title: Colonic Polyp Classification with Convolutional Neural Networks

abstract: Texture patch classification is an important task in many different computer-aided medical systems. Convolutional Neural Networks (CNN's) have become state-of-the-art for many computer vision tasks in recent years. In this paper, we propose the use of CNN's for the automated classification of colonic mucosa for colon polyp staging in the context of colon cancer screening. This deep learning approach has the property of extracting features and classifying images in the same architecture by exploiting directly the input image pixels being successful in handling distortions such as different light conditions, presence of partial occlusions, etc. For this type of deep learning approach it is common to require that the database contains large amounts of data, which is quite rare in the medical field. The method proposed allows the use of small patches (subimages) to increase the size of the database as well to classify different regions in the same image. We show experimentally that this model is more efficient than some of the commonly used features for colonic polyp classification.


title: A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem

abstract: The rapid urbanization of developing countries coupled with explosion in construction of high rising buildings and the high power usage in them calls for conservation and efficient energy program. Such a program require monitoring of end-use appliances energy consumption in real-time. The worldwide recent adoption of smart-meter in smart-grid, has led to the rise of Non-Intrusive Load Monitoring (NILM); which enables estimation of appliance-specific power consumption from building's aggregate power consumption reading. NILM provides households with cost-effective real-time monitoring of end-use appliances to help them understand their consumption pattern and become part and parcel of energy conservation strategy. This paper presents an up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem. This is followed by the review of the state-of-the art NILM algorithms. Furthermore, we review several performance metrics used by NILM researcher to evaluate NILM algorithms and discuss existing benchmarking framework for direct comparison of the state of the art NILM algorithms. Finally, the paper discuss potential NILM use-cases, presents an overview of the public available dataset and highlight challenges and future research directions.


title: From swiping to casual sex and/or committed relationships: Exploring the experiences of Tinder users

abstract: ABSTRACTTo better understand if and how the mechanics of the process Tinder imposes on its users (i.e., swiping, matching, and starting conversations) influences the resulting sexual or romantic interactions, we collected data from 1038 Belgian Tinder users. Our findings show that a user's swiping quantity does not guarantee a higher number of Tinder matches, women have generally more matches than men and men usually have to start a conversation on Tinder. Moreover, while conversations were positively associated with reported offline Tinder encounters, less than half of our sample reported having had an offline meeting with another Tinder user. Whereas more than one third of these offline encounters led to casual sex, more than a quarter resulted in the formation of a committed relationship. Such findings indicate that Tinder is not “just a hookup app”, as often assumed in public discourse. We argue it is plausible that sexual encounters will eventually lead to committed relationships in a society where i...


title: Evolutionary Divergence and Convergence in Proteins

abstract: Publisher Summary Informational macromolecules, or semantides, play a unique role in determining the properties of living matter in the perspectives that differ by the magnitude of time required for the processes involved—the short-timed biochemical reaction, the medium-timed ontogenetic event, and the long-timed evolutionary event. Although the slower processes should be broken down into linked faster processes, if one loses sight of the slower processes one also loses the links between the component faster processes. The relative importance of the contributions to evolution of changes in functional properties of polypeptides through their structural modification on the one hand, and of changes in the timing and the rate of synthesis of these polypeptides on the other hand, constitutes a problem that justifies the study of evolution at the level of informational macromolecules. The evaluation of the amount of differences between two organisms as derived from sequences in structural genes or in their polypeptide translation is likely to lead to quantities different from those obtained on the basis of observations made at any other, higher level of biological integration.


Momentum Investment Strategies of Mutual Funds Performance Persistence


title: Kinematic and dynamic analogies between planar biped robots and the reaction mass pendulum (RMP) model

abstract: In order to simplify dynamic analysis, humanoid robots are often abstracted with various versions of the inverted pendulum model. However, most of these models do not explicitly characterize the robotpsilas rotational inertia, a critical component of its dynamics, and especially of its balance. To remedy this, we have earlier introduced the reaction mass pendulum (RMP), an extension of the inverted pendulum, which models the rotational inertia and angular momentum of a robot through its centroidal composite rigid body (CCRB) inertia. However, we presented only the kinematic mapping between a robot and its corresponding RMP. Focussing in-depth on planar mechanisms, here we derive the dynamic equations of the RMP and explicitly compute the parameters that it must possess in order to establish equivalence with planar compass gait robot. In particular, we show that, a) an angular momentum equality between the robot and RMP does not necessarily guarantee kinetic energy equality, and b) a cyclic robot gait may not result in a cyclic RMP movement. The work raises the broader question of how quantitatively similar the simpler models of humanoid robot must be in order for them to be of practical use.


title: Automatic Facial Expression Recognition System Based on Geometric and Appearance Features

abstract: This paper presents a computer vision system for automatic facial expression recognition (AFER). The robust AFER system can be applied in many areas such as emotion science, clinical psychology and pain assessment it includes facial feature extraction and pattern recognition phases that discriminates among different facial expressions. In feature extraction phase a combination between holistic and analytic approaches is presented to extract 83 facial expression features. Expression recognition is performed by using radial basis function based artificial neural network to recognize the six basic emotions (anger, fear, disgust, joy, surprise, sadness). The experimental results show that 96% recognition rate can be achieved when applying the proposed system on person-dependent database and 93.5% when applying on person-independent one.


title: A Dual-Ported, Dual-Polarized Spherical Near-Field Probe,

abstract: Abstract : The spherical near-field measurement technique is a convenient and economical method for testing antennas. It is particularly useful for electrically small antennas at low frequencies. It requires much less real estate than conventional far-field ranges and yields more accurate results in a more controlled and secure environment. The spherical near-field measurement technique can be used to test antennas too large in physical size for the compact range technique and, unlike other near-field measurement techniques, it permits the use of standard spherical coordinate positioners. The most serious disadvantage of spherical near-field testing is the time required to perform a test.


title: The rotor-flux-oriented control of the induction machine used the multicarrier strategies for multilevel npc inverter

abstract: The rotor-flux-oriented control of the induction motor used the multicarrier strategies for the three-phase multilevel NPC inverter. It applies the PWM strategy of command making a difference between carriers superposition to create new PWM strategies. This work compares two commands of multilevel strategies based on PWM; the PDC-PWM strategies (Phases Disposition Carrier-Pulse Width Modulation) and the PSC-PWM strategies (Phases Shifted Carrier-Pulse Width Modulation).


title: Adaptive Attention Network for Review Sentiment Classification

abstract: Document-level sentiment classification is an important NLP task. The state of the art shows that attention mechanism is particularly effective on document-level sentiment classification. Despite the success of previous attention mechanism, it neglects the correlations among inputs (e.g., words in a sentence), which can be useful for improving the classification result. In this paper, we propose a novel Adaptive Attention Network (AAN) to explicitly model the correlations among inputs. Our AAN has a two-layer attention hierarchy. It first learns an attention score for each input. Given each input’s embedding and attention score, it then computes a weighted sum over all the words’ embeddings. This weighted sum is seen as a “context” embedding, aggregating all the inputs. Finally, to model the correlations among inputs, it computes another attention score for each input, based on the input embedding and the context embedding. These new attention scores are our final output of AAN. In document-level sentiment classification, we apply AAN to model words in a sentence and sentences in a review. We evaluate AAN on three public data sets, and show that it outperforms state-of-the-art baselines.


title: Multimodal feature fusion for CNN-based gait recognition: an empirical comparison

abstract: People identification in video based on the way they walk (i.e., gait) is a relevant task in computer vision using a noninvasive approach. Standard and current approaches typically derive gait signatures from sequences of binary energy maps of subjects extracted from images, but this process introduces a large amount of non-stationary noise, thus conditioning their efficacy. In contrast, in this paper we focus on the raw pixels, or simple functions derived from them, letting advanced learning techniques to extract relevant features. Therefore, we present a comparative study of different convolutional neural network (CNN) architectures by using three different modalities (i.e., gray pixels, optical flow channels and depth maps) on two widely adopted and challenging datasets: TUM-GAID and CASIA-B. In addition, we perform a comparative study between different early and late fusion methods used to combine the information obtained from each kind of modalities. Our experimental results suggest that (1) the raw pixel values represent a competitive input modality, compared to the traditional state-of-the-art silhouette-based features (e.g., GEI), since equivalent or better results are obtained; (2) the fusion of the raw pixel information with information from optical flow and depth maps allows to obtain state-of-the-art results on the gait recognition task with an image resolution several times smaller than the previously reported results; and (3) the selection and the design of the CNN architecture are critical points that can make a difference between state-of-the-art results or poor ones.


