title: Prefixes and the Delimitation of Events

abstract: In Slavic languages, verbal prefixes can be applied to perfective verbs deriving new perfective verbs, and multiple prefixes can occur in a single verb. This well-known type of data has not yet been adequately analyzed within current approaches to the semantics of Slavic verbal prefixes and aspect. The notion “aspect” covers “grammatical aspect”, or “viewpoint aspect” (see Smith 1991/1997), best characterized by the formal perfective vs. imperfective distinction, which is often expressed by inflectional morphology (as in Romance languages), and corresponds to propositional operators at the semantic level of representation. It also covers “lexical aspect”, “situation aspect” (see Smith ibid.), “eventuality types” (Bach 1981, 1986), or “Aktionsart” (as in Hinrichs 1985; Van Valin 1990; Dowty 1999; Paslawska and von Stechow 2002, for example), which regards the telic vs. atelic distinction and its Vendlerian subcategories (activities, accomplishments, achievements and states). It is lexicalized by verbs, encoded by derivational morphology, or by a variety of elements at the level of syntax, among which the direct object argument has a prominent role, however, the subject (external) argument is arguably a contributing factor, as well (see Dowty 1991, for example). These two “aspect” categories are orthogonal to each other and interact in systematic ways (see also Filip 1992, 1997, 1993/99; de Swart 1998; Paslawska and von Stechow 2002; Rothstein 2003, for example). Multiple prefixation and application of verbal prefixes to perfective bases is excluded by the common view of Slavic prefixes, according to which all perfective verbs are telic and prefixes constitute a uniform class of “perfective” markers that that are applied to imperfective verbs that are atelic and derive perfective verbs that are telic. Moreover, this view of perfective verbs and prefixes predicts rampant violations of the intuitive “one delimitation per event” constraint, whenever a prefix is applied to a perfective verb. This intuitive constraint is motivated by the observation that an event expressed within a single predication can be delimited only once: cp. *run a mile for ten minutes, *wash the clothes clean white.


title: Multiple HOG templates for gait recognition

abstract: In gait recognition field, template-based approaches such as Gait Energy Image (GEI) and Chrono-Gait Image (CGI) can achieve good recognition performance with low computational cost. Meanwhile, CGI can preserve temporal information better than GEI. However, they pay less attention to the local shape features. To preserve temporal information and generate more abundant local shape features, we generate multiple HOG templates by extracting Histogram of Oriented Gradients (HOG) of GEI and CGI templates. Experiments show that compared with several published approaches, our proposed multiple HOG templates achieve better performance for gait recognition.


title: Putting Feelings Into Words Affect Labeling Disrupts Amygdala Activity in Response to Affective Stimuli

abstract: Putting feelings into words (affect labeling) has long been thought to help manage negative emotional experiences; however, the mechanisms by which affect labeling produces this benefit remain largely unknown. Recent neuroimaging studies suggest a possible neuro- cognitive pathway for this process, but methodological limitations of previous studies have prevented strong in- ferences from being drawn. A functional magnetic reso- nance imaging study of affect labeling was conducted to remedy these limitations. The results indicated that affect labeling, relative to other forms of encoding, diminished the response of the amygdala and other limbic regions to negative emotional images. Additionally, affect labeling produced increased activity in a single brain region, right ventrolateral prefrontal cortex (RVLPFC). Finally, RVLPFC and amygdala activity during affect labeling were inversely correlated, a relationship that was medi- ated by activity in medial prefrontal cortex (MPFC). These results suggest that affect labeling may diminish emotional reactivity along a pathway from RVLPFC to MPFC to the


title: CNUSVM: Hybrid CNN-Uneven SVM Model for Imbalanced Visual Learning

abstract: Recently, deep Convolutional Neural Networks (CNNs) have been used to achieve state-of-the-art performance on a wide range of visual learning tasks. However, when facing some imbalanced learning tasks where the training samples are unevenly distributed among different classes, CNNs tend to produce performance bias toward the majority class, making them not suitable for applications in which the recognition ability on the minority class is highly valued. To address the problem, this paper proposes a hybrid classification model by combining CNN with Support Vector Machine (SVM) that has uneven margins. In this model, CNN works as a feature extractor and the extracted features are then sent into a L2-SVM with linear uneven margins. We also develop a gradient-descent learning approach for this hybrid CNN-uneven SVM (CNUSVM) model by minimizing an uneven margin based L2-hinge loss. Our experiments on two benchmark datasets show that the CNUSVM model can make more favorable decisions for imbalanced visual learning tasks in comparison with the standard CNN and the hybrid CNN-SVM model.


title: Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening

abstract: Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.


title: Analyzing Uplink SINR and Rate in Massive MIMO Systems Using Stochastic Geometry

abstract: This paper proposes a stochastic geometry framework to analyze the SINR and rate performance in a large-scale uplink massive MIMO network. Based on the model, expressions are derived for spatial average SINR distributions over user and base station distributions with maximum ratio combining (MRC) and zero-forcing (ZF) receivers. We show that using massive MIMO, the uplink SINR in certain urban marco-cell scenarios is limited by interference. In the interference-limited regime, the results reveal that for MRC receivers, a super-linear (polynomial) scaling law between the number of base station antennas and scheduled users per cell preserves the uplink SIR distribution, while a linear scaling applies to ZF receivers. ZF receivers are shown to outperform MRC receivers in the SIR coverage, and the performance gap is quantified in terms of the difference in the number of antennas to achieve the same SIR distribution. Numerical results verify the analysis. It is found that the optimal compensation fraction in fractional power control to optimize rate is generally different for MRC and ZF receivers. Besides, simulations show that the scaling results derived from the proposed framework apply to the networks where base stations are distributed according to a hexagonal lattice.


title: Detecting criminal organizations in mobile phone networks

abstract: Abstract The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as mobile phones and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first we provide a theoretical framework for the problem of detecting and characterizing criminal organizations in networks reconstructed from phone call records. Then, we introduce an expert system to support law enforcement agencies in the task of unveiling the underlying structure of criminal networks hidden in communication data. This platform allows for statistical network analysis, community detection and visual exploration of mobile phone network data. It enables forensic investigators to deeply understand hierarchies within criminal organizations, discovering members who play central role and provide connection among sub-groups. Our work concludes illustrating the adoption of our computational framework for a real-word criminal investigation.


title: Pre-Touch Sensing for Mobile Interaction

abstract: Touchscreens continue to advance including progress towards sensing fingers proximal to the display. We explore this emerging pre-touch modality via a self-capacitance touchscreen that can sense multiple fingers above a mobile device, as well as grip around the screen's edges. This capability opens up many possibilities for mobile interaction. For example, using pre-touch in an anticipatory role affords an "ad-lib interface" that fades in a different UI--appropriate to the context--as the user approaches one-handed with a thumb, two-handed with an index finger, or even with a pinch or two thumbs. Or we can interpret pre-touch in a retroactive manner that leverages the approach trajectory to discern whether the user made contact with a ballistic vs. a finely-targeted motion. Pre-touch also enables hybrid touch + hover gestures, such as selecting an icon with the thumb while bringing a second finger into range to invoke a context menu at a convenient location. Collectively these techniques illustrate how pre-touch sensing offers an intriguing new back-channel for mobile interaction.


