title: Personality-based refinement for sentiment classification in microblog

abstract: Abstract Microblog has become one of the most widely used social media for people to share information and express opinions. As information propagates fast in social network, understanding and analyzing public sentiment implied in user-generated content is beneficial for many fields and has been applied to applications such as social management, business and public security. Most previous work on sentiment analysis makes no distinctions of the tweets by different users and ignores the diverse word use of people. As some sentiment expressions are used by specific groups of people, the corresponding textual sentiment features are often neglected in the analysis process. On the other hand, previous psychological findings have shown that personality influences the ways people write and talk, suggesting that people with same personality traits tend to choose similar sentiment expressions. Inspired by this, in this paper we propose a method to facilitate sentiment classification in microblog based on personality traits. To this end, we first develop a rule-based method to predict users’ personality traits based on the most well-studied personality model, the Big Five model. In order to leverage more effective but not widely used sentiment features, we then extract those features grouped by different personality traits and construct personality-based sentiment classifiers. Moreover, we adopt an ensemble learning strategy to integrate traditional textual feature based and our personality-based sentiment classification. Experimental studies on Chinese microblog dataset show the effectiveness of our method in refining the performance of both the traditional and state-of-the-art sentiment classifiers. Our work is among the first to explicitly explore the role of user's personality in social media analytics and its application in sentiment classification.


title: Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains

abstract: Experimental studies have observed Long Term synaptic Potentiation (LTP) when a presynaptic neuron fires shortly before a postsynaptic neuron, and Long Term Depression (LTD) when the presynaptic neuron fires shortly after, a phenomenon known as Spike Timing Dependant Plasticity (STDP). When a neuron is presented successively with discrete volleys of input spikes STDP has been shown to learn ‘early spike patterns’, that is to concentrate synaptic weights on afferents that consistently fire early, with the result that the postsynaptic spike latency decreases, until it reaches a minimal and stable value. Here, we show that these results still stand in a continuous regime where afferents fire continuously with a constant population rate. As such, STDP is able to solve a very difficult computational problem: to localize a repeating spatio-temporal spike pattern embedded in equally dense ‘distractor’ spike trains. STDP thus enables some form of temporal coding, even in the absence of an explicit time reference. Given that the mechanism exposed here is simple and cheap it is hard to believe that the brain did not evolve to use it.


title: Let's learn with Kahoot!

abstract: Blended and computer supported collaborative learning (CSCL) are contemporary trends that are constantly and rapidly evolving. In parallel, digital games are considered to be excellent tools for teaching and learning and offer an engaging and interesting way of participating and learning. For example, games like Kahoot! can be a very good choice for teaching university students. In this study, we leverage Kahoot!'s features by implementing in the learning process the Quiz and Jumble types of games, during the laboratory part of the "Web Technologies" course. Also, we use the Challenge feature to assign homeworks for further engagement. Finally, we gather the students' opinions about this blended type of teaching and learning by using a suitably adjusted questionnaire. The results are very positive since students think of Kahoot! as a useful learning tool.


title: Linear tSNE optimization for the Web

abstract: The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data. tSNE reveals clusters of high-dimensional data points at different scales while it requires only minimal tuning of its parameters. Despite these advantages, the computational complexity of the algorithm limits its application to relatively small datasets. To address this problem, several evolutions of tSNE have been developed in recent years, mainly focusing on the scalability of the similarity computations between data points. However, these contributions are insufficient to achieve interactive rates when visualizing the evolution of the tSNE embedding for large datasets. In this work, we present a novel approach to the minimization of the tSNE objective function that heavily relies on modern graphics hardware and has linear computational complexity. Our technique does not only beat the state of the art, but can even be executed on the client side in a browser. We propose to approximate the repulsion forces between data points using adaptive-resolution textures that are drawn at every iteration with WebGL. This approximation allows us to reformulate the tSNE minimization problem as a series of tensor operation that are computed with TensorFlow.js, a JavaScript library for scalable tensor computations.


title: Detection and Localization of Image Forgeries Using Resampling Features and Deep Learning

abstract: Resampling is an important signature of manipulated images. In this paper, we propose two methods to detect and localize image manipulations based on a combination of resampling features and deep learning. In the first method, the Radon transform of resampling features are computed on overlapping image patches. Deep learning classifiers and a Gaussian conditional random field model are then used to create a heatmap. Tampered regions are located using a Random Walker segmentation method. In the second method, resampling features computed on overlapping image patches are passed through a Long short-term memory (LSTM) based network for classification and localization. We compare the performance of detection/localization of both these methods. Our experimental results show that both techniques are effective in detecting and localizing digital image forgeries.


title: (More) Efficient Reinforcement Learning via Posterior Sampling

abstract: Most provably-efficient reinforcement learning algorithms introduce optimism about poorly-understood states and actions to encourage exploration. We study an alternative approach for efficient exploration: posterior sampling for reinforcement learning (PSRL). This algorithm proceeds in repeated episodes of known duration. At the start of each episode, PSRL updates a prior distribution over Markov decision processes and takes one sample from this posterior. PSRL then follows the policy that is optimal for this sample during the episode. The algorithm is conceptually simple, computationally efficient and allows an agent to encode prior knowledge in a natural way. We establish an O(τS/√AT) bound on expected regret, where T is time, τ is the episode length and S and A are the cardinalities of the state and action spaces. This bound is one of the first for an algorithm not based on optimism, and close to the state of the art for any reinforcement learning algorithm. We show through simulation that PSRL significantly outperforms existing algorithms with similar regret bounds.


title: An Asynchronous Parallel Stochastic Coordinate Descent Algorithm

abstract: We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method achieves a linear convergence rate on functions that satisfy an essential strong convexity property and a sublinear rate (1/K) on general convex functions. Near-linear speedup on a multicore system can be expected if the number of processors is O(n1/2) in unconstrained optimization and O(n1/4) in the separable-constrained case, where n is the number of variables. We describe results from implementation on 40-core processors.


title: Automatic Caption Generation for News Images

abstract: This paper is concerned with the task of automatically generating captions for images, which is important for many image-related applications. Examples include video and image retrieval as well as the development of tools that aid visually impaired individuals to access pictorial information. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned and colocated with thematically related documents. Our model learns to create captions from a database of news articles, the pictures embedded in them, and their captions, and consists of two stages. Content selection identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. The model postulates that images and their textual descriptions are generated by a shared set of latent variables (topics) and is trained on a weakly labeled dataset (which treats the captions and associated news articles as image labels). Inspired by recent work in summarization, we propose extractive and abstractive surface realization models. Experimental results show that it is viable to generate captions that are pertinent to the specific content of an image and its associated article, while permitting creativity in the description. Indeed, the output of our abstractive model compares favorably to handwritten captions and is often superior to extractive methods.


title: News sharing in social media: The effect of gratifications and prior experience

abstract: Recent events indicate that sharing news in social media has become a phenomenon of increasing social, economic and political importance because individuals can now participate in news production and diffusion in large global virtual communities. Yet, knowledge about factors influencing news sharing in social media remains limited. Drawing from the uses and gratifications (U&G) and social cognitive theories (SCT), this study explored the influences of information seeking, socializing, entertainment, status seeking and prior social media sharing experience on news sharing intention. A survey was designed and administered to 203 students in a large local university. Results from structural equation modeling (SEM) analysis revealed that respondents who were driven by gratifications of information seeking, socializing, and status seeking were more likely to share news in social media platforms. Prior experience with social media was also a significant determinant of news sharing intention. Implications and directions for future work are discussed.


title: Analysis of factors affecting the adoption of smartphones

abstract: The mobile-phone market has changed from a new purchase market to a replacement market when it is matured. At the same time smartphones come into the spotlight as an alternative of feature phones in the mobile-phone market. Since the characteristics of ‘smartphones’ are greatly different from those of ‘feature phones’ in that more technologies are incorporated and more functionalities are provided with users by smartphones, factors that can affect the adoption of smartphones and feature phones might not be the same.


title: Image Processing Based Detection of Counterfeit Indian Bank Notes

abstract: Currency counterfeiting is a significant offense which has a profound impact on the assets and capital of the citizens thus having an adverse effect on the nation's finances. The schemes currently existing to combat the falsification of banknotes are complex, hardware-based and inaccessible to the common people. In this paper, a unique authentication system is proposed which is compact, mobile and devoid of any hardware components. Certain security features, such as security thread and latent image, embedded on the note are utilized to help ascertain its legitimacy. The methodology involves the extraction and encoding of these security features. Given the prominence of the security thread in certain image planes, a clustering algorithm, k-means is applied for classification. The latent image, segmented via template matching was encoded using HOG descriptor and classified with an SVM model. The result is illustrated with the aid of performance parameters and overall accuracy.


