title: Recommender Systems for Technology Enhanced Learning

abstract: As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.


title: DNA Solution of the Maximal Clique Problem

abstract: The maximal clique problem has been solved by means of molecular biology techniques. A pool of DNA molecules corresponding to the total ensemble of six-vertex cliques was built, followed by a series of selection processes. The algorithm is highly parallel and has satisfactory fidelity. This work represents further evidence for the ability of DNA computing to solve NP-complete search problems.


title: The oblique popliteal ligament: an anatomic and MRI investigation

abstract: Purpose ::: Despite being the largest ligament on the posterior aspect of the knee, relatively little is known about the normal morphology of the oblique popliteal ligament (OPL). The aim of this study was to investigate the detailed anatomy of the OPL in cadavers and healthy volunteers.


The genetics of Phycomyces blakesleeanus.


title: Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition

abstract: In this paper we present an OCR for Handwritten Devnagari Characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features are computed by dividing the character image into different segments. Weighted majority voting technique is used for combining the classification decision obtained from four Multi Layer Perceptron(MLP) based classifier. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.80% as we considered top five choices results. This method is compared with other recent methods for Handwritten Devnagari Character Recognition and it has been observed that this approach has better success rate than other methods.


title: AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference

abstract: Channel pruning is an important family of methods to speed up deep model's inference. Previous filter pruning algorithms regard channel pruning and model fine-tuning as two independent steps. This paper argues that combining them into a single end-to-end trainable system will lead to better results. We propose an efficient channel selection layer, namely AutoPruner, to find less important filters automatically in a joint training manner. Our AutoPruner takes previous activation responses as an input and generates a true binary index code for pruning. Hence, all the filters corresponding to zero index values can be removed safely after training. We empirically demonstrate that the gradient information of this channel selection layer is also helpful for the whole model training. By gradually erasing several weak filters, we can prevent an excessive drop in model accuracy. Compared with previous state-of-the-art pruning algorithms (including training from scratch), AutoPruner achieves significantly better performance. Furthermore, ablation experiments show that the proposed novel mini-batch pooling and binarization operations are vital for the success of filter pruning.


title: Distribution Aware Active Learning

abstract: Discriminative learning machines often need a large set of labeled samples for training. Active learning (AL) settings assume that the learner has the freedom to ask an oracle to label its desired samples. Traditional AL algorithms heuristically choose query samples about which the current learner is uncertain. This strategy does not make good use of the structure of the dataset at hand and is prone to be misguided by outliers. To alleviate this problem, we propose to distill the structural information into a probabilistic generative model which acts as a \emph{teacher} in our model. The active \emph{learner} uses this information effectively at each cycle of active learning. The proposed method is generic and does not depend on the type of learner and teacher. We then suggest a query criterion for active learning that is aware of distribution of data and is more robust against outliers. Our method can be combined readily with several other query criteria for active learning. We provide the formulation and empirically show our idea via toy and real examples.


title: 3D Human Pose Estimation with Relational Networks

abstract: In this paper, we propose a novel 3D human pose estimation algorithm from a single image based on neural networks. We adopted the structure of the relational networks in order to capture the relations among different body parts. In our method, each pair of different body parts generates features, and the average of the features from all the pairs are used for 3D pose estimation. In addition, we propose a dropout method that can be used in relational modules, which inherently imposes robustness to the occlusions. The proposed network achieves state-of-the-art performance for 3D pose estimation in Human 3.6M dataset, and it effectively produces plausible results even in the existence of missing joints.


title: Rumor Diffusion and Convergence during the 3.11 Earthquake: A Twitter Case Study

abstract: We focus on Internet rumors and present an empirical analysis and simulation results of their diffusion and convergence during emergencies. In particular, we study one rumor that appeared in the immediate aftermath of the Great East Japan Earthquake on March 11, 2011, which later turned out to be misinformation. By investigating whole Japanese tweets that were sent one week after the quake, we show that one correction tweet, which originated from a city hall account, diffused enormously. We also demonstrate a stochastic agent-based model, which is inspired by contagion model of epidemics SIR, can reproduce observed rumor dynamics. Our model can estimate the rumor infection rate as well as the number of people who still believe in the rumor that cannot be observed directly. For applications, rumor diffusion sizes can be estimated in various scenarios by combining our model with the real data.


title: Millimeter-wave large-scale phased-arrays for 5G systems

abstract: This talk will present our latest work on silicon RFICs for phased-array applications with emphasis on very large chips with built-in-self-test capabilities for 5G systems. SiGe is shown to be ideal for mm-wave applications due to its high temperature performance (automotive radars, base-stations, defense systems, etc.) and lower power consumption. These chips drastically reduce the cost of microwave and millimeter-wave phased arrays by combining many elements on the same chip, together with digital control and some cases, high-efficiency antennas. The phased-array chips also result in an easier packaging scheme using either a multi-layer PCB or wafer-level packages. We believe that this family of chips will be essential for millimeter-wave 5G communication systems.


title: Predicting mutation outcome from early stochastic variation in genetic interaction partners

abstract: Many mutations, including those that cause disease, only have a detrimental effect in a subset of individuals. The reasons for this are usually unknown, but may include additional genetic variation and environmental risk factors. However, phenotypic discordance remains even in the absence of genetic variation, for example between monozygotic twins, and incomplete penetrance of mutations is frequent in isogenic model organisms in homogeneous environments. Here we propose a model for incomplete penetrance based on genetic interaction networks. Using Caenorhabditis elegans as a model system, we identify two compensation mechanisms that vary among individuals and influence mutation outcome. First, feedback induction of an ancestral gene duplicate differs across individuals, with high expression masking the effects of a mutation. This supports the hypothesis that redundancy is maintained in genomes to buffer stochastic developmental failure. Second, during normal embryonic development we find that there is substantial variation in the induction of molecular chaperones such as Hsp90 (DAF-21). Chaperones act as promiscuous buffers of genetic variation, and embryos with stronger induction of Hsp90 are less likely to be affected by an inherited mutation. Simultaneously quantifying the variation in these two independent responses allows the phenotypic outcome of a mutation to be more accurately predicted in individuals. Our model and methodology provide a framework for dissecting the causes of incomplete penetrance. Further, the results establish that inter-individual variation in both specific and more general buffering systems combine to determine the outcome inherited mutations in each individual.


title: Hierarchical visuomotor control of humanoids

abstract: We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies. The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment. For a supplementary video link, see this https URL .


title: Siamese network features for image matching

abstract: Finding matching images across large datasets plays a key role in many computer vision applications such as structure-from-motion (SfM), multi-view 3D reconstruction, image retrieval, and image-based localisation. In this paper, we propose finding matching and non-matching pairs of images by representing them with neural network based feature vectors, whose similarity is measured by Euclidean distance. The feature vectors are obtained with convolutional neural networks which are learnt from labeled examples of matching and non-matching image pairs by using a contrastive loss function in a Siamese network architecture. Previously Siamese architecture has been utilised in facial image verification and in matching local image patches, but not yet in generic image retrieval or whole-image matching. Our experimental results show that the proposed features improve matching performance compared to baseline features obtained with networks which are trained for image classification task. The features generalize well and improve matching of images of new landmarks which are not seen at training time. This is despite the fact that the labeling of matching and non-matching pairs is imperfect in our training data. The results are promising considering image retrieval applications, and there is potential for further improvement by utilising more training image pairs with more accurate ground truth labels.


title: A forensic taxonomy of SCADA systems and approach to incident response

abstract: SCADA systems that monitor and control Critical National Infrastructure (CNI) are increasingly becoming the target of advanced cyber-attacks since their convergence with TCP/IP and other networks for efficient controlling. When a SCADA incident occurs the consequences can be catastrophic having an impact on the environment, economy and human life and therefore it is essential for a forensic investigation to take place. SCADA system forensics is an essential process within the cyber-security lifecycle that not only helps to identify the cause of an incident and those responsible but to help develop and design more secure systems of the future. This paper provides an overall forensic taxonomy of the SCADA system incident response model. It discusses the development of forensic readiness within SCADA system investigations, including the challenges faced by the SCADA forensic investigator and suggests ways in which the process may be improved.


title: Social self-efficacy, academic locus of control, and internet addiction

abstract: The purpose of this research is to examine the relationship of internet addiction, social self-efficacy, and academic locus of control. Participants were 311 university students who completed a questionnaire package that included the Online Cognition Scale, the Academic Locus of Control Scale, and the Perceived Social Self-efficacy Scale. The hypothesis model was tested through structural equation modeling. According to results internal academic locus of control was predicted positively by social self-efficacy. Also internet addiction was explained negatively by social self-efficacy and internal academic locus of control and positively by external academic locus of control. Results were discussed in the light of literature.