COMPARATIVE PERFORMANCE ANALYSIS OF CLUSTERING TECHNIQUES IN EDUCATIONAL DATA MINING


title: Collaboration-Based Cloud Computing Security Management Framework

abstract: Although the cloud computing model is considered to be a very promising internet-based computing platform, it results in a loss of security control over the cloud-hosted assets. This is due to the outsourcing of enterprise IT assets hosted on third-party cloud computing platforms. Moreover, the lack of security constraints in the Service Level Agreements between the cloud providers and consumers results in a loss of trust as well. Obtaining a security certificate such as ISO 27000 or NIST-FISMA would help cloud providers improve consumers trust in their cloud platforms' security. However, such standards are still far from covering the full complexity of the cloud computing model. We introduce a new cloud security management framework based on aligning the FISMA standard to fit with the cloud computing model, enabling cloud providers and consumers to be security certified. Our framework is based on improving collaboration between cloud providers, service providers and service consumers in managing the security of the cloud platform and the hosted services. It is built on top of a number of security standards that assist in automating the security management process. We have developed a proof of concept of our framework using. NET and deployed it on a test bed cloud platform. We evaluated the framework by managing the security of a multi-tenant SaaS application exemplar.


title: A compact printed Yagi type antenna for GPS application

abstract: An experiment is performed on a thin dielectric substrate for the compact printed Yagi type antenna described theoretically in [1]. This time the SMA connector at the feed is taken into design considerations. Measured results show that this implemented antenna has a high directivity of 7.3 dBi, a front-to-back ratio of 11.6 dB, a cross-polarization level of −33.5 dB, and 3-dB beamwidths of 73° and 112° in the planes of θ = 90° and ϕ= 90°, respectively. The bandwidth is 9.1%, and the antenna efficiency is 92%. The proposed antenna is suitable for the global positioning system application in the mobile devices whose volumes are usually not sufficient for embedded antennas with good axial ratio (AR) values of right hand circular polarizations and good angular coverage of preferred AR values.


title: IPv6 routing protocol for low-power and lossy networks implementation in network simulator — 3

abstract: Wireless Sensor Networks (WSN) are widely used to monitor and control physical environments. An efficient energy management system is needed to be able to deploy these networks in lossy environments while maintaining reliable communication. The IPv6 Routing Protocol for Low-Power and Lossy networks is a routing protocol designed to properly manage energy without compromising reliability. This protocol has currently been implemented in Contiki OS, TinyOS, and OMNeT++ Castalia. But these applications also simulate all operation mechanics of a specified hardware model instead of just simulating the protocol only, thus adding unnecessary overhead and slowing down simulations on RPL. In light of this, we have implemented a working ns-3 implementation of RPL with support for multiple RPL instances with the use of a global repair mechanism. The behavior and output of our simulator was compared to Cooja for verification, and the results are similar with a minor difference in rank computation.


title: Arrears prediction for electricity customer through Wgan-Gp

abstract: It is not common for electric company that arrears can be found accurately and in time. This phenomenon leads to shortage of arrears data. It also cause process of predicting arrears can not be further developed. Stratified sampling has become a general solution to the problem. In this paper, a new method for arrears prediction is proposed, which uses Wgan-Gp (Improved Training of Wasserstein GANs) to simulate the real data and then generate fake data to predict arrears with DBN. Electric power data (256 indicators), like the image pixel (16∗16), is used as input of the Wgan-Gp to train the generator. For the first time, this paper convert the prediction of arrears time to prediction of arrears interval. Next, we design a series of relevant indicators for experiment, which proves that the prediction accuracy can be improved with analogue data generated by Wgan-Gp.


title: The status of the P versus NP problem

abstract: It's one of the fundamental mathematical problems of our time, and its importance grows with the rise of powerful computers.


title: Practical Implementation of Private Cloud Based on Open Source ownCloud for Small Teams - Case Study

abstract: Various companies in the manufacturing, but also in non-manufacturing sector, research and public organization use technology based on Internet to share data among staff but also with customers or in collaboration with other companies. Depending on the requirements of individual companies widely differing demands are placed on the technology in terms of features, administration, as well as security and the cost of the solution usually considered as one of the key issues. Cloud promises lower costs, high availability and scalability and that in the long term. The essence of any choice in selecting cloud solutions for corporate environment is to recognize its purpose, scope of use and content that will be uploaded to the storage. The paper discusses the project of setting up your own private cloud solution based on the open software ownCloud.


title: BerkMin: A Fast and Robust Sat-Solver

abstract: We describe a SAT-solver, BerkMin, that inherits such features of GRASP, SATO, and Chaff as clause recording, fast BCP, restarts, and conflict clause ''aging''. At the same time BerkMin introduces a new decision-making procedure and a new method of clause database management. We experimentally compare BerkMin with Chaff, the leader among resolution-based SAT-solvers. Experiments show that our program is more robust than Chaff being able to solve more instances than Chaff in a reasonable amount of time.


title: History Driven Program Repair

abstract: Effective automated program repair techniques have great potential to reduce the costs of debugging and maintenance. Previously proposed automated program repair (APR) techniques often follow a generate-and-validate and test-case-driven procedure: They first randomly generate a large pool of fix candidates and then exhaustively validate the quality of the candidates by testing them against existing or provided test suites. Unfortunately, many real-world bugs cannot be repaired by existing techniques even after more than 12 hours of computation in a multi-core cloud environment. More work is needed to advance the capabilities of modern APR techniques. We propose a new technique that utilizes the wealth of bug fixesacross projects in their development history to effectively guide and drive a programrepair process. Our main insight is that recurring bug fixes are common inreal-world applications, and that previously-appearing fix patterns canprovide useful guidance to an automated repair technique. Based on this insight, our technique first automaticallymines bug fix patterns from the history of many projects. We then employ existingmutation operators to generate fix candidates for a given buggy program. Candidates that match frequently occurring historical bug fixes are consideredmore likely to be relevant, and we thus give them priority inthe random search process. Finally, candidates thatpass all the previously failed test cases are recommended as likely fixes. We compare our technique against existinggenerate-and-validate and test-driven APR approaches using 90 bugs from 5 Javaprograms. The experiment results show that our technique can producegood-quality fixes for many more bugs as compared to the baselines, while beingreasonably computationally efficient: it takes less than 20minutes, on average, to correctly fix a bug.


title: Dense and Diverse Capsule Networks: Making the Capsules Learn Better

abstract: Past few years have witnessed exponential growth of interest in deep learning methodologies with rapidly improving accuracies and reduced computational complexity. In particular, architectures using Convolutional Neural Networks (CNNs) have produced state-of-the-art performances for image classification and object recognition tasks. Recently, Capsule Networks (CapsNet) achieved significant increase in performance by addressing an inherent limitation of CNNs in encoding pose and deformation. Inspired by such advancement, we asked ourselves, can we do better? We propose Dense Capsule Networks (DCNet) and Diverse Capsule Networks (DCNet++). The two proposed frameworks customize the CapsNet by replacing the standard convolutional layers with densely connected convolutions. This helps in incorporating feature maps learned by different layers in forming the primary capsules. DCNet, essentially adds a deeper convolution network, which leads to learning of discriminative feature maps. Additionally, DCNet++ uses a hierarchical architecture to learn capsules that represent spatial information in a fine-to-coarser manner, which makes it more efficient for learning complex data. Experiments on image classification task using benchmark datasets demonstrate the efficacy of the proposed architectures. DCNet achieves state-of-the-art performance (99.75%) on MNIST dataset with twenty fold decrease in total training iterations, over the conventional CapsNet. Furthermore, DCNet++ performs better than CapsNet on SVHN dataset (96.90%), and outperforms the ensemble of seven CapsNet models on CIFAR-10 by 0.31% with seven fold decrease in number of parameters.


title: Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

abstract: Background: Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective: The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods: The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results: After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions: Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes. [JMIR Med Inform 2017;5(1):e7]


Optical-inertial Synchronization of MoCap Suit with Single Camera Setup for Reliable Position Tracking


title: Improved 8-point Approximate DCT for Image and Video Compression Requiring Only 14 Additions

abstract: Video processing systems such as HEVC requiring low energy consumption needed for the multimedia market has lead to extensive development in fast algorithms for the efficient approximation of 2-D DCT transforms. The DCT is employed in a multitude of compression standards due to its remarkable energy compaction properties. Multiplier-free approximate DCT transforms have been proposed that offer superior compression performance at very low circuit complexity. Such approximations can be realized in digital VLSI hardware using additions and subtractions only, leading to significant reductions in chip area and power consumption compared to conventional DCTs and integer transforms. In this paper, we introduce a novel 8-point DCT approximation that requires only 14 addition operations and no multiplications. The proposed transform possesses low computational complexity and is compared to state-of-the-art DCT approximations in terms of both algorithm complexity and peak signal-to-noise ratio. The proposed DCT approximation is a candidate for reconfigurable video standards such as HEVC. The proposed transform and several other DCT approximations are mapped to systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45 nm CMOS technology.