title: Gradient Agreement as an Optimization Objective for Meta-Learning

abstract: This paper presents a novel optimization method for maximizing generalization over tasks in meta-learning. The goal of meta-learning is to learn a model for an agent adapting rapidly when presented with previously unseen tasks. Tasks are sampled from a specific distribution which is assumed to be similar for both seen and unseen tasks. We focus on a family of meta-learning methods learning initial parameters of a base model which can be fine-tuned quickly on a new task, by few gradient steps (MAML). Our approach is based on pushing the parameters of the model to a direction in which tasks have more agreement upon. If the gradients of a task agree with the parameters update vector, then their inner product will be a large positive value. As a result, given a batch of tasks to be optimized for, we associate a positive (negative) weight to the loss function of a task, if the inner product between its gradients and the average of the gradients of all tasks in the batch is a positive (negative) value. Therefore, the degree of the contribution of a task to the parameter updates is controlled by introducing a set of weights on the loss function of the tasks. Our method can be easily integrated with the current meta-learning algorithms for neural networks. Our experiments demonstrate that it yields models with better generalization compared to MAML and Reptile.


title: Speech emotion recognition with deep learning

abstract: This paper describes a method for Speech Emotion Recognition (SER) using Deep Neural Network (DNN) architecture with convolutional, pooling and fully connected layers. We used 3 class subset (angry, neutral, sad) of German Corpus (Berlin Database of Emotional Speech) containing 271 labeled recordings with total length of 783 seconds. Raw audio data were standardized so every audio file has zero mean and unit variance. Every file was split into 20 millisecond segments without overlap. We used Voice Activity Detection (VAD) algorithm to eliminate silent segments and divided all data into TRAIN (80%) VALIDATION (10%) and TESTING (10%) sets. DNN is optimized using Stochastic Gradient Descent. As input we used raw data without and feature selection. Our trained model achieved overall test accuracy of 96.97% on whole-file classification.


title: Image Based Retrieval and Keyword Spotting in Documents.

abstract: The attempt to move towards paperless offices has led to the digitization of large quantities of printed documents for storage in image databases. Thanks to advances in computer and network technology, it is possible to generate and transmit huge amount of document images efficiently. An ensuing and pressing issue is then to find ways and means to provide highly reliable and efficient retrieval functionality over these document images from a vast variety of information sources. Optical Character Recognition (OCR) is one powerful tool to achieve retrieval tasks, but nowadays there is a debate over the trade-off between OCR-based and OCR-free retrieval, because of OCR errors and wastage of time to OCR the entire collection into text format. Instead, image-based retrieval using document image similarity measure is a much more economical alternative. Till now, many methods have been proposed to achieve different subtasks, all of which contribute to the final retrieval performance. This chapter will present different methods for presenting word images and preprocessing steps before similarity measure or training and testing and discuss different algorithms or models for achieving keyword spotting and document image retrieval.


title: A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

abstract: In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.


title: Beyond moulin mechanisms

abstract: The only known general technique for designing truthful, approximatelybudget-balanced cost-sharing mechanisms is due to Moulin. While Moulin mechanisms have been successfully designed for a widerange of applications, recent negative results show that for manyfundamental cost-sharing problems, Moulin mechanisms inevitably sufferfrom poor budget-balance, poor economic efficiency, or both. We propose acyclic mechanisms, a new framework for designingtruthful, approximately budget-balanced cost-sharing mechanisms. Acyclic mechanisms strictly generalize Moulin mechanisms andoffer three important advantages. First, it is easier to design acyclic mechanisms than Moulinmechanisms: many classical primal-dual algorithms naturallyinduce a non-Moulin acyclic mechanism with good performanceguarantees. Second, for several important classes of cost-sharing problems, acyclicmechanisms have exponentially better budget-balance and economicefficiency than Moulin mechanisms.Finally, while Moulin mechanisms have found application primarily in binary demand games, we extend acyclic mechanisms to general demand games, a multi-parameter setting in which each bidder can be allocated one of several levels of service.


title: Exploring Nearest Neighbor Approaches for Image Captioning

abstract: We explore a variety of nearest neighbor baseline approaches for image captioning. These approaches find a set of nearest neighbor images in the training set from which a caption may be borrowed for the query image. We select a caption for the query image by finding the caption that best represents the "consensus" of the set of candidate captions gathered from the nearest neighbor images. When measured by automatic evaluation metrics on the MS COCO caption evaluation server, these approaches perform as well as many recent approaches that generate novel captions. However, human studies show that a method that generates novel captions is still preferred over the nearest neighbor approach.


title: Clinical evaluation of a lumbar interspinous dynamic stabilization device (the Wallis system) with a 13-year mean follow-up

abstract: The authors determined current health status of patients who had been included in a long-term survivorship analysis of a lumbar dynamic stabilizer. Among 133 living patients, 107 (average age at surgery, 44.2 ± 9.9 years) completed health questionnaires. All patients had initially been scheduled for decompression and fusion for canal stenosis, herniated disc, or both. In 20 patients, the implant was removed, and fusion was performed. The other 87 still had the dynamic stabilizer. Satisfaction, Oswestry disability index, visual analog scales for back and leg pain, short-form (SF-36) quality-of-life physical composite score, physical function, and social function were significantly better (p ≤ 0.05) in the patients who still had the dynamic stabilization device. SF-36 scores of the fused subgroup were no worse than those reported elsewhere in patients who had primary pedicle-screw enhanced lumbar fusion. This anatomy-sparing device provided a good 13-year clinical outcome and obviated arthrodesis in 80% of patients.


title: Entering crossroads with blind corners. A safe strategy for autonomous vehicles

abstract: Recent advances in the field of environment perception and cognition enable automated vehicles to safely drive in a growing variety of complex situations. However, in situations where required information cannot be observed directly and thus the consequences of the vehicle's actions cannot be estimated with high certainty, generating a safe behavior is still an unsolved problem. This paper tackles the scenario of a left turn maneuver in an urban environment with the presence of blind corners. We consider pedestrians and vehicles possibly hidden by parking cars, buildings or vegetation. In these cases, our approach allows to safely merge into traffic by using an environment representation based on tracked objects as well as an object-free sensor fusion including the calculation of unobservable regions in a digital map. A free-to-drive section of our desired path is obtained by long-term propagation of observed or possibly unobservable movement. The presented approach allows advancing into the road in a cautious manner, successively increasing the observable area.


title: Interactive multimodal music transcription

abstract: Automatic music transcription has usually been performed as an autonomous task and its evaluation has been made in terms of precision, recall, accuracy, etc. Nevertheless, in this work, assuming that the state of the art is far from being perfect, it is considered as an interactive one, where an expert user is assisted in its work by a transcription tool. In this context, the performance evaluation of the system turns into an assessment of how many user interactions are needed to complete the work. The strategy is that the user interactions can be used by the system to improve its performance in an adaptive way, thus minimizing the workload. Also, a multimodal approach has been implemented, in such a way that different sources of information, like onsets, beats, and meter, are used to detect notes in a musical audio excerpt. The system is focused on monotimbral polyphonic transcription.


title: Academic Emotions in Students' Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research

abstract: Academic emotions have largely been neglected by educational psychology, with the exception of test anxiety. In 5 qualitative studies, it was found that students experience a rich diversity of emotions in academic settings. Anxiety was reported most often, but overall, positive emotions were described no less frequently than negative emotions. Based on the studies in this article, taxonomies of different academic emotions and a self-report instrument measuring students' enjoyment, hope, pride, relief, anger, anxiety, shame, hopelessness, and boredom (Academic Emotions Questionnaire [AEQ]) were developed. Using the AEQ, assumptions of a cognitive-motivational model of the achievement effects of emotions, and of a control/value theory of their antecedents (Pekrun, 1992b, 2000), were tested in 7 cross-sectional, 3 longitudinal, and 1 diary study using samples of university and school students. Results showed that academic emotions are significantly related to students' motivation, learning strategies, cognit...