title: Deep Representation Learning for Human Motion Prediction and Classification

abstract: Generative models of 3D human motion are often restricted to a small number of activities and can therefore not generalize well to novel movements or applications. In this work we propose a deep learning framework for human motion capture data that learns a generic representation from a large corpus of motion capture data and generalizes well to new, unseen, motions. Using an encoding-decoding network that learns to predict future 3D poses from the most recent past, we extract a feature representation of human motion. Most work on deep learning for sequence prediction focuses on video and speech. Since skeletal data has a different structure, we present and evaluate different network architectures that make different assumptions about time dependencies and limb correlations. To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units. Our method outperforms the recent state of the art in skeletal motion prediction even though these use action specific training data. Our results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.


title: Flavonoids and a New Calamenene-type Sesquiterpene from Rhizomes of Alpinia oxymitra K. Schum. (Zingiberaceae)

abstract: Chemical constituents of rhizomes of Alpinia oxymitra (Zingiberaceae) were investigated. Using chromatographic techniques, two known flavonoids, epicatechin and galloepicatechin, were isolated together with a new calamenene sesquiterpene, (-)-(1 R,4 S)-8-hydroxy-13-calamenenoic acid. Structure elucidation of the isolated compounds was accomplished by means of spectroscopic techniques, especially NMR spectroscopy and mass spectrometry.


title: Optimal Design of an Outer-Stator Magnetically Geared Permanent Magnet Machine

abstract: In this paper, a magnetically geared permanent magnet (MGPM) machine is proposed for the use in wind power generation. The structure layout, operating principles, and equivalent circuit of the MGPM machine are first described. Then, an efficient finite-element-method-based design optimization approach is formulated and implemented in the design of the machine. The optimized machine shows that a rated torque density close to 70 kNm/ $\text{m}^{3}$ can be achieved, which is significantly higher than that of the conventional electrical machines. Furthermore, a prototype machine based on the design is constructed and experimentally evaluated. The measured torque performance shows good agreement with the predicted one, but a significant difference exists between the measured and calculated efficiencies. This is mainly due to higher than the expected mechanical losses in the MGPM machine. Relevant conclusions are drawn based on the analysis of presented results and some key design-related aspects.


Road Traffic Data: Collection Methods and Applications


title: On the effectiveness of local binary patterns in face anti-spoofing

abstract: Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.


title: Introduction of optical camera communication for Internet of vehicles (IoV)

abstract: In this paper, we have introduced optical camera communication (OCC) as a new technology for internet of vehicles (IoV). OCC has already been established in many areas which can also be used in vehicular communication. There have some researches in the field of OCC-based vehicular communication but these are not mature enough. Here, we have proposed a new system which will provide great advantages to the vehicular system. We proposed a combination of OCC and cloud-based communication for IoV which will ensure secure, stable, and system. We have also proposed an algorithm to provide cloud based IoV service.


title: Ambulatory measurement of 3D knee joint angle

abstract: Abstract Three-dimensional measurement of joint motion is a promising tool for clinical evaluation and therapeutic treatment comparisons. Although many devices exist for joints kinematics assessment, there is a need for a system that could be used in routine practice. Such a system should be accurate, ambulatory, and easy to use. The combination of gyroscopes and accelerometers (i.e., inertial measurement unit) has proven to be suitable for unrestrained measurement of orientation during a short period of time (i.e., few minutes). However, due to their inability to detect horizontal reference, inertial-based systems generally fail to measure differential orientation, a prerequisite for computing the three-dimentional knee joint angle recommended by the Internal Society of Biomechanics (ISB). A simple method based on a leg movement is proposed here to align two inertial measurement units fixed on the thigh and shank segments. Based on the combination of the former alignment and a fusion algorithm, the three-dimensional knee joint angle is measured and compared with a magnetic motion capture system during walking. The proposed system is suitable to measure the absolute knee flexion/extension and abduction/adduction angles with mean (SD) offset errors of −1° (1°) and 0° (0.6°) and mean (SD) root mean square (RMS) errors of 1.5° (0.4°) and 1.7° (0.5°). The system is also suitable for the relative measurement of knee internal/external rotation (mean (SD) offset error of 3.4° (2.7°)) with a mean (SD) RMS error of 1.6° (0.5°). The method described in this paper can be easily adapted in order to measure other joint angular displacements such as elbow or ankle.


title: Using LSTM networks to predict engine condition on large scale data processing framework

abstract: As the Internet of Things technology is developing rapidly, companies have an ability to observe the health of engine components and constructed systems through collecting signals from sensors. According to output of IoT sensors, companies can build systems to predict the conditions of components. Practically the components are required to be maintained or replaced before the end of life in performing their assigned task. Predicting the life condition of a component is so crucial for industries that have intent to grow in a fast paced technological environment. Recent studies on predictive maintenance help industries to create an alert before the components are corrupted. Thanks to prediction of component failures, companies have a chance to sustain their operations efficiently while reducing their maintenance cost by repairing components in advance. Since maintenance affects production capacity and the service quality directly, optimized maintenance is the key factor for organizations to have more revenue and stay competitive in developing industrialized world. With the aid of well-designed prediction system for understanding current situation of an engine, components could be taken out of active service before malfunction occurs. With the help of inspection, effective maintenance extends component life, improves equipment availability and keeps components in a proper condition while reducing costs. Real time data collected from sensors is a great source to model component deteriorations. Markov Chain models, Survival Analysis, Optimization algorithms and several machine learning approaches have been implemented in order to model predictive maintenance. In this paper Long Short Term Memory (LSTM) networks has been performed to predict the current situation of an engine. LSTM model deals with a sequential input data. Training process of LSTM networks has been performed on large-scale data processing engine with high performance. Since huge amount of data is flowing into the predictive model, Apache Spark which is offering a distributed clustering environment has been used. The output of the LSTM network is deciding the current life condition of components and offering the alerts for components before the end of their life. The proposed model also trained and tested on an open source data that is about an engine degradation simulation provided by the Prognostics CoE at NASA Ames.


title: Technique for automatic emotion recognition by body gesture analysis

abstract: This paper illustrates our recent work on the analysis of expressive gesture related to the motion of the upper body (the head and the hands) in the context of emotional portrayals performed by professional actors. An experiment is presented which is the result of a multidisciplinary joint work. The experiment aims at (i) developing models and algorithms for analysis of such expressive content (ii) individuating which motion cues are involved in conveying the actorpsilas expressive intentions to portray four emotions (anger, joy, relief, sadness) via a scenario approach. The paper discusses the experiment in detail with reference to related conceptual issues, developed techniques, and the obtained results.