title: A fast face clustering method for indexing applications on mobile phones

abstract: Tagging of faces present in a photo or video at shot level has multiple applications related to indexing and retrieval. Face clustering, which aims to group similar faces corresponding to an individual, is a fundamental step of face tagging. We present a progressive method of applying easy-to-hard grouping technique that applies increasingly sophisticated feature descriptors and classifiers on reducing number of faces from each of the iteratively generated clusters. Our primary goal is to design a cost effective solution for deploying it on low-power devices like mobile phones. First, the method initiates the clustering process by applying K-Means technique with relatively large K value on simple LBP features to generate the first set of high precision clusters. Multiple clusters generated for each individual (low recall) are then progressively merged by applying linear and non-linear subspace modelling strategies on custom selected sophisticated features like Gabor filter, Gabor Jets, and Spin LGBP (Local Gabor Binary Patterns) with spatially spinning bin support for histogram computation. Our experiments on the standard face databases like YouTube Faces, YouTube Celebrities, Indian Movie Face database, eNTERFACE, Multi-Pie, CK+, MindReading and internally collected mobile phone samples demonstrate the effectiveness of proposed approach as compared to state-of-the-art methods and a commercial solution on a mobile phone.


title: A new method for the determination of nitrates

abstract: Summary A new method for the spectrophotometric determination of nitrate is proposed based on the use of chromotropic acid. The method is highly selective, very sensitive, and can be quickly and easily applied. Of special significance is the lack of chloride interference (a serious restriction to the classic phenoldisulfonic acid method) and the freedom from the tedium of evaporation of samples.


Design and Development of a Reconfigurable Cable Parallel Robot


title: An Investigation of Dehazing Effects on Image and Video Coding

abstract: This paper makes an investigation of the dehazing effects on image and video coding for surveillance systems. The goal is to achieve good dehazed images and videos at the receiver while sustaining low bitrates (using compression) in the transmission pipeline. At first, this paper proposes a novel method for single-image dehazing, which is used for the investigation. It operates at a faster speed than current methods and can avoid halo effects by using the median operation. We then consider the dehazing effects in compression by investigating the coding artifacts and motion estimation in cases of applying any dehazing method before or after compression. We conclude that better dehazing performance with fewer artifacts and better coding efficiency is achieved when the dehazing is applied before compression. Simulations for Joint Photographers Expert Group images in addition to subjective and objective tests with H.264 compressed sequences validate our conclusion.


title: Incremental Evolving Domain Adaptation

abstract: Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space in which the source domain and the current target domain are approximately indistinguishable. Therefore, source and target domain data are similarly distributed in the new feature space and we use a semi-supervised classification method to utilize both the unlabeled data of the target domain and the labeled data of the source domain. Since test data arrives sequentially, we propose an incremental approach both for finding the new feature space and for semi-supervised classification. Experiments on several real datasets demonstrate the superiority of our proposed method in comparison to the other recent methods.


title: Discriminative probabilistic models for passage based retrieval

abstract: The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main reason of the inconsistent performance is that there exists no unified framework to model the evidence of individual passages within a document. This paper proposes two probabilistic models to formally model the evidence of a set of top ranked passages in a document. The first probabilistic model follows the retrieval criterion that a document is relevant if any passage in the document is relevant, and models each passage independently. The second probabilistic model goes a step further and incorporates the similarity correlations among the passages. Both models are trained in a discriminative manner. Furthermore, we present a combination approach to combine the ranked lists of document retrieval and passage-based retrieval. An extensive set of experiments have been conducted on four different TREC test beds to show the effectiveness of the proposed discriminative probabilistic models for passage-based retrieval. The proposed algorithms are compared with a state-of-the-art document retrieval algorithm and a language model approach for passage-based retrieval. Furthermore, our combined approach has been shown to provide better results than both document retrieval and passage-based retrieval approaches.


title: Development of Pallet Recognition System using Kinect Camera

abstract: This study proposes a new pallet recognition system using Kinect camera. Depth image of Kinect camera is produced from the infrared ray data of random dot type. This system was applied to an automated guided vehicle(AGV) to recognize the pallet in various conditions. A modularized hardware and software of the pallet recognition system was developed. The performance of the developed pallet recognition system was tested through experiments under various environment, and it show good performance.


title: Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations

abstract: For applications such as Amazon warehouse order fulfillment, robots must grasp a desired object amid clutter: other objects that block direct access. This can be difficult to program explicitly due to uncertainty in friction and push mechanics and the variety of objects that can be encountered. Deep Learning networks combined with Online Learning from Demonstration (LfD) algorithms such as DAgger and SHIV have potential to learn robot control policies for such tasks where the input is a camera image and system dynamics and the cost function are unknown. To explore this idea, we introduce a version of the grasping in clutter problem where a yellow cylinder must be grasped by a planar robot arm amid extruded objects in a variety of shapes and positions. To reduce the burden on human experts to provide demonstrations, we propose using a hierarchy of three levels of supervisors: a fast motion planner that ignores obstacles, crowd-sourced human workers who provide appropriate robot control values remotely via online videos, and a local human expert. Physical experiments suggest that with 160 expert demonstrations, using the hierarchy of supervisors can increase the probability of a successful grasp (reliability) from 55% to 90%.


title: Fast penetration depth computation for physically-based animation

abstract: We present a novel and fast algorithm to compute penetration depth (PD) between two polyhedral models for physically-based animation. Given two overlapping polyhedra, it computes the minimal translation distance to separate them using a combination of object-space and image-space techniques. The algorithm computes pairwise Minkowski sums of decomposed convex pieces and performs a closest point query using rasterization hardware. It uses bounding volume hierarchies, object-space and image-space culling algorithms to further accelerate the computation and refines the estimated PD in a hierarchical manner. We demonstrate its application to contact response computation and a time-stepping method for dynamic simulation.


title: Elicitation and Modeling Non-Functional Requirements - A POS Case Study

abstract: Proper management of requirements is crucial to successful development software within limited time and cost. Nonfunctional requirements (NFR) are one of the key criteria to derive a comparison among various software systems. In most of software development NFR have be specified as an additional requirement of software. NFRs such as performance, reliability, maintainability, security, accuracy etc. have to be considered at the early stage of software development as functional requirement (FR). However, identifying NFR is not an easy task. Although there are well developed techniques for eliciting functional requirement, there is a lack of elicitation mechanism for NFR and there is no proper consensus regarding NFR elicitation techniques. Eliciting NFRs are considered to be one of the challenging jobs in requirement analysis. This paper proposes a UML use case based questionary approach to identifying and classifying NFR of a system. The proposed approach is illustrated by using a Point of Sale (POS) case study.


title: An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing

abstract: Collaborative filtering is one of the most important technologies in electronic commerce. With the development of recommender systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users’ ratings is the major reason causing the poor quality. To address this issue, an item-based collaborative filtering recommendation algorithm using slope one scheme smoothing is presented. This approach predicts item ratings that users have not rated by the employ of slope one scheme, and then uses Pearson correlation similarity measurement to find the target items’ neighbors, lastly produces the recommendations. The experiments are made on a common data set using different recommender algorithms. The results show that the proposed approach can improve the accuracy of the collaborative filtering recommender system.


title: The Mycology as Forensics Tool

abstract: In a murder case it is very common to find a corpse in a grave followed by the human decomposition. In a criminal act, the facts in a legal investigation are not clear enough to help clarify unnatural causes of death by suicide or homicide. Estimating the post-mortem interval (PMI), and mainly in cases where there are no witnesses, is crucial to the investigation process. However, the today study of certain species of fungi found and collected from soil in contact with a rotting human body; contribute to obtain important data useful to estimate the PMI of the victim in crime scene investigation. Dichotomomyces cejpii, Talaromyces trachyspermus, Talaromyces flavus and Talaromyces udagawae, teleomorphic Ascomycota fungal are the mycobiota currently found and clearly differs to associated mycobiota in control sample and from previously described species Buenos Aires Province, Argentina. Furthermore, additional tests are needed to finally rely on the mycology as a forensic tool.


electrochemical pH and free chlorine sensors for water quality monitoring : recent advances and research challenges


title: Improving DNN-based Music Source Separation using Phase Features

abstract: Music source separation with deep neural networks typically relies only on amplitude features. In this paper we show that additional phase features can improve the separation performance. Using the theoretical relationship between STFT phase and amplitude, we conjecture that derivatives of the phase are a good feature representation opposed to the raw phase. We verify this conjecture experimentally and propose a new DNN architecture which combines amplitude and phase. This joint approach achieves a better signal-to distortion ratio on the DSD100 dataset for all instruments compared to a network that uses only amplitude features. Especially, the bass instrument benefits from the phase information.


