title: Microwave ablation with cap-choke antenna: Result in computer simulation

abstract: This paper reports a novel three-dimensional finite element analysis of coaxial antenna for hepatic microwave ablation. We analyses the temperature distribution pattern and lesion size on hepatic cancer of cap-choke antenna at frequency of 2.45GHz. We set duration time for microwave ablation in the range 60s, 100s, 200s and 300s. We set the potential difference between the inner and an outer conductor was 50V. From our simulation results, if we considered the region where temperature exceeds 50 °C, the threshold for successful hepatic ablation. The temperature distributions that all time had similar characteristics but, there are differently at the lesion size. When we set the ablation time was 300s the lesion size was larger, with a lesion width of approximately 26 mm, compared to 12 mm for set the ablation time was 60s. If we considered the lesion volumes at the ablation time was 300s this case can be induced the highest temperature in cancer tissue and maximum ablation cancer tissue (10.22cm3). In addition, we also set the boundary conditions on temperature control mode which maximum temperature control within hepatic cancer at 96°C, duration time at 300s. From the simulation results, the volume of lesion size was 4.25 cm3, lesion width and depth approximately 18mm, 27mm. respectively


title: Predicting anxiety: The role of experiential avoidance and anxiety sensitivity

abstract: Anxiety sensitivity (AS), the tendency to fear arousal-related body sensations based on beliefs that they are harmful, is a strong psychological risk factor for development of anxiety psychopathology; however, in most studies AS explains only a portion of the variability in anxiety symptoms. Recent theoretical and research work has suggested that experiential avoidance (EA), unwillingness to endure unpleasant internal experiences (e.g., thoughts, emotions, memories), is related to anxiety disorders. The current study examined independent contributions of EA and AS in the prediction of anxiety symptoms in a sample of 42 adults with DSM-IV anxiety disorders. Participants completed measures of AS, EA, anxiety, and depression. Correlational analyses indicated associations between AS, EA, and anxiety, yet more conservative regression analyses indicated that the Physical Concerns dimension of AS predicted anxiety symptom severity independently of EA. Theoretical and treatment implications of the results are discussed.


title: Distributed Data Mining with Differential Privacy

abstract: With recent advances in communication and data storage technology, an explosive amount of information is being collected and stored in the Internet. Even though such vast amount of information presents great opportunities for knowledge discovery, organizations might not want to share their data due to legal or competitive reasons. This posts the challenge of mining knowledge while preserving privacy. Current efficient privacy-preserving data mining algorithms are based on an assumption that it is acceptable to release all the intermediate results during the data mining operations. However, it has been shown that such intermediate results can still leak private information. In this work, we use differential privacy to quantitatively limit such information leak. Differential privacy is a newly emerged privacy definition that is capable of providing strong measurable privacy guarantees. We propose Secure group Differential private Query(SDQ), a new algorithm that combines techniques from differential privacy and secure multiparty computation. Using decision tree induction as a case study, we show that SDQ can achieve stronger privacy than current efficient secure multiparty computation approach, and better accuracy than current differential privacy approach while maintaining efficiency.


Prevalence of Posttraumatic Stress Symptomatology and Distribution of Attachment Patterns 1 and 5 Months after a Terrorist Attack


title: Automatic people tagging for expertise profiling in . . .

abstract: In an enterprise search setting, there is a class of queries for which people, rather than documents, are desirable answers. However, presenting users with just a list of names of knowledgeable employees without any description of their expertise may lead to confusion, lack of trust in search results, and abandonment of the search engine. At the same time, building a concise meaningful description for a person is not a trivial summarization task. In this paper, we propose a solution to this problem by automatically tagging people for the purpose of profiling their expertise areas in the scope of the enterprise where they are employed. We address the novel task of automatic people tagging by using a machine learning algorithm that combines evidence that a certain tag is relevant to a certain employee acquired from different sources in the enterprise. We experiment with the data from a large distributed organization, which also allows us to study sources of expertise evidence that have been previously overlooked, such as personal click-through history. The evaluation of the proposed methods shows that our technique clearly outperforms state of the art approaches.


title: Potential Vulnerabilities of Neuronal Reward, Risk, and Decision Mechanisms to Addictive Drugs

abstract: How do addictive drugs hijack the brain's reward system? This review speculates how normal, physiological reward processes may be affected by addictive drugs. Addictive drugs affect acute responses and plasticity in dopamine neurons and postsynaptic structures. These effects reduce reward discrimination, increase the effects of reward prediction error signals, and enhance neuronal responses to reward-predicting stimuli, which may contribute to compulsion. Addictive drugs steepen neuronal temporal reward discounting and create temporal myopia that impairs the control of drug taking. Tonically enhanced dopamine levels may disturb working memory mechanisms necessary for assessing background rewards and thus may generate inaccurate neuronal reward predictions. Drug-induced working memory deficits may impair neuronal risk signaling, promote risky behaviors, and facilitate preaddictive drug use. Malfunctioning adaptive reward coding may lead to overvaluation of drug rewards. Many of these malfunctions may result in inadequate neuronal decision mechanisms and lead to choices biased toward drug rewards.


title: STUDY OF ABRASION OF RUBBER MATERIALS BY EXPERIMENTAL DESIGN, RESPONSE SURFACE AND ARTIFICIAL NEURAL NETWORK MODELING

abstract: Iranian Journal of Polymer Science and Technology Vol. 28, No. 3, 197-209 August-September 2015 ISSN: 1016-3255 Online ISSN: 2008-0883


title: Speech activity detection on youtube using deep neural networks.

abstract: Speech activity detection (SAD) is an important first step in speech processing. Commonly used methods (e.g., frame-level classification using gaussian mixture models (GMMs)) work well under stationary noise conditions, but do not generalize well to domains such as YouTube, where videos may exhibit a diverse range of environmental conditions. One solution is to augment the conventional cepstral features with additional, hand-engineered features (e.g., spectral flux, spectral centroid, multiband spectral entropies) which are robust to changes in environment and recording condition. An alternative approach, explored here, is to learn robust features during the course of training using an appropriate architecture such as deep neural networks (DNNs). In this paper we demonstrate that a DNN with input consisting of multiple frames of mel frequency cepstral coefficients (MFCCs) yields drastically lower frame-wise error rates (19.6%) on YouTube videos compared to a conventional GMM based system (40%).


title: Diversity of fall armyworm, Spodoptera frugiperda and their gut bacterial community in Kenya

abstract: Background ::: The invasive fall armyworm, Spodoptera frugiperda (J.E. Smith) is a polyphagous pest that causes widespread damage particularly to maize and sorghum in Africa. The microbiome associated with S. frugiperda could play a role in the insects' success and adaptability. However, bacterial communities in S. frugiperda remain poorly studied. ::: ::: ::: Methods ::: We investigated the composition, abundance and diversity of microbiomes associated with larval and adult specimens of S. frugiperda collected from four maize growing regions in Kenya through high throughput sequencing of the bacterial 16S rRNA gene. The population structure of S. frugiperda in Kenya was assessed through amplification of the mitochondrial cytochrome oxidase subunit I gene. ::: ::: ::: Results ::: We identified Proteobacteria and Firmicutes as the most dominant bacterial phyla and lesser proportions of Bacteroidetes and Actinobacteria. We also observed differences in bacterial microbiome diversity between larvae and adults that are a likely indication that some prominent larval bacterial groups are lost during metamorphosis. However, several bacterial groups were found in both adults and larvae suggesting that they are transmitted across developmental stages. Reads corresponding to several known entomopathogenic bacterial clades as well as the fungal entomopathogen, Metarhizium rileyi, were observed. Mitochondrial DNA haplotyping of the S. frugiperda population in Kenya indicated the presence of both "Rice" and "Corn" strains, with a higher prevalence of the "Rice" strain.


title: Taxonomy induction based on a collaboratively built knowledge repository

abstract: The category system in Wikipedia can be taken as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexico-syntactic matching. The result is a large scale taxonomy. For evaluation we propose a method which (1) manually determines the quality of our taxonomy, and (2) automatically compares its coverage with ResearchCyc, one of the largest manually created ontologies, and the lexical database WordNet. Additionally, we perform an extrinsic evaluation by computing semantic similarity between words in benchmarking datasets. The results show that the taxonomy compares favorably in quality and coverage with broad-coverage manually created resources.


title: Day-to-Day Travel Time Trends and Travel Time Prediction from Loop Detector Data

abstract: An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that r...