title: High-Quality Cartographic Roads on High-Resolution DEMs

abstract: The efficient and high quality rendering of complex road networks—given as vector data—and high-resolution digital elevation models (DEMs) poses a significant problem in 3D geographic information systems. As in paper maps, a cartographic representation of roads with rounded caps and accentuated clearly distinguishable colors is desirable. On the other hand, advances in the technology of remote sensing have led to an explosion of the size and resolution of DEMs, making the integration of cartographic roads very challenging. In this work we investigate techniques for integrating such roads into a terrain renderer capable of handling high-resolution data sets. We evaluate the suitability of existing methods for draping vector data onto DEMs, and we adapt two methods for the rendering of cartographic roads by adding analytically computed rounded caps at the ends of road segments. We compare both approaches with respect to performance and quality, and we outline application areas in which either approach is preferable.


title: Analogical Inference for Multi-Relational Embeddings

abstract: Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a novel framework for optimizing the latent representations with respect to the \textit{analogical} properties of the embedded entities and relations. By formulating the learning objective in a differentiable fashion, our model enjoys both theoretical power and computational scalability, and significantly outperformed a large number of representative baseline methods on benchmark datasets. Furthermore, the model offers an elegant unification of several well-known methods in multi-relational embedding, which can be proven to be special instantiations of our framework.


title: Lip contour extraction using a deformable model

abstract: The use of visual information from lip movements can improve the accuracy and robustness of a speech recognition system. Accurate extraction of visual features associated with the lips is thus very important. In this paper, we present a deformable model-based technique for lip contour extraction from color lip images. The geometric lip model we propose is able to fit different lip shapes. Our method starts by generating a probability map of the lip image using spatial fuzzy clustering. Then the optimum set of model parameters that partitions a lip probability map into lip region and non-lip region is found, such that the joint probability of the two regions is maximised. Experimental results indicate that accurate and robust lip contour extraction is possible using this approach.


title: A novel, compact, low-cost, impulse ground-penetrating radar for nondestructive evaluation of pavements

abstract: This paper reports on the development of a novel, compact, low-cost, impulse ground-penetrating radar (GPR) and demonstrate its use for nondestructive evaluation of pavement structures. This GPR consists of an ultrashort-monocycle-pulse transmitter (330 ps), an ultrawide-band (UWB) sampling receiver (0-6 GHz), and two UWB antennas (0.2-20 GHz)-completely designed using microwave-integrated circuits with seamless electrical connections between them. An approximate analysis is used to determine the signal loss and power budget. Performance of this GPR has been verified through the measurements of relative permittivity and thicknesses of various samples, and a good agreement between the experimental and theoretical results has been achieved.


title: Between homomorphic signal processing and deep neural networks: Constructing deep algorithms for polyphonic music transcription

abstract: This paper presents a new approach in understanding how deep neural networks (DNNs) work by applying homomorphic signal processing techniques. Focusing on the task of multi-pitch estimation (MPE), this paper demonstrates the equivalence relation between a generalized cepstrum and a DNN in terms of their structures and functionality. Such an equivalence relation, together with pitch perception theories and the recently established rectified-correlations-on-a-sphere (RECOS) filter analysis, provide an alternative way in explaining the role of the nonlinear activation function and the multi-layer structure, both of which exist in a cepstrum and a DNN. To validate the efficacy of this new approach, a new feature designed in the same fashion is proposed for pitch salience function. The new feature outperforms the one-layer spectrum in the MPE task and, as predicted, it addresses the issue of the missing fundamental effect and also achieves better robustness to noise.


title: Peekaboom: a game for locating objects in images

abstract: We introduce Peekaboom, an entertaining web-based game that can help computers locate objects in images. People play the game because of its entertainment value, and as a side effect of them playing, we collect valuable image metadata, such as which pixels belong to which object in the image. The collected data could be applied towards constructing more accurate computer vision algorithms, which require massive amounts of training and testing data not currently available. Peekaboom has been played by thousands of people, some of whom have spent over 12 hours a day playing, and thus far has generated millions of data points. In addition to its purely utilitarian aspect, Peekaboom is an example of a new, emerging class of games, which not only bring people together for leisure purposes, but also exist to improve artificial intelligence. Such games appeal to a general audience, while providing answers to problems that computers cannot yet solve.


title: Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

abstract: Analysis of Internet of Things (IoT) sensor data is a key for achieving city smartness. In this paper a multitier fog computing model with large-scale data analytics service is proposed for smart cities applications. The multitier fog is consisted of ad-hoc fogs and dedicated fogs with opportunistic and dedicated computing resources, respectively. The proposed new fog computing model with clear functional modules is able to mitigate the potential problems of dedicated computing infrastructure and slow response in cloud computing. We run analytics benchmark experiments over fogs formed by Rapsberry Pi computers with a distributed computing engine to measure computing performance of various analytics tasks, and create easy-to-use workload models. Quality of services (QoS) aware admission control, offloading, and resource allocation schemes are designed to support data analytics services, and maximize analytics service utilities. Availability and cost models of networking and computing resources are taken into account in QoS scheme design. A scalable system level simulator is developed to evaluate the fog-based analytics service and the QoS management schemes. Experiment results demonstrate the efficiency of analytics services over multitier fogs and the effectiveness of the proposed QoS schemes. Fogs can largely improve the performance of smart city analytics services than cloud only model in terms of job blocking probability and service utility.


title: Large population speaker identification using clean and telephone speech

abstract: This paper presents text-independent speaker identification results for varying speaker population sizes up to 630 speakers for both clean, wideband speech, and telephone speech. A system based on Gaussian mixture speaker models is used for speaker identification, and experiments are conducted on the TIMIT and NTIMIT databases. The TIMIT results show large population performance under near-ideal conditions, and the NTIMIT results show the corresponding accuracy loss due to telephone transmission. These are believed to be the first speaker identification experiments on the complete 630 speaker TIMIT and NTIMIT databases and the largest text-independent speaker identification task reported to date. Identification accuracies of 99.5 and 60.7% were achieved on the TIMIT and NTIMIT databases, respectively. >


title: Applicability of Clustering and Classification Algorithms for Recruitment Data Mining

abstract: of appropriate employees and their retention are the major concerns towards creating the competitive strength in the knowledge economy. Every year IT companies recruit fresh graduates through their campus selection programs after examining their skills by conducting tests, group discussion and a number of interviews. The recruitment process requires enormous amount of effort and investment. During each phase of the recruitment process, candidates are filtered based on some performance criteria. Intense analysis on the system indicates that a pattern exists among the candidates selected for an industry. The problem domain is complex and the aspects of candidates that impact the recruitment process is not explicit. In this research, the domain knowledge is extracted through knowledge acquisition techniques. Data mining techniques that fit the problem are determined. A study has been made by applying K-means and fuzzy C-means clustering and decision tree classification algorithms to the recruitment data of an industry. Experiments were conducted with the data collected from an IT industry to support their hiring decisions. Pruned and unpruned trees were constructed using ID3, C4.5 and CART algorithms. From the comparative study, it has been observed that the clustering algorithms are not much suitable for the problem and performance of the C4.5 decision tree algorithm is high. Using the constructed decision trees, discussions were made with the domain experts to deduce viable decision rules. candidate among those who graduate becomes a herculean task. The process involves lot of effort by the recruiting team and money spent for the process is phenomenal. One of the mechanisms used by the industries is to conduct tests and group discussions during the filtration process. The selection process uses different criteria that comprise the average of their semester marks, marks obtained in the aptitude, programming and technical tests conducted by the company, group discussion, technical and HR interviews. These criteria are common for all the students, but the skill level of the students vary since they are from different disciplines and backgrounds. With a varying curriculum, mode of delivery and the evaluation methodologies followed in the educational system, the recruitment process becomes much more challenging. The time taken and expenditure for conducting group discussions and interviews consumes more than 90% of the total effort for the recruitment process. It has been observed that 1 among 120 students who apply get selected and the ratio of number of candidates selected against the number of candidates interviewed after tests is approximately 1:20. Reducing these ratios will immensely help the industries to save the effort. Lot of effort is put to analyze the profiles of the applicants to determine the ones that suit the needs of the industry. The knowledge required for this process is not explicit as quantitative numbers but a hidden convention that may be extracted by mining the profiles of previous years and


title: Steganalysis of JPEG images using rich models

abstract: In this paper, we propose a rich model of DCT coefficients in a JPEG file for the purpose of detecting steganographic ::: embedding changes. The model is built systematically as a union of smaller submodels formed as joint ::: distributions of DCT coefficients from their frequency and spatial neighborhoods covering a wide range of statistical ::: dependencies. Due to its high dimensionality, we combine the rich model with ensemble classifiers and ::: construct detectors for six modern JPEG domain steganographic schemes: nsF5, model-based steganography, ::: YASS, and schemes that use side information at the embedder in the form of the uncompressed image: MME, ::: BCH, and BCHopt. The resulting performance is contrasted with previously proposed feature sets of both low ::: and high dimensionality. We also investigate the performance of individual submodels when grouped by their ::: type as well as the effect of Cartesian calibration. The proposed rich model delivers superior performance across ::: all tested algorithms and payloads.