title: Knowledge management infrastructure and processes on effectiveness of nursing care

abstract: The purpose of this study is to investigate the influence of knowledge management infrastructure and process on nursing care effectiveness in selected teaching hospitals in Southwest Nigeria. The organization of nursing knowledge resources is critical to health care organizations for providing safe and high quality care for patients. Therefore, it is necessary to study the role of knowledge management in clinical nursing practice and the outcomes on the effectiveness of nursing care. Despite the critical role of nursing care in defining high-performing health care delivery, knowledge management in this area is still at an early stage of development. Based on organizational capability theory and employing concurrent mixed-method research design, this study seeks to identify the components of knowledge infrastructure and processes that influence the efficiency and effectiveness of nursing care in clinical floor nursing of selected teaching hospitals in Nigeria, a developing country in Africa. The output from the research is expected to contribute to the domain of literature/knowledge; provide awareness of the knowledge management practices in nursing care in Nigeria; assist nursing administrators, health policy makers and hospital management to exploit and make effective use of knowledge-based resources to enhance nursing care which can improve the productivity of health care organizations.


title: Cognitive mechanisms underlying achievement deficits in children with mathematical learning disability

abstract: Using strict and lenient mathematics achievement cutoff scores to define a learning disability, respective groups of children who are math disabled (MLD, n = 15) and low achieving (LA, n = 44) were identified. These groups and a group of typically achieving (TA, n = 46) children were administered a battery of mathematical cognition, working memory, and speed of processing measures (M = 6 years). The children with MLD showed deficits across all math cognition tasks, many of which were partially or fully mediated by working memory or speed of processing. Compared with the TA group, the LA children were less fluent in processing numerical information and knew fewer addition facts. Implications for defining MLD and identifying underlying cognitive deficits are discussed.


title: Resistive Switching by Voltage-Driven Ion Migration in Bipolar RRAM—Part II: Modeling

abstract: Resistive-switching memory (RRAM) based on transition metal oxides is a potential candidate for replacing Flash and dynamic random access memory in future generation nodes. Although very promising from the standpoints of scalability and technology, RRAM still has severe drawbacks in terms of understanding and modeling of the resistive-switching mechanism. This paper addresses the modeling of resistive switching in bipolar metal-oxide RRAMs. Reset and set processes are described in terms of voltage-driven ion migration within a conductive filament generated by electroforming. Ion migration is modeled by drift–diffusion equations with Arrhenius-activated diffusivity and mobility. The local temperature and field are derived from the self-consistent solution of carrier and heat conduction equations in a 3-D axis-symmetric geometry. The model accounts for set–reset characteristics, correctly describing the abrupt set and gradual reset transitions and allowing scaling projections for metal-oxide RRAM.


title: Measurement and analysis of tennis swing motion using 3D gyro sensor

abstract: This paper describes the measurement and analysis of the motion of a tennis swing. Over the past decade, people have taken a greater interest in their physical condition in an effort to avoid health problems due to aging. Exercise, especially sports, is an integral part of a healthy lifestyle. As a popular lifelong sport, tennis was selected as the subject of this study, with the focus on the correct form for playing tennis, which is difficult to learn. We used a 3D gyro sensor fixed at the waist to detect the angular velocity in the movement of the stroke and serve of expert and novice tennis players for comparison.


title: Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

abstract: Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.


title: Quantum effects in biology: golden rule in enzymes, olfaction, photosynthesis and magnetodetection

abstract: Despite certain quantum concepts, such as superposition states, entanglement, 'spooky action at a distance' and tunnelling through insulating walls, being somewhat counterintuitive, they are no doubt extremely useful constructs in theoretical and experimental physics. More uncertain, however, is whether or not these concepts are fundamental to biology and living processes. Of course, at the fundamental level all things are quantum, because all things are built from the quantized states and rules that govern atoms. But when does the quantum mechanical toolkit become the best tool for the job? This review looks at four areas of 'quantum effects in biology'. These are biosystems that are very diverse in detail but possess some commonality. They are all (i) effects in biology: rates of a signal (or information) that can be calculated from a form of the 'golden rule' and (ii) they are all protein-pigment (or ligand) complex systems. It is shown, beginning with the rate equation, that all these systems may contain some degree of quantumeffect, and where experimental evidence is available, it is explored to determine how the quantum analysis aids in understanding of the process.


Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision


title: Stiffness Design of a Resonance-Based Planar Snake Robot With Parallel Elastic Actuators

abstract: This letter proposes an $N$ -link snake robot with parallel elastic actuators (PEA) and a stiffness property that reduces the energy consumption during fast movement. Each PEA couples the actuator output to a spring element, enhancing the periodic gait as demonstrated in previous studies of walking robots. The PEA can mimic each joint of a typical snake robot, which is also controlled by sinusoidal periodic signals. If the actuator output is synchronized with the resonance frequency of the spring, the PEA can greatly reduce the actuator torque and power. Therefore, this letter proposes PEAs to reduce the energy consumption of $N$ -link snake robots. After analyzing the robot dynamics, we explain how the torque of each joint is minimized. The energy consumption is evaluated by measuring the motor current in experimental tests. During fast movement of the robot, the PEA reduced the current consumption by 72%.


title: The Natural Selection of Words: Finding the Features of Fitness

abstract: We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future leader of a synset: the word in the synset that will have the highest frequency. The algorithm uses features based on a word’s length, the characters in the word, and the historical frequencies of the word. It can predict change of leadership (including the identity of the new leader) fifty years in the future, with an F-score considerably above random guessing. Analysis of the learned models provides insight into the causes of change in the leader of a synset. The algorithm confirms observations linguists have made, such as the trend to replace the -ise suffix with -ize, the rivalry between the -ity and -ness suffixes, and the struggle between economy (shorter words are easier to remember and to write) and clarity (longer words are more distinctive and less likely to be confused with one another). The results indicate that integration of the Google Books Ngram Corpus with WordNet has significant potential for improving our understanding of how language evolves.


title: Using Ant Algorithm to Arrange Taxiway Sequencing in Airport

abstract: Over the last decade, air traffic flow at airports experienced an increase never seen before. Whereas several improvements have been achieved in enlarging the en-route traffic capacity, little has been done in order to decrease congestion on the airport surface. Aircraft taxiing is an expensive process, responsible for causing delays for both passengers and airlines and pollution problems. This paper presents an optimization solution for aircraft taxi-scheduling problem using Ant Colony Optimization, a method for search and optimization inspired by the behavior of real ants in nature. The simulations presented were based on real data from Brasilia International Airport. However, the developed model can be used in any airport for optimal taxiing routes searching. The paper has shown the optimization of taxiing to be efficient in achieving minimization of aircraft taxi time and complies with security constraints at airports. Index terms – Ant algorithm, air traffic flow management, optimization, simulation, taxiway sequencing


title: Maximum Likelihood Sound Source Localization and Beamforming for Directional Microphone Arrays in Distributed Meetings

abstract: In distributed meeting applications, microphone arrays have been widely used to capture superior speech sound and perform speaker localization through sound source localization (SSL) and beamforming. This paper presents a unified maximum likelihood framework of these two techniques, and demonstrates how such a framework can be adapted to create efficient SSL and beamforming algorithms for reverberant rooms and unknown directional patterns of microphones. The proposed method is closely related to steered response power-based algorithms, which are known to work extremely well in real-world environments. We demonstrate the effectiveness of the proposed method on challenging synthetic and real-world datasets, including over six hours of recorded meetings.


title: Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

abstract: Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for fine-grained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics. We show that mid-level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class-specific discriminative patches without extra part or bounding box annotations. Such a filter bank is well structured, properly initialized and discriminatively learned through a novel asymmetric multi-stream architecture with convolutional filter supervision and a non-random layer initialization. Experimental results show that our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach.


title: Trajectories as Topics: Multi-Object Tracking by Topic Discovery

abstract: This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet process mixture model. The tracking problem is cast as a topic-discovery task, where the video sequence is treated analogously to a document. It addresses tracking issues such as object exclusivity constraints as well as tracking management without the need for heuristic thresholds. Variation of object appearance is modeled as the dynamics of word co-occurrence and handled by updating the cluster parameters across the sequence in the dynamical clustering procedure. We develop two kinds of visual representation based on super-pixel and deformable part model and integrate them into the model of automatic topic discovery for tracking rigid and non-rigid objects, respectively. In experiments on public data sets, we demonstrate the effectiveness of the proposed algorithm.