title: Stress and anxiety detection using facial cues from videos

abstract: Abstract This study develops a framework for the detection and analysis of stress/anxiety emotional states through video-recorded facial cues. A thorough experimental protocol was established to induce systematic variability in affective states (neutral, relaxed and stressed/anxious) through a variety of external and internal stressors. The analysis was focused mainly on non-voluntary and semi-voluntary facial cues in order to estimate the emotion representation more objectively. Features under investigation included eye-related events, mouth activity, head motion parameters and heart rate estimated through camera-based photoplethysmography. A feature selection procedure was employed to select the most robust features followed by classification schemes discriminating between stress/anxiety and neutral states with reference to a relaxed state in each experimental phase. In addition, a ranking transformation was proposed utilizing self reports in order to investigate the correlation of facial parameters with a participant perceived amount of stress/anxiety. The results indicated that, specific facial cues, derived from eye activity, mouth activity, head movements and camera based heart activity achieve good accuracy and are suitable as discriminative indicators of stress and anxiety.


title: Shakeout: a new regularized deep neural network training scheme

abstract: Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice. We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR-10 show that Shakeout deals with over-fitting effectively.


title: Statistics-free sports prediction

abstract: We use a simple machine learning model, logistically-weighted regularized linear least squares regression, in order to predict baseball, basketball, football, and hockey games. We do so using only the thirty-year record of which visiting teams played which home teams, on what date, and what the final score was. No real "statistics" are used. The method works best in basketball, likely because it is high-scoring and has long seasons. It works better in football and hockey than in baseball, but in baseball the predictions are closer to a theoretical optimum. The football predictions, while good, can in principle be made much better, and the hockey predictions can be made somewhat better. These findings tells us that in basketball, most statistics are subsumed by the scores of the games, whereas in football, further study of game and player statistics is necessary to predict games as well as can be done. Baseball and hockey lie somewhere in between.


title: Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform

abstract: This paper proposes a Green Light Optimized Speed Advisory (GLOSA) application implementation in a typical reference area, and presents the results of its performance analysis using an integrated cooperative ITS simulation platform. Our interest was to monitor the impacts of GLOSA on fuel and traffic efficiency by introducing metrics for average fuel consumption and average stop time behind a traffic light, respectively. For gathering the results we implemented a traffic scenario defining a single route through an urban area including two traffic lights. The simulations are varied for different penetration rates of GLOSA-equipped vehicles and traffic density. Our results indicate that GLOSA systems could improve fuel consumption and reduce traffic congestion in junctions.


title: Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis

abstract: Detecting and recognizing human faces automatically in digital images strongly enhance content-based video indexing systems. In this paper, a novel scheme for human faces detection in color images under nonconstrained scene conditions, such as the presence of a complex background and uncontrolled illumination, is presented. Color clustering and filtering using approximations of the YCbCr and HSV skin color subspaces are applied on the original image, providing quantized skin color regions. A merging stage is then iteratively performed on the set of homogeneous skin color regions in the color quantized image, in order to provide a set of potential face areas. Constraints related to shape and size of faces are applied, and face intensity texture is analyzed by performing a wavelet packet decomposition on each face area candidate in order to detect human faces. The wavelet coefficients of the band filtered images characterize the face texture and a set of simple statistical deviations is extracted in order to form compact and meaningful feature vectors. Then, an efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage.


title: How to compute variance-based sensitivity indicators with your spreadsheet software

abstract: The use of sensitivity indicators is explicitly recommended by authorities like the EC, the US EPA and others in model valuation and audit. In this note, we want to draw the attention to a numerically efficient algorithm that computes first order global sensitivity effects from given data using a discrete cosine transformation.


title: A neural network with a case based dynamic window for stock trading prediction

abstract: Stock forecasting involves complex interactions between market-influencing factors and unknown random processes. In this study, an integrated system, CBDWNN by combining dynamic time windows, case based reasoning (CBR), and neural network for stock trading prediction is developed and it includes three different stages: (1) screening out potential stocks and the important influential factors; (2) using back propagation network (BPN) to predict the buy/sell points (wave peak and wave trough) of stock price and (3) adopting case based dynamic window (CBDW) to further improve the forecasting results from BPN. The system developed in this research is a first attempt in the literature to predict the sell/buy decision points instead of stock price itself. The empirical results show that the CBDW can assist the BPN to reduce the false alarm of buying or selling decisions. Nine different stocks with different trends, i.e., upward, downward and steady, are studied and one individual stock (AUO) will be studied as case example. The rates of return for upward, steady, and downward trend stocks are higher than 93.57%, 37.75%, and 46.62%, respectively. These results are all very promising and better than using CBR or BPN alone.


title: Measuring emotional valence during interactive experiences: boys at video game play

abstract: This paper describes the use of facial electromyography (EMG) as a measure of positive and negative emotional valence during interactive experience. Thirteen boys played a car racing video game on an Xbox platform while facial EMG data were collected. Through video review positive and negative events during play were identified. The zygomaticus muscle EMG, which controls smiling, was found to be significantly greater during positive events as compared to negative. The corrugator muscle EMG, which controls frowning, was found to be significantly greater during negative events. The results of this study demonstrate that positive valence can be measured during interactive experiences with physiologic measures. This study also found that the corrugator EMG can still measure negative valence during high intensity interactive play in spite of the confounding factor of mental effort. These methods appear useful for associating the player's emotion with game events, and could be applied to HCI in general.


title: A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern

abstract: In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation (S) channels in the HSV color space has been explored which was not done earlier. We have quantized the H channel into a number of bins and performed the voting with saturation values and vice versa by following a principle similar to that of the HOG descriptor, where orientation of the gradient is quantized into a certain number of bins and voting is done with gradient magnitude. This helps us to study the nature of variation of saturation with variation in Hue and nature of variation of Hue with the variation in saturation. The texture component of our descriptor considers the co-occurrence relationship between the pixels symmetric about both the diagonals of a 3 × 3 window. Our work is inspired from the work done by Dubey et al. (IEEE Signal Process Lett 22(9):1215–1219, [2015]). These two components, viz. color and texture information individually perform better than existing texture and color descriptors. Moreover, when concatenated the proposed descriptors provide a significant improvement over existing descriptors for content base color image retrieval. The proposed descriptor has been tested for image retrieval on five databases, including texture image databases—MIT-VisTex database and Salzburg texture database and natural scene databases Corel 1K, Corel 5K and Corel 10K. The precision and recall values experimented on these databases are compared with some state-of-art local patterns. The proposed method provided satisfactory results from the experiments.


title: Secure the internet of things with challenge response authentication in fog computing

abstract: As the Internet of Things (IoT) continues to grow, there arises concerns and challenges with regard to the security and privacy of the IoT system. In this paper, we propose a FOg CompUting-based Security (FOCUS) system to address the security challenges in the IoT. The proposed FOCUS system leverages the virtual private network (VPN) to secure the access channel to the IoT devices. In addition, FOCUS adopts a challenge-response authentication to protect the VPN server against distributed denial of service (DDoS) attacks, which can further enhance the security of the IoT system. FOCUS is implemented in fog computing that is close to the end users, thus achieving a fast and efficient protection. We demonstrate FOCUS in a proof-of-concept prototype, and conduct experiments to evaluate its performance. The results show that FOCUS can effectively filter out malicious attacks with a very low response latency.


title: Towards proactive SDN-controller attack and failure resilience

abstract: SDN networks rely mainly on a set of software defined modules, running on generic hardware platforms, and managed by a central SDN controller. The tight coupling and lack of isolation between the controller and the underlying host limit the controller resilience against host-based attacks and failures. That controller is a single point of failure and a target for attackers. “Linux-containers” is a successful thin virtualization technique that enables encapsulated, host-isolated execution-environments for running applications. In this paper we present PAFR, a controller sandboxing mechanism based on Linux-containers. PAFR enables controller/host isolation, plug-and-play operation, failure-and-attack-resilient execution, and fast recovery. PAFR employs and manages live remote checkpointing and migration between different hosts to evade failures and attacks. Experiments and simulations show that the frequent employment of PAFR's live-migration minimizes the chance of successful attack/failure with limited to no impact on network performance.


title: Action understanding and active inference

abstract: We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference.


title: Active visual SLAM for robotic area coverage: Theory and experiment

abstract: This paper reports on an integrated navigation algorithm for the visual simultaneous localization and mapping SLAM robotic area coverage problem. In the robotic area coverage problem, the goal is to explore and map a given target area within a reasonable amount of time. This goal necessitates the use of minimally redundant overlap trajectories for coverage efficiency; however, visual SLAM's navigation estimate will inevitably drift over time in the absence of loop closures. Therefore, efficient area coverage and good SLAM navigation performance represent competing objectives. To solve this decision-making problem, we introduce perception-driven navigation, an integrated navigation algorithm that automatically balances between exploration and revisitation using a reward framework. This framework accounts for SLAM localization uncertainty, area coverage performance, and the identification of good candidate regions in the environment for visual perception. Results are shown for both a hybrid simulation and real-world demonstration of a visual SLAM system for autonomous underwater ship hull inspection.