Dynamic properties of the virtual synchronous machine (VISMA)


title: Improving GAN with neighbors embedding and gradient matching

abstract: We propose two new techniques for training Generative Adversarial Networks (GANs) in the unsupervised setting. Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. Our code is available at: https://github.com/tntrung/gan


title: The semantic web: The roles of XML and RDF

abstract: XML and RDF are the current standards for establishing semantic interoperability on the Web, but XML addresses only document structure. RDF better facilitates interoperation because it provides a data model that can be extended to address sophisticated ontology representation techniques. We explain the role of ontologies in the architecture of the Semantic Web. We then briefly summarize key elements of XML and RDF, showing why using XML as a tool for semantic interoperability will be ineffective in the long run. We argue that a further representation and inference layer is needed on top of the Web's current layers, and to establish such a layer, we propose a general method for encoding ontology representation languages into RDF/RDF schema. We illustrate the extension method by applying it to Ontology Interchange Language, an ontology representation and inference language.


title: An empirical investigation of electronic word-of-mouth: Informational motive and corporate response strategy

abstract: The proliferation of the Internet has given birth to a number of complaint web sites where dissatisfied and frustrated consumers can easily articulate their opinions and comments on products, services, or companies. Little attention, however, has been paid to the influence of online complaints on potential consumers' behaviors. This study attempts to provide the understanding of causal attribution process in the online complaining behaviors. The results showed that informational factors, such as vividness and consensus, facilitated consumers' attribution to companies' responsibility for the negative events, and subsequently led to changing their evaluation of the companies. In addition, we found that corporate response strategies to online complaints should be different from the conventional response strategies.


title: Prospective detection of open spina bifida at 11-13 weeks by assessing intracranial translucency and posterior brain

abstract: We describe a case series of six fetuses with open spina bifida (OSB) from four different prenatal units, where the anomaly was detected at the routine 11-13-week ultrasound examination. Crown-rump length ranged from 49 to 78 mm. All cases were first suspected during nuchal translucency thickness measurement in the mid-sagittal plane of the face. OSB was lumbosacral in five fetuses and cervical in one. The intracranial translucency (IT) was obliterated in two cases, but some fluid was found in the other four cases. However, in all cases the typical landmarks of a normal posterior brain and normal IT were absent. In all six cases the ratio of brainstem diameter to brainstem-occipital bone distance was increased (≥ 1). This detection of an abnormal posterior brain led to a targeted examination and detection of the spinal lesion during the same examination in five cases, whereas in one suspicious case the patient was recalled at 17 weeks, when the abnormality was detected. Two fetuses had both multiple anomalies and trisomy 18. These prospective cases demonstrate the feasibility of using the standard mid-sagittal plane commonly used for NT measurement to assess the IT and the posterior brain and to determine the presence of OSB during NT screening.


title: Hidden Automotive Antenna Performance and Simulation

abstract: A new printed antenna system mounted in an aperture in the roof of a car is evaluated for broadcast signal reception from 88-108 MHz. Results from simulations on the vehicle are compared with measurements. The overall performance of the antenna is also compared to common types of vehicle antennas, the roof monopole and a glass based antenna. Overall the average gain of the antennas was better than -3 dBi for vertical polarization and -10 dBi for horizontal while the input return loss was generally better than -2 dB before matching sections in the tuners and comparable with common automotive antennas. The antennas in the roof aperture offer an alternative place to mount antennas on automobiles


title: Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

abstract: The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio. For the test images with synthetic noise, our method reduces the number of flipped pixels by 48.2% and improves the compression ratio by 36.36% as compared with the best encoding methods. For the test images with real noise, our method visually improves the image quality, and outperforms the cutting-edge method by 28.27% in terms of the compression ratio.


title: An Outlook on Lithium Ion Battery Technology

abstract: Lithium ion batteries as a power source are dominating in portable electronics, penetrating the electric vehicle market, and on the verge of entering the utility market for grid-energy storage. Depending on the application, trade-offs among the various performance parameters—energy, power, cycle life, cost, safety, and environmental impact—are often needed, which are linked to severe materials chemistry challenges. The current lithium ion battery technology is based on insertion-reaction electrodes and organic liquid electrolytes. With an aim to increase the energy density or optimize the other performance parameters, new electrode materials based on both insertion reaction and dominantly conversion reaction along with solid electrolytes and lithium metal anode are being intensively pursued. This article presents an outlook on lithium ion technology by providing first the current status and then the progress and challenges with the ongoing approaches. In light of the formidable challenges with some of the...


title: Stochastic Approximation for Canonical Correlation Analysis

abstract: We propose novel first-order stochastic approximation algorithms for canonical correlation analysis (CCA). Algorithms presented are instances of inexact matrix stochastic gradient (MSG) and inexact matrix exponentiated gradient (MEG), and achieve $\epsilon$-suboptimality in the population objective in $\operatorname{poly}(\frac{1}{\epsilon})$ iterations. We also consider practical variants of the proposed algorithms and compare them with other methods for CCA both theoretically and empirically.


title: Point-Based POMDP Algorithms: Improved Analysis and Implementation

abstract: Existing complexity bounds for point-based POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity.


title: Smoothing LUT classifiers for robust face detection

abstract: Look-up table (LUT) classifiers are often used to construct concise classifiers for rapid object detection due to their favorable convergent ability. However, their poor generalization ability imposes restrictions on their applications. A novel improvement to LUT classifiers is proposed in this paper where the new confident of each partition is recalculated by smoothing the old ones within its neighbor partitions. The new confidents are more generalizable than the old ones because each of the new predicts is supported by more training samples and the high frequency components in the old predict sequences are suppressed greatly through smoothing operation. Both weight sum smoothing method and confident smoothing method are introduced here, which all bring negligible extra computation cost in training time and no extra cost in test time. Experimental results in the domain of upright frontal face detection using smoothed LUT classifiers with identical smoothing width and smoothing factor for all partitions based on Haar-like rectangle features show that smoothed LUT classifiers generalize much better and also converge more or less worse than unsmooth LUT classifiers. Specifically, smoothed LUT classifiers can delicately balance between generalization ability and convergent ability through carefully set smoothing parameters.


title: The Spatial Semantic Hierarchy

abstract: Abstract The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving. The control level represents useful patterns of sensorimotor interaction with the world in the form of trajectory-following and hill-climbing control laws leading to locally distinctive states. Local geometric maps in local frames of reference can be constructed at the control level to serve as observers for control laws in particular neighborhoods. The causal level abstracts continuous behavior among distinctive states into a discrete model consisting of states linked by actions. The topological level introduces the external ontology of places, paths and regions by abduction to explain the observed pattern of states and actions at the causal level. Quantitative knowledge at the control, causal and topological levels supports a “patchwork map” of local geometric frames of reference linked by causal and topological connections. The patchwork map can be merged into a single global frame of reference at the metrical level when sufficient information and computational resources are available. We describe the assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems. Evidence is presented from several partial implementations of the SSH on simulated and physical robots.


