database experiments have, however, revealed that the performance of the state-of-the-art methods drops drastically as they fail to cope with new attacks scenarios and other operating conditions that have not been seen during training and development phases. So far, even the popular convolutional neural networks (CNN) have failed to derive well-generalizing features for face anti-spoofing. In this work, we explore the effect of different factors, such as acquisition conditions and presentation attack instrument (PAI) variation, on the generalization of color texture-based face anti-spoofing. Our extensive cross-database evaluation of seven color texture-based methods demonstrates that most of the methods are unable to generalize to unseen spoofing attack scenarios. More importantly, the experiments show that some facial color texture representations are more robust to particular PAIs than others. From this observation, we propose a face PAD solution of attack-specific countermeasures based solely on color texture analysis and investigate how well it generalizes under display and print attacks in different conditions. The evaluation of the method combining attack-specific detectors on three benchmark face anti-spoofing databases showed remarkable generalization ability against display attacks while print attacks require still further attention.


title: A survey on sentiment analysis and opinion mining for social multimedia

abstract: Social media sentiment analysis (also known as opinion mining) which aims to extract people's opinions, attitudes and emotions from social networks has become a research hotspot. Conventional sentiment analysis concentrates primarily on the textual content. However, multimedia sentiment analysis has begun to receive attention since visual content such as images and videos is becoming a new medium for self-expression in social networks. In order to provide a reference for the researchers in this active area, we give an overview of this topic and describe the algorithms of sentiment analysis and opinion mining for social multimedia. Having conducted a brief review on textual sentiment analysis for social media, we present a comprehensive survey of visual sentiment analysis on the basis of a thorough investigation of the existing literature. We further give a summary of existing studies on multimodal sentiment analysis which combines multiple media channels. We finally summarize the existing benchmark datasets in this area, and discuss the future research trends and potential directions for multimedia sentiment analysis. This survey covers 100 articles during 2008---2018 and categorizes existing studies according to the approaches they adopt.


title: Hate Speech Detection on Twitter: Feature Engineering v.s. Feature Selection

abstract: The increasing presence of hate speech on social media has drawn significant investment from governments, companies, and empirical research. Existing methods typically use a supervised text classification approach that depends on carefully engineered features. However, it is unclear if these features contribute equally to the performance of such methods. We conduct a feature selection analysis in such a task using Twitter as a case study, and show findings that challenge conventional perception of the importance of manual feature engineering: automatic feature selection can drastically reduce the carefully engineered features by over 90% and selects predominantly generic features often used by many other language related tasks; nevertheless, the resulting models perform better using automatically selected features than carefully crafted task-specific features.


title: Do Long-Tailed Bats Alter Their Evening Activity in Response to Aircraft Noise?

abstract: Human-generated noise may alter animal activity patterns and mask vocal signals. We used field-based observations and a playback experiment to investigate whether aircraft activity and noise alter the evening activity of New Zealand long-tailed bats (Chalinolobus tuberculatus) a cryptic threatened species. Low-altitude aircraft activity temporally overlapped bat activity near the runway of an international airport, but was unlikely to mask echolocation pulses as aircraft noise was most intense at ≤ 10 kHz. There was no statistically significant difference in mean bat activity during and after overflights compared with pre-aircraft activity. The experiment revealed that playback stimuli (aircraft passes and two controls: silent tracks and blackbird (Turdus merula) calls) differed in their effect on bat activity at two sites, one with low and one with high aircraft activity. Simulated aircraft noise and silent tracks reduced bat activity when compared with blackbird calls (P < 0.05). Bats may have found it ...


title: The periosteum. Part 1: Anatomy, histology and molecular biology.

abstract: The periosteum is a thin layer of connective tissue that covers the outer surface of a bone in all places except at joints (which are protected by articular cartilage). As opposed to bone itself, it has nociceptive nerve endings, making it very sensitive to manipulation. It also provides nourishment in the form of blood supply to the bone. The periosteum is connected to the bone by strong collagenous fibres called Sharpey’s fibres, which extend to the outer circumferential and interstitial lamellae of bone. The periosteum consists of an outer ‘‘fibrous layer’’ and inner ‘‘cambium layer’’. The fibrous layer contains fibroblasts while the cambium layer contains progenitor cells which develop into osteoblasts that are responsible for increasing bone width. After a bone fracture the progenitor cells develop into osteoblasts and chondroblasts which are essential to the healing process. This review discusses the anatomy, histology and molecular biology of the periosteum in detail.