Learning Word Meta-Embeddings by Autoencoding


title: An Ontology-Based Approach for Mining Radicalization Indicators from Online Messages

abstract: Detecting radicalization on social networks is crucial to the fight against violent extremism and terrorism. In most cases, online radicalization has clear warning indicators that can be detected at the early stages of the radicalization process. In this paper, we focus on mining radicalization indicators from online messages by exploiting structured domain knowledge. More precisely, we propose an approach to automatically annotate social messages with concepts from a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. We conducted a set of experiments on a sample extracted from a public dataset that contains radicalized individuals along with their social messages (i.e. Tweets). Obtained results show the effectiveness of our approach compared to a baseline method.


title: The gcd-sum function

abstract: The gcd-sum is an arithmetic function defined as the sum of the gcd’s of the first n integers with n : g(n) = P n=1 (i,n). The function arises in deriving asymptotic estimates for a lattice point counting problem. The function is multiplicative, and has polynomial growth. Its Dirichlet series has a compact representation in terms of the Riemann zeta function. Asymptotic forms for values of partial sums of the Dirichlet series at real values are derived, including estimates for error terms.


Euthanasia and physician-assisted suicide: focus on the data


title: FPGA-realization of a novel sliding mode observer for a sensorless PMSM drive

abstract: Based on the FPGA (Field Programmable Gate Array), this paper presents an implementation of a sensorless PMSM speed control system which the rotor speed and rotor position are estimated by novel SMO method. The estimated rotor position can be sent back to the current loop for vector control and the estimated rotor speed can be fed back to the speed loop for closed-loop speed control. In realization, the digital hardware IP (Intellectual Property) regarding as the speed controller, the current vector controller and novel SMO algorithm are described by VHDL. Finally, to confirm the correctness and effectiveness of the proposed sensorless speed control IP (Intellectual Property), a design procedure from ModelSim/Simulink co-simulation to FPGA realization is applied and an experimental PMSM drive system is demonstrated.


title: Invariant characterization of DOVID security features using a photometric descriptor

abstract: Diffractive Optically Variable Image Devices (DOVIDs) are popular security features used to protect security documents such as banknotes, ID cards, passports, etc. Checking authenticity of these security features on both user as well as forensic level remains a challenging task, requiring sophisticated hardware tools and expert knowledge. Recently, we proposed a technique exploiting a large-scale photometric behavior of DOVIDs in order to discriminate denominations and detect counterfeits. Here we investigate invariance properties of the proposed method and demonstrate its robustness against various common perturbations, which may have negative impact on the acquisition quality in practice. Presented results show a great potential of this approach primarily for security and forensic purposes, but also for other applications, where automated inspection of DOVIDs is of interest.


title: Domain Adaptation for Statistical Machine Translation with Domain Dictionary and Monolingual Corpora

abstract: Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to perform domain adaptation for statistical machine translation, where in-domain bilingual corpora do not exist. This method first uses out-of-domain corpora to train a baseline system and then uses in-domain translation dictionaries and in-domain monolingual corpora to improve the in-domain performance. We propose an algorithm to combine these different resources in a unified framework. Experimental results indicate that our method achieves absolute improvements of 8.16 and 3.36 BLEU scores on Chinese to English translation and English to French translation respectively, as compared with the baselines using only out-of-domain corpora.


title: Landmark Free Face Attribute Prediction

abstract: Face attribute prediction in the wild is important for many facial analysis applications, yet it is very challenging due to ubiquitous face variations. In this paper, we address face attribute prediction in the wild by proposing a novel method, lAndmark Free Face AttrIbute pRediction (AFFAIR). Unlike traditional face attribute prediction methods that require facial landmark detection and face alignment, AFFAIR uses an end-to-end learning pipeline to jointly learn a hierarchy of spatial transformations that optimize facial attribute prediction with no reliance on landmark annotations or pre-trained landmark detectors. AFFAIR achieves this through simultaneously: 1) learning a global transformation which effectively alleviates negative effect of global face variation for the following attribute prediction tailored for each face; 2) locating the most relevant facial part for attribute prediction; and 3) aggregating the global and local features for robust attribute prediction. Within AFFAIR, a new competitive learning strategy is developed that effectively enhances global transformation learning for better attribute prediction. We show that with zero information about landmarks, AFFAIR achieves the state-of-the-art performance on three face attribute prediction benchmarks, which simultaneously learns the face-level transformation and attribute-level localization within a unified framework.


title: Probabilistic Elastic Matching for Pose Variant Face Verification

abstract: Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic matching method. We take a part based representation by extracting local features (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each feature with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of all face images in the training corpus. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms. Each Gaussian component builds correspondence of a pair of features to be matched between two faces/face tracks. For face verification, we train an SVM on the vector concatenating the difference vectors of all the feature pairs to decide if a pair of faces/face tracks is matched or not. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that our method outperforms the state-of-the-art in the most restricted protocol on Labeled Face in the Wild (LFW) and the YouTube video face database by a significant margin.


title: Towards Characterisation of Music via Rhythmic Patterns

abstract: A central problem in music information retrieval is finding suitable representations which enable efficient and accurate computation of musical similarity and identity. Low level audio features are ideal for calculating identity, but are of limited use for similarity measures, as many aspects of music can only be captured by considering high level features. We present a new method of characterising music by typical bar-length rhythmic patterns which are automatically extracted from the audio signal, and demonstrate the usefulness of this representation by its application in a genre classification task. Recent work has shown the importance of tempo and periodicity features for genre recognition, and we extend this research by employing the extracted temporal patterns as features. Standard classification algorithms are utilised to discriminate 8 classes of Standard and Latin ballroom dance music (698 pieces). Although pattern extraction is error-prone, and patterns are not always unique to a genre, classification by rhythmic pattern alone achieves up to 50% correctness (baseline 16%), and by combining with other features, a classification rate of 96% is obtained.


title: A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study

abstract: The main real-coded genetic algorithm (RCGA) research effort has been spent on developing efficient crossover operators. This study presents a taxonomy for this operator that groups its instances in different categories according to the way they generate the genes of the offspring from the genes of the parents. The empirical study of representative crossovers of all the categories reveals concrete features that allow the crossover operator to have a positive influence on RCGA performance. They may be useful to design more effective crossover models. © 2003 Wiley Periodicals, Inc. Genetic algorithms (GAs) are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. 1‐3 Under their initial formulation, the search space solutions are coded using the binary alphabet; however, other coding types have been taken into account for the representation issue such as real coding. The real coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A chromosome is a vector of floating point numbers in which their size is kept the same as the length of the vector, which is the solution to the problem. GAs based on real-number representation are called real-coded GAs


title: Making Computers Laugh: Investigations In Automatic Humor Recognition

abstract: Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this paper, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines.


title: Understanding Information Security Culture: A Conceptual Framework

abstract: The importance of establishing an information security culture in an organization has become a well established idea. The aim of such a culture is to address the various human factors that can affect an organization’s overall information security efforts. However, understanding both the various elements of an information security culture, as well as the relationships between these elements, can still be problematic. Schein’s definition of a corporate culture is often used to aid understanding of an information security culture. This paper briefly introduces Schein’s model. It then incorporates the important role knowledge plays in information security into this definition. Finally, a conceptual framework to aid understanding of the interactions between the various elements of such a culture, is presented. This framework is explained by means of illustrative examples, and it is suggested that this conceptual framework can be a useful aid to understanding information security culture.


title: Effects of neonatal dexamethasone treatment on hypothalamo-pituitary adrenal axis and immune system of the rat

abstract: Abstract Synthetic glucocorticoids (GC) are widely used drugs, also in the prevention of diseases that occur in the preterm newborn. Previously we have found that GC treatment of pregnant rats resulted in a persistent increase in the ratio of AVP over CRH in the mediobasal hypothalamus, and in an increased CD4/CD8 ratio in the thymus of the newborn. The objective of the present study was to investigate whether such effects were also seen after neonatal GC exposure, given in clinically-relevant doses. Dexamethasone-21-phosphate (DEX; 1.2 μ g/g BW, i.p.) was given at day 5 and day 7 after birth. At day 18, 33, and 48 after birth effects of GC on the HPA-axis, and on CD4 + and CD8 + T cells in thymus and spleen were examined. Neonatal DEX treatment temporarily increased ( p p p + CD8 − over CD4 − CD8 + thymocytes on day 18 after birth, but not on day 33 and day 48 after birth. In conclusion, neonatal DEX treatment has temporary effects on peptide expression in hypothalamic CRH neurons, and on thymocyte maturation. Apparently, neonatal exposure to GC affects potentially sensitive targets within the endocrine system and immune system but these alterations are reversible and readjusted during development.