title: Determinants of the Capital Structure of Small and Medium Sized Brazilian Enterprises

abstract: This research investigates the determinants of the capital structure of small and medium enterprises (SMEs) using a unique database that includes over 19,000 Brazilian firms and spans 13 years of data. The econometric analysis employs the System Generalized Method of Moments estimator (GMM-Sys) and two strong results emerge: (a) profitability is negatively related to leverage, and (b) asset growth is positively related to leverage. Both results are consistent with the pecking order theory of capital structure and suggest that SMEs tend to finance their expansion with debt only after exhausting their internal resources. Additionally, we find weaker evidence for the following: (a) size is positively related to leverage, which can be interpreted as evidence that larger firms have more access to credit markets; (b) riskier SMEs tend to be less financially leveraged, consistent with the bankruptcy cost arguments from trade-off theories; and (c) the age of the firm is negatively related to financial leverage, suggesting that older SMEs may be slightly more conservative in their financing choices. Finally, the magnitude of the coefficient of lagged leverage shows the high persistence of this variable and is compatible with the hypothesis that SMEs adjust their debt/equity ratio towards a target value, although at a low speed.


title: An Intelligent Agent that Learns How to Tutor Students: Design and Results

abstract: In this paper, we describe an intelligent agent that presents different learning content such as tutorials, examples, and problems adaptively to individual students and learns from its interaction with the students how to improve its performance. We have built an end-to-end intelligent tutoring system, premised on the above goal, with a graphical user interface (GUI) front-end, an agent powered by case-based reasoning (CBR), and a mySQL database backend. We use a casebase to store the pedagogical strategies, embedded in the individual cases and the similarity retrieval and adaptation heuristics. Each case has situation, solution and outcome parameters. The situation parameters include the students' static and dynamic profiles and the instructional content's characteristics while the solution parameters specify the characteristics of the example or problem to be delivered to the student. We developed a set of CS1 content that includes five topics and deployed our system in the laboratories. Our results show that when the machine learning mechanism is activated, our agent is able to learn to tutor students more efficiently.


title: A Multimedia English Learning System Using HMMs to Improve Phonemic Awareness for English Learning

abstract: This paper proposes a multimedia English learning (MEL) system, based on Hidden Markov Models (HMMs) and mastery theory strategy, for teaching students with the aim of enhancing their English phonetic awareness and pronunciation. It can analyze phonetic structures, identify and capture pronunciation errors to provide students with targeted advice in pronunciation, intonation, rhythm and volume. In addition, the paper also applies the mastery learning to effectively help students practice pronunciation of English words and sentences. Finally, this paper adopts a quasi-experimental design and lasting for 12 weeks and 120 third-graders, aged 9-10 years, from an elementary school in Yunlin County in Taiwan. These students were recruited and randomly assigned as the experimental group and the control group, respectively. The former used the MEL system, while the latter received the conventional English teaching. Research data were collected through the Phonemic Awareness Test and the English Achievement Test. The results showed that the experimental group with low phonemic awareness performed significantly better than the control group in the English Achievement Test.


Ordinal Regression Analysis: Fitting the Proportional Odds Model Using Stata, SAS and SPSS


title: Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D Model Retrieval

abstract: Aggregating a set of local features has become one of the most common approaches for representing a multi-media data such as 2D image and 3D model. The success of Bag-of-Features (BF) aggregation [2] prompted several extensions to BF, that are, VLAD [12], Fisher Vector (FV) coding [22] and Super Vector (SV) coding [34]. They all learn small number of codewords, or representative local features, by clustering a set of large number of local features. The set of local features extracted from a media data (e.g., an image) is encoded by considering distribution of features around the codewords; BF uses frequency, VLAD and FV uses displacement vector, and SV uses a combination of both. In doing so, these encoding algorithms assume linearity of feature space about a codeword. Consequently, even if the set of features form a non-linear manifold, its non-linearity would be ignored, potentially degrading quality of aggregated features. In this paper, we propose a novel feature aggregation algorithm called Diffusion-on-Manifold (DM) that tries to take into account, via diffusion distance, structure of non-linear manifold formed by the set of local features. In view of 3D shape retrieval, we also propose a local 3D shape feature defined for oriented point set. Experiments using shape-based 3D model retrieval scenario show that the DM aggregation results in better retrieval accuracy than the existing aggregation algorithms we've compared against, that are, VLAD, FV, and SV, etc..


title: Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis

abstract: Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. Using an entropy measure, we infer that users at Digg are not highly focused and participate across a wide range of topics. We also use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We show promising results for predicting the popularity scores even after limiting our feature extraction to the first few hours of comment activity that follows a Digg submission.


title: A capability-based security approach to manage access control in the Internet of Things

abstract: Abstract Resource and information protection plays a relevant role in distributed systems like the ones present in the Internet of Things (IoT). Authorization frameworks like RBAC and ABAC do not provide scalable, manageable, effective, and efficient mechanisms to support distributed systems with many interacting services and are not able to effectively support the dynamicity and scaling needs of IoT contexts that envisage a potentially unbound number of sensors, actuators and related resources, services and subjects, as well as a more relevance of short-lived, unplanned and dynamic interaction patterns. Furthermore, as more end-users start using smart devices (e.g. smart phones, smart home appliances, etc.) the need to have more scalable, manageable, understandable and easy to use access control mechanisms increases. This paper describes a capability based access control system that enterprises, or even individuals, can use to manage their own access control processes to services and information. The proposed mechanism supports rights delegation and a more sophisticated access control customization. The proposed approach is being developed within the European FP7 IoT@Work project to manage access control to some of the project’s services deployed in the shop floor.


title: Security system using raspberry Pi

abstract: In recent times we tend to use a number of surveillance systems for monitoring the targeted area. This requires an enormous amount of storage space along with a lot of human power in order to implement and monitor the area under surveillance. This is supposed to be costly and not a reliable process. In this paper we propose an intelligent surveillance system that continuously monitors the targeted area and detects motion in each and every frame. If the system detects motion in the targeted area then a notification is automatically sent to the user by sms and the video starts getting recorded till the motion is stopped. Using this method the required memory space for storing the video is reduced since it doesn't store the entire video but stores the video only when a motion is detected. This is achieved by using real time video processing using open CV (computer vision / machine vision) technology and raspberry pi system.


title: Bim: a novel member of the Bcl-2 family that promotes apoptosis

abstract: Certain members of the Bcl-2 family inhibit apoptosis while others facilitate this physiological process of cell death. An expression screen for proteins that bind to Bcl-2 yielded a small novel protein, denoted Bim, whose only similarity to any known protein is the short (nine amino acid) BH3 motif shared by most Bcl-2 homologues. Bim provokes apoptosis, and the BH3 region is required for Bcl-2 binding and for most of its cytotoxicity. Like Bcl-2, Bim possesses a hydrophobic C-terminus and localizes to intracytoplasmic membranes. Three Bim isoforms, probably generated by alternative splicing, all induce apoptosis, the shortest being the most potent. Wild-type Bcl-2 associates with Bim in vivo and modulates its death function, whereas Bcl-2 mutants that lack survival function do neither. Significantly, Bcl-xL and Bcl-w, the two closest homologues of Bcl-2, also bind to Bim and inhibit its activity, but more distant viral homologues, adenovirus E1B19K and Epstein-Barr virus BHRF-1, can do neither. Hence, Bim appears to act as a 'death ligand' which can only neutralize certain members of the pro-survival Bcl-2 sub-family.


title: A Markovian Decision Process Analysis of Experienced Agents Joining Ad-Hoc Teams

abstract: As the number of agents grows in a multi-agent system, it is very impractical to have a team that is pre-programmed to share a cooperation protocol. In this situation experienced agents that have been trained to cooperate with different teams come in handy. In this paper based on some UAV teams, we investigate the behavior of an experienced agent which we call ad-hoc agent. First, we train the ad-hoc agent to cooperate with different teams and in different environmental situations. This training is based on an approximated version of MDP which is very fast. Then the ad-hoc agent joins the teams and tries to cooperate with them in a Persistent Surveillance Mission(PSM). Our experiment for the ad-hoc agent starts with joining different fixed strategy teams and ends with joining a fully cooperative team. The results of the simulation show that the performance of a team having an ad-hoc agent is even comparable to a team that has completely trained together.


title: Deep Learning-based Cooperative Trail Following for Multi-Robot System

abstract: Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learningbased approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot visionbased trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the singlerobot system.