title: Autonomous Mobile Robot Navigation Using Passive RFID in Indoor Environment

abstract: This paper proposes an efficient method for localization and pose estimation for mobile robot navigation using passive radio-frequency identification (RFID). We assume that the robot is able to identify IC tags and measure the robot's pose based on the relation between the previous and current location according to the IC tags. However, there arises the problem of uncertainty of location due to the nature of the antenna and IC tags. In other words, an error is always present which is relative to the sensing area of the antenna. Many researches have used external sensors in order to reduce the location errors, with few researches presented involving purely RFID driven systems. Our proposed algorithm that uses only passive RFID is able to estimate the robot's location and orientation more precisely by using trigonometric functions and the IC tags' Cartesian coordinates in a regular gridlike pattern. The experimental results show that the proposed method effectively estimates both the location and the pose of a mobile robot during navigation.


title: DART: Noise Injection for Robust Imitation Learning

abstract: One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy. A known problem with this"off-policy"approach is that the robot's errors compound when drifting away from the supervisor's demonstrations. On-policy, techniques alleviate this by iteratively collecting corrective actions for the current robot policy. However, these techniques can be tedious for human supervisors, add significant computation burden, and may visit dangerous states during training. We propose an off-policy approach that injects noise into the supervisor's policy while demonstrating. This forces the supervisor to demonstrate how to recover from errors. We propose a new algorithm, DART (Disturbances for Augmenting Robot Trajectories), that collects demonstrations with injected noise, and optimizes the noise level to approximate the error of the robot's trained policy during data collection. We compare DART with DAgger and Behavior Cloning in two domains: in simulation with an algorithmic supervisor on the MuJoCo tasks (Walker, Humanoid, Hopper, Half-Cheetah) and in physical experiments with human supervisors training a Toyota HSR robot to perform grasping in clutter. For high dimensional tasks like Humanoid, DART can be up to $3x$ faster in computation time and only decreases the supervisor's cumulative reward by $5\%$ during training, whereas DAgger executes policies that have $80\%$ less cumulative reward than the supervisor. On the grasping in clutter task, DART obtains on average a $62\%$ performance increase over Behavior Cloning.


title: Analysis of Power Cycling Capability of IGBT Modules in a Conventional Matrix Converter

abstract: This paper analyzes the power cycling capability of IGBT modules in a conventional matrix converter used as a motor drive. The analysis is made under various condition for this topology, including low speed operation capability, high speed thermal and power cycle capability and etc. It was found that the power cycling mean time to failure (MTTF) of IGBT in a matrix converter is low when the input and output frequency is close to each other or when the output frequency of the converter is low. As a result, the chip size of the conventional matrix converter may be larger than the other candidates. In the end of the paper, some guidance of designing a matrix converter for long term reliabilities is also discussed.


Detecting Sarcasm in Text An Obvious Solution to a Trivial Problem


The role of counseling training in the leadership style of secondary school principals as perceived by themselves and their subordinates.


String Matching and its Applications in Diversified Fields


title: MF3D: Model-free 3D semantic scene parsing

abstract: We present a novel model-free method for online 3D semantic scene parsing from video sequences. MF3D (Model-Free 3D) is different from conventional methods for 3D scene parsing in that voxel labelling is approached via search-based label transfer instead of discriminative classification. This non-parametric approach makes MF3D easy to scale with an online growth in the database, as no model re-training is required with the addition of new examples or categories. Experimental results on the KITTI benchmark demonstrate that our model-free approach enables accurate online 3D scene parsing while retaining extensibility to new categories. In addition, we show that unsupervised binary encoding (hashing) techniques can be easily incorporated into our framework for scalability to larger databases.


title: Progressive Neural Networks for Transfer Learning in Emotion Recognition

abstract: Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another. In this paper, we investigate how knowledge can be transferred between three paralinguistic tasks: speaker, emotion, and gender recognition. Further, we extend this problem to cross-dataset tasks, asking how knowledge captured in one emotion dataset can be transferred to another. We focus on progressive neural networks and compare these networks to the conventional deep learning method of pre-training and fine-tuning. Progressive neural networks provide a way to transfer knowledge and avoid the forgetting effect present when pre-training neural networks on different tasks. Our experiments demonstrate that: (1) emotion recognition can benefit from using representations originally learned for different paralinguistic tasks and (2) transfer learning can effectively leverage additional datasets to improve the performance of emotion recognition systems.