Q Methodology, Communication, and the Behavioral Text.


title: Elicitation for Preferences Single Peaked on Trees

abstract: In multiagent systems, we often have a set of agents each of which have a preference ordering over a set of items and one would like to know these preference orderings for various tasks, for example, data analysis, preference aggregation, voting etc. However, we often have a large number of items which makes it impractical to ask the agents for their complete preference ordering. In such scenarios, we usually elicit these agents' preferences by asking (a hopefully small number of) comparison queries --- asking an agent to compare two items. Prior works on preference elicitation focus on unrestricted domain and the domain of single peaked preferences and show that the preferences in single peaked domain can be elicited by much less number of queries compared to unrestricted domain. We extend this line of research and study preference elicitation for single peaked preferences on trees which is a strict superset of the domain of single peaked preferences. We show that the query complexity crucially depends on the number of leaves, the path cover number, and the distance from path of the underlying single peaked tree, whereas the other natural parameters like maximum degree, diameter, pathwidth do not play any direct role in determining query complexity. We then investigate the query complexity for finding a weak Condorcet winner for preferences single peaked on a tree and show that this task has much less query complexity than preference elicitation. Here again we observe that the number of leaves in the underlying single peaked tree and the path cover number of the tree influence the query complexity of the problem.


title: Discriminative Deep Random Walk for Network Classification

abstract: Deep Random Walk (DeepWalk) can learn a latent space representation for describing the topological structure of a network. However, for relational network classification, DeepWalk can be suboptimal as it lacks a mechanism to optimize the objective of the target task. In this paper, we present Discriminative Deep Random Walk (DDRW), a novel method for relational network classification. By solving a joint optimization problem, DDRW can learn the latent space representations that well capture the topological structure and meanwhile are discriminative for the network classification task. Our experimental results on several real social networks demonstrate that DDRW significantly outperforms DeepWalk on multilabel network classification tasks, while retaining the topological structure in the latent space. DDRW is stable and consistently outperforms the baseline methods by various percentages of labeled data. DDRW is also an online method that is scalable and can be naturally parallelized.


title: CapWalk: a capacitive recognition of walking-based activities as a wearable assistive technology

abstract: In this research project, we present an alternative approach to recognize various walking-based activities based on the technology of capacitive sensing. While accelerometry-based walking detections suffer from reduced accuracy at low speeds, the technology of capacitive sensing uses physical distance parameters, which makes it invariant to the duration of step performance. Determining accurate levels of walking activity is a crucial factor for people who perform walking with tiny step lengths such as elderlies or patients with pathologic conditions. In contrast to other gait analysis solutions, CapWalk is mobile and less affected by external influences such as bad lighting conditions, while it is also invariant to external acceleration artifacts. Our approach enables a reliable recognition of very slow walking speeds, in which accelerometer-based implementations can fail or provide high deviations. In CapWalk we present three different capacitive sensing prototypes (Leg Band, Chest Band, Insole) in the setup of loading mode to demonstrate recognition of sneaking, normal walking, fast walking, jogging, and walking while carrying weight. Our designs are wearable and could easily be integrated into wearable objects, such as shoes, pants or jackets. We envision such gathered information to be used to assist certain user groups such as diabetics, whose optimal insulin dose is depending on bread units and physical activity or elderlies whose personalized dosage of medication can be better determined based on their physical activity.


title: Viden: Attacker Identification on In-Vehicle Networks

abstract: Various defense schemes --- which determine the presence of an attack on the in-vehicle network --- have recently been proposed. However, they fail to identify which Electronic Control Unit (ECU) actually mounted the attack. Clearly, pinpointing the attacker ECU is essential for fast/efficient forensic, isolation, security patch, etc. To meet this need, we propose a novel scheme, called Viden (Voltage-based attacker identification), which can identify the attacker ECU by measuring and utilizing voltages on the in-vehicle network. The first phase of Viden, called ACK learning, determines whether or not the measured voltage signals really originate from the genuine message transmitter. Viden then exploits the voltage measurements to construct and update the transmitter ECUs' voltage profiles as their fingerprints. It finally uses the voltage profiles to identify the attacker ECU. Since Viden adapts its profiles to changes inside/outside of the vehicle, it can pinpoint the attacker ECU under various conditions. Moreover, its efficiency and design-compliance with modern in-vehicle network implementations make Viden practical and easily deployable. Our extensive experimental evaluations on both a CAN bus prototype and two real vehicles have shown that Viden can accurately fingerprint ECUs based solely on voltage measurements and thus identify the attacker ECU with a low false identification rate of 0.2%.


title: A survey on issues in non-functional requirements elicitation

abstract: One of the primary responsibilities of requirement engineer is the elicitation of non-functional and functional requirements. Functional requirements sketch out the functionality, the system has to be performed while non-functional requirements compel the restrictions on this functionality. Software industry is raising its demand for not only the required functionality but the necessity of non-functional requirements too such as performance, security, usability, security, privacy etc. The current practice in the industry is to deal with functional requirements as first class requirements while non-functional requirements are only catered at design and implementation level. This approach leaves a serious flaw in the system design towards user satisfaction and often results in the failure of projects such as the classic example of London Ambulance System[1]. The chances of software success can be maximized when dealing with NFR at the requirements level. This literature survey is a first effort of its kind to seek key NFR challenges and issues while elicitation level of requirement engineering. It also find-outs the approaches and methods that are suggested in literature to deal with these issues.


title: Adaptive controller for motion control of an omni-directional walker

abstract: Walking is a vital exercise for health promotion and a fundamental ability necessary for everyday life. In the authors' previous studies, an omni-directional walker was developed for walking rehabilitation. Walking training programs are stored in the walker and the walker must precisely follow the paths defined in the walking training programs to guarantee the effectiveness of rehabilitation. In the previous study, an adaptive control method has been proposed for path tracking of the walker considering a center of gravity shift and load change. In this paper simulations and running experiments are carried out to verify the proposed adaptive control method. First, the kinematics and the kinetics of the omni-directional walker motion are described. Second, the adaptive control strategy is presented. Finally, path tracking simulations and experiments are carried out using the proposed method. Comparing with the proportional-integral-derivative control (PID control), the simulation and experiment results demonstrate the feasibility and effectiveness of the adaptive control method.


title: Data structures and algorithms for nearest neighbor search in general metric spaces

abstract: We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation 1s very high. Also relevant are high-dimensional Euclidian settings in which the distribution of data is in some sense of lower dimension and embedded in the space. The up-tree (vantage point tree) is introduced in several forms, together‘ with &&ciated algorithms, as an improved method for these difficult search nroblems. Tree construcI tion executes in O(nlog(n i ) time, and search is under certain circumstances and in the imit, O(log(n)) expected time. The theoretical basis for this approach is developed and the results of several experiments are reported. In Euclidian cases, kd-tree performance is compared.


title: MO-SLAM: Multi object SLAM with run-time object discovery through duplicates

abstract: In this paper, we present MO-SLAM, a novel visual SLAM system that is capable of detecting duplicate objects in the scene during run-time without requiring an offline training stage to pre-populate a database of objects. Instead, we propose a novel method to detect landmarks that belong to duplicate objects. Further, we show how landmarks belonging to duplicate objects can be converted to first-order entities which generate additional constraints for optimizing the map. We evaluate the performance of MO-SLAM with extensive experiments on both synthetic and real data, where the experimental results verify the capabilities of MO-SLAM in detecting duplicate objects and using these constraints to improve the accuracy of the map.