title: Implementing ultra-high-value floating tunable CMOS resistors

abstract: A compact high-value ﬂoating resistor utilising PMOS devices in the subthreshold region is introduced. A test chip has been fabricated in 0.18 um CMOS technology to verify the proposed concept. This technique has been applied to design a reconﬁgurable sixth-order very-low cutoff-frequency MOSFET-C ﬁlter.


title: Automatic generation of software behavioral models

abstract: Dynamic analysis of software systems produces behavioral models that are useful for analysis, verification and testing. The main techniques for extracting models of functional behavior generate either models of constraints on data, usually in the form of Boolean expressions, or models of interactions between components, usually in the form of finite state machines. Both data and interaction models are useful for analyzing and verifying different aspects of software behavior, but none of them captures the complex interplay between data values and components interactions. Thus related analysis and testing techniques can miss important information. In this paper, we focus on the generation of models of relations between data values and component interactions, and we present GK-tail, a technique to automatically generate extended finite state machines (EFSMs) from interaction traces. EFSMs model the interplay between data values and component interactions by annotating FSM edges with conditions on data values. We show that EFSMs include details that are not captured by either Boolean expressions or (classic) FSM alone, and allow for more accurate analysis and verification than separate models, even if considered jointly.


title: Guidelines for an acceptable euthanasia procedure.

abstract: Abstract The Netherlands is one of the very few countries that has guidelines for the practice of euthanasia. Each year there are about 9700 explicit requests for euthanasia or physician-assisted suicide (EAS), of which approximately 3600 patients are agreed upon in The Netherlands. Other countries have criticized the Dutch policy concerning EAS. First of all, it has been suggested that palliative care in The Netherlands is not adequate and that euthanasia is often requested by patients with depression. Additionally, part of the criticism is based on the regulation of the euthanasia procedure in The Netherlands. This chapter describes the guidelines for the procedure for euthanasia in The Netherlands, and focuses on some of the practical problems and issues of euthanasia. Also, the current situation concerning euthanasia and physician-assisted suicide in The Netherlands is summarized and described.


title: Findings of the 2017 Conference on Machine Translation (WMT17)

abstract: This paper presents the results of the WMT17 shared tasks, which included ::: three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task.


Reduction of Ringing Losses in Flyback Converter by Using the RC-RCD Clamp Circuit


title: SPECTROPHOTOMETRIC DETERMINATION OF KETOPROFEN AND ITS APPLICATION IN PHARMACEUTICAL ANALYSIS

abstract: A new simple rapid and sensitive spectrophotometric method has been developed for the determina- tion of ketoprofen in pharmaceutical preparations. The method is based on the reaction of ketoprofen with an analytical reagent n Astra Phloxin FF n at pH 8.0 n 10.8 and followed by the extraction of formed ion associ- ate in toluene with spectrophotometric detection (it has an absorption maximum at 563 nm, e = 7.6 × 10 4 L×mol -1 ×cm -1 ). The calibration plot was linear from 0.8 n 16.0 µg×mL -1 of ketoprofen, and the detection limit was 0.037 µg×mL -1 .


title: Examining the Affects of Student Multitasking with Laptops during the Lecture

abstract: 1. INTRODUCTION Laptop computers are widely used in many college classrooms today (Weaver and Nilson, 2005); however, there is an ongoing debate regarding the purpose and value of laptop initiative programs that encourage or even require students to purchase laptops, and the role of laptops in classrooms. Although the use of laptops in the classroom has the potential to motivate and contribute to student learning (Efaw, Hampton, Martinez, Smith, 2004; Trimmel and Bachmann, 2004), they also have the potential to negatively impact student attention, motivation, student-teacher interactions, and academic achievement (Young, 2006; Meierdiercks, 2005). Previous research has shown that students who bring laptops to class often engage in electronic multitasking that involves switching their cognitive focus back and forth between tasks that are directly related to the lecture material and tasks that are not directly related to the lecture material (Fried, 2008; Hembrooke and Gay, 2003; Grace-Martin and Gay, 2001). Although many students may believe they can switch back and forth between different tasks with no serious consequences to their academic performance, multitasking has been shown to dramatically increase the number of memory errors and the processing time required to "learn" topics that involve a significant cognitive load (Rubenstein, Meyer, and Evans, 2001). Attempting to "learn" while engaged in multitasking behavior can result in the acquisition of less flexible knowledge that cannot be easily recalled and/or applied in new situations (Foerde, Knowlton, and Poldrack, 2006). Furthermore, it takes time and effort to refocus after switching from one task to another (Bailey and Konstan, 2006). It can be argued, that multitasking is a natural part of the modern classroom and work environments and students need to learn to multitask effectively--especially in today's high tech world. Research that investigates how students use laptops in the classroom and what affects laptop usage has on performance outcomes does exist, but there is a lack of research that focuses on the unstructured or unsanctioned use of computers in the classroom, that explicitly measures learning outcomes, and that incorporates actual use data (1). In general, multitasking has been shown to negatively impact productivity (Foerde, Knowlton, and Poldrack, 2006; Rubenstein, Meyer, and Evans, 2001); however, the affects of different types of computer-based multitasking behaviors in the classroom have not been measured and examined in detail to date. This paper presents the results of an exploratory study that investigates different types of student multitasking behavior while using laptop computers in an unstructured manner during class. A number of novel contributions are made. First, we collect both self reported laptop usage data and actual laptop usage data from spyware installed on student laptops. This allows us to directly measure student laptop use, and then compare student's actual usage to self-reported usage. Second, we categorize different types of software multitasking activities and identify which activities are performed most frequently and for how long. We then examine how different categories of distractive software activity impact class performance. We define distractive multitasking as tasks or activities where cognitive resources are used to process information that is not directly related to the course material. Productive multitasking is defined as tasks or activities that are directly related to completing a primary task associated with the course material. Finally, we introduce quantifiable metrics for measuring the frequency, duration, and extent of student multitasking behavior in class, and evaluate the impact this behavior has on academic performance. Three primary research questions are addressed. (1) How does the frequency of multitasking related to each multitasking category affect learning outcomes? …


title: Generative Adversarial Product Quantisation

abstract: Product Quantisation (PQ) has been recognised as an effective encoding technique for scalable multimedia content analysis. In this paper, we propose a novel learning framework that enables an end-to-end encoding strategy from raw images to compact PQ codes. The system aims to learn both PQ encoding functions and codewords for content-based image retrieval. In detail, we first design a trainable encoding layer that is pluggable into neural networks, so the codewords can be trained in back-forward propagation. Then we integrate it into a Deep Convolutional Generative Adversarial Network (DC-GAN). In our proposed encoding framework, the raw images are directly encoded by passing through the convolutional and encoding layers, and the generator aims to use the codewords as constrained inputs to generate full image representations that are visually similar to the original images. By taking the advantages of the generative adversarial model, our proposed system can produce high-quality PQ codewords and encoding functions for scalable multimedia retrieval tasks. Experiments show that the proposed architecture GA-PQ outperforms the state-of-the-art encoding techniques on three public image datasets.


title: A Neural Compositional Paradigm for Image Captioning

abstract: Mainstream captioning models often follow a sequential structure to generate cap- tions, leading to issues such as introduction of irrelevant semantics, lack of diversity in the generated captions, and inadequate generalization performance. In this paper, we present an alternative paradigm for image captioning, which factorizes the captioning procedure into two stages: (1) extracting an explicit semantic representation from the given image; and (2) constructing the caption based on a recursive compositional procedure in a bottom-up manner. Compared to conventional ones, our paradigm better preserves the semantic content through an explicit factorization of semantics and syntax. By using the compositional generation procedure, caption construction follows a recursive structure, which naturally fits the properties of human language. Moreover, the proposed compositional procedure requires less data to train, generalizes better, and yields more diverse captions.


title: Joint Global and Co-Attentive Representation Learning for Image-Sentence Retrieval

abstract: In image-sentence retrieval task, correlated images and sentences involve different levels of semantic relevance. However, existing multi-modal representation learning paradigms fail to capture the meaningful component relation on word and phrase level, while the attention-based methods still suffer from component-level mismatching and huge computation burden. We propose a Joint Global and Co-Attentive Representation learning method (JGCAR) for image-sentence retrieval. We formulate a global representation learning task which utilizes both intra-modal and inter-modal relative similarity to optimize the semantic consistency of the visual/textual component representations. We further develop a co-attention learning procedure to fully exploit different levels of visual-linguistic relations. We design a novel softmax-like bi-directional ranking loss to learn the co-attentive representation for image-sentence similarity computation. It is capable of discovering the correlative components and rectifying inappropriate component-level correlation to produce more accurate sentence-level ranking results. By joint global and co-attentive representation learning, the latter benefits from the former by producing more semantically consistent component representation, and the former also benefits from the latter by back-propagating the contextual information. Image-sentence retrieval is performed as a two-step process in the testing stage, inheriting advantages on both effectiveness and efficiency. Experiments show that JGCAR outperforms existing methods on MSCOCO and Flickr30K image-sentence retrieval tasks.