EXPERIMENTAL INVESTIGATION OF THE EFFECT EFFECTIVE OF FACTORS AND PARAMETERS ON THE PUNCH ON THE PROPERTIES OF QUASI-STATIC PUNCHING SHEAR THE GLASS/EPOXY COMPOSITE


title: Online Multi-object Tracking via Structural Constraint Event Aggregation

abstract: Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera movements and thus not always smooth or predictable. To deal with such unexpected camera motion for online 2D MOT, a structural motion constraint between objects has been utilized thanks to its robustness to camera motion. In this paper, we propose a new data association method that effectively exploits structural motion constraints in the presence of large camera motion. In addition, to further improve the robustness of data association against mis-detections and false positives, a novel event aggregation approach is developed to integrate structural constraints in assignment costs for online MOT. Experimental results on a large number of datasets demonstrate the effectiveness of the proposed algorithm for online 2D MOT.


title: A syntax-independent approach to forgetting in disjunctive logic programs

abstract: In this paper, we present an approach to forgetting in disjunctive logic programs, where forgetting an atom from a program amounts to a reduction in the signature of that program. Notably, the approach is syntax-independent, so that if two programs are strongly equivalent, then the result of forgetting a given atom in each program is also strongly equivalent. Our central definition of forgetting is abstract: forgetting an atom from program P is characterised by the set of those SE consequences of P that do not mention the atom to be forgotten. We provide an equivalent, syntactic, characterization in which forgetting an atom p is given by those rules in the program that do not mention p, together with rules obtained by a single inference step from those rules that do mention p. Forgetting is shown to have appropriate properties; in particular, answer sets are preserved in forgetting an atom. As well, forgetting an atom via the syntactic characterization results in a modest (at worst quadratic) blowup in the program size. Finally, we provide a prototype implementation of this approach to forgetting.


title: General background on the hypothalamic-pituitary-thyroid (HPT) axis.

abstract: This article reviews the thyroid system, mainly from a mammalian standpoint. However, the thyroid system is highly conserved among vertebrate species, so the general information on thyroid hormone production and feedback through the hypothalamic-pituitary-thyroid (HPT) axis should be considered for all vertebrates, while species-specific differences are highlighted in the individual articles. This background article begins by outlining the HPT axis with its components and functions. For example, it describes the thyroid gland, its structure and development, how thyroid hormones are synthesized and regulated, the role of iodine in thyroid hormone synthesis, and finally how the thyroid hormones are released from the thyroid gland. It then progresses to detail areas within the thyroid system where disruption could occur or is already known to occur. It describes how thyroid hormone is transported in the serum and into the tissues on a cellular level, and how thyroid hormone is metabolized. There is an in-depth description of the alpha and beta thyroid hormone receptors and their functions, including how they are regulated, and what has been learned from the receptor knockout mouse models. The nongenomic actions of thyroid hormone are also described, such as in glucose uptake, mitochondrial effects, and its role in actin polymerization and vesicular recycling. The article discusses the concept of compensation within the HPT axis and how this fits into the paradigms that exist in thyroid toxicology/endocrinology. There is a section on thyroid hormone and its role in mammalian development: specifically, how it affects brain development when there is disruption to the maternal, the fetal, the newborn (congenital), or the infant thyroid system. Thyroid function during pregnancy is critical to normal development of the fetus, and several spontaneous mutant mouse lines are described that provide research tools to understand the mechanisms of thyroid hormone during mammalian brain development. Overall this article provides a basic understanding of the thyroid system and its components. The complexity of the thyroid system is clearly demonstrated, as are new areas of research on thyroid hormone physiology and thyroid hormone action developing within the field of thyroid endocrinology. This review provides the background necessary to review the current assays and endpoints described in the following articles for rodents, fishes, amphibians, and birds.


title: Variation in top level soccer match performance.