A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms


title: A wireless handheld probe with spectrally constrained evolution strategies for diffuse optical imaging of tissue

abstract: We present a low-cost, portable, wireless diffuse optical imaging device. The handheld device is fast, portable, and can be applied to a wide range of both static and dynamic imaging applications including breast cancer, functional brain imaging, and peripheral artery disease. The continuous-wave probe has four near-infrared wavelengths and uses digital detection techniques to perform measurements at 2.3 Hz. Using a multispectral evolution algorithm for chromophore reconstruction, we can measure absolute oxygenated and deoxygenated hemoglobin concentration as well as scattering in tissue. Performance of the device is demonstrated using a series of liquid phantoms comprised of Intralipid®, ink, and dye.


title: The Security and Performance of the Galois/Counter Mode (GCM) of Operation

abstract: The recently introduced Galois/Counter Mode (GCM) of operation for block ciphers provides both encryption and message authentication, using universal hashing based on multiplication in a binary finite field. We analyze its security and performance, and show that it is the most efficient mode of operation for high speed packet networks, by using a realistic model of a network crypto module and empirical data from studies of Internet traffic in conjunction with software experiments and hardware designs. GCM has several useful features: it can accept IVs of arbitrary length, can act as a stand-alone message authentication code (MAC), and can be used as an incremental MAC. We show that GCM is secure in the standard model of concrete security, even when these features are used. We also consider several of its important system-security aspects.


title: What Is the Utility of Biomarkers for Assessing the Pathophysiology of Hip Osteoarthritis? A Systematic Review

abstract: Background ::: Innovations in biologics offer great promise in the treatment of patients with orthopaedic conditions and in advancing our ability to monitor underlying disease pathophysiology. Our understanding of the pathophysiology of hip osteoarthritis (OA) has improved significantly in the last decade. Femoroacetabular impingement (FAI) and hip dysplasia are increasingly recognized and treated as forms of prearthritic hip disease, yet the inability of radiographic and MR imaging to identify patients before the onset of irreversible articular cartilage injury limits their use for early diagnosis and treatment of patients with these conditions. Molecular biomarkers, as objectively measureable indicators of the pathophysiology of hip OA, have the potential to improve diagnosis, disease staging, and prognosis of hip OA and prearthritic hip disease. Although research into molecular biomarkers of hip OA has been conducted, investigations in prearthritic hip disease have only recently begun.


title: Design and evaluation of a Twitter hashtag recommendation system

abstract: Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests. We propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations more stable and reliable than ranking tags based on tweet content similarity. Our results show that HF-IHU can achieve over 30% hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet.


title: Identifying learning features and models for context-aware ubiquitous learning with phenomenological research method

abstract: Context-aware ubiquitous learning u-learning is an emerging paradigm of e-learning, and it provides new educational practices for knowledge and skill development. This paper focuses on the identification and analysis of u-learning effectiveness to develop a learning design model to guide interested practitioners via phenomenological research method. The authors first examined 21 identified u-learning studies cited at least five times in the Web of Science to conduct the first step of phenomenological research method, bracketing the phenomenon. Later, the researchers followed the needed procedure of the phenomenological research method, including collecting interview data, identifying meaningful statements, giving meanings to those statements, and creating rich descriptions. The results demonstrate possible features of u-learning and the learning design model based the features to increase the learning effectiveness, and interested practitioners can apply the proposed learning design model proposed after the identification of learning effectiveness of the selected u-learning studies when constructing u-learning projects.


Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning


title: Rotations and Interpretability of Word Embeddings: the Case of the Russian Language

abstract: Consider a continuous word embedding model. Usually, the cosines between word vectors are used as a measure of similarity of words. These cosines do not change under orthogonal transformations of the embedding space. We demonstrate that, using some canonical orthogonal transformations from SVD, it is possible both to increase the meaning of some components and to make the components more stable under re-learning. We study the interpretability of components for publicly available models for the Russian language (RusVectores, fastText, RDT).


title: Automatic SAR Simulation Technique for Object Identification in Complex Urban Scenarios

abstract: Most of the existing SAR simulators provide simulated images only for visual interpretation. This paper presents a new approach for supporting the automatic interpretation of meter resolution SAR images in complex urban scenarios. To this end, an automatic system for generating and geocoding of simulated images has been developed, based on the SAR simulator RaySAR and digital surface models as geometric information for urban scenes. Various simulated images and different layers (layover, shadow, double bounce, and ground) are generated for the whole scene as well as for individual buildings, with consideration of neighborhood influences. The proposed approach is tested on an area in central Munich with a LiDAR digital surface model as the input. The simulated images are compared with a TerraSAR-X spotlight image and show good geocoding accuracy, reasonable mask layers, and precise individual building layover contours.


title: A Deep Learning Approach for Classification of Cleanliness in Restrooms

abstract: Facilities hire cleaning companies to maintain and manage cleaning operations on their restrooms by deploying cleaners who are responsible for performing frequent checks to ensure the cleanliness of restrooms. Nevertheless, the perception of quality and word, clean is very subjective to the observer. Hence, it is not an easy task to quantify the cleanliness. This paper presents a deep learning approach using deep convolutional neural networks (DCNN) to detect and classify the level of cleanliness in restrooms into three different categories; namely dirty, average, and clean. Our method sheds new lights on data augmentation, feature extraction and knowledge transfer between models. The proposed architecture achieved a precision of 0.98, 0.95, 0.80 and recall of 0.99, 0.83, 0.95 for dirty, average, and clean categories respectively utilizing a dataset collected from an active restroom facility.


A Survey on Different Approaches used for Credit Card Fraud Detection


Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining


title: Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting

abstract: Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. Keywords—Parametric and Nonparametric Techniques,


title: Non-rigid range-scan alignment using thin-plate splines

abstract: We present a nonrigid alignment algorithm for aligning high-resolution range data in the presence of low-frequency deformations, such as those caused by scanner calibration error. Traditional iterative closest points (ICP) algorithms, which rely on rigid-body alignment, fail in these cases because the error appears as a nonrigid warp in the data. Our algorithm combines the robustness and efficiency of ICP with the expressiveness of thin-plate splines to align high-resolution scanned data accurately, such as scans from the Digital Michelangelo Project [M. Levoy et al. (2000)]. This application is distinguished from previous uses of the thin-plate spline by the fact that the resolution and size of warping are several orders of magnitude smaller than the extent of the mesh, thus requiring especially precise feature correspondence.


title: Multi-Label Active Learning from Crowds

abstract: Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle. In this paper, we consider the poolbased multi-label active learning under the crowdsourcing setting, where during the active query process, instead of resorting to a high cost oracle for the ground-truth, multiple low cost imperfect annotators with various expertise are available for labeling. To deal with this problem, we propose the MAC (Multi-label Active learning from Crowds) approach which incorporate the local influence of label correlations to build a probabilistic model over the multi-label classifier and annotators. Based on this model, we can estimate the labels for instances as well as the expertise of each annotator. Then we propose the instance selection and annotator selection criteria that consider the uncertainty/diversity of instances and the reliability of annotators, such that the most reliable annotator will be queried for the most valuable instances. Experimental results demonstrate the effectiveness of the proposed approach.


title: A randomized linear-time algorithm for finding minimum spanning trees

abstract: We present a randomized linear-time algorithm for finding a minimum spanning tree in a connected graph with edge weights. The algorithm is a modification of one proposed by Karger and uses random sampling in combination with a recently discovered linear-time algorithm for verifying a minimum spanning tree. Our computational model is a unit-cost random-access machine with the restriction that the only operations allowed on edge weights are binary comparisons.


title: Stacked PMOS clamps for high voltage power supply protection

abstract: Abstract Large p-channel MOS (PMOS) field-effect transistors (FETs) with multiple gates can be arranged to provide ESD protection to high voltage on-chip power supplies in submicron integrated circuits. These clamps divide the supply voltage among several gate oxides; the circuitry accompanying the large series FETs provides near-maximum gate drive during the ESD for high pulsed current. Layouts are densely packed because minimum dimensions can be used and because no contact is needed between the stacked gates. The designs for high voltage are extensions of the large PMOS FET ESD clamps and timed drive circuitry that are used to clamp ordinary on-chip power supply lines.


title: FPGAs in the Cloud: Booting Virtualized Hardware Accelerators with OpenStack

abstract: We present a new approach for integrating virtualized FPGA-based hardware accelerators into commercial-scale cloud computing systems, with minimal virtualization overhead. Partially reconfigurable regions across multiple FPGAs are offered as generic cloud resources through OpenStack (opensource cloud software), thereby allowing users to “boot” custom designed or predefined network-connected hardware accelerators with the same commands they would use to boot a regular Virtual Machine. We propose a hardware and software framework to enable this virtualization. This is a first attempt at closely fitting FPGAs into existing cloud computing models, where resources are virtualized, flexible, and have the illusion of infinite scalability. Our system can set up and tear down virtual accelerators in approximately 2.6 seconds on average, much faster than regular virtual machines. The static virtualization hardware on the physical FPGAs causes only a three cycle latency increase and a one cycle pipeline stall per packet in accelerators when compared to a non-virtualized system. We present a case study analyzing the design and performance of an application-level load balancer using a fully implemented prototype of our system. Our study shows that FPGA cloud compute resources can easily outperform virtual machines, while the system's virtualization and abstraction significantly reduces design iteration time and design complexity.