title: Autoencoders trained with relevant information: Blending Shannon and Wiener's perspectives

abstract: It is almost seventy years after the publication of Claude Shannon's “A Mathematical Theory of Communication” [1] and Norbert Wiener's “Extrapolation, Interpolation and Smoothing of Stationary Time Series” [2]. The pioneering works of Shannon and Wiener lay the foundation of communication, data storage, control, and other information technologies. This paper briefly reviews Shannon and Wiener's perspectives on the problem of message transmission over noisy channel and also experimentally evaluates the feasibility of integrating these two perspectives to train autoencoders close to the information limit. To this end, the principle of relevant information (PRI) is used and validated to optimally encode input imagery in the presence of noise.


title: Recommender Systems for Self-Actualization

abstract: Every day, we are confronted with an abundance of decisions that require us to choose from a seemingly endless number of choice options. Recommender systems are supposed to help us deal with this formidable task, but some scholars claim that these systems instead put us inside a "Filter Bubble" that severely limits our perspectives. This paper presents a new direction for recommender systems research with the main goal of supporting users in developing, exploring, and understanding their unique personal preferences.


title: On the Beauty and Usability of Tag Clouds

abstract: Tag clouds are text-based visual representations of a set of tags usually depicting tag importance by font size. Recent trends in social and collaborative software have greatly increased the popularity of this type of visualization. This paper proposes a family of novel algorithms for tag cloud layout and presents evaluation results obtained from an extensive user study and a technical evaluation. The algorithms address issues found in many common approaches, for example large whitespaces, overlapping tags and restriction to specific boundaries. The layouts computed by these algorithms are compact and clear, have small whitespaces and may feature arbitrary convex polygons as boundaries. The results of the user study and the technical evaluation enable designers to devise a combination of algorithm and parameters which produces satisfying tag cloud layouts for many application scenarios.


title: Design and identification of a lumped-parameter thermal network for permanent magnet synchronous motors based on heat transfer theory and particle swarm optimisation

abstract: A lumped-parameter thermal network (LPTN) for a permanent magnet motor (PMSM) is developed to estimate the most crucial motor temperatures. In this contribution a 60 kW PMSM prototype designed for automotive traction drives is used as the investigation basis. Aiming at real-time motor monitoring well-known analytic equations from the heat transfer theory are used to model the dominant heat paths. Based on a three-dimensional approach in cylindrical coordinates a differential-algebraic state-space model with varying parameters (LPV) is proposed. Due to the chosen level of model abstraction as well as motor material data uncertainties significant estimation errors between the LPTN and experimental test bench measurements result. To improve the estimation accuracy particle swarm optimisation (PSO) is applied for strategic fitting of uncertain model parameters with respect to a maximum likelihood cost function. To avoid converging in suboptimal local minima, which is a typical problem of gradient-based standard optimisation methods, the meta-heuristic PSO is utilised for the resulting multi-variable, non-linear and constrained optimisation problem. For the identification process experimental training data is used which is statistically independent from the (cross-)validation profiles. As a result the maximum estimation error (worst-case) regarding all considered motor component temperatures can be drastically reduced to 8 °C.


title: Food region segmentation in meal images using touch points

abstract: We propose an interactive scheme for segmenting meal images for automated dietary assessment. A smartphone user photographs a meal and marks a few touch points on the resulting image. The segmentation algorithm initializes a set of food segments with the touch points, and grows them using local image features. We evaluate the algorithm with a data set consisting of 300 manually segmented meal images. The precision of segmentation is 0.87, compared with 0.70 for fully automatic segmentation. The results show that the precision of segmentation was significantly improved by incorporating minimal user intervention.


title: Displacement: A Review of the Empirical Literature

abstract: This paper reviews 55 published articles on crime prevention measures In which researchers specifically looked for evidence of displacement These articles are classified by the type of crime prevented and the nature of the preventive measure. Analysis of the evidence suggests that displacement Is not Inevitable, and that If displacement occurs, It will be limited In scope.


title: Public Stigma of Mental Illness in the United States: A Systematic Literature Review

abstract: Public stigma is a pervasive barrier that prevents many individuals in the U.S. from engaging in mental health care. This systematic literature review aims to: (1) evaluate methods used to study the public's stigma toward mental disorders, (2) summarize stigma findings focused on the public's stigmatizing beliefs and actions and attitudes toward mental health treatment for children and adults with mental illness, and (3) draw recommendations for reducing stigma towards individuals with mental disorders and advance research in this area. Public stigma of mental illness in the U.S. was widespread. Findings can inform interventions to reduce the public's stigma of mental illness.


title: LiDAR Ranging System Based on Automatic Gain Control and Timing Discriminators

abstract: A LiDAR ranging system has been developed based on automatic gain control and timing discriminators in this paper. Timing discriminators in the time-pickoff circuit, which employ constant fraction discriminator, were designed to reduce the walk error caused by the variation of pulse amplitude. Automatic gain control was used to concentrate the input pulse amplitude of timing discriminator in a limited range, which can meet the demand of timing discriminators and reduce the timing jitter error of timing discriminators. The time-pickoff circuit using automatic gain control (AGC) and the timing discriminators can improve the precision of LiDAR ranging system. The performance of the designed circuits of AGC and timing discriminators has been tested through simulations and experiments. The results of experiments showed that the timing discriminators were able to effectively improve the precision even if the detected distance changes.


title: Imaging in scattering media using correlation image sensors and sparse convolutional coding

abstract: Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity.


title: Mechanism of syncope in patients with isolated syncope and in patients with tilt-positive syncope

abstract: BACKGROUND ::: Because of its episodic behavior, the correlation of spontaneous syncope with an abnormal finding can be considered a reference standard. ::: ::: ::: METHODS AND RESULTS ::: We inserted an implantable loop recorder in 111 patients with syncope, absence of significant structural heart disease, and a normal ECG; tilt-testing was negative in 82 (isolated syncope) and positive in 29 (tilt-positive). The patients had had >/=3 episodes of syncope in the previous 2 years and were followed up for 3 to 15 months. Results were similar in the isolated syncope group and the tilt-positive group: syncope recurred in 28 (34%) and 10 patients (34%), respectively, and electrocardiographic correlation was found in 24 (23%) and 8 (28%) patients, respectively. The most frequent finding, which was recorded in 46% and 62% of patients, respectively, was one or more prolonged asystolic pauses, mainly due to sinus arrest, preceded for a few minutes by progressive bradycardia or progressive tachycardia-bradycardia. Bradycardia without pauses was observed in 8% and 12% of cases, respectively. The remaining patients had normal sinus rhythm or sinus tachycardia, except for one, who had ectopic atrial tachycardia. In the tilt-positive group, an asystolic syncope was also recorded when the type of response to tilt-testing was vasodepressor or mixed. Presyncopal episodes were never characterized by asystolic pauses; normal sinus rhythm was the most frequent finding. ::: ::: ::: CONCLUSIONS ::: Homogeneous findings were observed during syncope. In most patients, the likely cause was neurally-mediated, and the most frequent mechanism was a bradycardic reflex. In the other cases, a normal sinus rhythm was frequently recorded. Presyncope was not an accurate surrogate for syncope in establishing a diagnosis.


title: Stochastic Modeling and Optimization in a Microgrid: A Survey

abstract: The future smart grid is expected to be an interconnected network of small-scale and self-contained microgrids, in addition to a large-scale electric power backbone. By utilizing microsources, such as renewable energy sources and combined heat and power plants, microgrids can supply electrical and heat loads in local areas in an economic and environment friendly way. To better adopt the intermittent and weather-dependent renewable power generation, energy storage devices, such as batteries, heat buffers and plug-in electric vehicles (PEVs) with vehicle-to-grid systems can be integrated in microgrids. However, significant technical challenges arise in the planning, operation and control of microgrids, due to the randomness in renewable power generation, the buffering effect of energy storage devices and the high mobility of PEVs. The two-way communication functionalities of the future smart grid provide an opportunity to address these challenges, by offering the communication links for microgrid status information collection. However, how to utilize stochastic modeling and optimization tools for efficient, reliable and economic planning, operation and control of microgrids remains an open issue. In this paper, we investigate the key features of microgrids and provide a comprehensive literature survey on the stochastic modeling and optimization tools for a microgrid. Future research directions are also identified.