abstract: This study examined the influence of the opposing team, seasonal variations and the influence of first half activity on match performance in top-level soccer players. Physical performance measures were collected using the ProZone match analysis system from 20 professional soccer players from the same team and their opponents (n = 188) during a season. Match activities (standing, walking, jogging, running, high-speed running and sprinting), distances (total distance [TD], high-intensity running [HIR] and very high-intensity running [VHIR]) and other measures including involvement with the ball and peak running speed were collected. The influence of opponent team, the level of opposition, first half physical activities on second half activities, and playing position were analysed. The main finding was that TD (r = 0.62, p < 0.05), HIR (r = 0.51, p < 0.05), and VHIR (r = 0.65, p < 0.05) of the reference team was influenced by the activity profile of the opponent teams. The TD and HIR was higher against Best opponent teams compared to Worst opponent teams (p < 0.05), and the TD, HIR and VHIR travelled in the first half significantly influenced the distances covered in the second half. TD, HIR and VHIR were greater at the end of the season. These results may be used to interpret meaningful changes in match performance in top level soccer.


title: Universals and cultural differences in the judgments of facial expressions of emotion.

abstract: We present here new evidence of cross-cultural agreement in the judgment of facial expression. Subjects in 10 cultures performed a more complex judgment task than has been used in previous cross-cultural studies. Instead of limiting the subjects to selecting only one emotion term for each expression, this task allowed them to indicate that multiple emotions were evident and the intensity of each emotion. Agreement was very high across cultures about which emotion was the most intense. The 10 cultures also agreed about the second most intense emotion signaled by an expression and about the relative intensity among expressions of the same emotion. However, cultural differences were found in judgments of the absolute level of emotional intensity.


title: Introduction To The CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

abstract: We describe the CoNLL-2003 shared task: language-independent named entity recognition. We give background information on the data sets (English and German) and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.


title: Cooperative Interference Mitigation and Handover Management for Heterogeneous Cloud Small Cell Networks

abstract: Heterogeneous small cell networks have attracted much attention for satisfying users’ explosive data traffic requirements. The heterogeneous cloud small cell network (HCSNet), which combines cloud computing and a heterogeneous small cell network, will likely play an important role in 5G mobile communication networks. However, with massive deployment of small cells, co-channel interference and handover management are two important problems in an HCSNet, especially for cell edge users. In this article, we examine the problems of cooperative interference mitigation and handover management in an HCSNet. A network architecture is described to combine cloud radio access network with small cells. An effective CoMP clustering scheme using affinity propagation is adopted to mitigate cell edge users’ interference. A low-complexity handover management scheme is presented, and its signaling procedure is analyzed in an HCSNet. Numerical results show that with the proposed network architecture, CoMP clustering and handover management schemes can significantly increase the capacity of HCSNet while maintaining users’ quality of service.


title: Modeling dwell time to predict click-level satisfaction

abstract: Clicks on search results are the most widely used behavioral signals for predicting search satisfaction. Even though clicks are correlated with satisfaction, they can also be noisy. Previous work has shown that clicks are affected by position bias, caption bias, and other factors. A popular heuristic for reducing this noise is to only consider clicks with long dwell time, usually equaling or exceeding 30 seconds. The rationale is that the more time a searcher spends on a page, the more likely they are to be satisfied with its contents. However, having a single threshold value assumes that users need a fixed amount of time to be satisfied with any result click, irrespective of the page chosen. In reality, clicked pages can differ significantly. Pages have different topics, readability levels, content lengths, etc. All of these factors may affect the amount of time spent by the user on the page. In this paper, we study the effect of different page characteristics on the time needed to achieve search satisfaction. We show that the topic of the page, its length and its readability level are critical in determining the amount of dwell time needed to predict whether any click is associated with satisfaction. We propose a method to model and provide a better understanding of click dwell time. We estimate click dwell time distributions for SAT (satisfied) or DSAT (dissatisfied) clicks for different click segments and use them to derive features to train a click-level satisfaction model. We compare the proposed model to baseline methods that use dwell time and other search performance predictors as features, and demonstrate that the proposed model achieves significant improvements.


title: TripletGAN: Training Generative Model with Triplet Loss

abstract: As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts. The main innovation of triplet loss is using feature map to replace softmax in the classification task. Inspired by this concept, we propose here a new adversarial modeling method by substituting the classification loss of discriminator with triplet loss. Theoretical proof based on IPM (Integral probability metric) demonstrates that such setting will help the generator converge to the given distribution theoretically under some conditions. Moreover, since triplet loss requires the generator to maximize distance within a class, we justify tripletGAN is also helpful to prevent mode collapse through both theory and experiment.