title: Wearable Medical Systems for p-Health

abstract: Driven by the growing aging population, prevalence of chronic diseases, and continuously rising healthcare costs, the healthcare system is undergoing a fundamental transformation, from the conventional hospital-centered system to an individual-centered system. Current and emerging developments in wearable medical systems will have a radical impact on this paradigm shift. Advances in wearable medical systems will enable the accessibility and affordability of healthcare, so that physiological conditions can be monitored not only at sporadic snapshots but also continuously for extended periods of time, making early disease detection and timely response to health threats possible. This paper reviews recent developments in the area of wearable medical systems for p-Health. Enabling technologies for continuous and noninvasive measurements of vital signs and biochemical variables, advances in intelligent biomedical clothing and body area networks, approaches for motion artifact reduction, strategies for wearable energy harvesting, and the establishment of standard protocols for the evaluation of wearable medical devices are presented in this paper with examples of clinical applications of these technologies.


title: Early-onset hidradenitis suppurativa.

abstract: A 9-year-old girl developed hidradenitis suppurativa 3 months after the first signs of adrenarche. Such a close temporal relationship is consistent with the hypothesis that the disease is androgen dependent. Less than 2% of patients have onset of the disease before the age of 11 years. The exceptionally early age of onset in our patient may be partly explained by the fact that she had an early puberty.


title: Sharing Economy in Smart City Transportation Services

abstract: Sharing economy has created a number of opportunities for Smart Cities and their communities around the world to create a better and smarter working and living environment. With economic transactions that usually happen through a variety of interconnected data-driven digital platforms, sharing economy has a potential to improve asset utilization and reduce transaction cost or waste effectively and efficiently. Similar to the aims of Smart Cities in providing better and smarter working and living environment, sharing economy is seemingly a natural match for Smart Cities. Nonetheless, the critics around sharing economy, highlight several potential challenges in several fronts, i.e. regulation, inequality and sustainability. Based on the insights gained from User Research conducted in Indonesia, Australia and USA for the last four months, we build our initial arguments to validate the challenges of the current sharing economy practice embedded in app-based transportation services.


Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques


title: AGV global localization using indistinguishable artificial landmarks

abstract: In this paper we consider the global localization problem for an industrial AGV moving in a known environment. The problem consists of determining the pose of the vehicle without any prior information about its location. The vehicle is supposed to be equipped with a laser scanner that allows to measure the range and bearing of the vehicle with respect to a set of anonymous landmarks. A map with the positions of all landmarks in the environment is available to the localization system. We propose a novel algorithm for AGV self-localization based on landmarks identification that can take into account also false detections, very common in industrial environments. The pose is computed with a single scan (2D), without any sensor fusion. The performance of the proposed strategy is shown both by simulations and experiments on real industrial plants.


title: The future of social is personal: the potential of the personal data store

abstract: This chapter argues that technical architectures that facilitate the longitudinal, decentralised and individual-centric personal collection and curation of data will be an important, but partial, response to the pressing problem of the autonomy of the data subject, and the asymmetry of power between the subject and large scale service providers/data consumers. Towards framing the scope and role of such Personal Data Stores (PDSes), the legalistic notion of personal data is examined, and it is argued that a more inclusive, intuitive notion expresses more accurately what individuals require in order to preserve their autonomy in a data-driven world of large aggregators. Six challenges towards realising the PDS vision are set out: the requirement to store data for long periods; the difficulties of managing data for individuals; the need to reconsider the regulatory basis for third-party access to data; the need to comply with international data handling standards; the need to integrate privacy-enhancing technologies; and the need to future-proof data gathering against the evolution of social norms. The open experimental PDS platform INDX is introduced and described, as a means of beginning to address at least some of these six challenges.


title: CUDA Based Fast Implementation of Very Large Matrix Computation

abstract: CUDA (Compute Unified Device Architecture) acceleration of very large scale matrix-vector and matrix-matrix multiplication is presented in this paper. The intrinsic parallelism in the matrix computations are exploited thoroughly. By dividing the entire matrix computation to multiple sub-groups, scalable performance improvement can be achieved using multiple GPUs. The key operations are accelerated by GPU. And the CUDA related data storage, threads hierarchy, and kernel implementation are proposed. Several optimization methods including coalesced global memory access, on-the-fly reduction, bank conflict free shared memory usage, loop unrolling, removing unnecessary synchronization, and concurrent execution on the device through streams are also employed. Experiment results show that about 8.5 times speedup can be achieved for CUDA accelerated matrix multiplication maximally.


Multi-Objective particle swarm optimizers: A survey of the state-of-the-art


title: A System for Acquiring, Processing, and Rendering Panoramic Light Field Stills for Virtual Reality

abstract: We present a system for acquiring, processing, and rendering panoramic light field still photography for display in Virtual Reality (VR). We acquire spherical light field datasets with two novel light field camera rigs designed for portable and efficient light field acquisition. We introduce a novel real-time light field reconstruction algorithm that uses a per-view geometry and a disk-based blending field. We also demonstrate how to use a light field prefiltering operation to project from a high-quality offline reconstruction model into our real-time model while suppressing artifacts. We introduce a practical approach for compressing light fields by modifying the VP9 video codec to provide high quality compression with real-time, random access decompression. ::: ::: We combine these components into a complete light field system offering convenient acquisition, compact file size, and high-quality rendering while generating stereo views at 90Hz on commodity VR hardware. Using our system, we built a freely available light field experience application called Welcome to Light Fields featuring a library of panoramic light field stills for consumer VR which has been downloaded over 15,000 times.


title: Joint Deep Modeling of Users and Items Using Reviews for Recommendation

abstract: A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.


title: A Turing test for free will

abstract: Before Alan Turing made his crucial contributions to the theory of computation, he studied the question of whether quantum mechanics could throw light on the nature of free will. This article investigates the roles of quantum mechanics and computation in free will. Although quantum mechanics implies that events are intrinsically unpredictable, the `pure stochasticity' of quantum mechanics adds only randomness to decision making processes, not freedom. By contrast, the theory of computation implies that even when our decisions arise from a completely deterministic decision-making process, the outcomes of that process can be intrinsically unpredictable, even to -- especially to -- ourselves. I argue that this intrinsic computational unpredictability of the decision making process is what give rise to our impression that we possess free will. Finally, I propose a `Turing test' for free will: a decision maker who passes this test will tend to believe that he, she, or it possesses free will, whether the world is deterministic or not.


title: Electrolytic Capacitor-Less Single-Phase to Three-Phase Inverter with Harmonics Suppression Control for Air Conditioner

abstract: Global demand for highly energy-efficient inverter air conditioners is rising. However, the power converter must comply with harmonics regulations. This requires the converter to have a large inductor, PFC circuit and electrolytic capacitor. Therefore, the converter becomes expensive, which prevents inverter air conditioners from becoming popular around the world. In order to solve this problem, electrolytic-capacitor-less inverters were proposed.This paper proposes a harmonics suppression control of an electrolytic capacitor-less inverter. As a result of applying this control, the inductance and the capacitance of power converter have been reduced to 1/6 and 1/50, respectively, compared with those for conventional power converters, which has resulted in the commercialization of low-cost inverter air conditioners.


title: Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

abstract: Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. However, it is non-trivial to manually design a robot controller that combines modalities with very different characteristics. While deep reinforcement learning has shown success in learning control policies for high-dimensional inputs, these algorithms are generally intractable to deploy on real robots due to sample complexity. We use self-supervision to learn a compact and multimodal representation of our sensory inputs, which can then be used to improve the sample efficiency of our policy learning. We evaluate our method on a peg insertion task, generalizing over different geometry, configurations, and clearances, while being robust to external perturbations. We present results in simulation and on a real robot.


title: A 1.4–2.3-GHz Tunable Diplexer Based on Reconfigurable Matching Networks

abstract: An idea for implementing reconfigurable matching networks to realize a tunable diplexer is investigated and demonstrated. The reconfigurable matching networks ensure that the rejection band impedance of every filter is transformed to an open circuit over a wide range of frequencies, allowing two tunable filters to be connected together to form a tunable diplexer without affecting each other. The tunable diplexer is built using Schottky diodes and combline resonators on Duroid substrates, and can operate from 1.4 to 2.3 GHz with a closest frequency separation of 110 MHz. Measurements show virtually no difference in the frequency response between a standalone filter and a filter placed in the tunable diplexer. The work shows that a wideband tunable diplexer can therefore be realized using tunable bandpass filters and reconfigurable matching networks.


title: Socket Shield Technique for Ridge Preservation : A Case Report

abstract: e " socket shie ld technique " has de mon strated the pot ent ial in preventing buccal tissue from resorption in animal and clinical studies. It is assumed that retaining a root fragment aached to the buccal bone plate in this technique can avoid tissue alteration after tooth extraction. This article presents a 58-year-old healthy man with a failing upper right second premolar which would be replaced by an implant-supported single crown. Leaving a partial r oot fragment at buccal side in combination with immediate implant placement lingual to the retained fragment was performed. Four months aer implant placement, clinical examination showed healthy peri-implant sotissue and the ridge was well preserved. A denit ive metal ceramic crown was fabricated and cemented on a titanium abutment. The prosthesis successfully restored the function of the patie nt. A maximum amount of horizontal resorption at the buccal side was 0.72mm. Applying socket shield technique and immediate implant placement may be a feasible treatment option in case with high esthetic concern.