title: The Human Factors Analysis and Classification System – HFACS

abstract: BACKGROUND ::: The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based on Reason's (1990) model of latent and active failures, HFACS addresses human error at all levels of the system, including the condition of aircrew and organizational factors. The purpose of the present study was to assess the utility of the HFACS framework as an error analysis and classification tool outside the military. ::: ::: ::: METHODS ::: The HFACS framework was used to analyze human error data associated with aircrew-related commercial aviation accidents that occurred between January 1990 and December 1996 using database records maintained by the NTSB and the FAA. ::: ::: ::: RESULTS ::: Investigators were able to reliably accommodate all the human causal factors associated with the commercial aviation accidents examined in this study using the HFACS system. In addition, the classification of data using HFACS highlighted several critical safety issues in need of intervention research. ::: ::: ::: CONCLUSION ::: These results demonstrate that the HFACS framework can be a viable tool for use within the civil aviation arena. However, additional research is needed to examine its applicability to areas outside the flight deck, such as aircraft maintenance and air traffic control domains.


title: Fast 3D Salient Region Detection in Medical Images using GPUs

abstract: Automated detection of visually salient regions is an activearea of research in computer vision. Salient regions can serve as inputs for object detectors as well as inputs for region-based registration algorithms. In this paper, we consider the problem of speeding up computationally intensive bottom-up salient region detection in 3D medical volumes. The method uses the Kadir–Brady formulation of saliency. We show that in the vicinity of a salient region, entropy is a monotonically increasing function of the degree of overlap of a candidate window with the salient region. This allows us to initialize a sparse seed point grid as the set of tentative salient region centers and iteratively converge to the local entropy maxima, thereby reducing the computation complexity compared to the Kadir–Brady approach of performing this computation at every point in the image. We propose two different approaches for achieving this. The first approach involves evaluating entropy in the four quadrants around the seed point and iteratively moving in the direction that increases entropy. The second approach we propose makes use of mean shift tracking framework to affect entropy maximizing moves. Specifically, we propose the use of uniform pmf as the target distribution to seek high entropy regions. We demonstrate the use of our algorithm on medical volumes for left ventricle detection in PET images and tumor localization in brain MR sequences.


title: Uncovering Social Media Data For Public Health Surveillance

abstract: Social media is playing an increasingly important role as the sources of health related information. The goal of this study is to investigate the extent social media appear in search engine results in the context of health-related information search. We simulate an information seeker’s use of a search engine for health consultation using a set of pre-defined keywords in combination with 5 types of complaints. The results showed that social media constitute a significant part of the search results, indicating that search engines likely direct information seekers to social media sites. This study confirms the growing importance of social media in health communication. It also provides evidence regarding opportunities and challenges faced by health professionals and general public.


title: Comparing Virtual and Physical Robotics Environments for Supporting Complex Systems and Computational Thinking

abstract: Both complex systems methods (such as agent-based modeling) and computational methods (such as programming) provide powerful ways for students to understand new phenomena. To understand how to effectively teach complex systems and computational content to younger students, we conducted a study in four urban middle school classrooms comparing 2-week-long curricular units—one using a physical robotics participatory simulation and one using a virtual robotics participatory simulation. We compare the two units for their effectiveness in supporting students’ complex systems thinking and computational thinking skills. We find that while both units improved student outcomes to roughly the same extent, they engendered different perspectives on the content. That is, students using the physical system were more likely to interpret situations from a bottom-up (“agent”) perspective, and students using the virtual system were more likely to employ a top-down (“aggregate”) perspective. Our outcomes suggest that the medium of students’ interactions with systems leads to differences in their learning from and about those systems. We explore the reasons for and effects of these differences, challenges in teaching this content, and student learning gains. The paper contributes operationalizable definitions of complex systems perspectives and computational perspectives and provides both a theoretical framework for and empirical evidence of a relationship between those two perspectives.


title: Precise Recovery of Latent Vectors from Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) transform latent vectors into visually plausible images. It is generally thought that the original GAN formulation gives no out-of-the-box method to reverse the mapping, projecting images back into latent space. We introduce a simple, gradient-based technique called stochastic clipping. In experiments, for images generated by the GAN, we precisely recover their latent vector pre-images 100% of the time. Additional experiments demonstrate that this method is robust to noise. Finally, we show that even for unseen images, our method appears to recover unique encodings.


title: Delegate the smartphone user? Security awareness in smartphone platforms

abstract: Smartphone users increasingly download and install third-party applications from official application repositories. Attackers may use this centralized application delivery architecture as a security and privacy attack vector. This risk increases since application vetting mechanisms are often not in place and the user is delegated to authorize which functionality and protected resources are accessible by third-party applications. In this paper, we mount a survey to explore the security awareness of smartphone users who download applications from official application repositories (e.g. Google Play, Apple's App Store, etc.). The survey findings suggest a security complacency, as the majority of users trust the app repository, security controls are not enabled or not added, and users disregard security during application selection and installation. As a response to this security complacency we built a prediction model to identify users who trust the app repository. The model is assessed, evaluated and proved to be statistically significant and efficient.


title: Secure Control Systems: A Quantitative Risk Management Approach

abstract: Critical infrastructures must continuously operate safely and reliably, despite a variety of potential system disturbances. Given their strict operating requirements, such systems are automated and controlled in real time by several digital controllers receiving measurements from sensors and transmitting control signals to actuators. Since these physical systems are often spatially distributed, there is a need for information technology (IT) infrastructures enabling the timely data flow between the system components. These networked control systems are ubiquitous in modern societies [1]. Examples include the electric power network, intelligent transport systems, and industrial processes.


title: Translational biomarker discovery in clinical metabolomics: an introductory tutorial

abstract: Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start “speaking the same language” in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.


Viewpoint Could Artificial Intelligence Create an Unemployment Crisis ?


title: Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations

abstract: We present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence assumptions. The model is composed of two parts: a feature-rich relation extractor, which predicts a semantic relation between two entities, and a factorization model, which reconstructs arguments (i.e., the entities) relying on the predicted relation. The two components are estimated jointly so as to minimize errors in recovering arguments. We study factorization models inspired by previous work in relation factorization and selectional preference modeling. Our models substantially outperform the generative and agglomerative-clustering counterparts and achieve state-of-the-art performance.


title: Learning dictionaries for information extraction by multi-level bootstrapping

abstract: Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories.


title: Introducing instant messaging and chat in the workplace

abstract: We report on our experiences of introducing an instant messaging and group chat application into geographically distributed workgroups. We describe a number of issues we encountered, including privacy concerns, individual versus group training, and focusing on teams or individuals. The perception of the tool's utility was a complex issue, depending both on users' views of the importance of informal communication, and their perceptions of the nature of cross-site communication issues. Finally, we conclude with a discussion of critical mass, which is related to the features each user actually uses. More generally, we encountered a dilemma that imposes serious challenges for user-centered design of groupware systems


title: A framework for seed user identification across multiple online social networks

abstract: With the advent of smart mobile phone social networking services are becoming the backbone of online virtual communication. Every year, several new social networks introduced with unique features that bring the opportunity for social network users' to migrate on the newly introduced social platform. In order to enjoy the services of newly introduced social media it is necessary that a user must have an account on that particular network. Finding the same user across the social network is a challenging task because of platform diversity. In this paper, we propose a hybrid personal information based approach to identify users across multiple online social networks. In this work, we utilize one content feature; ‘cross link posts’ and one network feature; ‘following network’ relation to identify unique seed users' across two social networks containing syntactically similar usernames namely, Twitter and Instagram. Matching user's ‘following network’ relationship using native approximate distance (Levenshtein) method, it is found that one cross link can give on an average 52 seed users. This result can be used as preprocessing step for large scale user identification across multiple social networks.