title: Qcorp: an annotated classification corpus of Chinese health questions

abstract: Health question-answering (QA) systems have become a typical application scenario of Artificial Intelligent (AI). An annotated question corpus is prerequisite for training machines to understand health information needs of users. Thus, we aimed to develop an annotated classification corpus of Chinese health questions (Qcorp) and make it openly accessible. We developed a two-layered classification schema and corresponding annotation rules on basis of our previous work. Using the schema, we annotated 5000 questions that were randomly selected from 5 Chinese health websites within 6 broad sections. 8 annotators participated in the annotation task, and the inter-annotator agreement was evaluated to ensure the corpus quality. Furthermore, the distribution and relationship of the annotated tags were measured by descriptive statistics and social network map. The questions were annotated using 7101 tags that covers 29 topic categories in the two-layered schema. In our released corpus, the distribution of questions on the top-layered categories was treatment of 64.22%, diagnosis of 37.14%, epidemiology of 14.96%, healthy lifestyle of 10.38%, and health provider choice of 4.54% respectively. Both the annotated health questions and annotation schema were openly accessible on the Qcorp website. Users can download the annotated Chinese questions in CSV, XML, and HTML format. We developed a Chinese health question corpus including 5000 manually annotated questions. It is openly accessible and would contribute to the intelligent health QA system development.


title: IoT-enabled smart lighting systems for smart cities

abstract: Over the past few decades, the rate of urbanization has increased enormously. More enhanced services and applications are needed in urban areas to provide a better lifestyle. Smart city, which is a concept of interconnecting modern digital technologies in the context of a city, is a potential solution to enhance the quality and performance of urban services. With the introduction of Internet-of-Things (IoT) in the smart city, new opportunities have emerged to develop new services and integrate different application domains with each other using Information and Communication Technologies. However, to ensure seamless services in an IoT-enabled smart city environment, all the applications have to be maintained with limited energy resources. One of the core sectors that can be improved significantly by implementing IoT is the lighting system of a city since it consumes more energy than other parts of a city. In a smart city, the lighting system is integrated with advanced sensors and communication channels to obtain a Smart Lighting System (SLS). The goal of an SLS is to obtain an autonomous and more efficient lighting management system. In this paper, we provide an overview of the SLS and review different IoT-enabled communication protocols, which can be used to realize the SLS in the context of a smart city. Moreover, we analyzed different usage scenarios for IoT-enabled indoor and outdoor SLS and provide an analysis of the power consumption. Our results reveal that IoT-enabled smart lighting systems can reduce power consumption up to 33.33% in both indoor and outdoor settings. Finally, we discussed the future research directions in SLS in the smart city.


title: A Soft Robotic Gripper With Gecko-Inspired Adhesive

abstract: Previous work has demonstrated the versatility of soft robotic grippers using simple control inputs. However, these grippers still face challenges in grasping large objects and in achieving high-strength grasps. This work investigates the combination of fluidic elastomer actuators and gecko-inspired adhesives to both enhance existing soft gripper properties and generate new capabilities. On rocky or dirty surfaces where adhesion is limited, the gripper retains the functionality of a pneumatically actuated elastomer gripper with no measured loss in performance. Design strategies for using the unique properties of the gecko-inspired adhesives are presented. By modeling fluidic elastomer actuators as a series of joints with associated joint torques, we designed an actuator that takes advantage of the unique properties of the gecko-inspired adhesive. Experiments showed higher strength grasps at lower pressures compared to nongecko actuators, in many cases enabling the gripper to actuate more quickly and use less energy. The gripper weighs 48.7 g, uses $7.25 of raw materials, and can support loads of over 50 N. A second gripper, using three fingers for a larger adhesive surface, demonstrated a grasping force of 111 N (25 lbf) when actuated at an internal pressure of 40 kPa.