Defining Virtual Reality: Dimensions Determining Telepresence


title: Predicting positive and negative links in online social networks

abstract: We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.


title: Wolverine: A Wearable Haptic Interface for Grasping in VR

abstract: The Wolverine is a mobile, wearable haptic device designed for simulating the grasping of rigid objects in virtual environment. In contrast to prior work on force feedback gloves, we focus on creating a low cost, lightweight, and wireless device that renders a force directly between the thumb and three fingers to simulate objects held in pad opposition type grasps. Leveraging low-power brake-based locking sliders, the system can withstand over 100N of force between each finger and the thumb, and only consumes 2.78 Wh(10 mJ) for each braking interaction. Integrated sensors are used both for feedback control and user input: time-of-flight sensors provide the position of each finger and an IMU provides overall orientation tracking. This design enables us to use the device for roughly 6 hours with 5500 full fingered grasping events. The total weight is 55g including a 350 mAh battery.


title: VisGraB: A Benchmark for Vision-Based Grasping

abstract: We present a database and a software tool, VisGraB, for benchmarking of methods for vision-based grasping of unknown objects with no prior object knowledge. The benchmark is a combined real-world and simulated experimental setup. Stereo images of real scenes containing several objects in different configurations are included in the database. The user needs to provide a method for grasp generation based on the real visual input. The grasps are then planned, executed, and evaluated by the provided grasp simulator where several grasp-quality measures are used for evaluation. This setup has the advantage that a large number of grasps can be executed and evaluated while dealing with dynamics and the noise and uncertainty present in the real world images. VisGraB enables a fair comparison among different grasping methods. The user furthermore does not need to deal with robot hardware, focusing on the vision methods instead. As a baseline, benchmark results of our grasp strategy are included.


title: Edge detection using on partial differential equation and anisotropic diffusion

abstract: In this paper we propose a new edge detection operator using partial differential equation (PDE) for grey level and color images. We propose a new approach in order to obtain a new anisotropic edge detection method based on the crossing of the second derivative. We show here that edges can be robustly extracted without lost of precision. We present various results on synthetic and real images.


title: A comparative study of the current technologies and approaches of relation extraction in biomedical literature using text mining

abstract: Many techniques in Relation Extraction (RE) require an understanding of the concept and association of relations between entities. This paper presents advanced approaches that combined many methods to extract simple and complex relations between different entities from the biomedical literature. The literature comparative study includes a wide variety of techniques for RE all of which fall into one of the following approaches such as supervised, semi-supervised, higher order relation and self-supervised approaches. Supervised approach needs very detailed data, while in semi-supervised approach only a small knowledge base to automatically annotates features. This approach does not need an initial set of labeled pages for extraction rules. Self-supervised is an integration of these two approaches. On the other hand, the term higher order relation indicates a relation that connects different biomedical relations together. The F-score performance differs from relation to relation when it is extracted. Finally, an extensive performance evaluations with real datasets for both supervised, semi-supervised, higher order relation and self-supervised approaches are described for entity — RE tasks.


title: Predicting the success of bank telemarketing using deep convolutional neural network

abstract: Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. The number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.


title: Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

abstract: Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.


Impact of Training on Employee Performance ( Banking Sector Karachi )


title: Traffic sign recognition using convolutional neural networks

abstract: Traffic sign recognition (TSR) represents an important feature of advanced driver assistance systems, contributing to the safety of the drivers, pedestrians and vehicles as well. Developing TSR systems requires the use of computer vision techniques, which could be considered fundamental in the field of pattern recognition in general. Despite all the previous works and research that has been achieved, traffic sign detection and recognition still remain a very challenging problem, precisely if we want to provide a real time processing solution. In this paper, we present a comparative and analytical study of the two major approaches for traffic sign detection and recognition. The first approach is based on the color segmentation technique and convolutional neural networks (C-CNN), while the second one is based on the fast region-based convolutional neural networks approach (Fast R-CNN).


title: Stretchable circuits and sensors for robotic origami

abstract: Programmable materials based on robotic origami have been demonstrated with the capability to fold into 3D shapes starting from a nominally 2D sheet. This concept requires high torque density actuators, flexible electronics and an integrated substrate. We report on two types of stretchable circuitry that are directly applicable to robotic origami: meshed copper traces and liquid-metal-filled channels in an elastomer substrate. Both methods maintain conductivity even at large strains (during stretching) and curvatures (during folding). Both circuit designs are integrated with a tiled origami module actuated by a shape memory alloy actuator. We also integrate a soft curvature sensor into the robotic origami module that measures the full range of motion of the module in real-time.


title: An Efficient Keyframe Extraction from Motion Capture Data

abstract: This paper proposes a keyframe extraction method based on a novel layered curve simplification algorithm for motion capture data. Bone angles are employed as motion features and keyframe candidates can be selected based on them. After that, the layered curve simplification algorithm will be used to refine those candidates and the keyframe collection can be gained. To meet different requirements for compression and level of detail of motion abstraction, adaptive extraction parameters are also applied. The experiments demonstrate that our method can not only compress and summarize the motion capture data efficiently, but also keep the consistency of keyframe collection between similar human motion sequences, which is of great benefit to further motion data retrieval or editing.


title: An implementation of the earliest deadline first algorithm in Linux

abstract: Recently, many projects have been started to introduce some real-time mechanisms into general purpose operating systems (GPOS) in order to make them capable of providing the users with some temporal guarantees. Many of these projects focused especially on Linux for its capillary and widespread adoption throughout many different research and industrial environments. By tracking the kernel release cycle, we propose an efficient Earliest Deadline First implementation in the form of a patch-set against the 2.6.27 version, that is the latest released one, as of now. Our implementation provides the user with the possibility to choose SCHED_EDF as one of the possible scheduling policies for a task, with an enhanced version of the standard algorithm. In fact, we propose a new approach to shared resources' access which, differently from many other previous existing works, does not require the user to specify any parameters about the critical sections every task will enter during its execution.


title: Design of radio-frequency powered coils for implant instruments

abstract: Radio frequency (r.f.) has been investigated as a means of externally powering miniature and long term implant telemetry systems. Optimum power transfer from the transmitter to the receiving coil is desired for total system efficiency. A seven step design procedure for the transmitting and receiving coils is described based on r.f., coil diameter, coil spacing, load and the number of turns of the coil. An inductance tapping circuit and a voltage doubler circuit have been built in accordance with the design procedure. Experimental results were within the desired total system efficiency ranges of 18% and 23%, respectively.


title: Design analysis of XOR (4T) based low voltage CMOS full adder circuit

abstract: This paper presents a comparative study of highspeed, low-power and low voltage full adder circuits. Our approach is based on XOR-XNOR (4T) design full adder circuits combined in a single unit. This technique helps in reducing the power consumption and the propagation delay while maintaining low complexity of logic design. Simulation results illustrate the superiority of the designed adder circuits against the conventional CMOS, TG and Hybrid adder circuits in terms of power, delay and power delay product (PDP) at low voltage. Noise analysis shows designed full adder circuit's work at high frequency and high temperature satisfactorily. Simulation results reveal that the designed circuits exhibit lower PDP, more power efficiency and faster when compared to the available full adder circuits at low voltage. The design is implemented on UMC 0.18µm process models in Cadence Virtuoso Schematic Composer at 1.8 V single ended supply voltage and simulations are carried out on Spectre S.