title: A Novel Rectangular Ring Planar Monopole Antennas for Ultra-Wideband Applications

abstract: This paper proposes two rectangular ring planar monopole antennas for wideband and ultra-wideband applications. Simple planar rectangular rings are used to design the planar antennas. These rectangular rings are designed in a way to achieve the wideband operations. The operating frequency band ranges from 1.85 GHz to 4.95 GHz and 3.12 GHz to 14.15 GHz. The gain varies from 1.83 dBi to 2.89 dBi for rectangular ring wideband antenna and 1.89 dBi to 5.2 dBi for rectangular ring ultra-wideband antenna. The design approach and the results are discussed.


title: Usability testing and system evaluation : a guide for designing useful computer systems

abstract: Designing useful systems. Data gathering tools. Introduction to experimental design. Types of usability evaluation techniques: applying what we have just learned. Integrating usability evaluation into the design process. Develop a usability test and evaluation plan. Help, user documentation and tutoring systems.


title: Dynamics of conflicts in Wikipedia

abstract: In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.


title: Reliability Qualification of CoSi2 Electrical Fuse for 90Nm Technology

abstract: The reliability of CoSi2/p-poly Si electrical fuse (eFUSE) programmed by electromigration for 90nm technology will be presented. Both programmed and unprogrammed fuse elements were shown to be stable through extensive reliability evaluations. A qualification methodology is demonstrated to define an optimized reliable electrical fuse programming window by combining fuse resistance measurements, physical analysis, and functional sensing data. This methodology addresses the impact on electrical fuse reliability caused by process variation and device degradation (e.g., NBTI) in the sensing circuit and allows an adequate margin to ensure electrical fuse reliability over the chip lifetime.


title: Design and Development of an Assistive Hand Device for Enhancing Compatibility and Comfortability

abstract: Currently, there are plenty of hand exoskeletons for rehabilitation and assistive purpose that have been developed. They have been designed for working with patient's limbs and can be used for hand positioning and executing task which help reduce therapist's workload and give them an alternative way to rehabilitate patients. However, there are some patients with permanent impairments, which cannot improve from rehabilitation. Thus, portable assistive devices could greatly help them performing simple activities of daily living (ADL), but such device has not been fully explored, the present design of tendon routing cannot closely replicate finger orientation and motion during grasping. Improving the flexion tendon would increase the possibility of grasping for patients with hand disability. This research aims to develop a tendon routing design of a soft exoskeleton to increase compatibility with human hand, while it can execute tasks and exercises more naturally compared to normal limbs to help stroke patients to live independently. The results convinced that the proposed flexion routing can closely replicate human grasping motion with sufficient fingertip force to perform daily activities.


title: Deep Learning Based HEVC In-Loop Filtering for Decoder Quality Enhancement

abstract: High Efficiency Video Coding (HEVC), which is the latest video coding standard currently, achieves up to 50% bit rate reduction compared to previous H.264/AVC standard. While performing the block based video coding, these lossy compression techniques produce various artifacts like blurring, distortion, ringing, and contouring effects on output frames, especially at low bit rates. To reduce those compression artifacts HEVC adopted two post processing filtering technique namely de-blocking filter (DBF) and sample adaptive offset (SAO) on the decoder side. While DBF applies to samples located at block boundaries, SAO nonlinear operation applies adaptively to samples satisfying the gradient based conditions through a lookup table. Again SAO filter corrects the quantization errors by sending edge offset values to decoders. This operation consumes extra signaling bit and becomes an overhead to network. In this paper, we proposed a Convolutional Neural Network (CNN) based architecture for SAO in-loop filtering operation without modifying anything on encoding process. Our experimental results show that our proposed model outperformed previous state-of-the-art models in terms of BD-PSNR (0.408 dB) and BD-BR (3.44%), measured on a widely available standard video sequences.


title: Tagging English Text With A Probabilistic Model

abstract: In this paper we present some experiments on the use of a probabilistic model to tag English text, i.e. to assign to each word the correct tag (part of speech) in the context of the sentence. The main novelty of these experiments is the use of untagged text in the training of the model. We have used a simple triclass Markov model and are looking for the best way to estimate the parameters of this model, depending on the kind and amount of training data provided. Two approaches in particular are compared and combined:using text that has been tagged by hand and computing relative frequency counts,using text without tags and training the model as a hidden Markov process, according to a Maximum Likelihood principle.Experminents show that the best training is obtained by using as much tagged text as possible. They also show that Maximum Likelihood training, the procedure that is routinely used to estimate hidden Markov models parameters from training data, will not necessarily improve the tagging accuracy. In fact, it will generally degrade this accuracy, except when only a limited amount of hand-tagged text is available.


title: A simple PEM fuel cell emulator using electrical circuit model

abstract: This paper presents the development of a simple emulator for proton exchange membrane (PEM) fuel cell. This emulator is built based on the dynamic model of PEM fuel cells using electrical circuits. The electrical circuit model has been modeled in Matlab Simulink environments. The double-layer charging effect characteristic inside the fuel cell is included in the models. The steady-state and transient response of the model is investigated. Emulator performance is validated using experimental data from a 500-W commercial PEM fuel-cell stack. The emulator could be used in PEM fuel-cell control related studies.


Driver Fusions and Their I mplications in the Development and Treatment of Human Cancers Graphical


title: Identifying Anomalous Citations for Objective Evaluation of Scholarly Article Impact

abstract: Evaluating the impact of a scholarly article is of great significance and has attracted great attentions. Although citation-based evaluation approaches have been widely used, these approaches face limitations e.g. in identifying anomalous citations patterns. This negligence would inevitably cause unfairness and inaccuracy to the article impact evaluation. In this study, in order to discover the anomalous citations and ensure the fairness and accuracy of research outcome evaluation, we investigate the citation relationships between articles using the following factors: collaboration times, the time span of collaboration, citing times and the time span of citing to weaken the relationship of Conflict of Interest (COI) in the citation network. Meanwhile, we study a special kind of COI, namely suspected COI relationship. Based on the COI relationship, we further bring forward the COIRank algorithm, an innovative scheme for accurately assessing the impact of an article. Our method distinguishes the citation strength, and utilizes PageRank and HITS algorithms to rank scholarly articles comprehensively. The experiments are conducted on the American Physical Society (APS) dataset. We find that about 80.88% articles contain contributed citations by co-authors in 26,366 articles and 75.55% articles among these articles are cited by the authors belonging to the same affiliation, indicating COI and suspected COI should not be ignored for evaluating impact of scientific papers objectively. Moreover, our experimental results demonstrate COIRank algorithm significantly outperforms the state-of-art solutions. The validity of our approach is verified by using the probability of Recommendation Intensity.


title: Investigation of effect of slot opening on fractional slot and integer slot axial flux permanent magnet machine

abstract: This research attempts to compare the effects of the slot opening on axial flux permanent magnet machines with the fractional slot concentrated winding and the integer slot toroid winding. Analytical derivations of winding function, flux linkage, and back-EMF are presented, paying special attention to the slot opening. Specific machines having 24 slots 28 poles, 24 slots 20 poles, and 84 slots 28 poles are compared. Comparison results shows that the slot opening contributes to reducing the harmonics of the back-EMF in the axial flux permanent magnet machines having fractional slot concentrated winding, while it increases harmonics of the back-EMF in the axial flux permanent magnet machines having integer slot toroid winding. Slot opening ratio considerably varies total harmonic distortion of back-EMF in axial flux permanent magnet machine with fractional slot concentrated winding while it has negligible effect on the harmonic distortion of axial flux permanent magnet machine with integer slot toroid winding.


title: Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization

abstract: In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs’ hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users’ distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users.


title: CloudSeer: Workflow Monitoring of Cloud Infrastructures via Interleaved Logs

abstract: Cloud infrastructures provide a rich set of management tasks that operate computing, storage, and networking resources in the cloud. Monitoring the executions of these tasks is crucial for cloud providers to promptly find and understand problems that compromise cloud availability. However, such monitoring is challenging because there are multiple distributed service components involved in the executions. CloudSeer enables effective workflow monitoring. It takes a lightweight non-intrusive approach that purely works on interleaved logs widely existing in cloud infrastructures. CloudSeer first builds an automaton for the workflow of each management task based on normal executions, and then it checks log messages against a set of automata for workflow divergences in a streaming manner. Divergences found during the checking process indicate potential execution problems, which may or may not be accompanied by error log messages. For each potential problem, CloudSeer outputs necessary context information including the affected task automaton and related log messages hinting where the problem occurs to help further diagnosis. Our experiments on OpenStack, a popular open-source cloud infrastructure, show that CloudSeer's efficiency and problem-detection capability are suitable for online monitoring.