title: A 3.5–6.8GHz wide-bandwidth DTC-assisted fractional-N all-digital PLL with a MASH ΔΣ TDC for low in-band phase noise

abstract: We present a digital-to-time converter (DTC)-assisted fractional-N wide-bandwidth all-digital PLL (ADPLL). It employs a MASH ΔΣ time-to-digital converter (TDC) to achieve low in-band phase noise, and a wide-tuning range digitally-controlled oscillator (DCO). Fabricated in 40nm CMOS, the ADPLL consumes 10.7 mW while outputting 1.73 to 3.38 GHz (after a ÷2 division) and achieves better than −109 dBc/Hz in-band phase noise and 420fs rms integrated jitter.


title: Real-Time Active Tampering Detection of Surveillance Camera and Implementation on Digital Signal Processor

abstract: Active and automatic camera tampering detection is more and more important, especially for wide area surveillance systems with large amount of video cameras installed. Generally, a camera functions for video recording and lacks the ability to detect whether tampering has occurred. Most tampering solutions are passive, as evidenced by many surveillance systems frequently left unattended. Tampering detection of real-time automated cameras is thus of priority concern important for timely operator warning. This work describes a novel algorithm for camera tamper detection scheme. Developed for video surveillance and security applications, the pro-posed approach identifies camera tampering by detecting large edge differences and grayscale histogram comparisons between current and previous frames. The optimized algorithm is implemented on PC and TI DAVINCI DM6437 DSP as well. Extensive experiments were performed. Experimental results indicate that the proposed approach can detect various tampering cases. The overall recognition performance was 94%.


title: Learning Distributed Representations of Data in Community Question Answering for Question Retrieval

abstract: We study the problem of question retrieval in community question answering (CQA). The biggest challenge within this task is lexical gaps between questions since similar questions are usually expressed with different but semantically related words. To bridge the gaps, state-of-the-art methods incorporate extra information such as word-to-word translation and categories of questions into the traditional language models. We find that the existing language model based methods can be interpreted using a new framework, that is they represent words and question categories in a vector space and calculate question-question similarities with a linear combination of dot products of the vectors. The problem is that these methods are either heuristic on data representation or difficult to scale up. We propose a principled and efficient approach to learning representations of data in CQA. In our method, we simultaneously learn vectors of words and vectors of question categories by optimizing an objective function naturally derived from the framework. In question retrieval, we incorporate learnt representations into traditional language models in an effective and efficient way. We conduct experiments on large scale data from Yahoo! Answers and Baidu Knows, and compared our method with state-of-the-art methods on two public data sets. Experimental results show that our method can significantly improve on baseline methods for retrieval relevance. On 1 million training data, our method takes less than 50 minutes to learn a model on a single multicore machine, while the translation based language model needs more than 2 days to learn a translation table on the same machine.


title: Classification of human emotions from EEG signals using SVM and LDA Classifiers

abstract: Emotion Detection has been a topic of great research in the last few decades. It plays a very important role in establishing human computer interface. We as humans are able to understand the emotions of other person but it is literally impossible for the computer to do so. The present work is to achieve the same as accurately as possible. Emotion detection can be done either through text, speech, facial expression or gesture. In the present work the emotions are detected using Electroencephalography (EEG) signals. EEG records the electrical activity within the neurons of the brain. The main advantage of using EEG signals is that it detects real emotions arising straight from our mind and ignores external features like facial expressions or gesture. Hence EEG can act as real indicator of the emotion depicted by the subject. We have employed Independent Component Analysis (ICA) and Machine Learning techniques such as Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) to classify EEG signals into seven different emotions. The accuracy achieved with both the algorithms is computed and compared. We are able to recognize seven emotions using the two algorithms, SVM and LDA with an average overall accuracy of 74.13% and 66.50% respectively. This accuracy was achieved after performing a 4-fold cross-validation. Future applications of emotion detection includes neuro-marketing, market survey, EEG based music therapy and music player.