title: Potential risk factors for brain tumors in children

abstract: Two hundred cases of verified brain tumors occurring in patients under 15 years of age were studied in relation to possible etiologic, genetic, and environmental risk factors. They were compared with 100 age-matched patients harboring solid neoplasms outside the nervous system, as well as with 100 normal children. In our study, first-degree relatives of a brain tumor child did not show a higher incidence of either tumors or of epilepsy and strokes as compared with controls. First-born children (46%) with higher birth weights showed a greater tendency to present brain tumors. Dystocia (18.5%), previous miscarriages (18%), and dietary restrictions during pregnancy (3%) were also noted in this study and compared with data in the literature. No evidence of a role of maternal chickenpox and toxoplasmosis could be found. The pharmacological risk also seemed to be minimal. The mother's hormonal profile is deduced from the age at menarche and delivery, as well as from a tendency to miscarriages and complicated pregnancies. With regard to the immunologic aspect, it is worth noting that 15% of the mothers complained of allergies. Live polio vaccine and zoonosis might suggest a possible role of virus-related factors in the oncogenesis of brain tumors in children. Radiation-related risk is possibly present in less than 5% of cases. Parental occupation is not relevant in this series.


title: Detection of Eye Blinking Using Doppler Sensor With Principal Component Analysis

abstract: We propose the detection of human eye blinking using a Doppler sensor. Eye blinking is one of the most common and convenient human activities that can be used as an input modality for computer devices, a simple communication methodology, and fatigue diagnostics. The reflected wave from the blinking eye has a unique Doppler signature. To investigate this signature, several measurements were performed with/without the noise caused by human movement when the sensor was placed near the eyes. We analyzed the Doppler signal in the joint time–frequency domain. It was found that the Doppler frequency produced by eye blinking is approximately 115 Hz. Furthermore, unconscious and conscious eye blinking exhibited different Doppler characteristics. In order to classify these characteristics, we employed a principal component analysis to extract the features. The truncated eigenvectors were multiplied with an image of eye blinking, and the resulting coefficients were used for classification. The proposed method successfully differentiated conscious eye blinking in the presence of noise from human motion in diverse scenarios.


title: Where Science and Magic Meet: The Illusion of a "Science of Magic"

abstract: Recent articles calling for a scientific study of magic have been the subject of widespread interest. This article considers the topic from a broader perspective and argues that to engage in a science of magic, in any meaningful sense, is misguided. It argues that those who have called for a scientific theory of magic have failed to explain either how or why such a theory might be constructed, that a shift of focus to a neuroscience of magic is simply unwarranted, and that a science of magic is itself an inherently unsound idea. It seeks to provide a more informed view of the relationship between science and magic and suggests a more appropriate way forward for scientists.


Understanding email communication patterns


title: DEVELOPMENT OF AN ANTIBODY-BASED CAPTURE ENZYME-LINKED IMMUNOSORBENT ASSAY FOR DETECTING ECHINOSTOMA CAPRONI (TREMATODA) IN EXPERIMENTALLY INFECTED RATS: KINETICS OF COPROANTIGEN EXCRETION

abstract: The present study reports on the development of a coproantigen capture enzyme-linked immunosorbent assay (ELISA) for detecting Echinostoma caproni in experimentally infected rats. The capture ELISA was based on polyclonal rabbit antibodies that recognize excretory-secretory (ES) antigens. The detection limit of pure ES was 3 ng/ml in sample buffer and 60 ng/ml in fecal samples. The test was evaluated using a follow-up of 10 rats experimentally infected with 100 metacercariae of E. caproni, and the results were compared with those of other diagnostic methods such as parasitological examination and antibody titers determined by indirect ELISA. Coproantigens were detected in all the infected rats from the first day postinfection (DPI). The period of maximal coproantigen excretion was between 7 and 21 DPI. The values remained positive until 49-56 DPI, coinciding with the disappearance of the eggs in the stool samples of the infected rats. The kinetics of coproantigen detection were correlated with those of egg output. The present assay provides an alternative tool for the diagnosis of the echinostome infections. The proposed capture ELISA makes possible an earlier diagnosis than that provided by parasitological examination and indirect ELISA and also allows for the differentiation of past and current infections. Our results show that this assay can also be used to monitor the course of echinostome infections.


title: Designing Strategy for Resolving Maldistribution of Vehicles in One-Way Car-Sharing through Active Trip Request to Potential Users

abstract: Recently, one-way car-sharing service that allows a user to drop the vehicle off at the destination has been started. However, it has not been as popular as expected mainly because it cannot assure the existence of an available vehicle at the start station or a vacant parking slot at the destination station. At this moment, the company staff are responsible for moving the vehicles to certain stations manually. Therefore, it is very costly. In order to reduce costs and make more efficient use of vehicles in one-way car-sharing service, we propose to involve a future potential user to move vehicles. We assume that there exist some requested one-way trips. Our method inserts a new trip among the existing trips for maximizing the number of successful trips. This trip is called “a system request trip.” We assume that the system request trip can be realized by changing a future schedule or asking future potential users who want to get some rewards. In this paper, we analyzed how each user uses vehicles in one-way car-sharing service by using the past data of demonstration experiment conducted by a car-sharing company in Tokyo, Japan. Based on the analysis result, we examined how to request each user.


title: Visual Twitter Analytics (Vista)

abstract: Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes. Findings – A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be...


title: The Development, Evolution, and Modifications of ICD-10: Challenges to the International Comparability of Morbidity Data

abstract: BACKGROUND ::: The United States is about to make a major nationwide transition from ICD-9-CM coding of hospital discharges to ICD-10-CM, a country-specific modification of the World Health Organization's ICD-10. As this transition occurs, the WHO is already in the midst of developing ICD-11. Given this context, we undertook this review to discuss: (1) the history of the International Classification of Diseases (a core information "building block" for health systems everywhere) from its introduction to the current era of ICD-11 development; (2) differences across country-specific ICD-10 clinical modifications and the challenges that these differences pose to the international comparability of morbidity data; (3) potential strategic approaches to achieving better international ICD-11 comparability. ::: ::: ::: LITERATURE REVIEW AND DISCUSSION ::: A literature review and stakeholder consultation was carried out. The various ICD-10 clinical modifications (ICD-10-AM [Australia], ICD-10-CA [Canada], ICD-10-GM [Germany], ICD-10-TM [Thailand], ICD-10-CM [United States]) were compared. These ICD-10 modifications differ in their number of codes, chapters, and subcategories. Specific conditions are present in some but not all of the modifications. ICD-11, with a similar structure to ICD-10, will function in an electronic health records environment and also provide disease descriptive characteristics (eg, causal properties, functional impact, and treatment). ::: ::: ::: CONCLUSION ::: The threat to the comparability of international clinical morbidity is growing with the development of many country-specific ICD-10 versions. One solution to this threat is to develop a meta-database including all country-specific modifications to ensure more efficient use of people and resources, decrease omissions and errors but most importantly provide a platform for future ICD updates.


title: A Consolidated View of Context for Intelligent Systems

abstract: This paper's main objective is to consolidate the knowledge on context in the realm of intelligent systems, systems that are aware of their context and can adapt their behavior accordingly. We provide an overview and analysis of 36 context models that are heterogeneous and scattered throughout multiple fields of research. In our analysis, we identify five shared context categories: social context, location, time, physical context, and user context. In addition, we compare the context models with the context elements considered in the discourse on intelligent systems and find that the models do not properly represent the identified set of 3,741 unique context elements. As a result, we propose a consolidation of the findings from the 36 context models and the 3,741 unique context elements. The analysis reveals that there is a long tail of context categories that are considered only sporadically in context models. However, particularly these context elements in the long tail may be necessary for improving intelligent systems' context awareness.


title: Diabetes mellitus in Saudi Arabia

abstract: OBJECTIVES ::: Diabetes mellitus (DM) is a major public health problem worldwide, and it is a known risk factor for coronary artery disease (CAD). New recommendations for the diagnosis of diabetes have changed the epidemiology of DM. Therefore, we designed this study with the objective to determine the prevalence of DM among Saudis of both sexes, between the ages of 30-70-years in rural as well as urban communities. This work is part of a major national project: Coronary Artery Disease in Saudis study (CADISS) that is designed to look at CAD and its risk factors in Saudi population. ::: METHODS ::: This study is a community-based national epidemiological health survey, conducted by examining Saudi subjects in the age group of 30-70-years of selected households over a 5-year period between 1995 and 2000. Data were obtained from history, fasting plasma glucose levels, and body mass index. The data were analyzed to classify individuals as diabetic, impaired fasting glucose and normal, using 1997 American Diabetes Association (ADA) criteria, which was adopted by the World Health Organization (WHO) in 1998, to provide prevalence of DM in the Kingdom of Saudi Arabia (KSA). ::: RESULTS ::: A total of 17232 Saudi subjects were selected in the study, and 16917 participated (98.2% response rate). Four thousand and four subjects (23.7%), out of 16917 were diagnosed to have DM. Thus, the overall prevalence of DM obtained from this study is 23.7% in KSA. The prevalence in males and females were 26.2% and 21.5% (p<0.00001). The calculated age-adjusted prevalence for Saudi population for the year 2000 is 21.9%. Diabetes mellitus was more prevalent among Saudis living in urban areas of 25.5% compared to rural Saudis of 19.5% (p<0.00001). Despite the readily available access to healthcare facilities in KSA, a large number of diabetics 1116 (27.9%) were unaware of having DM. ::: CONCLUSIONS ::: The overall prevalence of DM in adults in KSA is 23.7%. A national prevention program at community level targeting high risk groups should be implemented sooner to prevent DM. We further recommend a longitudinal study to demonstrate the importance of modifying risk factors for the development of DM and reducing its prevalence in KSA.