title: A review on human action analysis in videos for retrieval applications

abstract: Today, the number of available videos on the Internet is significantly increased. Content-based video retrieval is used for finding the users' desired items among these big video data. Memorizing details of the videos and intricate relations between included objects in videos can be considered as the major challenges of this big data topic. A large portion of video data relates to the humans. Thus, human action retrieval has been introduced as a new big data topic that seeks to find video objects based on the included human action. Human action retrieval has been applicated in different domains such as video search, intelligent human---computer interaction, robotics, video surveillance and human behavior analysis. There are some challenges such as variations in rotation, scale, style and above-mentioned challenges for the big video data that can impress the retrieval accuracy. In this paper, a survey on human action retrieval studies is presented that the methodologies have been analyzed from action representation and retrieving perspectives. Moreover, limitations and common datasets of human action retrieval are introduced before describing the state-of-the-arts' methodologies.


title: An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation

abstract: This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.


title: Multimodal iron oxide (Fe3O4)-saturated lactoferrin nanocapsules as nanotheranostics for real-time imaging and breast cancer therapy of claudin-low, triple-negative (ER-/PR-/HER2-)

abstract: Aim: To unravel the multimodal nanotheranostic ability of Fe3O4-saturated bovine lactoferrin nanocapsules (FebLf NCs) in claudin-low, triple-negative breast cancer model. Materials & methods: Xenograft study was performed to examine biocompatibility, antitumor efficacy and multimodal nanotheranostic action in combination with near-infrared live mice imaging. Results: FebLf NCs exhibited a size range of 80 nm ± 5 nm with observed superparamagnetism. FebLf NCs successfully internalized into breast cancer cells through receptor-mediated endocytosis and induced apoptosis through the downregulation of inhibitor of apoptosis survivin and livin proteins. Investigations revealed a remarkable biocompatibility, anticancer efficacy of the FebLf NCs. Near-infrared imaging observations confirmed selective localization of multimodal FebLf NCs at the tumor site and lead to time-dependent reduction of tumor growth. Conclusion: FebLf NCs can be safe, biocompatible nanotheranostic approach for real-time imaging and monitor...


title: Black Swans and the Domains of Statistics

abstract: The Black Swan: The Impact of the Highly Improbable (hence TBS) is only critical of statistics, statisticians, or users of statistics in a very narrow (but consequential) set of circumstances. It was written by a veteran practitioner of uncertainty whose profession (a mixture of quantitative research, derivatives pricing, and risk management) estimates and deals with exposures to higher order statistical properties. Derivatives depend on some nonlinear function of random variables (often square or cubes) and are therefore extremely sensitive to estimation errors of the higher moments of probability distributions. This is the closest to applied statistician one can possibly get. Furthermore, TBS notes the astonishing success of statistics as an engine of scientific knowledge in (1) some well-charted domains such as measurement errors, gambling theory, thermodynamics, and quantum mechanics (these fall under the designation of “mild randomness”), or (2) some applications in which our vulnerability to errors is small. Indeed, statistics has been very successful in “low moment” applications such as “significance testing” for problems based on probability, not expectation or higher moments. In psychological experiments, for instance, the outlier counts as a single observation, and does not cause a high impact beyond its frequency. TBS is critical of some statistics in the following areas:


title: Log-time and Log-space Extreme Classification

abstract: We present LTLS, a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. We train LTLS with stochastic gradient descent on a number of multiclass and multilabel datasets and show that despite its small memory footprint it is often competitive with existing approaches.


title: Electrophysiology in the age of light

abstract: Electrophysiology, the 'gold standard' for investigating neuronal signalling, is being challenged by a new generation of optical probes. Together with new forms of microscopy, these probes allow us to measure and control neuronal signals with spatial resolution and genetic specificity that already greatly surpass those of electrophysiology. We predict that the photon will progressively replace the electron for probing neuronal function, particularly for targeted stimulation and silencing of neuronal populations. Although electrophysiological characterization of channels, cells and neural circuits will remain necessary, new combinations of electrophysiology and imaging should lead to transformational discoveries in neuroscience.


title: Total variation based image restoration with free local constraints

abstract: A new total variation based approach was developed by Rudin, Osher and Fatemi (see Physica D., vol.60, p.259, 1992) to overcome the basic limitations of all smooth regularization algorithms. The TV-based technique use the L/sup 1/ norm of the magnitude of a gradient, thus making discontinuous and nonsmooth solutions possible. In TV image restoration, the solution is obtained by solving a time-dependent, nonlinear PDE on a manifold that satisfies the degradation constraints. In practical applications, one assumes a space-varying blurring kernel and signal-dependent (e.g. multiplicative) noise. The evolution part of the TV-based PDE turned out to be related to the curve shortening equation, but scaled by an inverse. >


title: Sparse Dueling Bandits

abstract: The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a new approach for finding the"best"arm according to the Borda criterion using noisy comparisons. We prove that in the absence of structural assumptions, the sample complexity of this problem is proportional to the sum of the inverse squared gaps between the Borda scores of each suboptimal arm and the best arm. We explore this dependence further and consider structural constraints on the pairwise comparison matrix (a particular form of sparsity natural to this problem) that can significantly reduce the sample complexity. This motivates a new algorithm called Successive Elimination with Comparison Sparsity (SECS) that exploits sparsity to find the Borda winner using fewer samples than standard algorithms. We also evaluate the new algorithm experimentally with synthetic and real data. The results show that the sparsity model and the new algorithm can provide significant improvements over standard approaches.


title: LCM: Lightweight Communications and Marshalling

abstract: We describe the Lightweight Communications and Marshalling (LCM) library for message passing and data marshalling. The primary goal of LCM is to simplify the development of low-latency message passing systems, especially for real-time robotics research applications.


title: Heel and Toe Clearance Estimation for Gait Analysis Using Wireless Inertial Sensors

abstract: Tripping is considered a major cause of fall in older people. Therefore, foot clearance (i.e., height of the foot above ground during swing phase) could be a key factor to better understand the complex relationship between gait and falls. This paper presents a new method to estimate clearance using a foot-worn and wireless inertial sensor system. The method relies on the computation of foot orientation and trajectory from sensors signal data fusion, combined with the temporal detection of toe-off and heel-strike events. Based on a kinematic model that automatically estimates sensor position relative to the foot, heel and toe trajectories are estimated. 2-D and 3-D models are presented with different solving approaches, and validated against an optical motion capture system on 12 healthy adults performing short walking trials at self-selected, slow, and fast speed. Parameters corresponding to local minimum and maximum of heel and toe clearance were extracted and showed accuracy ± precision of 4.1 ± 2.3 cm for maximal heel clearance and 1.3 ± 0.9 cm for minimal toe clearance compared to the reference. The system is lightweight, wireless, easy to wear and to use, and provide a new and useful tool for routine clinical assessment of gait outside a dedicated laboratory.


title: Colposcopy: An Evidence-Based Update

abstract: Colposcopy is a diagnostic procedure, most commonly used in the diagnosis of cervical intraepithelial neoplasia and lower genital tract carcinoma. In this article, evidence-based management strategies are updated with discussion of the 2001 American Society for Colposcopy and Cervical Pathology Consensus Guidelines. Practice management issues include methods to improve cervical cancer screening rates, coding and billing, and telemedicine. Textbooks, CD-ROMs, and courses are listed for new learners and experienced providers who want to update and sharpen their skills.


title: Sentimental analysis on web mining using statistical measures

abstract: Opinion mining or sentimental analysis plays important role in the data mining process. In the proposed method, opinions are classified using various statistical measures to provide ratings to help the sentimental analysis of big data. Experimental results demonstrate the efficiency of the proposed method to help in analysis of quality of product, marketers evaluation of success of a new product launched, determine which versions of a product or service are popular and identify demographics like or dislike of product features, etc.


title: Complex Graphs and Networks

abstract: Graph theory in the information age Old and new concentration inequalities A generative model--the preferential attachment scheme Duplication models for biological networks Random graphs with given expected degrees The rise of the giant component Average distance and the diameter Eigenvalues of the adjacency matrix of $G(\mathbf{w})$ The semi-circle law for $G(\mathbf{w})$ Coupling on-line and off-line analyses of random graphs The configuration model for power law graphs The small world phenomenon in hybrid graphs Bibliography Index.