title: Shape2Pose: human-centric shape analysis

abstract: As 3D acquisition devices and modeling tools become widely available there is a growing need for automatic algorithms that analyze the semantics and functionality of digitized shapes. Most recent research has focused on analyzing geometric structures of shapes. Our work is motivated by the observation that a majority of man-made shapes are designed to be used by people. Thus, in order to fully understand their semantics, one needs to answer a fundamental question: "how do people interact with these objects?" As an initial step towards this goal, we offer a novel algorithm for automatically predicting a static pose that a person would need to adopt in order to use an object. Specifically, given an input 3D shape, the goal of our analysis is to predict a corresponding human pose, including contact points and kinematic parameters. This is especially challenging for man-made objects that commonly exhibit a lot of variance in their geometric structure. We address this challenge by observing that contact points usually share consistent local geometric features related to the anthropometric properties of corresponding parts and that human body is subject to kinematic constraints and priors. Accordingly, our method effectively combines local region classification and global kinematically-constrained search to successfully predict poses for various objects. We also evaluate our algorithm on six diverse collections of 3D polygonal models (chairs, gym equipment, cockpits, carts, bicycles, and bipedal devices) containing a total of 147 models. Finally, we demonstrate that the poses predicted by our algorithm can be used in several shape analysis problems, such as establishing correspondences between objects, detecting salient regions, finding informative viewpoints, and retrieving functionally-similar shapes.


title: Development of a low cost anthropomorphic robot hand with high capability

abstract: This paper presents a development of an anthropomorphic robot hand, ‘KITECH Hand’ that has 4 full-actuated fingers. Most robot hands have small size simultaneously many joints as compared with robot manipulators. Components of actuator, gear, and sensors used for building robots are not small and are expensive, and those make it difficult to build a small sized robot hand. Differently from conventional development of robot hands, KITECH hand adopts a RC servo module that is cheap, easily obtainable, and easy to handle. The RC servo module that have been already used for several small sized humanoid can be new solution of building small sized robot hand with many joints. The feasibility of KITECH hand in object manipulation is shown through various experimental results. It is verified that the modified RC servo module is one of effective solutions in the development of a robot hand.


title: Deep Learning with Nonparametric Clustering

abstract: Clustering is an essential problem in machine learning and data mining. One vital factor that impacts clustering performance is how to learn or design the data representation (or features). Fortunately, recent advances in deep learning can learn unsupervised features effectively, and have yielded state of the art performance in many classification problems, such as character recognition, object recognition and document categorization. However, little attention has been paid to the potential of deep learning for unsupervised clustering problems. In this paper, we propose a deep belief network with nonparametric clustering. As an unsupervised method, our model first leverages the advantages of deep learning for feature representation and dimension reduction. Then, it performs nonparametric clustering under a maximum margin framework -- a discriminative clustering model and can be trained online efficiently in the code space. Lastly model parameters are refined in the deep belief network. Thus, this model can learn features for clustering and infer model complexity in an unified framework. The experimental results show the advantage of our approach over competitive baselines.


title: Detecting Duplicate Posts in Programming QA Communities via Latent Semantics and Association Rules

abstract: Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30% improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public.


title: Vision meets robotics: The KITTI dataset

abstract: We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.


title: Radical-Enhanced Chinese Character Embedding

abstract: In this paper, we present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role for modelling character semantics as characters with the same radical usually have similar semantic meaning and grammatical usage. However, most existing character (or word) embedding learning algorithms typically only model the syntactic contexts but ignore the radical information. As a result, they do not explicitly capture the inner semantic connections of characters via radical into the embedding space of characters. To solve this problem, we propose to incorporate the radical information for enhancing the Chinese character embedding. We present a dedicated neural architecture with a hybrid loss function, and integrate the radical information through softmax upon each character. To verify the effectiveness of the learned character embedding, we apply it on Chinese word segmentation. Experiment results on two benchmark datasets show that, our radical-enhanced method outperforms two widely-used context-based embedding learning algorithms.


title: LODifier: generating linked data from unstructured text

abstract: The automated extraction of information from text and its transformation into a formal description is an important goal in both Semantic Web research and computational linguistics. The extracted information can be used for a variety of tasks such as ontology generation, question answering and information retrieval. LODifier is an approach that combines deep semantic analysis with named entity recognition, word sense disambiguation and controlled Semantic Web vocabularies in order to extract named entities and relations between them from text and to convert them into an RDF representation which is linked to DBpedia and WordNet. We present the architecture of our tool and discuss design decisions made. An evaluation of the tool on a story link detection task gives clear evidence of its practical potential.


title: A brief study of Wannacry Threat: Ransomware Attack 2017

abstract: Recently Ransomware virus software spread like a cyclone winds. A cyclone wind creates atmospheric instability; likewise ransomware creates computer data instability. Every user is moving towards digitization. User keep data secure in his or her computer. But what if data is hijacked. A ransomware is one of the software virus that hijack users data. A ransomware may lock the system in a way which is not for a knowledgeable person to reverse.It not only targets home computersbut business also gets affected. It encrypts data in such a way that normal person can no longer decrypt. A person has to pay ransom to decrypt it. But it does not generate that files will be released.This paper gives a brief study of WannaCry ransomware, its effect on computer world and its preventive measures to control ransomwareon computer system.