title: A CMOS time-to-digital converter with better than 10 ps single-shot precision

abstract: A high-precision CMOS time-to-digital converter IC has been designed. Time interval measurement is based on a counter and two-level interpolation realized with stabilized delay lines. Reference recycling in the delay line improves the integral nonlinearity of the interpolator and enables the use of a low frequency reference clock. Multi-level interpolation reduces the number of delay elements and registers and lowers the power consumption. The load capacitor scaled parallel structure in the delay line permits very high resolution. An INL look-up table reduces the effect of the remaining nonlinearity. The digitizer measures time intervals from 0 to 204 /spl mu/s with 8.1 ps rms single-shot precision. The resolution of 12.2 ps from a 5-MHz external reference clock is divided by means of only 20 delay elements.


Cost-Effective Active Learning from Diverse Labelers


title: Fixed-film Anaerobic Digestion of Flushed Dairy Manure after Primary Treatment: Wastewater Production and Characterisation

abstract: The characteristics of flushed dairy manure after primary treatment at a Florida dairy were monitored daily over a 1-year period along with cow numbers and water use, with the aim of developing fixed-film anaerobic digestion of flushed dairy manure wastewater (FDMW). Primary treatment included screening through a mechanical separator followed by sedimentation. The FDMW exhibited average total solids (TS), volatile solids (VS) and total chemical oxygen demand (COD) levels of 3580, 2210 and 3530 mg l � 1 , respectively, for a milking herd average of 359 cows and a wastewater production of 502 m 3 d � 1 . Analyses of FDMW parameters and manure excretion estimates indicated that 53% of the TS, 40% of the VS and 58% of the total COD remained in the wastewater after screening and sedimentation. The separation of fibrous solids increased the COD to VS ratio of the FDMW. Wastewater temperature was found to significantly affect both the separation of TS, VS, and total COD, and the partitioning of wastewater parameters between the particulate and soluble phases. Higher FDMW temperatures tended to reduce TS, VS, and total COD removal efficiencies. The ratio of soluble COD to dissolved VS increased with temperature, while the ratio of particulate COD to volatile suspended solids decreased, indicating that significant levels of biological activity occurred in the sedimentation basin at higher temperatures. The VS measurement does not accurately determine the methane potential of components in FDMW compared to COD measurements. In spite of the fact that 60% of the VS is removed by screening and sedimentation, the major portion of the methane potential remains in the wastewater since the solids that are removed are relatively non-degradable fibres and do not contribute to methane production. Model equations were developed to predict total COD levels in flushed dairy manure wastewater as a function of fresh flushwater usage per animal unit for similar dairy operations, providing an important design parameter for implementation of fixed-film anaerobic digestion systems.


title: Non-invasive blood glucose measurement

abstract: Impedance of blood relatively affected by blood-glucose concentration. Blood electrical impedance value is varied with the content of blood glucose in a human body. This characteristic between glucose and electrical impedance has been proven by using four electrode method's measurement. The bioelectrical voltage output shows a difference between fasting and non-fasting blood glucose measured by using designed four tin lead alloy electrode. 10 test subjects ages between 20–25 years old are UniMAP student has been participated in this experiment and measurement of blood glucose using current clinical measurement and designed device is obtained. Preliminary study using the developed device, has shown that glucose value in the range of 4–5mol/Liter having the range of 0.500V to −1.800V during fasting, and 0.100V or less during normal glucose condition, 5 to 11 mol/liter. On the other hand, It also shows that prediction of blood glucose using this design device could achieve relevant for measurement accuracy compared to gold standard measurement, the hand prick invasive measurement. This early result has support that there is an ample scope in blood electrical study for the non-invasive blood glucose measurement.


title: Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection

abstract: This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes prediction of intervals of multiple actions. Specifically, we first assemble video clips according to class labels by an attention mechanism that learns class-variable attention weights and thus helps the noise relieving from background or other actions. Secondly, we build temporal relationship between actions by feeding the assembled features into an enhanced recurrent neural network. Finally, we transform the output of recurrent neural network into the corresponding action distribution. In order to generate more precise temporal proposals, we design a score term called segregated temporal gradient-weighted class activation mapping (ST-GradCAM) fused with attention weights. Experiments on THUMOS’14 and ActivityNet1.3 datasets show that our approach outperforms the state-of-theart weakly-supervised method, and performs at par with the fully-supervised counterparts.


title: Accuracy of profile retrievals from mm-wave spectra of ClO and N/sub 2/O

abstract: The retrieval accuracy of a Chahine-Twomey retrieval algorithm, acting on pressure-broadened mm-wave rotational spectra measured with ground-based equipment, is explored for two radically different profiles, the first representing a two-layered Antarctic ClO distribution having a maximum mixing ratio of /spl sim/1.5 ppbv and no tropospheric component, and the second representing a typical polar N/sub 2/O profile, with a tropospheric mixing ratio of 300 ppbv dropping rapidly in the low to mid-stratosphere. The uncertainties or errors due to intrinsic retrieval limits, practical levels of random noise, uncertainties in input parameters (atmospheric temperature and pressure profiles, pressure broadening), and in calibration of instrument sensitivity are all explored, and an overall error budget established a function of altitude. >


title: Should Robots be Obedient?

abstract: Intuitively, obedience -- following the order that a human gives -- seems like a good property for a robot to have. But, we humans are not perfect and we may give orders that are not best aligned to our preferences. We show that when a human is not perfectly rational then a robot that tries to infer and act according to the human's underlying preferences can always perform better than a robot that simply follows the human's literal order. Thus, there is a tradeoff between the obedience of a robot and the value it can attain for its owner. We investigate how this tradeoff is impacted by the way the robot infers the human's preferences, showing that some methods err more on the side of obedience than others. We then analyze how performance degrades when the robot has a misspecified model of the features that the human cares about or the level of rationality of the human. Finally, we study how robots can start detecting such model misspecification. Overall, our work suggests that there might be a middle ground in which robots intelligently decide when to obey human orders, but err on the side of obedience.


title: Augmented Reality Interfaces for Additive Manufacturing

abstract: This paper explores potential use cases for using augmented reality (AR) as a tool to operate industrial machines. As a baseline we use an additive manufacturing system, more commonly known as a 3D printer. We implement novel augmented interfaces and controls using readily available open source frameworks and low cost hardware. Our results show that the technology enables richer and more intuitive printer control and performance monitoring than currently available on the market. Therefore, there is a great deal of potential for these types of technologies in future digital factories.


title: VIAC: An out of ordinary experiment

abstract: This paper presents the preliminary results of VIAC, the VisLab Intercontinental Autonomous Challenge, a test of autonomous driving along an unknown route from Italy to China. It took 3 months to run the entire test; all data have been logged, including all data generated by the sensors, vehicle data, and GPS info. This huge amount of information has been packed during the trip, compressed, and transferred back to Parma for further processing. This data is now ready for a deep analysis of the various systems performance, with the aim of virtually running the whole trip multiple times with improved versions of the software. This paper discusses some preliminary figures obtained by the analysis of the data collected during the test. More information will be generated by a deeper analysis, which will take additional time, being the data about 40 terabyte in size.


title: An electronic throttle control strategy including compensation of friction and limp-home effects

abstract: An electronic throttle is a low-power dc servo drive which positions the throttle plate. Its application in modern automotive engines leads to improvements in vehicle drivability, fuel economy, and emissions. Transmission friction and the return spring limp-home nonlinearity significantly affect the electronic throttle performance. The influence of these effects is analyzed by means of computer simulations, experiments, and analytical calculations. A dynamic friction model is developed in order to adequately capture the experimentally observed characteristics of the presliding-displacement and breakaway effects. The linear part of electronic throttle process model is also analyzed and experimentally identified. A nonlinear control strategy is proposed, consisting of a proportional-integral-derivative (PID) controller and a feedback compensator for friction and limp-home effects. The PID controller parameters are analytically optimized according to the damping optimum criterion. The proposed control strategy is verified by computer simulations and experiments.