Relapses of chronic hepatitis C in HIV-infected patients who responded to interferon therapy. Hepatitis/HIV Spanish Study Group.


title: Youth Exposure to Violence: Prevalence, Risks, and Consequences

abstract: Recent empirical work on the distribution, determinants, and consequences of children and adolescents' witnessing of community violence are reviewed. Major findings across studies indicate that males, ethnic minorities, and urban residents are at increased risk for witnessing violence, and that higher rates of PTSD, depression, distress, aggression, and externalizing behavior disturbances are reported among those who witness violence. Degree of family conflict, domestic violence, and family support were demonstrated to modify the impact of exposure to violence. Research and policy recommendations are offered.


title: CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages

abstract: The CoNLL-SIGMORPHON 2017 shared task on supervised morphological generation required systems to be trained and tested in each of 52 typologically diverse languages. In sub-task 1, submitted systems were asked to predict a specific inflected form of a given lemma. In sub-task 2, systems were given a lemma and some of its specific inflected forms, and asked to complete the inflectional paradigm by predicting all of the remaining inflected forms. Both sub-tasks included high, medium, and low-resource conditions. Sub-task 1 received 24 system submissions, while sub-task 2 received 3 system submissions. Following the success of neural sequence-to-sequence models in the SIGMORPHON 2016 shared task, all but one of the submissions included a neural component. The results show that high performance can be achieved with small training datasets, so long as models have appropriate inductive bias or make use of additional unlabeled data or synthetic data. However, different biasing and data augmentation resulted in disjoint sets of inflected forms being predicted correctly, suggesting that there is room for future improvement.


title: Clinical depression and anxiety among ST-elevation myocardial infarction hospitalizations: Results from Nationwide Inpatient Sample 2004–2013

abstract: Abstract Depression and anxiety are common among patients who have a major cardiovascular event. However, despite their frequency, there is a lack of evidence regarding the relationship between depression and/or anxiety and receiving revascularization in ST-elevation myocardial infarction (STEMI) hospitalizations. Using data from the Nationwide Inpatient Sample (NIS) from the years 2004 to 2013, we assessed whether a clinical co-diagnosis of depression and/or anxiety decreases the likelihood of revascularization among STEMI hospitalizations. Our central finding is that, paradoxically, the odds of in-hospital mortality were lower among STEMI hospitalizations with a clinical co-diagnosis of depression and/or anxiety as compared to those without. We further discovered that clinical diagnoses of depression and/or anxiety were less prevalent among revascularized as compared to non-revascularized STEMI hospitalizations. However, the percentage of clinical diagnoses of depression and/or anxiety among STEMI hospitalizations increased at a similar rate over a 10-year period irrespective of revascularization status. In conclusion, these results are suggestive of the potentially underdiagnosed mental health issues surrounding major cardiovascular events, and indeed, chronic disease as a whole. To our knowledge, this is the first study to document and examine the “depression paradox” among a population of cardiac patients.


title: Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier

abstract: Atrial Fibrillation (AF) is characterized by chaotic electrical impulses in the atria, which leads to irregular heartbeats and can develop blood clots and stroke. Therefore, early detection of AF is crucial for increasing the success rate of the treatment. This study is focused on detection of AF rhythm using hand-held ECG monitoring devices, in addition to three other classes: normal or sinus rhythm, other rhythms, and too noisy to analyze. The pipeline of the proposed method consists of three major components: preprocessing and feature extraction, feature selection, and classification. In total, 491 hand-crafted features are extracted. Then, 150 features are selected in a feature ranking procedure. The selected features are from time, frequency, time-frequency domains, and phase space reconstruction of the ECG signals. In the final stage, a random forest classifier is used to classify the selected features into one of the four aforementioned ECG classes. Using the scoring mechanism provided by PhysioNet/Computing in Cardiology (CinC) Challenge 2017, the overall score (mean±std) of 81.9±2.6% is achieved over the training dataset in 10-fold cross-validation. The proposed algorithm tied for the first place in the PhysioNet/CinC Challenge 2017 with an overall score of 82.6% (rounded to 83%) on the unseen test dataset.


title: How android app developers manage power consumption?: an empirical study by mining power management commits

abstract: As Android platform becomes more and more popular, a large amount of Android applications have been developed. When developers design and implement Android applications, power consumption management is an important factor to consider since it affects the usability of the applications. Thus, it is important to help developers adopt proper strategies to manage power consumption. Interestingly, today, there is a large number of Android application repositories made publicly available in sites such as GitHub. These repositories can be mined to help crystalize common power management activities that developers do. These in turn can be used to help other developers to perform similar tasks to improve their own Android applications.In this paper, we present an empirical study of power management commits in Android applications. Our study extends that of Moura et al. who perform an empirical studyon energy aware commits; however they do not focus on Android applications and only a few of the commits that they study come from Android applications. Android applications are often different from other applications (e.g., those running on a server) due to the issue of limited battery life and the use of specialized APIs. As subjects of our empirical study, we obtain a list of open source Android applications from F-Droid and crawl their commits from Github. We get 468 power management commits after we filter the commits using a set of keywords and by performing manual analysis. These 468 power management commits are from 154 different Android applications and belong to 15 different application categories. Furthermore, we use open card sort to categorize these power management commits and we obtain 6 groups which correspond to different power management activities. Our study also reveals that for different kinds of Android application (e.g., Games, Connectivity, Navigation, etc.), the dominant power management activities differ.For example, the percentageof power management commits belonging to Power Adaptation activity is larger for Navigation applications than those belonging to other categories.


title: A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highway Safety

abstract: This paper presents a much advanced and efficient lane detection algorithm. The algorithm is based on (ROI) Region of Interest segmentation. In this algorithm images are pre-processed by a top-hat transform for de-noising and enhancing contrast. ROI of a test image is then extracted. For detecting lines in the ROI, Hough transform is used. Estimation of the distance between Hough origin and lane-line midpoint is made. Lane departure decision is made based on the difference between these distances. As for the simulation part we have used Matlab software.Experiments show that the proposed algorithm can detect the lane markings accurately and quickly.


Recommending and evaluating choices in a virtual community of use


title: Waqf in Historical Perspective: Online fatāwā and Contemporary Discourses by Muslim Scholars

abstract: AbstractSince the beginnings of Islam, awqāf or religious endowments have been the medium through which various public services (i.e. schools, hospitals, and mosques) have been provided to communities. Historically, endowments were both insulated from state authority and an emanation of imperial and/or state power. Modern day Muslim scholars have taken a renewed interest in waqf, particularly as Muslim societies look to revive those indigenous institutions which promote cultural sustainability. This paper examines perceptions of the role of waqf in Muslim society as evidenced in current online fatāwā and writings by Muslim scholars. These sources are drawn from English-medium, Muslim web organizations and sites which have particular appeal among Muslims living in the West. This literature propagates a historical narrative of waqf which highlights the institution's moral significance, civic identity, and economic efficiency, and plays down its pre-nineteenth-century links to state power and its potential i...


title: A New Robust Digital Image Watermarking Algorithm Based on LWT-SVD and Fractal Images

abstract: This paper presents a robust copyright protection scheme based on Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). We have used fractal decoding to make a very compact representation of watermark image. The fractal code is presented by a binary image. In the embedding phase of watermarking scheme, at first, we perform decomposing of the host image with 2D-LWT transform, then SVD is applied to sub-bands of the transformed image, and then the watermark, “binary image,” is embedded by modifying the singular values. In the watermark extraction phase, after the reverse steps are applied, the embedded binary image and consequently the fractal code are extracted from the watermarked image. The original watermark image is rendered by running the code. To verify the validity of the proposed watermarking scheme, several experiments are carried out and the results are compared with the results of the other algorithms. In order to evaluate the quality of image, we use parameter peak value signal-to-noise ratio (PSNR). To measure the robustness of the proposed algorithm, the NC coefficient is evaluated. The experimental results indicate that, in addition to high transparency, the proposed scheme is strong enough to resist various signal processing operations, such as average filter, median filter, Jpeg compression, contrast adjustment, cropping, histogram equalization, rotation, etc.


title: Object Detection from Video Tubelets with Convolutional Neural Networks

abstract: Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. For object detection, particularly in still images, the performance has been significantly increased last year thanks to powerful deep networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN features (RCNN)). The lately introduced ImageNet [6] task on object detection from video (VID) brings the object detection task into the video domain, in which objects' locations at each frame are required to be annotated with bounding boxes. In this work, we introduce a complete framework for the VID task based on still-image object detection and general object tracking. Their relations and contributions in the VID task are thoroughly studied and evaluated. In addition, a temporal convolution network is proposed to incorporate temporal information to regularize the detection results and shows its effectiveness for the task. Code is available at https://github.com/ myfavouritekk/vdetlib.