An investigation into the relationship between music preference, personality and psychological wellbeing


title: Crowdsourcing On-street Parking Space Detection

abstract: As the number of vehicles continues to grow, parking spaces are at a premium in city streets. Additionally, due to the lack of knowledge about street parking spaces, heuristic circling the blocks not only costs drivers' time and fuel, but also increases city congestion. In the wake of recent trend to build convenient, green and energy-efficient smart cities, we rethink common techniques adopted by high-profile smart parking systems, and present a user-engaged (crowdsourcing) and sonar-based prototype to identify urban on-street parking spaces. The prototype includes an ultrasonic sensor, a GPS receiver and associated Arduino micro-controllers. It is mounted on the passenger side of a car to measure the distance from the vehicle to the nearest roadside obstacle. Multiple road tests are conducted around Wheatley, Oxford to gather results and emulate the crowdsourcing approach. By extracting parked vehicles' features from the collected trace, a supervised learning algorithm is developed to estimate roadside parking occupancy and spot illegal parking vehicles. A quantity estimation model is derived to calculate the required number of sensing units to cover urban streets. The estimation is quantitatively compared to a fixed sensing solution. The results show that the crowdsourcing way would need substantially fewer sensors compared to the fixed sensing system.


title: Fabrication of GaInP/GaAs//Si Solar Cells by Surface Activated Direct Wafer Bonding

abstract: GaInP/GaAs//Si solar cells with three active p-n junctions were fabricated by surface activated direct wafer bonding between GaAs and Si. The direct wafer bond is performed at room temperature and leads to a conductive and transparent interface. This allows the fabrication of high-efficiency monolithic tandem solar cells with active junctions in both Si and the III-V materials. This technology overcomes earlier challenges of III-V and Si integration caused by the large difference in lattice constant and thermal expansion. Transmission electron microscopy revealed a 5-nm thin amorphous interface layer formed by the argon fast atom beam treatment before bonding. No further defects or voids are detected in the photoactive layers. First triple-junction solar cell devices on Si reached an efficiency of 23.6% under concentrated illumination.


title: A 3V CMOS-MEMS oscillator in 0.35μm CMOS technology

abstract: This paper presents the design, fabrication and characterization of a fully monolithic 11-MHz oscillator circuit operating with a low voltage MEMS resonator biased below the nominal 3.3V operation of the commercial 0.35-μm CMOS technology used to fabricate the device. The CMOS-MEMS oscillator comprises a polysilicon double-ended tuning fork (DETF) resonator embedded in a differential Pierce oscillator scheme. The device is suitable for RF applications and ultrasensitive mass sensing.


title: An Integrated Battery Charger With High Power Density and Efficiency for Electric Vehicles

abstract: Power conversion systems for electric vehicles (EVs) have been researched to improve power density and efficiency at low cost. To satisfy these needs for EVs, this paper proposes a novel battery charging system that integrates a nonisolated on-board charger (OBC) and low-voltage dc–dc converters (LDCs) by sharing the semiconductor devices and mechanical elements. Thus, the volume of LDCs is reduced dramatically compared with a conventional nonintegrated charging system. The proposed integrated system is configured based on a driving condition that is derived from the analysis of vehicle operating modes. In order to improve system's performance, an asynchronous control algorithm is applied to control the OBC optimally. In the LDC system, two LLC resonant converters are composed by sharing a transformer and secondary-side components. To increase the efficiency of each LDC, which is operated in the wide input and output voltage range, a duty and frequency control algorithm is proposed. The theoretical analysis, operating strategy, and experimental results on a 6.6-kW OBC and 1.9-kW LDC are presented to evaluate the performance of the proposed system; the total volume of LDCs is 1.87 L, and peak efficiencies of OBC and LDC are 97.3% and 93.13%, respectively. Moreover, a comparative analysis is presented to evaluate the performance of the proposed system.


title: A parallel non-conforming multi-element DGTD method for the simulation of electromagnetic wave interaction with metallic nanoparticles

abstract: The present work is about the development of a parallel non-conforming multi-element discontinuous Galerkin time-domain (DGTD) method for the simulation of the scattering of electromagnetic waves by metallic nanoparticles. Such nanoparticles most often have curvilinear shapes, therefore we propose a numerical modeling strategy which combines the use of an unstructured tetrahedral mesh for the discretization of the scattering structures with a structured (uniform cartesian) mesh for treating efficiently the rest of the domain. The overall goal is to increase the flexibility in the meshing process while decreasing the needs in computational resources for the target applications. The latter are here modeled by the system of 3D time-domain Maxwell equations coupled to a Drude dispersion model for taking into account the material properties of nanoparticles at optical frequencies. We propose an auxiliary differential equation (ADE) based DGTD method for solving the resulting system and present numerical results demonstrating the benefits of using non-conforming multi-element meshes in this particular application context.


title: Mean Field Theory for Sigmoid Belief Networks

abstract: We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition---the classification of handwritten digits.


AXONAL TRANSECTION IN THE LESIONS OF MULTIPLE SCLEROSIS B


title: From Identification to Authentication - A Review of RFID Product Authentication Techniques

abstract: Authentication has an important role in many RFID applications for providing security and privacy. In this paper we focus on investigating how RFID can be used in product authentication in supply chain applications and a review of existing approaches is provided. The different categories of RFID product authentication approaches are analyzed within the context of anticounterfeiting and fields where future research is needed are identified.


Ordinal Zero-Shot Learning


title: Capital Market-Driven Corporate Finance

abstract: Much of empirical corporate finance focuses on sources of the demand for various forms of capital, not the supply. Recently, this has changed. Supply effects of equity and credit markets can arise from a combination of three ingredients: investor tastes, limited intermediation, and corporate opportunism. Investor tastes when combined with imperfectly competitive intermediaries lead prices and interest rates to deviate from fundamental values. Opportunistic firms respond by issuing securities with high prices and investing the proceeds. A link between capital market prices and corporate finance can in principle come from either supply or demand. This framework helps to organize empirical approaches that more precisely identify and quantify supply effects through variation in one of these three ingredients. Taken as a whole, the evidence shows that shifting equity and credit market conditions play an important role in dictating corporate finance and investment.


title: A Combination-based Semantic Similarity Measure using Multiple Information Sources

abstract: The semantic similarity techniques are interested in determining how much two concepts, or terms, are similar according to a given ontology. This paper proposes a method for measuring semantic similarity/distance between terms. The measure combines strengths and complements weaknesses of existing measures that use ontology as primary source. The proposed measure uses a new feature of common specificity (CSpec) besides the path length feature. The CSpec feature is derived from (1) information content of concepts, and (2) information content of the ontology given a corpus. We evaluated the proposed measure with benchmark test set of term pairs scored for similarity by human experts. The experimental results demonstrated that our similarity measure is effective and outperforms the existing measures. The proposed semantic similarity measure gives the best correlation (0.874) with human scores in the benchmark test set compared to the existing measures.


title: Automated analysis of free speech predicts psychosis onset in high-risk youths

abstract: A computer program that analyses natural speech could help predict the onset of psychosis in young people at risk. People with schizophrenia have subtle disorganization in speech, even before they first develop psychosis. In a collaboration between IBM, Columbia University Medical Center, and researchers in South America, an automated program that simulates how the human brain understands language was used to analyze interview transcripts from 34 ‘at risk’ youths. Decrease in the flow of meaning from one spoken phrase to the next, and grammatical markers of speech complexity, identified the five individuals who later developed psychosis. The computer program outperformed clinical assessments in predicting psychosis. While numbers are small in this proof-of-principle study, the authors suggest automated analysis could lay the foundation for a simple clinical test of emerging schizophrenia, which would inform early intervention.