title: Multistage Object Detection With Group Recursive Learning

abstract: Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately. However, the important semantic and spatial layout correlations among proposals are often ignored, which are actually useful for more accurate object detection. In this work, we propose a new EM-like group recursive learning approach to iteratively refine object proposals by incorporating such context of surrounding proposals and provide an optimal spatial configuration of object detections. In addition, we propose to incorporate the weakly-supervised object segmentation cues and region-based object detection into a multi-stage architecture in order to fully exploit the learned segmentation features for better object detection in an end-to-end way. The proposed architecture consists of three cascaded networks which respectively learn to perform weakly-supervised object segmentation, object proposal generation and recursive detection refinement. Combining the group recursive learning and the multi-stage architecture provides competitive mAPs of 78.6% and 74.9% on the PASCAL VOC2007 and VOC2012 datasets respectively, which outperforms many well-established baselines [10] [20] significantly.


title: Campaign extraction from social media

abstract: In this manuscript, we study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns—ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing—are growing in significance and reach with the commensurate rise in massive-scale social systems. Specifically, we propose and evaluate a content-driven framework for effectively linking free text posts with common “talking points” and extracting campaigns from large-scale social media. Three of the salient features of the campaign extraction framework are: (i) first, we investigate graph mining techniques for isolating coherent campaigns from large message-based graphs; (ii) second, we conduct a comprehensive comparative study of text-based message correlation in message and user levels; and (iii) finally, we analyze temporal behaviors of various campaign types. Through an experimental study over millions of Twitter messages we identify five major types of campaigns—namely Spam, Promotion, Template, News, and Celebrity campaigns—and we show how these campaigns may be extracted with high precision and recall.


title: A fully-integrated, high-conversion-ratio and dual-output voltage boost converter with MPPT for low-voltage energy harvesting

abstract: This paper proposes a fully-integrated high-conversion-ratio dual-output voltage boost converter (VBC) with maximum power point tracking (MPPT) circuits for low-voltage energy harvesting. The VBC consists of two voltage generators that generate Vout1 and VOUT2. Vout1 and VOUT2 are three and nine times higher than the harvester's output Vin, respectively. Vout1 is used as a supply voltage for on-chip application circuits while VOUT2 is used as the charging voltage for a Li-ion battery. The VBC achieves a high voltage conversion ratio (max. × 9) and a high power conversion efficiency with a small number of charge pumps (CPs). The MPPT circuits control the operating frequencies of the CPs to extract maximum power at each output. The measurement results demonstrated that the circuit converted a 0.59-V input to a 1.41-V output with 75.8% efficiency when the output powers of VOUT1 and VOUT2 were 396 and 0 μW, respectively, and a 0.62-V input to a 4.54-V output with 49.1% efficiency when the output powers were 0 and 114 μW, respectively.


title: Acquisition of word-object associations by 14-month-old infants

abstract: The following experiments were designed to determine the age at which infants can first readily learn word-object pairings with only minimal exposure and without social or contextual support. To address this question, 8- to 14-month-old infants were tested on their ability to form word-object associations in a switch design. Infants were habituated to 2 word-object pairings and then tested with 1 trial that maintained a familiar word-object pairing and I that involved a familiar word and object in a new combination. Across 6 experiments, only 14-month-old infants formed word-object associations under these controlled testing conditions but appeared to do so only when the objects were moving. Although 8- to 12-month-olds did not form the associations, they appeared to process both the word and the object information. These studies provide strong evidence that 14-month-old infants can rapidly learn arbitrary associations between words and objects, that this ability appears to develop at about 14 months of age, and that the Switch design is a useful method for assessing word-object learning in infancy.


title: Selecting the signals for a brain–machine interface

abstract: Brain–machine interfaces are being developed to assist paralyzed patients by enabling them to operate machines with recordings of their own neural activity. Recent studies show that motor parameters, such as hand trajectory, and cognitive parameters, such as the goal and predicted value of an action, can be decoded from the recorded activity to provide control signals. Neural prosthetics that use simultaneously a variety of cognitive and motor signals can maximize the ability of patients to communicate and interact with the outside world. Although most studies have recorded electroencephalograms or spike activity, recent research shows that local field potentials (LFPs) offer a promising additional signal. The decode performances of LFPs and spike signals are comparable and, because LFP recordings are more long lasting, they might help to increase the lifetime of the prosthetics.


title: Toxic releases: An environmental performance index for coal-fired power plants☆

abstract: In order to assess the performance of electric power plants that produce both good and bad outputs, this study uses data from the toxic release inventory to construct an Environmental Performance Index (EPI). Fare, Grosskopf, and Pasurka (2006) demonstrated that for the one good output and one air pollutant case, the EPI simplifies to the ratio of good to bad output. In this paper, we extend the EPI to include an index of multiple bad outputs. After deriving the index as a Malmquist Quantity Index, we assemble data from 1998 to 2005 on releases of selected toxic chemicals and electricity generation for a sample of coal-fired power plants in the United States to demonstrate how the EPI can provide initial perspectives on trends in releases of toxic chemicals by coal-fired power plants.


A Variable-Impedance Prosthetic Socket for a Transtibial Amputee Designed from Magnetic Resonance Imaging Data


title: Estimation of behavioral user state based on eye gaze and head pose—application in an e-learning environment

abstract: Most e-learning environments which utilize user feedback or profiles, collect such information based on questionnaires, resulting very often in incomplete answers, and sometimes deliberate misleading input. In this work, we present a mechanism which compiles feedback related to the behavioral state of the user (e.g. level of interest) in the context of reading an electronic document; this is achieved using a non-intrusive scheme, which uses a simple web camera to detect and track the head, eye and hand movements and provides an estimation of the level of interest and engagement with the use of a neuro-fuzzy network initialized from evidence from the idea of Theory of Mind and trained from expert-annotated data. The user does not need to interact with the proposed system, and can act as if she was not monitored at all. The proposed scheme is tested in an e-learning environment, in order to adapt the presentation of the content to the user profile and current behavioral state. Experiments show that the proposed system detects reading- and attention-related user states very effectively, in a testbed where children's reading performance is tracked.


title: Novel Broadband Bandpass Filters Using Y-Shaped Dual-Mode Microstrip Resonators

abstract: A Y-shaped dual-mode microstrip bandpass filter (BPF) with input-output cross-coupling is presented. The characteristic of the Y-shaped dual-mode resonator has been investigated. The resonance frequencies of the degenerate modes can be adjusted easily to satisfy the bandwidth of the BPF. A parallel-coupling feed structure with a cross coupling has been used to generate two transmission zeros at the lower and upper stopband, which can improve the out-of-band performance. A broadband dual-mode microstrip BPF has been designed, fabricated, and measured. The simulated and measured results are in agreement generally. Within the 3.1 to 5.4 GHz bandwidth, the measured insertion loss, return loss and group delay variation are below 0.5 dB, above 15 dB, and below 0.2 ns, respectively.


title: A Flexible Model-Driven Game Development Approach

abstract: Game developers are facing an increasing demand for new games every year. Game development tools can be of great help, but require highly specialized professionals. Also, just as any software development effort, game development has some challenges. Model-Driven Game Development (MDGD) is suggested as a means to solve some of these challenges, but witha loss in flexibility. We propose a MDGD approach that combines multiple domain-specific languages (DSLs) with design patterns to provide flexibility and allow generated code to be integrated with manual code. After experimentation, we observed that, with the approach, less experienced developers can create games faster and more easily, and the product of code generation can be customized with manually written code, providing flexibility. However, with MDGD, developers become less familiar with the code, making manual codification more difficult.


title: Detection of Harassment on Web 2.0

abstract: Web 2.0 has led to the development and evolution of web-based communities and applications. These communities provide places for information sharing and collaboration. They also open t he door for inappropriate online activities, such as harassment, i n which some users post messages in a virtual community that are intention- ally offensive to other members of the community. It is a new and challenging task to detect online harassment; currently fe w systems attempt to solve this problem. In this paper, we use a supervised learning approach for dete ct- ing harassment. Our technique employs content features, sentiment features, and contextual features of documents. The experi mental results described herein show that our method achieves significant improvements over several baselines, including Term Frequency- Inverse Document Frequency (TFIDF) approaches. Identifica tion of online harassment is feasible when TFIDF is supplemented with sentiment and contextual feature attributes.