MULTI STAGE PHISHING EMAIL CLASSIFICATION


title: Neonatal Marfan syndrome: A case report

abstract: Abstract: A case of neonatal Marfan syndrome is presented. The patient was noted to have cardiomegaly and tricuspid regurgitation on antenatal ultrasound scan. She was born with long, slender fingers and toes, an aged appearance and non-paralytic hypotonia. Echocardiogram revealed a dilated right atrium, right ventricle, dysplastic tricuspid valve and severe tricuspid regurgitation. She subsequently died of severe heart failure. Post-mortem examination showed the pathological features of lobar emphysema and cystic medial necrosis of the aorta. These features supported the diagnosis of neonatal Marfan syndrome. Nucleotide sequencing showed substitution of G by A at codon 1032 in exon 25 located in the long arm of chromosome 15. This resulted in the substitution of a cysteine by a tyrosine. A de novo mutation is suggested by the absence of affected family members.


title: Modeling, control of a two-wheeled self-balancing robot

abstract: Two wheeled self-balancing robot is based on an inverted pendulum system. This paper describes the development of kinematics model of a two wheeled self-balancing robot and its control using various control techniques. The analysis of the developed kinematic model can be divided into wheel and frame structure, after one is done with design and development of mechatronics system is finalized. The said test platform is two Degree of Freedom highly non-linear and unstable system. Main focus of this research is to develop an efficient controller required to enable the robot to act in real time and test different controllers to analyze their performance by the vertical balance, position, its control signal and disturbance rejection capabilities.


title: Effect of Finger Pitch on the Driving Ability of a 40-nm MOSFET With Contact Etch Stop Layer Strain in Multifinger Gated Structure

abstract: Effects of the poly gate finger pitch on, hot-carrier-nduced reliability degradation, and radio frequency characteristics of the 40-nm n-channel metal-oxide-semiconductor field-effect transistors with contact-etch-stop-layer (CESL) strain and multifinger gate structures were systematically investigated by both experiment and technology computer-aided design simulation. The finger pitch influences both the transfer of CESL-induced stress into a channel and the shadow effect of a poly gate on a pocket implantation. The results showed that the effects of the poly gate finger pitch were more obvious for pitches less than 0.12 . Additionally, the change in stress on the channel was dominant for pitches larger than 0.12 , but for pitches less than 0.12 , the modulation of pocket implantation shadow effects became the main controlling factor.


title: Compiler directed write-mode selection for high performance low power volatile PCM

abstract: Micro-Controller Units (MCUs) are widely adopted ubiquitous computing devices. Due to tight cost and energy constraints, MCUs often integrate very limited internal RAM memory on top of Flash storage, which exposes Flash to heavy write traffic and results in short system lifetime. Architecting emerging Phase Change Memory (PCM) is a promising approach for MCUs due to its fast read speed and long write endurance. However, PCM, especially multi-level cell (MLC) PCM, has long write latency and requires large write energy, which diminishes the benefits of its replacement of traditional Flash. By studying MLC PCM write operations, we observe that writing MLC PCM can take advantages of two write modes --- fast write leaves cells in volatile state, and slow write leaves cells in non-volatile state. In this paper, we propose a compiler directed dual-write (CDDW) scheme that selects the best write mode for each write operation to maximize the overall performance and energy efficiency. Our experimental results show that CDDW reduces dynamic energy by 32.4%(33.8%) and improves performance by 6.3%(35.9%) compared with an all fast(slow) write approach.


title: Managing risks in enterprise systems implementations

abstract: What determines if a system implementation will be successful?


title: Limit theorems for Parrondo's paradox

abstract: That there exist two losing games that can be combined, either by random mixture or by nonrandom alternation, to form a winning game is known as Parrondo's paradox. We establish a strong law of large numbers and a central limit theorem for the Parrondo player's sequence of profits, both in a one-parameter family of capital-dependent games and in a two-parameter family of history-dependent games, with the potentially winning game being either a random mixture or a nonrandom pattern of the two losing games. We derive formulas for the mean and variance parameters of the central limit theorem in nearly all such scenarios; formulas for the mean permit an analysis of when the Parrondo effect is present.


title: Feature Extraction Based Multi-Structure Manifold Embedding for Hyperspectral Remote Sensing Image Classification

abstract: Hyperspectral remote sensing image is a typical high-dimensional data with a large number of redundant informantion, which will impact the classification accuracy. Feature extraction is an effective method to reduce the redundancy of hyperspectral image (HSI) and improve the classification performance. However, most feature extraction methods just consider a single structure information of HSI that will lose some valuable information. To address the drawback, we proposed an unsupervised feature extraction method termed multi-structure manifold embedding (MSME) for HSI classification. First, MSME utilizes sparse representation to obtain the sparse coefficients of HSI data. Then, it constructs a sparse graph and a sparse hypergraph with the sparse coefficients. We use the sparse graph, the sparse hypergraph, and the local linear property to represent different intrinsic structures of HSI. Finally, we construct a feature learning method with these structures to achieve an optimal projection matrix for feature extraction. MSME makes full use of the complementarity of different structures to reveal the intrinsic properties of HSI and improve the discriminating power of features for classification. Experiments on the Salinas and PaviaU data sets show that the proposed MSME algorithm achieves the best classification results than other state-of-the-art methods.


title: Calpain-mediated cleavage of Atg5 switches autophagy to apoptosis

abstract: Autophagy-related gene (Atg) 5 is a gene product required for the formation of autophagosomes. Here, we report that Atg5, in addition to the promotion of autophagy, enhances susceptibility towards apoptotic stimuli. Enforced expression of Atg5-sensitized tumour cells to anticancer drug treatment both in vitro and in vivo. In contrast, silencing the Atg5 gene with short interfering RNA (siRNA) resulted in partial resistance to chemotherapy. Apoptosis was associated with calpain-mediated Atg5 cleavage, resulting in an amino-terminal cleavage product with a relative molecular mass of 24,000 (Mr 24K). Atg5 cleavage was observed independent of the cell type and the apoptotic stimulus, suggesting that calpain activation and Atg5 cleavage are general phenomena in apoptotic cells. Truncated Atg5 translocated from the cytosol to mitochondria, associated with the anti-apoptotic molecule Bcl-xL and triggered cytochrome c release and caspase activation. Taken together, calpain-mediated Atg5 cleavage provokes apoptotic cell death, therefore, represents a molecular link between autophagy and apoptosis--a finding with potential importance for clinical anticancer therapies.


title: Spikes in Mammalian Bipolar Cells Support Temporal Layering of the Inner Retina

abstract: In the mammalian retina, 10-12 different cone bipolar cell (BC) types decompose the photoreceptor signal into parallel channels, providing the basis for the functional diversity of retinal ganglion cells (RGCs). BCs differing in their temporal properties appear to project to different strata of the retina's inner synaptic layer, based on somatic recordings of BCs and excitatory synaptic currents measured in RGCs. However, postsynaptic currents in RGCs are influenced by dendritic morphology and receptor types, and the BC signal can be transformed at the axon terminals both through interactions with amacrine cells and through the generation of all-or-nothing spikes. Therefore, the temporal properties of the BC output have not been analyzed systematically across different types of mammalian BCs. We recorded calcium signals directly within axon terminals using two-photon imaging and show that BCs can be divided into ≥eight functional clusters. The temporal properties of the BC output were directly reflected in their anatomical organization within the retina's inner synaptic layer: faster cells stratified closer to the border between ON and OFF sublamina. Moreover, ≥three fastest groups generated clear all-or-nothing spikes. Therefore, the systematic projection pattern of BCs provides distinct temporal "building blocks" for the feature extracting circuits of the inner retina.


title: Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers

abstract: In this paper, we develop novel, efficient 2D encodings for 3D geometry, which enable reconstructing full 3D shapes from a single image at high resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction problem. To that end, we first develop a simple baseline network that predicts entire voxel tubes at each pixel of a reference view. By leveraging well-proven architectures for 2D pixel-prediction tasks, we attain state-of-the-art results, clearly outperforming purely voxel-based approaches. We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll. This allows reconstructing highly detailed shapes with complex topology, as demonstrated in extensive experiments; we clearly outperform previous octree-based approaches despite having a much simpler architecture using standard network components. Our Matryoshka networks further enable reconstructing shapes from IDs or shape similarity, as well as shape sampling.


title: An Event Detection Approach Based On Twitter Hashtags

abstract: Twitter is one of the most popular microblogging services in the world. The great amount of information within Twitter makes it an important information channel for people to learn and share news. Twitter hashtag is an popular feature that can be viewed as human-labeled information which people use to identify the topic of a tweet. Many researchers have proposed event-detection approaches that can monitor Twitter data and determine whether special events, such as accidents, extreme weather, earthquakes, or crimes take place. Although many approaches use hashtags as one of their features, few of them explicitly focus on the effectiveness of using hashtags on event detection. In this study, we proposed an event detection approach that utilizes hashtags in tweets. We adopted the feature extraction used in STREAMCUBE and applied a clustering K-means approach to it. The experiments demonstrated that the K-means approach performed better than STREAMCUBE in the clustering results. A discussion on optimal K values for the K-means approach is also provided.