title: Clinical-epidemiological profile of hospitalised patients in paediatric intensive care unit

abstract: Introduction : Paediatric intensive care units have made important advances in technology and assistance since the 1980s, which have made more favourable the prognosis of critically ill children all over the world. Objective : Identify the epidemiological profile and clinical outcomes of hospitalised children and adolescents in the Paediatric Intensive Care Unit of Hospital Vitoria, Espirito Santo, Brazil. Methods : A descriptive and retrospective study carried out in the Paediatric Intensive Care Unit of Hospital Infantil Nossa Senhora da Gloria in the city of Vitoria, Espirito Santo, Brazil. Age, sex, ospitalisation diagnosis, progression to discharge/death, and length of hospital admission were obtained from the Sector of Medical and Hospital Statistics Files between 2011 and 2012. For the descriptive analysis, categorical variables were expressed as absolute and percentage, and the continuous variables in average and standard deviation. For comparison, Pearson’s chi-squared tests, Fisher’s test, and Student’s t test were used and p values <0.05 with confidence interval of 95% were considered statistically significant. Results : Of the 609 patients analysed, 342 (56.2%) were male (mean age: 72.7 ± 71.3 months). Respiratory disease, postoperative processes, and trauma were the main causes of hospitalisation. The average duration of hospitalization was 6.9 ± 5.5 days; 514 (84.4%) patients were discharged, 95 (15.6%) died, and of those, 53 (55.7%) died in less than 72 hours of hospitalisation. Conclusion : Most of the patients were less than 2 years of age. The leading causes of hospitalisation were respiratory disease (pneumonia, bronchiolitis, and asthma), sepsis, and head injury, which was the major cause of the trauma motivated by violence. The average hospital admission in the unit was one week and the mortality rate was 15.6%, with one third of the deaths recorded in the first 72 hours of hospitalisation.


Data-driven biped control


title: Adaptive neural-fuzzy control of uncertain constrained multiple coordinated nonholonomic mobile manipulators

abstract: Most studies on the coordination of multiple mobile manipulators system assume exact knowledge of system kinematics and dynamics, and deal only with motion tracking control. However, actual applications may involve tasks in which multiple coordinated mobile manipulators system is required to keep contact on the contour of the constraint surface in tasks. In this paper, we consider multiple mobile manipulators grasping a rigid object in contact with deformable working surfaces, whose geometric and physical model is unknown. The contact forces are nonlinear and unknown. Adaptive neuro-fuzzy (NF) control for coordinated mobile manipulators is proposed for robust force/motion tracking on the constraint surface while it is in motion. The control law is decoupled in three subspaces and adaptive tuning mechanism is developed to deal with the uncertain environmental constraints, disturbances, and unknown robotic dynamics. The proposed adaptive NF hybrid force/motion controller guarantees robust tracking of the desired motion and force trajectories. Simulation examples are presented to illustrate the results.


title: A SELECTIVE LEARNING METHOD TO IMPROVE THE GENERALIZATION OF MULTILAYER FEEDFORWARD NEURAL NETWORKS

abstract: Multilayer feedforward neural networks with backpropagation algorithm have been used successfully in many applications. However, the level of generalization is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the training patterns more appropriate to the new sample to be predicted. This training method follows a lazy learning strategy, in the sense that it builds approximations centered around the novel sample. The proposed method has been applied to three different domains: two artificial approximation problems and a real time series prediction problem. Results have been compared to standard backpropagation using the complete training data set and the new method shows better generalization abilities.


Antibiotic prophylaxis in penetrating abdominal injuries.


title: An expert evaluation of word-sized visualizations for analyzing eye movement data

abstract: Word-sized visualizations for eye movement data allow analysts to compare a variety of experiment conditions or participants at the sametime. Weimplementedasetofsuchword-sizedvisualizations as part of an analysis framework. We want to ﬁnd out which of the visualizations is most suitable for different analysis tasks. To this end, we applied the framework to data from an eye tracking study on the reading behavior of users studying metro maps. In anexpertevaluationwithﬁveanalysts,weidentiﬁeddistinguishing characteristics of the different word-sized visualizations.


title: CAN: Creative Adversarial Networks, Generating"Art"by Learning About Styles and Deviating from Style Norms

abstract: We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.


title: A survey on Ambient Intelligence projects

abstract: Intelligence is increasingly emerging in our ambients. Evidences of this emergence are the existence of smart homes, smart vehicles, intelligent manufacturing systems and most importantly, the appearance of the concept of intelligent cities. Humans are presently surrounded by technology that is intended to increase their quality of life and simplify their daily activities. Multi-Agent Systems are an example of technology that can be used in these activities. The concept of ubiquitous computing is implicit in these technologies and can generate an invisible ambient of interactivity. This paper presents a survey and a comparative analysis of some of the research projects concerning Ambient Intelligence (AmI). The main objective of this work was to understand the current necessities, devices and the main results in the development of these projects. By analysing these projects using several evaluation criteria one of the main conclusions is that most projects do not explore the potential of human profiles in the context of ambient adaptation. Thus, this may be a very intersting research area for future work.


title: A survey on techniques of detecting identity documents forgery

abstract: Identity Document (ID) forgery is a process by which authorized identity document is modified and\or copied by unauthorized party or parties to be used illegally. The rapid development of personal computers, scanners, and color printers a long with their affordability raises up the risk of identity document forgery. They represent rich tools and techniques that highly support the creation of faked identities. This review paper investigates the current techniques for countering document forgery threats long with their achievements and limitations. These techniques are insufficient to mitigate ID forgery threats. New methods and techniques need to be developed to reduce or eliminate the risk of the Identity documents forgery.


title: Full paper iCub : the design and realization of an open humanoid platform for cognitive and neuroscience research

abstract: The development of robotic cognition and the advancement of understanding of human cognition form two of the current greatest challenges in robotics and neuroscience, respectively. The RobotCub project aims to develop an embodied robotic child (iCub) with the physical (height 90 cm and mass less than 23 kg) and ultimately cognitive abilities of a 2.5-year-old human child. The iCub will be a freely available open system which can be used by scientists in all cognate disciplines from developmental psychology to epigenetic robotics to enhance understanding of cognitive systems through the study of cognitive development. The iCub will be open both in software, but more importantly in all aspects of the hardware and mechanical design. In this paper the design of the mechanisms and structures forming the basic 'body' of the iCub are described. The papers considers kinematic structures dynamic design criteria, actuator specification and selection, and detailed mechanical and electronic design. The paper conclude...


title: Twitter Sentiment Analysis with Deep Convolutional Neural Networks

abstract: This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a new model for initializing the parameter weights of the convolutional neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning model on a distant supervised corpus. At a final stage, the pre-trained parameters of the network are used to initialize the model. We train the latter on the supervised training data recently made available by the official system evaluation campaign on Twitter Sentiment Analysis organized by Semeval-2015. A comparison between the results of our approach and the systems participating in the challenge on the official test sets, suggests that our model could be ranked in the first two positions in both the phrase-level subtask A (among 11 teams) and on the message-level subtask B (among 40 teams). This is an important evidence on the practical value of our solution.