title: Mind wandering and reading comprehension: Examining the roles of working memory capacity, interest, motivation, and topic experience.

abstract: Abstract Individual differences in mind wandering and reading comprehension were examined in the current study. In particular, individual differences in mind wandering, working memory capacity, interest in the current topic, motivation to do well on the task, and topic experience and their relations with reading comprehension were examined in the current study. Using confirmatory factor analysis and structural equation modeling it was found that variation in mind wandering while reading was influenced by working memory capacity, topic interest, and motivation. Furthermore, these same factors, along with topic experience, influenced individual differences in reading comprehension. Importantly, several factors had direct effects on reading comprehension (and mind wandering), while the relation between reading comprehension (and mind wandering) and other factors occurred via indirect effects. These results suggest that both domain-general and domain-specific factors contribute to mind wandering while reading and to reading comprehension.


title: A novel actuator with adjustable stiffness (AwAS)

abstract: This paper describes the design and development of a new actuator with adjustable stiffness (AwAS) which can be used in robots which are necessary to work close to or physically interact with humans, e.g. humanoids and exoskeletons. The actuator presented in this work can independently control equilibrium position and stiffness by two motors. The first motor controls the equilibrium position while the second motor regulates the compliance. The novelty of the proposed design with respect to the existing systems is on the principle used to regulate the compliance. This is done not through the tuning of the pretension of the elastic element as in the majority of existing system but by controlling the fixation of the elastic elements (springs) using a linear drive. An important consequence of this approach is that the displacement needed to change the stiffness is perpendicular to the forces generated by the springs, thus this helps to minimize the energy/power required to change the stiffness. This permits the use of a small motor for the stiffness adjustment resulting in a lighter setup. Experimental results are presented to show the ability of AwAS to control position and regulate the stiffness independently.


Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation.


title: A Framework for State-Space Exploration of Java-Based Actor Programs

abstract: The actor programming model offers a promising model for developing reliable parallel and distributed code. Actors provide flexibility and scalability: local execution may be interleaved, and distributed nodes may operate asynchronously. The resulting nondeterminism is captured by nondeterministic processing of messages. To automate testing, researchers have developed several tools tailored to specific actor systems. As actor languages and libraries continue to evolve, such tools have to be reimplemented. Because many actor systems are compiled to Java bytecode, we have developed Basset, a general framework for testing actor systems compiled to Java bytecode. We illustrate Basset by instantiating it for the Scala programming language and for the ActorFoundry library for Java. Our implementation builds on Java PathFinder, a widely used model checker for Java. Experiments show that Basset can effectively explore executions of actor programs; e.g., it discovered a previously unknown bug in a Scala application.


title: FAMPER: A fully autonomous mobile robot for pipeline exploration

abstract: Pipeline-based applications have become an integral part of life. However, knowing that the pipeline systems can be largely deployed in an inaccessible and hazardous environment, active monitoring and frequent inspection of the pipeline systems are highly expensive using the traditional maintenance systems. Robot agents have been considered as an attractive alternative. Although many different types of pipeline exploration robots have been proposed, they were suffered from various limitations. In this paper, we present the design and implementation of a single-moduled fully autonomous mobile pipeline exploration robot, called FAMPER, that can be used for the inspection of 150mm pipelines. This robot consists of four wall-press caterpillars operated by two DC motors each. The speed of each caterpillar is controlled independently to provide steering capability to go through 45 degree elbows, 90 degree elbows, T-branches, and Y-branches. The uniqueness of this paper is to show the opportunity of using 4 caterpillar configuration for superior performance in all types of complex networks of pipelines. The robot system has been developed and experimented in different pipeline layouts.


title: Cyber-crime Science = Crime Science + Information Security

abstract: Cyber-crime Science is an emerging area of study aiming to prevent cyber-crime by combining security protection techniques from Information Security with empirical research methods used in Crime Science. Information security research has developed techniques for protecting the confidentiality, integrity, and availability of information assets but is less strong on the empirical study of the effectiveness of these techniques. Crime Science studies the effect of crime prevention techniques empirically in the real world, and proposes improvements to these techniques based on this. Combining both approaches, Cyber-crime Science transfers and further develops Information Security techniques to prevent cyber-crime, and empirically studies the effectiveness of these techniques in the real world. In this paper we review the main contributions of Crime Science as of today, illustrate its application to a typical Information Security problem, namely phishing, explore the interdisciplinary structure of Cyber-crime Science, and present an agenda for research in Cyber-crime Science in the form of a set of suggested research questions.


title: Looking up data in P2P systems

abstract: The main challenge in P2P computing is to design and implement a robust and scalable distributed system composed of inexpensive, individually unreliable computers in unrelated administrative domains. The participants in a typical P2P system might include computers at homes, schools, and businesses, and can grow to several million concurrent participants.


title: Nonisolated High Step-Up DC–DC Converters Adopting Switched-Capacitor Cell

abstract: In a photovoltaic (PV)- or fuel-cell-based grid-connected power system, a high step-up dc-dc converter is required to boost the low voltage of a PV or fuel cell to a relatively high bus voltage for the downstream dc-ac grid-connected inverter. To integrate the advantages of the high voltage gain of a switched-capacitor (SC) converter and excellent output regulation of a switching-mode dc-dc converter, a method of combining the two types of converters is proposed in this paper. The basic idea is that when the switch is turned on, the inductor is charged, and the capacitors are connected in series to supply the load, and when the switch is turned off, the inductor releases energy to charge multiple capacitors in parallel, whose voltages are controlled by a pulsewidth modulation technique. Thus, a high voltage gain of the dc-dc converter can be obtained with good regulation. Based on this principle, a series of new topologies are derived, and the operating principles and voltage gains of the proposed converters are analyzed. Finally, the design of the proposed converter is given, and the experiment results are provided to verify the theoretical analysis.


title: Optimization of image description metrics using policy gradient methods

abstract: In this paper, we propose a novel training procedure for image captioning models based on policy gradient methods. This allows us to directly optimize for the metrics of interest, rather than just maximizing likelihood of human generated captions. We show that by optimizing for standard metrics such as BLEU, CIDEr, METEOR and ROUGE, we can develop a system that improve on the metrics and ranks first on the MSCOCO image captioning leader board, even though our CNN-RNN model is much simpler than state of the art models. We further show that by also optimizing for the recently introduced SPICE metric, which measures semantic quality of captions, we can produce a system that significantly outperforms other methods as measured by human evaluation. Finally, we show how we can leverage extra sources of information, such as pre-trained image tagging models, to further improve quality.


title: Protective Effects of Miswak (Salvadora persica) against Experimentally Induced Gastric Ulcers in Rats

abstract: Gastric ulcers are among the most broadly perceived illnesses affecting individuals. Alcohol consumption is the main cause of gastric ulceration. This study assessed the protective effects of Salvadora persica (SP) extract against ethanol-induced gastric ulcer and elucidated the conceivable underlying mechanisms involved. For this purpose, 40 rats were allotted into 4 equal groups (control, ethanol- (EtOH-) treated, and SP-treated "SP200 and SP400" groups). The control and EtOH-treated groups were given phosphate buffer saline (PBS), and both the SP200 and SP400 groups were given SP extract dissolved in PBS at doses of 200 and 400 mg/kg b.w., respectively. All treatments were given orally for 7 constitutive days. On the 8th day, all rats were fasted for 24 h followed by oral gavage of PBS in the control group and chilled absolute ethanol solution (5 ml/kg b.w.) in the EtOH- and SP-treated groups to induce gastric lesions. One hour later, the rats were sacrificed and the stomachs were harvested. Gross and microscopic examinations of the EtOH-treated group showed severe gastric hemorrhagic necrosis, submucosal edema, destruction of epithelial cells, and reduced glycoprotein content at the mucus surface. These pathological lesions were defeated by SP extract treatment. Administration of SP extract modulated the oxidative stress and augmented the antioxidant defenses. The elevated ethanol-expressed tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) genes, as well as bcl-2-like protein 4 (Bax) and inducible nitric oxide synthase (iNOS), were diminished in the SP-treated group. Curiously, SP extract upregulated endothelial nitric oxide synthase (eNOS) and transforming growth factor-β1 (TGF-β1) gene expression comparable to that of the EtOH-treated rats. Aggregately, SP exerted antiulcer activities in ethanol-induced gastric ulcer rat models via modulation of oxidant/antioxidant status, mitigation of proinflammatory cytokines, and apoptosis, as well as remodeling of both NOS isoforms.