title: Meticulous optimization of cardiomyocyte yields in a 3-stage continuous integrated agitation bioprocess

abstract: Abstract Human pluripotent stem cells (hPSCs) can be a renewable source for generating cardiomyocyte (CM) for treating myocardial infraction. In our previous publication, we described an integrated microcarrier-based wave reactor process for the expansion and differentiation of hPSCs to CMs on a rocker based platform. However, this platform is limited in terms of linear scalability and CMs purity. The present study describes ways to overcome these limitations by the use of a stirred scalable platform and incorporation of an additional lactate based purification step which increases CM purity. Efficient CM differentiation in stirred spinners was achieved by (1) Addition of ascorbic acid (AS) during the differentiation phase which resulted in an increase of 38.42% in CM yield (0.84 ± 0.03 × 10 6 vs 1.17 ± 0.07 × 10 6 CM/mL for cultures without AS and with AS respectively) and (2) Change of agitation regime to a shorter static intervals one (from 66 min off/6 min on (66/6) to 8 min off/1 min on (8/1)) during the first 3 days of differentiation which resulted in 22% increase in CM yield (1.50 ± 0.10 × 10 6 vs 1.23 ± 0.07 × 10 6 CM/mL). The combination of AS addition and change in agitation regime resulted in a production yield of 1.50 ± 0.10 × 10 6 CM/mL which is comparable to that achieved in the rocker platform as described before (1.61 ± 0.36 × 10 6 CM/mL). Increase in CM purity was achieved by changing of culture medium to RPMI1640 (without glucose) + 5 mM lactate +0.6 mM AS at day 10 of differentiation which resulted in 44.5% increase in CM purity at day 15. The increase in purity of CMs was due to the death of the non-CM cells (~76% of cell death). It is important to note that in the absence of glucose, lactate was consumed at a rate of 0.01 mmol/10 6 cells/h. Addition of glucose, even in small amounts, during the purification step prevents the process of CM purification, due to the growth of the non-CM cell population. In summary, hPSC (hESC-HES3 and hiPSC-IMR90) can be efficiently differentiated to CMs in a scalable spinner process which integrates 7 days of expansion (3.01 ± 0.51 × 10 6 to 3.50 ± 0.65 × 10 6 cells/mL) followed by 10 days of WNT modulated CM differentiation and 5 days of lactate based purification. CM yield of 1.38 ± 0.22 × 10 6 to 1.29 ± 0.42 × 10 6 CM/mL with 72.5 ± 8.35% to 83.12 ± 8.73% cardiac troponin-T positive cells were obtained from these cultures.


title: The role of deoxycytidine-metabolizing enzymes in the cytotoxicity induced by 3′-amino-2′,3′-dideoxycytidine and cytosine arabinoside

abstract: The cellular metabolism of 3'-amino-2',3'-dideoxycytidine (3'-NH2-dCyd), a cytotoxic agent previously reported to be a poor substrate for purified Cyd/dCyd deaminase (dCydD), was compared with that of cytosine arabinoside (ara-C) in cells that displayed dCydD activity (HeLa) and in cells that did not (L1210). Growth inhibition induced by 3'-NH2-dCyd was dependent on the levels of anabolic enzymes, particularly dCyd kinase (dCydK), whereas cytotoxicity induced by ara-C was dependent on the expression of both anabolic and catabolic enzyme activities. Competition kinetics using purified enzyme revealed that the binding affinity of ara-C to dCydK was 5-fold that of the amino analog. However, this binding advantage is apparently offset in cells that contain high levels of dCydD, since the Ki values for this enzyme were 0.2 and 23 mM for ara-C and 3'-NH2-dCyd, respectively. This was reflected in the decrease in analog sensitivity observed between the two cell lines, whereby the concentrations of ara-C and 3'-NH2-dCyd required to inhibit growth by 50% were 200 and 7 times higher, respectively, in the dCydD-containing HeLa cells as compared with the dCydD-deficient L1210 cells. The metabolic stability and cytotoxicity of 3'-NH2-dCyd was independent of cell number. An unexpected finding was the extent to which the effectiveness of ara-C could be mitigated by the number of dCydD-containing cells. A completely cytotoxic concentration of ara-C was rendered nontoxic by a 10-fold increase in cell number. This observation was supported by an increase in I-beta-D-arabinofuranosyluracil (ara-U) formation, a decrease in ara-C 5'-triphosphate (ara-CTP) accumulation, and a rise in cell viability with increasing cell number. These findings indicate that unlike ara-C, the effectiveness of 3'-NH2-dCyd is independent of the level of deaminase, which suggests its possible utility in situations in which high levels of deaminase are manifest.


title: A Formation Energy Predictor for Crystalline Materials Using Ensemble Data Mining

abstract: Formation energy is one of the most important properties of a compound that is directly related to its stability. More negative the formation energy, the more stable the compound is likely to be. Here we describe the development and deployment of predictive models for formation energy, given the chemical composition of the material. The data-driven models described here are built using nearly 100,000 Density Functional Theory (DFT) calculations, which is a quantum mechanical simulation technique based on the electron density within the crystal structure of the material. These models are deployed in an online web-tool that takes a list of material compositions as input, generates over hundred composition-based attributes for each material and feeds them into the predictive models to obtain the predictions of formation energy. The online formation energy predictor is available at http://info.eecs.northwestern.edu/FEpredictor.


title: Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans

abstract: Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.


title: Stock market prediction with multiple classifiers

abstract: Stock market prediction is attractive and challenging. According to the efficient market hypothesis, stock prices should follow a random walk pattern and thus should not be predictable with more than about 50 percent accuracy. In this paper, we investigated the predictability of the Dow Jones Industrial Average index to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto-mutual information and false nearest neighbor methods. Some inductive machine-learning classifiers--artificial neural network, decision tree, and k-nearest neighbor were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved prediction accuracy up to 65 percent.


title: How Crawlers Aid Regression Testing in Web Applications: The State of the Art

abstract: today's world web applications have increasingly become very popular, and with the same speed they are been upgraded frequently. This poses a big challenge for web application testing. To ensure web application quality, we must perform adequate testing for the new features as well as regression testing the web application in each of iteration. This requires us to systematically identify/locate changes introduced in the new iteration. This paper surveys various tools which identify/locate new changes, which greatly facilitates web application testing in short release cycles. Also we will describe what are web crawler's and show how crawlers can aid in regression testing of web applications.


title: Open Platform Strategies and Innovation: Granting Access vs. Devolving Control

abstract: This paper studies two fundamentally distinct approaches to opening a technology platform and their different impacts on innovation. One approach is to grant access to a platform and thereby open up markets for complementary components around the platform. Another approach is to give up control over the platform itself. Using data on 21 handheld computing systems (1990--2004), I find that granting greater levels of access to independent hardware developer firms produces up to a fivefold acceleration in the rate of new handheld device development, depending on the precise degree of access and how this policy was implemented. Where operating system platform owners went further to give up control (beyond just granting access to their platforms) the incremental effect on new device development was still positive but an order of magnitude smaller. The evidence from the industry and theoretical arguments both suggest that distinct economic mechanisms were set in motion by these two approaches to opening.


title: Adaptable Service Oriented Infrastructure Provisioning with Lightweight Containers Virtualization Technology

abstract: Modern computing infrastructures should enable realization of converged provisioning and governance operations on virtualized computing, storage and network resources used on behalf of users' workloads. These workloads must have ensured sufficient access to the resources to satisfy required QoS. This requires flexible platforms providing functionality for construction, activation and governance of Runtime Infrastructure which can be realized according to Service Oriented Infrastructure (SOI) paradigm. Implementation of the SOI management framework requires definition of flexible architecture and utilization of advanced software engineering and policy-based techniques. The paper presents an Adaptable SOI Provisioning Platform which supports adaptable SOI provisioning with lightweight virtualization, compliant with the structured process model suitable for construction, activation and governance of IT environments. The requirements, architecture and implementation of the platform are all discussed. Practical usage of the platform is presented on the basis of a complex case study for provisioning JEE middleware on top of the Solaris 10 lightweight virtualization platform.


title: Azathioprine treatment of ulcerative colitis, granulomatous colitis and regional enteritis

abstract: Twenty-four patients with ulcerative colitis, granulomatous colitis or regional enteritis were treated with azathioprine, starting with 2.5 mg/kg body weight. Seven received treatment for less than 4 months. Seventeen received treatment for 4 months or longer. Of these, 14 had partial or full benefit; all of those in the latter group who were on steroids were taken off the drugs. One patient died from complications that arose partly from preexisting renal disease. One patient was found to have a small carcinoma at colectomy after 22 months of therapy. Side effects were minor and easily reversible. Azathioprine is reasonably safe and effective for treating inflammatory bowel disease in patients who cannot tolerate or do not respond to the usual therapy with salicylazosulfapyridine, corticosteroids or surgery.


title: Project selection in project portfolio management: An artificial neural network model based on critical success factors

abstract: Abstract While a growing body of literature focuses in detecting and analyzing the main reasons affecting project success, the use of these results in project portfolio management is still under investigation. Project critical success factors (CSFs) can serve as the fundamental criteria to prevent possible causes of failures with an effective project selection process, taking into account company strategic objectives, project manager’s experience and the competitive environment. This research proposes an innovative methodology to help managers in assessing projects during the selection phase. The paper describes the design, development and testing stages of a decision support system to predict project performances. An artificial neural network (ANN), scalable to any set of CSFs, classifies the level of project’s riskiness by extracting the experience of project managers from a set of past successful and unsuccessful projects.