title: Towards Security as a Service (SecaaS): On the modeling of Security Services for Cloud Computing

abstract: The security of software services accessible via the Internet has always been a crosscutting non-functional requirement of uttermost importance. The recent advent of the Cloud Computing paradigm and its wide diffusion has given birth to new challenges towards the securing of existing Cloud services, by properly accounting the issues related to their delivery models and their usage patterns, and has opened the way to the new concept of Security as a Service(SecaaS), i.e. the ability of developing reusable software services which can be composed with standard Cloud services in order to offer them the suitable security features. In this context, there is a strong need for methods and tools for the modeling of security concerns, as well as for evaluation techniques, for supporting both the comparison of different design choices and the analysis of their impact on the behavior of new services before their actual realization. This paper proposes a meta-model for supporting the modeling of Security Services in a Cloud Computing environment as well as an approach for guiding the identification and the integration of security services within the standard Cloud delivery models. The proposal is exemplified through a case study.


title: The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching

abstract: The ASSISTments project is an ecosystem of a few hundred teachers, a platform, and researchers working together. Development professionals help train teachers and get teachers to participate in studies. The platform and these teachers help researchers (sometimes explicitly and sometimes implicitly) simply by using content the teacher selects. The platform, hosted by Worcester Polytechnic Institute, allows teachers to write individual ASSISTments (composed of questions with answers and associated hints, solutions, web-based videos, etc.) or to use pre-built ASSISTments, bundle them together in a problem set, and assign these to students. The system gives immediate feedback to students while they are working and provides student-level data to teachers on any assignment. The word “ASSISTments” blends tutoring “assistance” with “assessment” reporting to teachers and students. While originally focused on mathematics, the platform now has content from many other subjects (e.g., science, English, Statistics, etc.). Due to the large library of mathematics content, however, it is mostly used by math teachers. Over 50,000 students used ASSISTments last school year (2013–4) and this number has been doubling each year for the last 8 years. The platform allows any user, mostly researchers, to create randomized controlled trials in the content, which has helped us use the tool in over 18 published and an equal number of unpublished studies. The data collected by the system has also been used in a few dozen peer-reviewed data mining publications. This paper will not seek to review these publications, but instead we will share why ASSISTments has been successful and what lessons were learned along the way. The first lesson learned was to build a platform for learning sciences, not a product that focused on a math topic. That is, ASSISTments is a tool, not a curriculum. A second lesson learned is expressed by the mantra “Put the teacher in charge, not the computer.” This second lesson is about building a flexible system that allows teachers to use the tool in concert with the classroom routine. Once teachers are using the tool they are more likely to want to participate in research studies. These lessons were born from the design decisions about what the platform supports and does not support. In conclusion, goals for the future will be presented.


title: An empirical study of an informal knowledge repository in a medium-sized software consulting company

abstract: Numerous studies have been conducted on design and architecture of knowledge repositories. This paper addresses the need for looking at practices where knowledge repositories are actually used in concrete work situations. This insight should be used when developing knowledge repositories in the future. Through methods inspired by ethnography this paper investigates how an unstructured knowledge repository is used for different purposes by software developers and managers in a medium-sized software consulting company. The repository is a part of the company's knowledge management tool suite on the Intranet. We found five distinct ways of using the tool, from solving specific technical problems to getting an overview of competence in the company. We highlight the importance of informal organization and the social integration of the tool in the daily work practices of the company.


title: Anomaly Detection Using Real-Valued Negative Selection

abstract: This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.


title: Passenger Flow Predictions at Sydney International Airport: A Data-Driven Queuing Approach

abstract: Time spent in processing zones at an airport are an important part of the passenger's airport experience. It undercuts the time spent in the rest of the airport, and therefore the revenue that could be generated from shopping and dining. It can also result in passengers missing flights and connections, which has significant operational repercussions. Inadequate staffing levels are often to blame for large congestion at an airport. In this paper, we present a stochastic simulation that estimates the operational uncertainty in passenger processing at immigration. Congestion and delays are estimated on arrivals and departures based on scheduled flight departures and arrivals. We demonstrate the use of cellular tracking data in refining the model, and an approach to controlling congestion by adjusting staffing levels.


title: Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks

abstract: Vasculature is known to be of key biological significance, especially in the study of cancer. As such, considerable effort has been focused on the automated measurement and analysis of vasculature in medical and pre-clinical images. In tumors in particular, the vascular networks may be extremely irregular and the appearance of the individual vessels may not conform to classical descriptions of vascular appearance. Typically, vessels are extracted by either a segmentation and thinning pipeline, or by direct tracking. Neither of these methods are well suited to microscopy images of tumor vasculature. In order to address this we propose a method to directly extract a medial representation of the vessels using Convolutional Neural Networks. We then show that these two-dimensional centerlines can be meaningfully extended into 3D in anisotropic and complex microscopy images using the recently popularized Convolutional Long Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this hybrid convolutional-recurrent architecture over both 2D and 3D convolutional comparators.


title: Improving frequency stability in low inertia power systems using synthetic inertia from wind turbines

abstract: In response to ongoing worldwide theme of sustainable development, more and more fossil energy based conventional synchronous generators are being replaced by renewable energy sources for the electricity production in modern power systems. However, some low inertia issues will be brought into future power systems, which will not be conducive to system frequency stability. This paper aims at improving frequency stability in low inertia power systems by using synthetic inertia from DFIG based wind turbines. All simulation work involved in this paper is performed on the software platform MATLAB/SIMULINK. The test model is chosen to be a power system connected by an integrated DFIG based wind farm. After modification and validation the chosen test model will be applied to simulate the GB equivalent large system model. It can be eventually verified that the impacts of synthetic inertia from wind turbines will become more significant on system frequency stability in terms of both ROCOF and frequency nadir when more wind farms are being integrated into future low inertia GB power system.


title: U.S. Minority Homeless Youth’s Access to and Use of Mobile Phones: Implications for mHealth Intervention Design

abstract: Few interventions for homeless youth have leveraged the potential of mHealth technologies, in part because of the limited data on phone behaviors, perceptions, and intervention preferences among youth experiencing homelessness. We conducted 9 focus groups (n = 52 homeless youth) and 41 individual structured interviews also with homeless youth in underserved communities in Baltimore and Washington, DC, to ascertain how youth perceived their mobile phone, acquired and maintained mobile services over time, and thought mHealth programs for this population should be designed. We also measured phone use, functionality, source, duration of ownership, and reasons for changing phones or numbers. Results showed that mobile coverage was high, as most youth self-purchased phones or received gift payments from others. Maintaining mobile connectivity was often challenging because of financial constraints and interpersonal conflict. Youth valued phones to access social support but used several tactics to avoid perceived...


title: New disinfection and sterilization methods.

abstract: Address for correspondence: William A. Rutala, 547 Burnett-Womack, CB #7030, Division of Infectious Diseases, UNC at Chapel Hill, Chapel Hill, NC 27599-7030; fax: 919-966-6714; e-mail: brutala@unch.unc.edu New disinfection methods include a persistent antimicrobial coating that can be applied to inanimate and animate objects (Surfacine), a high-level disinfectant with reduced exposure time (orthophthalaldehyde), and an antimicrobial agent that can be applied to animate and inanimate objects (superoxidized water). New sterilization methods include a chemical sterilization process for endoscopes that integrates cleaning (Endoclens), a rapid (4-hour) readout biological indicator for ethylene oxide sterilization (Attest), and a hydrogen peroxide plasma sterilizer that has a shorter cycle time and improved efficacy (Sterrad 50).


title: Frequency-to-output-power transfer function measurement of a resonant inverter for domestic induction heating applications

abstract: This paper presents a method to measure off-line the frequency-to-output-power transfer function of a resonant inverter in open-loop. This method is applied to a half-bridge series resonant inverter for domestic induction heating applications. A sinusoidal perturbation is injected to the nominal value of the switching frequency, leading to a perturbation in the power transferred to the load. The Discrete Fourier Transform (DFT) is used off-line to process the digitized input and outputs in order to compute the gain and phase of the transfer function at the injected perturbation frequency. The digitized input is the saw-tooth waveform used in the trailing-edge modulator. This waveform is generated with a commercial function generator that allows modulating the frequency with an internal sine waveform. Since the internal modulation signal is not accessible, the sinusoidal switching frequency perturbation is off-line obtained by processing the ramp slope of the saw-tooth waveform. The digitized outputs are the inductor current and load voltage, which are processed to obtain the moving average output power. The algorithm accuracy is firstly evaluated in simulation, leading to an appropriate method to extract the ramp slope of the saw-tooth waveform. Finally, the proposed method is experimentally verified, evaluating the validity range of the EDF model extended for several harmonics applied to resonant power converters.