title: Targeted Topic Modeling for Focused Analysis

abstract: One of the overarching tasks of document analysis is to find what topics people talk about. One of the main techniques for this purpose is topic modeling. So far many models have been proposed. However, the existing models typically perform full analysis on the whole data to find all topics. This is certainly useful, but in practice we found that the user almost always also wants to perform more detailed analyses on some specific aspects, which we refer to as targets (or targeted aspects). Current full-analysis models are not suitable for such analyses as their generated topics are often too coarse and may not even be on target. For example, given a set of tweets about e-cigarette, one may want to find out what topics under discussion are specifically related to children. Likewise, given a collection of online reviews about a camera, a consumer or camera manufacturer may be interested in finding out all topics about the camera's screen, the targeted aspect. As we will see in our experiments, current full topic models are ineffective for such targeted analyses. This paper studies this problem and proposes a novel targeted topic model (TTM) to enable focused analyses on any specific aspect of interest. Our experimental results demonstrate the effectiveness of the TTM.


title: Design of charge pump circuit with consideration of gate-oxide reliability in low-voltage CMOS processes

abstract: A new charge pump circuit with consideration of gate-oxide reliability is designed with two pumping branches in this paper. The charge transfer switches in the new proposed circuit can be completely turned on and turned off, so its pumping efficiency is higher than that of the traditional designs. Moreover, the maximum gate-source and gate-drain voltages of all devices in the proposed charge pump circuit do not exceed the normal operating power supply voltage (VDD). Two test chips have been implemented in a 0.35-/spl mu/m 3.3-V CMOS process to verify the new proposed charge pump circuit. The measured output voltage of the new proposed four-stage charge pump circuit with each pumping capacitor of 2 pF to drive the capacitive output load is around 8.8 V under 3.3-V power supply (VDD = 3.3 V), which is limited by the junction breakdown voltage of the parasitic pn-junction in the given process. The new proposed circuit is suitable for applications in low-voltage CMOS processes because of its high pumping efficiency and no overstress across the gate oxide of devices.


title: Review of Current Methods, Applications, and Data Management for the Bioinformatics Analysis of Whole Exome Sequencing

abstract: The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, preprocessing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.


title: Classification of Brain Cancer using Artificial Neural Network

abstract: A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement, morphological operations and feature extraction have been developed for detection of the brain tumor in the MRI images of the cancer affected patients. The extraction of texture features in the detected tumor has been achieved by using Gray Level Co-occurrence Matrix (GLCM). These features are compared with the stored features in the Knowledge Base. Finally a Neuro Fuzzy Classifier has been developed to recognize different types of brain cancers. The whole system has been tested in two phases firstly Learning/Training Phase and secondly Recognition/Testing Phase. The known MRI images of affected brain cancer patients obtained from Radiology Department of Tata Memorial Hospital (TMH) were used to train the system. The unknown samples of brain cancer affected MRI images are also obtained from TMH and were used to test the system. The system was found efficient in classification of these samples and responds any abnormality.


title: Fast road sign detection using hough transform for assisted driving of road vehicles

abstract: A system for real-time traffic sign detection is described in this paper. The system uses restricted Hough transform for circumferences in order to detect circular signs, and for straight lines for triangular ones. Some results obtained from a set of real road images captured under both normal and adverse weather conditions are presented as well in order to illustrate the robustness of the detection system. The average processing time is 30 ms per frame, what makes the system a good approach to work in real time conditions.


title: Research Directions in Requirements Engineering

abstract: In this paper, we review current requirements engineering (RE) research and identify future research directions suggested by emerging software needs. First, we overview the state of the art in RE research. The research is considered with respect to technologies developed to address specific requirements tasks, such as elicitation, modeling, and analysis. Such a review enables us to identify mature areas of research, as well as areas that warrant further investigation. Next, we review several strategies for performing and extending RE research results, to help delineate the scope of future research directions. Finally, we highlight what we consider to be the "hot" current and future research topics, which aim to address RE needs for emerging systems of the future.