title: How to Make an Image More Memorable?: A Deep Style Transfer Approach

abstract: Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "Can we make an image more memorable?". Methods for automatically increasing image memorability would have an impact in many application fields like education, gaming or advertising. Our work is inspired by the popular editing-by-applying-filters paradigm adopted in photo editing applications, like Instagram and Prisma. In this context, the problem of increasing image memorability maps to that of retrieving ``memorabilizing'' filters or style ``seeds''. Still, users generally have to go through most of the available filters before finding the desired solution, thus turning the editing process into a resource and time consuming task. In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match. Our approach leverages from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style a novel learning-based solution, also relying on deep models, is proposed. Our experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selection.


title: Teaching Compositionality to CNNs

abstract: Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions. In this paper, we propose a method for augmenting and training CNNs so that their learned features are compositional. It encourages networks to form representations that disentangle objects from their surroundings and from each other, thereby promoting better generalization. Our method is agnostic to the specific details of the underlying CNN to which it is applied and can in principle be used with any CNN. As we show in our experiments, the learned representations lead to feature activations that are more localized and improve performance over non-compositional baselines in object recognition tasks.


title: A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

abstract: Healthcare scientific applications, such as body area network, require of deploying hundreds of interconnected sensors to monitor the health status of a host. One of the biggest challenges is the streaming data collected by all those sensors, which needs to be processed in real time. Follow-up data analysis would normally involve moving the collected big data to a cloud data center for status reporting and record tracking purpose. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real time streaming data applications. The current cloud platform either lacks of such a module to process streaming data, or scales in regard to coarse-grained compute nodes.In this paper, we propose a task-level adaptive MapReduce framework. This framework extends the generic MapReduce architecture by designing each Map and Reduce task as a consistent running loop daemon. The beauty of this new framework is the scaling capability being designed at the Map and Task level, rather than being scaled from the compute-node level. This strategy is capable of not only scaling up and down in real time, but also leading to effective use of compute resources in cloud data center. As a first step towards implementing this framework in real cloud, we developed a simulator that captures workload strength, and provisions the amount of Map and Reduce tasks just in need and in real time.To further enhance the framework, we applied two streaming data workload prediction methods, smoothing and Kalman filter, to estimate the unknown workload characteristics. We see 63.1% performance improvement by using the Kalman filter method to predict the workload. We also use real streaming data workload trace to test the framework. Experimental results show that this framework schedules the Map and Reduce tasks very efficiently, as the streaming data changes its arrival rate. We propose a task-level adaptive MapReduce framework for healthcare applications.We prove the optimal runtime Map and Reduce task count when workload changes.Our method extends the MapReduce programming model to process streaming data.Our method also includes two methods to predict streaming data workload in run time.We demonstrate the effectiveness of the method on real-life workload benchmark.


title: Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data

abstract: The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifying skin lesions as either benign or malignant. In this setting, the authors' proposed approach - the semi-supervised, denoising adversarial autoencoder - is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. They perform an ablation study to analyse the contributions of both the adversarial and denoising components and compare their work with state-of-the-art results. They find that their model yields superior classification performance, especially when evaluating their model at high sensitivity values.


title: The Use of Social Networking Sites in Education: A Case Study of Facebook

abstract: The purpose of this research is to find out how Facebook and Web 2.0 tools create a positive effect when used in education and to investigate teachers' opinions about the Online learning environment. This experimental study was carried out in primary and secondary schools with teachers who use Facebook. The study took six weeks and 30 hours. The teachers attended lessons and accessed materials online and offline, in face-to-face learning environment. Moreover, they were able to co-operate and share information with their colleagues via Facebook. The study sample consisted of 35 teachers from primary and secondary school who constituted a blended learning group and 36 teachers from primary and high school who formed an online learning group that enrolled in the material development for Facebook course. These teachers were chosen randomly. The participation was anonymous and the study was carried out amongst volunteered teachers. Data was collected using a 5-point Likert scale questionnaire created by the authors and entitled "Teachers' Opinions about Facebook in Education". The questionnaire consisted of 39 positive statements about Facebook. It was completed by teachers at the beginning (pre-experience test) and the end of the study (post-experience test). Results show that, if used for educational purposes, Facebook could bring about a positive change in teachers' opinions. Results also indicate that Facebook virtual environment helps teachers to do many activities with online classes, which is not possible to do in schools. Teachers are convinced that this environment helps students not only to improve their team work, but also to improve their learning skills. Based on the findings, recommendations are made about using Facebook in education.


Hydrocolloids as emulsifiers and emulsion stabilizers q


title: Modeling of photovoltaic module from commercial specification in datasheet

abstract: This paper proposes a modeling method of a photovoltaic (PV) module by a MATLAB. The main contribution is to investigate all PV parameters necessary to model a PV module. This is achieved based on the given commercial specification in datasheet of PV producer. This method is appropriate for single-diode PV model. The environmental factors, which are considered in this paper, consist of light irradiation and ambient temperature. These main factors affect the parameters of a PV module. The simulation results also show that these factors and the corresponding PV model influence the power obtained from PV modules.


title: Improving Patent Search by Search Result Diversification

abstract: Patent retrieval has some unique features relative to web search. One major task in this domain is finding existing patents that may invalidate new patents, known as prior-art or invalidity search, where search queries can be formulated from query patents (i.e., new patents). Since a patent document generally contains long and complex descriptions, generating effective search queries can be complex and difficult. Typically, these queries must cover diverse aspects of the new patent application in order to retrieve relevant documents that cover the full scope of the patent. Given this context, search diversification techniques can potentially improve the retrieval performance of patent search by introducing diversity into the document ranking. In this paper, we examine the effectiveness for patent search of a recent term-based diversification framework. Using this framework involves developing methods to identify effective phrases related to the topics mentioned in the query patent. In our experiments, we evaluate our diversification approach using standard measures of retrieval effectiveness and diversity, and show significant improvements relative to state-of-the-art baselines.


title: A 7.8-mW 5-b 5-GS/s Dual-Edges-Triggered Time-Based Flash ADC

abstract: This paper proposes a 5-b 5-GS/s time-based flash ADC in 65-nm digital CMOS technology, which utilizes both rising and falling edges of the clock for sampling and quantization. A dual-edge-triggered scheme reduces the dynamic power consumption of a voltage-to-time converter and the clock buffers by half. We doubled both the reset and the available regeneration times by interleaving the time comparators. The ADC has a low input capacitance and the calibration circuit is included on-chip for suppressing various mismatches. The prototype running at 5 GS/s consumes 7.8 mW from a 1-V supply and achieves a signal-to-noise and distortion ratio of 26.19 dB at Nyquist. The resulting figure of merit is 94.6 fJ/conversion-step and the core area is only 0.004 mm2.


title: Support vector channel selection in BCI

abstract: Designing a brain computer interface (BCI) system one can choose from a variety of features that may be useful for classifying brain activity during a mental task. For the special case of classifying electroencephalogram (EEG) signals we propose the usage of the state of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization which are based on the training of support vector machines (SVM) . These algorithms can provide more accurate solutions than standard filter methods for feature selection . We adapt the methods for the purpose of selecting EEG channels. For a motor imagery paradigm we show that the number of used channels can be reduced significantly without increasing the classification error. The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks. Furthermore we show how time dependent task specific information can be visualized.


title: IMPROVING FRAME INTERPOLATION WITH SPATIAL MOTION SMOOTHING FOR PIXEL DOMAIN DISTRIBUTED VIDEO CODING 1

abstract: Distributed video coding (DVC) is a new compression paradigm based on two key Information Theory results: the Slepian-Wolf and Wyner-Ziv theorems. A particular case of DVC deals with lossy source coding with side information at the decoder (Wyner-Ziv) and enables to shift the coding complexity from the encoder to the decoder. The solution here described is based on a very lightweight encoder leaving for the decoder the time consuming motion estimation/compensation task. In this paper, the performance of the pixel domain distributed video codec is improved by using better side information based derived by motion compensated frame interpolation algorithms at the decoder. Besides forward and bidirectional motion estimation, a spatial motion smoothing algorithm to eliminate motion outliers is proposed. This allows significant improvements in the rate-distortion (RD) performance without sacrificing the encoder complexity.