title: Audio Music Genre Classification Using Different Classifiers and Feature Selection Methods

abstract: We examine performance of different classifiers on different audio feature sets to determine the genre of a given music piece. For each classifier, we also evaluate performances of feature sets obtained by dimensionality reduction methods. Finally, we experiment on increasing classification accuracy by combining different classifiers. Using a set of different classifiers, we first obtain a test genre classification accuracy of around 79.6 plusmn 4.2% on 10 genre set of 1000 music pieces. This performance is better than 71.1 plusmn 7.3% which is the best that has been reported on this data set. We also obtain 80% classification accuracy by using dimensionality reduction or combining different classifiers. We observe that the best feature set depends on the classifier used


Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems


title: Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

abstract: Background ::: With the fast advances in nextgen sequencing technology, high-throughput RNA sequencing has emerged as a powerful and cost-effective way for transcriptome study. De novo assembly of transcripts provides an important solution to transcriptome analysis for organisms with no reference genome. However, there lacked understanding on how the different variables affected assembly outcomes, and there was no consensus on how to approach an optimal solution by selecting software tool and suitable strategy based on the properties of RNA-Seq data.


title: DiSMEC: Distributed Sparse Machines for Extreme Multi-label Classification

abstract: Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. Datasets in extreme classification exhibit fit to power-law distribution, i.e. a large fraction of labels have very few positive instances in the data distribution. Most state-of-the-art approaches for extreme multi-label classification attempt to capture correlation among labels by embedding the label matrix to a low-dimensional linear sub-space. However, in the presence of power-law distributed extremely large and diverse label spaces, structural assumptions such as low rank can be easily violated. In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size. Unlike most state-of-the-art methods, DiSMEC does not make any low rank assumptions on the label matrix. Using double layer of parallelization, DiSMEC can learn classifiers for datasets consisting hundreds of thousands labels within few hours. The explicit capacity control mechanism filters out spurious parameters which keep the model compact in size, without losing prediction accuracy. We conduct extensive empirical evaluation on publicly available real-world datasets consisting upto 670,000 labels. We compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which is a leading approach for learning sparse local embeddings, and FastXML which is a tree-based approach optimizing ranking based loss function. On some of the datasets, DiSMEC can significantly boost prediction accuracies - 10% better compared to SLECC and 15% better compared to FastXML, in absolute terms.


A Universal Modular ACTOR Formalism for Artificial Intelligence


title: GEOMETRIC APPROACH TO SOLVING THE INVERSE DISPLACEMENT PROBLEM OF CALIBRATED DECOUPLED 6R SERIAL ROBOTS

abstract: This paper presents a simple but efficient way to numerically calculate the inverse displacement problem of calibrated decoupled 6R serial robots. The method is iterative and works with any type of calibrated robot model, such as level-3 models, since it requires no algebraic computation and no resolution of high-order polynomials, only the computation of the forward displacement problem of the calibrated robot model and the inverse kinematics of the nominal robot model. The method proposed can find up to eight possible solutions for a given end-effector pose. A numerical example is presented, with one million arbitrary end-effector poses of a level-3 calibrated ABB IRB 120 robot. The computation time for solving the inverse problem is analyzed, and in most cases is found to be only four times the time needed to calculate the nominal inverse kinematics and the calibrated direct kinematics. Furthermore, the method is fast enough to be implemented directly into the robot controller using the RAPID programmi...


Gender and Gaming : A Literature


title: A hidden Markov model information retrieval system

abstract: We present a new method for information retrieval using hidden Markov models (HMMs). We develop a general framework for incorporating multiple word generation mechanisms within the same model. We then demonstrate that an extremely simple realization of this model substantially outperforms standard tf :idf ranking on both the TREC-6 and TREC-7 ad hoc retrieval tasks. We go on to present a novel method for performing blind feedback in the HMM framework, a more complex HMM that models bigram production, and several other algorithmic re nements. Together, these methods form a state-of-the-art retrieval system that ranked among the best on the TREC-7 ad hoc retrieval task.


title: From Abstraction to Implementation: Can Computational Thinking Improve Complex Real-World Problem Solving? A Computational Thinking-Based Approach to the SDGs

abstract: Utilizing concepts derived from computational thinking—a method of thinking coined by Jeanette Wing—a problem-solving paradigm is presented to demonstrate the applicability of thinking computationally beyond the realm of computer science. The 17 Sustainable Development Goals (SDGs) are used as a set of real world problems to elaborate the function of the proposed four-stage paradigm. The paradigm seeks to provide a method of approaching problems with the aim of finding local and contextualized solutions that reach all members of different societies. This paper also serves as the foundation of further research in the development of computational thinking as a fundamental tool for finding solutions in a broad scope of disciplines and real-world situations.


title: Transfer Learning for Speech Recognition on a Budget

abstract: End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources. We explore transfer learning based on model adaptation as an approach for training ASR models under constrained GPU memory, throughput and training data. We conduct several systematic experiments adapting a Wav2Letter convolutional neural network originally trained for English ASR to the German language. We show that this technique allows faster training on consumer-grade resources while requiring less training data in order to achieve the same accuracy, thereby lowering the cost of training ASR models in other languages. Model introspection revealed that small adaptations to the network's weights were sufficient for good performance, especially for inner layers.


title: Network Models of Financial Systemic Risk 13frg184

abstract: In the aftermath of the 2008 financial meltdown, scientists and economists have rushed into the field of network science as it can be applied financial systemic risk. This exciting new area of research brings challenges from both the understanding of complex adaptive systems and the economics of financial institutions. Since this event, research groups around the world have been created, all with a common goal to better understand the linkages between banks, and the chains of defaults that may be transmitted through these linkages. Current understanding sees financial systemic risk as being the risk that a modest sized shock to the financial system triggers a domino-type cascade of bank failures that could engulf a significant fraction of the network. One can identify four key channels through which systemic risk may flow: the impact of large correlated losses across asset classes, cascades of default, cascades of funding illiquidity and market illiquidity leading to major firesales of assets. The first and fourth effects are not really network effects, but network science can have a great deal to say about the risks associated with cascades of default and illiquidity. The stakes are high in this research area. The potential consequences of preventing the next global financial crisis are of course enormous. Understanding the factors that drive systemic risk, has the potential to yield valuable tools for central bankers and financial regulators, and to inform the current international debate on financial regulation. Our focussed research group consisted of members of the research team working on a project called “Financial Systemic Risk: a Network Science Approach”, sponsored by the Global Risk Institute in Toronto. The major aim of this project is to combine Monte Carlo methods and the analytical framework based on Watts’ 2002 information cascade paper [10], and apply these techniques to increasingly sophisticated models of financial risk networks.


title: Randomized controlled trial of cognitive behavioral therapy and acceptance and commitment therapy for social phobia: Outcomes and moderators.

abstract: Objective: Cognitive behavioral therapy (CBT) is an empirically supported treatment for social phobia. However, not all individuals respond to treatment and many who show improvement do not maintain their gains over the long-term. Thus, alternative treatments are needed. Method: The current study (N = 87) was a 3-arm randomized clinical trial comparing CBT, acceptance and commitment therapy (ACT), and a wait-list control group (WL) in participants with a diagnosis of social phobia based on criteria of the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994). Participants completed 12 sessions of CBT or ACT or a 12-week waiting period. All participants completed assessments at baseline and posttreatment, and participants assigned to CBT and ACT also completed assessments 6 and 12 months following baseline. Assessments consisted of self-report measures, a public-speaking task, and clinician ratings. Results: Multilevel modeling was used to examine between-group differences on outcomes measures. Both treatment groups outperformed WL, with no differences observed between CBT and ACT on self-report, independent clinician, or public-speaking outcomes. Lower self-reported psychological flexibility at baseline was associated with greater improvement by the 12-month follow-up in CBT compared with ACT. Self-reported fear of negative evaluation significantly moderated outcomes as well, with trends for both extremes to be associated with superior outcomes from CBT and inferior outcomes from ACT. Across treatment groups, higher perceived control and extraversion were associated with greater improvement, whereas comorbid depression was associated with poorer outcomes. Conclusions: Implications for clinical practice and future research are discussed. (PsycINFO Database Record © 2014 APA, all rights reserved).


title: Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes

abstract: We explore approximate policy iteration, replacing the usual cost-function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-specific planners for classical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.


title: An Examination of the Communication Styles Associated with Psychopathy and Their Influence on Observer Impressions

abstract: Psychopathic individuals are characterized as “intra-species predators”—callous, impulsive, aggressive, and proficient at interpersonal manipulation. For example, despite their high risk for re-offending, psychopathic offenders often receive early release on parole. While reputed to be social chameleons, research suggests that even naive observers can accurately infer high levels of psychopathic traits in others with very brief exposures to behavior, but accuracy degrades with extended observation. We utilized a lens model approach to examine the communication styles (emotional facial expressions, body language, and verbal content) of offenders varying in levels of psychopathic traits using “thin slice” video clips of psychological assessment interviews and to reveal which cues observers use to inform their evaluations of psychopathy. Psychopathic traits were associated with more (a) Duchenne smiles, (b) negative (angry) emotional language, and (c) hand gestures (illustrators). Further, psychopathy was associated with a marked behavioral incongruence; when individuals scoring high in psychopathic traits engaged in Duchenne smiles they were also more likely to use angry language. Naive observers relied on each of these valid behavioral signals to quickly and accurately detect psychopathic traits. These findings provide insight into psychopathic communication styles, opportunities for improving the detection of psychopathic personality traits, and may provide an avenue for understanding successful psychopathic manipulation.


title: Evolutionary software development using Test Driven approach

abstract: Test-Driven Development (TDD) is an evolutionary approach in which unit tests are incrementally written prior to code implementation. This paper will discuss about TDD and will compare its development procedure with that of traditional (i.e. test-last) methodology. The TDD approach is radically different from traditional approach; in traditional approach, tests are written once the production code has been completely written down, unlike TDD where tests are written prior to writing the code. Although Test driven development has attracted the attention of professionals, the empirical evidence of its superiority over traditional approach is very limited. In this report we will discuss both approaches and also try to put some light over how does TDD stand along with other SDLC models. In addition, we will look whether TDD has any influence on real world projects; if yes then how?


title: Seized designer supplement named “1-Androsterone”: Identification as 3β-hydroxy-5α-androst-1-en-17-one and its urinary elimination

abstract: New analogues of androgens that had never been available as approved drugs are marketed as "dietary supplement" recently. They are mainly advertised to promote muscle mass and are considered by the governmental authorities in various countries, as well as by the World Anti-doping Agency for sport, as being pharmacologically and/or chemically related to anabolic steroids. In the present study, we report the detection of a steroid in a product seized by the State Bureau of Criminal Investigation Schleswig-Holstein, Germany. The product "1-Androsterone" of the brand name "Advanced Muscle Science" was labeled to contain 100mg of "1-Androstene-3b-ol,17-one" per capsule. The product was analyzed underivatized and as bis-TMS derivative by GC-MS. The steroid was identified by comparison with chemically synthesized 3β-hydroxy-5α-androst-1-en-17-one, prepared by reduction of 5α-androst-1-ene-3,17-dione with LS-Selectride (Lithium tris-isoamylborohydride), and by nuclear magnetic resonance. Semi-quantitation revealed an amount of 3β-hydroxy-5α-androst-1-en-17-one in the capsules as labeled. Following oral administration to a male volunteer, the main urinary metabolites were monitored. 1-Testosterone (17β-hydroxy-5α-androst-1-en-3-one), 1-androstenedione (5α-androst-1-ene-3,17-dione), 3α-hydroxy-5α-androst-1-en-17-one, 5α-androst-1-ene-3α,17β-diol, and 5α-androst-1-ene-3β,17β-diol were detected besides the parent compound and two more metabolites (up to now not finally identified but most likely C-18 and C-19 hydroxylated 5α-androst-1-ene-3,17-diones). Additionally, common steroids of the urinary steroid profile were altered after the administration of "1-Androsterone". Especially the ratios of androsterone/etiocholanolone and 5α-/5β-androstane-3α,17β-diol and the concentration of 5α-dihydrotestosterone were influenced. 3α-Hydroxy-5α-androst-1-en-17-one appears to be suitable for the long-term detection of the steroid (ab-)use, as this characteristic metabolite was detectable in screening up to nine days after a single administration of one capsule.


title: IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks

abstract: In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the recently-developed interleaved group convolutions (IGC) and its variants (e.g., Xception), has been attracting increasing interests. Motivated by the observation that the convolutions contained in a group convolution in IGC can be further decomposed in the same manner, we present a modularized building block, {IGCV$2$:} interleaved structured sparse convolutions. It generalizes interleaved group convolutions, which is composed of two structured sparse kernels, to the product of more structured sparse kernels, further eliminating the redundancy. We present the complementary condition and the balance condition to guide the design of structured sparse kernels, obtaining a balance among three aspects: model size, computation complexity and classification accuracy. Experimental results demonstrate the advantage on the balance among these three aspects compared to interleaved group convolutions and Xception, and competitive performance compared to other state-of-the-art architecture design methods.


title: Motion compensation and efficient array design for TDMA FMCW MIMO radar systems

abstract: In this paper we present methods for the design of planar frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) arrays with an emphasis on the problem of moving targets in time-division multiple-access (TDMA) systems. We discuss the influence of target motion and boundaries of operation and present a method to compensate for its effects, which requires special attention in the array design and in signal processing. Array design techniques, examples including an implementation, and measurement results are also covered in this article.


title: Transient effects in the backscattered current of a Luttinger liquid

abstract: We study the backscattered current in a Luttinger liquid in the presence of a point like weak impurity switched on at finite time, taking into account finite-temperature effects. We show how the well-known results for a static impurity are distorted. We derive a dimensionless parameter $\tau_{R}$ as function of the electron-electron interaction and the temperature, such that for $\tau_{R}<1$ ($>1$) the switching process is relevant (irrelevant). Our results suggest the possibility of determining the value of the Luttinger parameter $K$ through time measurements in transport experiments at fixed voltage.


title: Algorithmic and human teaching of sequential decision tasks

abstract: A helpful teacher can significantly improve the learning rate of a learning agent. Teaching algorithms have been formally studied within the field of Algorithmic Teaching. These give important insights into how a teacher can select the most informative examples while teaching a new concept. However the field has so far focused purely on classification tasks. In this paper we introduce a novel method for optimally teaching sequential decision tasks. We present an algorithm that automatically selects the set of most informative demonstrations and evaluate it on several navigation tasks. Next, we explore the idea of using this algorithm to produce instructions for humans on how to choose examples when teaching sequential decision tasks. We present a user study that demonstrates the utility of such instructions.


title: Towards distributed use of large-scale ontologies

abstract: Large scale knowledge bases systems are difficult and expensive to construct. If we could share knowledge across systems, costs would be reduced. However, because knowledge bases are typically constructed from scratch, each with their own idiosyncratic structure, sharing is difficult. Recent research has focused on the use of ontologies to promote sharing. An ontology is a hierarchically structured set of terms for describing a domain that can be used as a skeletal foundation for a knowledge base. If two knowledge bases are built on a common ontology, knowledge can be more readily shared, since they share a common underlying structure. This paper outlines a set of desiderata for ontologies, and then describes how we have used a large-scale (50,000+ concept) ontology develop a specialized, domain-specific ontology semiautomatically. We then discuss the relation between ontologies and the process of developing a system, arguing that to be useful, an ontology needs to be created as a "living document", whose development is tightly integrated with the system’s. We conclude with a discussion of Web-based ontology tools we are developing to support this approach.


title: Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks

abstract: Learning distributed sentence representations is one of the key challenges in natural language processing. Previous work demonstrated that a recurrent neural network (RNNs) based sentence encoder trained on a large collection of annotated natural language inference data, is efficient in the transfer learning to facilitate other related tasks. In this paper, we show that joint learning of multiple tasks results in better generalizable sentence representations by conducting extensive experiments and analysis comparing the multi-task and single-task learned sentence encoders. The quantitative analysis using auxiliary tasks show that multi-task learning helps to embed better semantic information in the sentence representations compared to single-task learning. In addition, we compare multi-task sentence encoders with contextualized word representations and show that combining both of them can further boost the performance of transfer learning.


title: A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

abstract: This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.