title: Digital Predistortion for Power Amplifiers in Hybrid MIMO Systems with Antenna Subarrays

abstract: We consider digital predistortion (DPD) for linearizing power amplifiers (PAs) of hybrid MIMO systems with subarrays. Designing such a DPD scheme is difficult because one DPD should linearize all PAs of a subarray whose beamforming coefficients are time-varying. We develop an adaptive DPD technique for each subarray based on direct learning method minimizing the sum of squared errors between the input and output signals of PAs. Use of the least mean square (LMS) Newton algorithm is proposed to adaptively control the DPD parameters. It is shown that the proposed DPD can simultaneously linearize multiple PAs in a subarray irrespective of its beamforming coefficients. Computer simulation results demonstrate that the proposed scheme can perform better than an alternative scheme that designs the DPD based on one of the PA outputs and uses the resulting DPD for all PAs of the subarray.


title: Hurdles to Herd Immunity: Distrust of Government and Vaccine Refusal

abstract: Abstract High rates of nonmedical exemptions (NMEs) from required childhood vaccinations have contributed to outbreaks of vaccine-preventable diseases, such as measles and pertussis. Understanding the parental decision to obtain an NME could help health professionals and public health programs improve vaccination rates in areas with high vaccine refusal. Using a 2002–2003 multi-state survey of parents of school age children ( n = 2445), this study found that parental distrust of the government and of healthcare providers is a significant factor related to a number of vaccine-related beliefs and behaviors. The odds that parents who distrust the government have seen a complementary/alternative medicine (CAM) provider were 2.11 times greater than those of parents who trust the government (70.1% vs 52.6%; OR, 2.11; 95% CI, 1.59–2.84; P P P P P


title: Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy

abstract: Knowledge base(KB) plays an important role in artificial intelligence. Much effort has been taken to both manually and automatically construct web-scale knowledge bases. Comparing with manually constructed KBs, automatically constructed KB is broader but with more noises. In this paper, we study the problem of improving the quality for automatically constructed web-scale knowledge bases, in particular, lexical taxonomies of isA relationships. We find that these taxonomies usually contain cycles, which are often introduced by incorrect isA relations. Inspired by this observation, we introduce two kinds of models to detect incorrect isA relations from cycles. The first one eliminates cycles by extracting directed acyclic graphs, and the other one eliminates cycles by grouping nodes into different levels. We implement our models on Probase, a state-of-the-art, automatically constructed, web-scale taxonomy. After processing tens of millions of relations, our models eliminate 74 thousand wrong relations with 91% accuracy.


title: Detecting Disordered Breathing and Limb Movement Using In-Bed Force Sensors

abstract: We present and evaluate measurement fusion and decision fusion for recognizing apnea and periodic limb movement in sleep episodes. We used an in-bed sensor system composed of an array of strain gauges to detect pressure changes corresponding to respiration and body movement. The sensor system was placed under the bed mattress during sleep and continuously recorded pressure changes. We evaluated both fusion frameworks in a study with nine adult participants that had mixed occurrences of normal sleep, apnea, and periodic limb movement. Both frameworks yielded similar recognition accuracies of 72.1 $\pm \sim$ 12% compared to 63.7 $\pm$ 17.4% for a rule-based detection reported in the literature. We concluded that the pattern recognition methods can outperform previous rule-based detection methods for classifying disordered breathing and period limb movements simultaneously.


title: Smart music player integrating facial emotion recognition and music mood recommendation

abstract: Songs, as a medium of expression, have always been a popular choice to depict and understand human emotions. Reliable emotion based classification systems can go a long way in helping us parse their meaning. However, research in the field of emotion-based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user's face as an input and makes use of deep learning algorithms to identify their mood with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood classes. The Recommendation Module suggests songs to the user by mapping their emotions to the mood type of the song, taking into consideration the preferences of the user.


title: Anatomy of the Third-Party Web Tracking Ecosystem

abstract: The presence of third-party tracking on websites has become customary. However, our understanding of the third-party ecosystem is still very rudimentary. We examine third-party trackers from a geographical perspective, observing the third-party tracking ecosystem from 29 countries across the globe. When examining the data by region (North America, South America, Europe, East Asia, Middle East, and Oceania), we observe significant geographical variation between regions and countries within regions. We find trackers that focus on specific regions and countries, and some that are hosted in countries outside their expected target tracking domain. Given the differences in regulatory regimes between jurisdictions, we believe this analysis sheds light on the geographical properties of this ecosystem and on the problems that these may pose to our ability to track and manage the different data silos that now store personal data about us all.


title: DeFlaker: automatically detecting flaky tests

abstract: Dynamic taint analysis is a well-known information flow analysis problem with many possible applications. Taint tracking allows for analysis of application data flow by assigning labels to data, and then propagating those labels through data flow. Taint tracking systems traditionally compromise among performance, precision, soundness, and portability. Performance can be critical, as these systems are often intended to be deployed to production environments, and hence must have low overhead. To be deployed in security-conscious settings, taint tracking must also be sound and precise. Dynamic taint tracking must be portable in order to be easily deployed and adopted for real world purposes, without requiring recompilation of the operating system or language interpreter, and without requiring access to application source code. ::: We present Phosphor, a dynamic taint tracking system for the Java Virtual Machine (JVM) that simultaneously achieves our goals of performance, soundness, precision, and portability. Moreover, to our knowledge, it is the first portable general purpose taint tracking system for the JVM. We evaluated Phosphor's performance on two commonly used JVM languages (Java and Scala), on two successive revisions of two commonly used JVMs (Oracle's HotSpot and OpenJDK's IcedTea) and on Android's Dalvik Virtual Machine, finding its performance to be impressive: as low as 3% (53% on average; 220% at worst) using the DaCapo macro benchmark suite. This paper describes our approach toward achieving portable taint tracking in the JVM.


title: Influence of a passive sonic irrigation system on the elimination of bacteria from root canal systems: a clinical study.

abstract: INTRODUCTION ::: The present investigation evaluated the ability of a new passive sonic irrigation (sonic group) system (EndoActivator) to eliminate cultivable bacteria from root canals in vivo and compared it with that of standard syringe irrigation (control group). ::: ::: ::: METHODS ::: Data were obtained by using bacteriologic sampling of root canals treated by endodontic residents. Sampling results from 1 session of treatment were then compared with results obtained after intervisit calcium hydroxide disinfection and a second session of treatment. ::: ::: ::: RESULTS ::: There was no significant difference in the ability of sonic group and control group to eliminate cultivable bacteria from root canals (P > .05). A second session and intervisit calcium hydroxide disinfection were able to eliminate cultivable bacteria from significantly more teeth than a single session of treatment (P < .05). ::: ::: ::: CONCLUSIONS ::: These in vivo results strengthen the case for a multi-visit approach to the treatment of apical periodontitis.


title: A Wavelet-Based Mammographic Image Denoising and Enhancement with Homomorphic Filtering

abstract: Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. This paper describes a new method for mammographic image enhancement and denoising based on wavelet transform and homomorphic filtering. The mammograms are acquired from the Faculty of Medicine of the University of Akdeniz and the University of Istanbul in Turkey. Firstly wavelet transform of the mammograms is obtained and the approximation coefficients are filtered by homomorphic filter. Then the detail coefficients of the wavelet associated with noise and edges are modeled by Gaussian and Laplacian variables, respectively. The considered coefficients are compressed and enhanced using these variables with a shrinkage function. Finally using a proposed adaptive thresholding the fine details of the mammograms are retained and the noise is suppressed. The preliminary results of our work indicate that this method provides much more visibility for the suspicious regions.


title: "Got You!": Automatic Vandalism Detection in Wikipedia with Web-based Shallow Syntactic-Semantic Modeling

abstract: Discriminating vandalism edits from non-vandalism edits in Wikipedia is a challenging task, as ill-intentioned edits can include a variety of content and be expressed in many different forms and styles. Previous studies are limited to rule-based methods and learning based on lexical features, lacking in linguistic analysis. In this paper, we propose a novel Web-based shallow syntactic-semantic modeling method, which utilizes Web search results as resource and trains topic-specific n-tag and syntactic n-gram language models to detect vandalism. By combining basic task-specific and lexical features, we have achieved high F-measures using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems.


title: A 900-MHz/1.8-GHz CMOS Receiver for Dual-Band Applications

abstract: This dual-band CMOS receiver for GSM and DCS1800 applications incorporates hardware sharing between two paths to reduce the number of off-chip components. The receiver is an extension of the Weaver single-sideband architecture. The Weaver architecture performs two consecutive quadrature downconversion operations on the signal and the image such that if the final outputs are added, the signal is obtained and the image is suppressed and if they are subtracted, the reverse occurs. The fact that the addition or subtraction of the outputs in the Weaver architecture can select or reject two bands symmetrically located around a local oscillator (LO) frequency provides the foundation for the work reported here.


title: Training a Natural Language Generator From Unaligned Data

abstract: We present a novel syntax-based natural language generation system that is trainable from unaligned pairs of input meaning representations and output sentences. It is divided into sentence planning, which incrementally builds deep-syntactic dependency trees, and surface realization. Sentence planner is based on A* search with a perceptron ranker that uses novel differing subtree updates and a simple future promise estimation; surface realization uses a rule-based pipeline from the Treex NLP toolkit. Our first results show that training from unaligned data is feasible, the outputs of our generator are mostly fluent and relevant.


title: A multidimensional approach for CSR assessment: The importance of the stakeholder perception

abstract: ''If a tree falls in the forest and no one is around to hear it, does it make a sound?'' and, paraphrasing the proverbial philosophy question, if a company has a strong CSR commitment but nobody recognizes it, does it produce any benefits? Business returns from corporate social responsibility (CSR) practices, such as customers loyalty and company reputation, depend heavily on how stakeholders perceive the company social behavior, making the measure of stakeholder perception a key issue in the process of CSR assessment. In this paper the analysis of CSR activities, as perceived by stakeholders, is realized utilizing global reporting initiative (GRI) indicators structured under balanced scorecard (BSC) perspectives and sustainability dimensions. We utilize a multi-criteria approach combined with fuzzy linguistic variables, in the variation of the 2-tuple, creating a hierarchy of CSR components with the purpose of integrating financial and non-financial sustainability dimensions and strategic perspectives. The hierarchy provides a multidimensional model that allows to evaluate the multifaceted social behavior of a company: the same company can be perceived simultaneously as responsible or irresponsible depending on the considered dimension and perspective.


title: Rectangular Slot Antenna with Patch Stub for Ultra Wideband Applications and Phased Array Systems

abstract: This paper presents a coplanar waveguide fed rectangular slot antenna tuned by a patch stub. The presented antenna has 98% impedance bandwidth, and 6 dB average gain. The antenna can be used in phased array applications with more than 61% usable bandwidth.


title: Simultaneous Estimation of Chords and Musical Context From Audio

abstract: Chord labels provide a concise description of musical harmony. In pop and jazz music, a sequence of chord labels is often the only written record of a song, and forms the basis of so-called lead sheets. We devise a fully automatic method to simultaneously estimate from an audio waveform the chord sequence including bass notes, the metric positions of chords, and the key. The core of the method is a six-layered dynamic Bayesian network, in which the four hidden source layers jointly model metric position, key, chord, and bass pitch class, while the two observed layers model low-level audio features corresponding to bass and treble tonal content. Using 109 different chords our method provides substantially more harmonic detail than previous approaches while maintaining a high level of accuracy. We show that with 71% correctly classified chords our method significantly exceeds the state of the art when tested against manually annotated ground truth transcriptions on the 176 audio tracks from the MIREX 2008 Chord Detection Task. We introduce a measure of segmentation quality and show that bass and meter modeling are especially beneficial for obtaining the correct level of granularity.


title: Rumor Source Identification in Social Networks with Time-Varying Topology

abstract: Identifying rumor sources in social networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of social networks and the ongoing dynamic processes challenge our traditional source identification techniques that are considered in static networks. In this paper, we borrow an idea from criminology and propose a novel method to overcome the challenges. First, we reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, we adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification. Third, to determine the real source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can provide the largest ML estimate is considered as the real source. The evaluations are carried out on real social networks with time-varying topology. The experiment results show that our method can reduce 60 - 90 percent of the source seeking area in various time-varying social networks. The results further indicate that our method can accurately identify the real source, or an individual who is very close to the real source. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in time-varying social networks.


title: Mental Training Affects Distribution of Limited Brain Resources

abstract: The information processing capacity of the human mind is limited, as is evidenced by the so-called “attentional-blink” deficit: When two targets (T1 and T2) embedded in a rapid stream of events are presented in close temporal proximity, the second target is often not seen. This deficit is believed to result from competition between the two targets for limited attentional resources. Here we show, using performance in an attentional-blink task and scalp-recorded brain potentials, that meditation, or mental training, affects the distribution of limited brain resources. Three months of intensive mental training resulted in a smaller attentional blink and reduced brain-resource allocation to the first target, as reflected by a smaller T1-elicited P3b, a brain-potential index of resource allocation. Furthermore, those individuals that showed the largest decrease in brain-resource allocation to T1 generally showed the greatest reduction in attentional-blink size. These observations provide novel support for the view that the ability to accurately identify T2 depends upon the efficient deployment of resources to T1. The results also demonstrate that mental training can result in increased control over the distribution of limited brain resources. Our study supports the idea that plasticity in brain and mental function exists throughout life and illustrates the usefulness of systematic mental training in the study of the human mind.


title: Pedestrian detection aided by deep learning semantic tasks

abstract: Deep learning methods have achieved great successes in pedestrian detection, owing to its ability to learn discriminative features from raw pixels. However, they treat pedestrian detection as a single binary classification task, which may confuse positive with hard negative samples (Fig.1 (a)). To address this ambiguity, this work jointly optimize pedestrian detection with semantic tasks, including pedestrian attributes (e.g. ‘carrying backpack’) and scene attributes (e.g. ‘vehicle’, ‘tree’, and ‘horizontal’). Rather than expensively annotating scene attributes, we transfer attributes information from existing scene segmentation datasets to the pedestrian dataset, by proposing a novel deep model to learn high-level features from multiple tasks and multiple data sources. Since distinct tasks have distinct convergence rates and data from different datasets have different distributions, a multi-task deep model is carefully designed to coordinate tasks and reduce discrepancies among datasets. Extensive evaluations show that the proposed approach outperforms the state-of-the-art on the challenging Caltech [9] and ETH [10] datasets where it reduces the miss rates of previous deep models by 17 and 5.5 percent, respectively.


title: A Low-Cost Gate Driver Design Using Bootstrap Capacitors for Multilevel MOSFET Inverters

abstract: Multilevel inverters require a large number of power semiconductors. The gate driver for each power semiconductor requires its own floating or isolated DC voltage source. Traditional methods on meeting the requirement are expensive and bulky. This paper presents a gate driver design for floating voltage source type multilevel inverters. Bootstrap capacitors are used to form the floating voltage sources in the design, which allows a single DC power supply to be used by all the gate drivers. Specially configured diode plus the proper charging cycles maintain adequate capacitor voltage levels. Such a simple and low cost solution allows user to utilize the easy accessible single-channel gate drivers for multilevel inverter applications without the extra cost on the bulky isolated DC power supplies for each gate driver. A prototype 3-cell 8-level floating voltage source inverter using the method illustrates the technique


title: Human-aware task planning for mobile robots

abstract: Robots that share their workspace with people, like household or service robots, need to take into account the presence of humans when planning their actions. In this paper, we present a framework for human-aware planning that would make the robots capable of performing their tasks without interfering with the user in his every day life. We focus in particular on the core module of the framework, a human-aware planner that generates a sequence of actions for a robot, taking into account the state of the environment and the goals of the robot, together with a set of forecasted possible plans of the human. We describe the planner and its relations to other system components like a plan recognizer, and present a series of experiments performed with a household robot in a small apartment.


title: A comparison of cellular vehicle-to-everything and dedicated short range communication

abstract: Vehicle to Everything (V2X) communication, which involves vehicles disseminating information about its type, location and movement are known to help increasing road safety. At physical layer, there are two main solutions: 1) Dedicated Short Range Communication, which is based on IEEE 802.11p standard and 2) Cellular V2X, which is a recent development in 3GPP LTE standard to support V2X use case. In this paper, we provide our comparison of the performance of the two aforementioned technologies at both link level and system level. Based on this study, one can see that Cellular V2X provides significant improvement over Dedicated Short Range Communication in terms of communication range, without suffering in other aspects. This improvement is equivalent to longer reaction time and more lives saved in the road.