Effects of Platform and Content Attributes on Information Dissemination on We Media: A Case Study on WeChat Platform


title: Hidden Markov processes

abstract: An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie (1966) on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed.


title: Offline bilingual word vectors, orthogonal transformations and the inverted softmax

abstract: Usually bilingual word vectors are trained "online''. Mikolov et al. showed they can also be found "offline"; whereby two pre-trained embeddings are aligned with a linear transformation, using dictionaries compiled from expert knowledge. In this work, we prove that the linear transformation between two spaces should be orthogonal. This transformation can be obtained using the singular value decomposition. We introduce a novel "inverted softmax" for identifying translation pairs, with which we improve the precision @1 of Mikolov's original mapping from 34% to 43%, when translating a test set composed of both common and rare English words into Italian. Orthogonal transformations are more robust to noise, enabling us to learn the transformation without expert bilingual signal by constructing a "pseudo-dictionary" from the identical character strings which appear in both languages, achieving 40% precision on the same test set. Finally, we extend our method to retrieve the true translations of English sentences from a corpus of 200k Italian sentences with a precision @1 of 68%.


title: Universal Audio Steganalysis Based on Calibration and Reversed Frequency Resolution of Human Auditory System

abstract: Calibration and higher-order statistics are standard components of image steganalysis. However, these techniques have not yet found adequate attention in audio steganalysis. Specifically, most of current studies are either non-calibrated or only based on noise removal. The goal of this study is to fill these gaps and to show that calibrated features based on re-embedding technique improve performance of audio steganalysis. Furthermore, the authors show that least significant bit is the most sensitive bit plane to data hiding algorithms, and therefore it can be employed as a universal embedding method. The proposed features also benefit from an efficient model which is tailored to the needs for audio steganalysis and represent the maximum deviation from human auditory system. Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. The results show the effectiveness of the proposed method in detecting the finest traces of data hiding algorithms in very low embedding rates. The system detects Steghide at capacity of 0.06 bit per symbol with sensitivity of 98.6% (music) and 78.5% (speech). These figures are, respectively, 7.1% and 27.5% higher than the state-of-the-art results based on R-Mel-frequency cepstral coefficient features.


title: Polluino: An efficient cloud-based management of IoT devices for air quality monitoring

abstract: The Internet of Things paradigm originates from the proliferation of intelligent devices that can sense, compute and communicate data streams in a ubiquitous information and communication network. The great amounts of data coming from these devices introduce some challenges related to the storage and processing capabilities of the information. This strengthens the novel paradigm known as Big Data. In such a complex scenario, the Cloud computing is an efficient solution for the managing of sensor data. This paper presents Polluino, a system for monitoring the air pollution via Arduino. Moreover, a Cloud-based platform that manages data coming from air quality sensors is developed.


title: Cell phone induced failures of visual attention during simulated driving

abstract: This research examined the effects of hands-free cell phone conversations on simulated driving. The authors found that these conversations impaired driver’s reactions to vehicles braking in front of them. The authors assessed whether this impairment could be attributed to a withdrawal of attention from the visual scene, yielding a form of inattention blindness. Cell phone conversations impaired explicit recognition memory for roadside billboards. Eye-tracking data indicated that this was due to reduced attention to foveal information. This interpretation was bolstered by data showing that cell phone conversations impaired implicit perceptual memory for items presented at fixation. The data suggest that the impairment of driving performance produced by cell phone conversations is mediated, at least in part, by reduced attention to visual inputs.


title: Framework for SCADA cyber-attack dataset creation

abstract: Cyber-security research and development for SCADA is being inhibited by the lack of available SCADA attack datasets. This paper presents a modular dataset generation framework for SCADA cyber-attacks, to aid the development of attack datasets. The presented framework is based on requirements derived from related prior research, and is applicable to any standardised or proprietary SCADA protocol. We instantiate our framework and validate the requirements using a Python implementation. This paper provides experiments of the framework's usage on a state-of-the-art DNP3 critical infrastructure test-bed, thus proving framework's ability to generate SCADA cyber-attack datasets.


title: Real-time Human Pose Estimation from Video with Convolutional Neural Networks

abstract: In this paper, we present a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks. Our method is aimed for use case specific applications, where good accuracy is essential and variation of the background and poses is limited. This enables us to use a generic network architecture, which is both accurate and fast. We divide the problem into two phases: (1) pre-training and (2) finetuning. In pre-training, the network is learned with highly diverse input data from publicly available datasets, while in finetuning we train with application specific data, which we record with Kinect. Our method differs from most of the state-of-the-art methods in that we consider the whole system, including person detector, pose estimator and an automatic way to record application specific training material for finetuning. Our method is considerably faster than many of the state-of-the-art methods. Our method can be thought of as a replacement for Kinect, and it can be used for higher level tasks, such as gesture control, games, person tracking, action recognition and action tracking. We achieved accuracy of 96.8\% (PCK@0.2) with application specific data.


title: Variation in Antioxidant Attributes at Three Ripening Stages of Guava (Psidium guajava L.) Fruit from Different Geographical Regions of Pakistan

abstract: The present investigation was carried out to appraise the levels of total phenols and vitamin C as well as antioxidant potential at three different ripening stages (un-ripe, semi-ripe and fully-ripe) of guava (Psidium guajava L.) fruit collected from three different geographical regions of Pakistan (Islamabad, Faisalabad and Bhakkar). The antioxidant potential of guava fruit extracts was assessed by means of different in-vitro antioxidant assays, namely inhibition of peroxidation in linoleic acid system, reducing power and radical scavenging capability. Overall, fruit at the un-ripe stage (G1) exhibited the highest levels of TPC, TFC, reducing power and DPPH radical scavenging activity, followed by the semi-ripe (G2) and fully-ripe (G3) stages. On the other hand, vitamin C content increased as the fruit maturity progressed, with highest value seen at the fully-ripe stage (G3) followed by the semi-ripe (G2) and un-ripe stage (G1). The concentration of vitamin C in fruits varied as: Faisalabad (136.4–247.9 mg 100 g−1), Islamabad (89.7–149.7 mg 100 g−1) and Bhakkar (73.1–129.5 mg 100 g−1). The results showed that different stages of maturation and geographical locations had profound effects on the antioxidant activity and vitamin C contents of guava fruit.


title: Embodied Conversational Agents as Conversational Partners

abstract: SUMMARY Conversational agents are becoming more widespread in computer technologies but there has been little research in how humans interact with them. Two eye tracking studies investigated how humans distribute eye gaze towards conversational agents in complex tutoring systems. In Study 1, participants interacted with the single-agent tutoring system AutoTutor. Fixation times showed that the agent received most attention throughout the interaction, even when display size was statistically controlled. In Study 2, participants interacted with iSTART. Fixations were on the relevant agents when these agents spoke. Both studies provided evidence that humans regard animated conversational agents as conversational partners in the communication process. Copyright# 2008 John Wiley & Sons, Ltd. Embodied conversational agents (ECAs) are animated characters that emulate human behaviour and communication. There has been a boom of these agents in computer environments that vary from conventional web systems to advanced virtual worlds. A fundamental question arises as to how humans interpret these agents. Are these ECAs distracting artefacts or human-like conversational partners? Answers to these questions require an in-depth analysis of the humans’ perceptions, attention and interactions with the agents. The present studies were designed to address these questions. Some studies have suggested that users interact with ECAs as they would with humans (Reeves & Nass, 1996), presumably because these computer-generated characters demonstrate many of the same multi-modal properties as do humans in face-to-face conversation (Cassell, Sullivan, Prevost, & Churchill, 2000). Studies in cognitive psychology, social psychology, artificial intelligence and education have investigated the effects of ECAs on user impressions, comprehension and learning gains (Atkinson, 2002; Baylor & Ryu, 2003; Graesser, Moreno, Marineau, Adcock, Olney, & Person, 2003). Some studies have shown that there are conditions in which users interacting with ECAs displayed on a computer monitor benefit from these interfaces more than from interfaces


title: Dataset Coverage for Testing Machine Learning Computer Programs

abstract: Machine learning programs are non-testable, and thus testing with pseudo oracles is recommended. Although metamorphic testing is effective for testing with pseudo oracles, identifying metamorphic properties has been mostly ad hoc. This paper proposes a systematic method to derive a set of metamorphic properties for machine learning classifiers, support vector machines. The proposal includes a new notion of test coverage for the machine learning programs; this test coverage provides a clear guideline for conducting a series of metamorphic testing.


Improving Project Management with Lean Thinking?


title: Roman-txt: forms and functions of roman urdu texting

abstract: In this paper, we present a user study conducted on students of a local university in Pakistan and collected a corpus of Roman Urdu text messages. We were interested in forms and functions of Roman Urdu text messages. To this end, we collected a mobile phone usage dataset. The data consists of 116 users and 346, 455 text messages. Roman Urdu text, is the most widely adopted style of writing text messages in Pakistan. Our user study leads to interesting results, for instance, we were able to quantitatively show that a number of words are written using more than one spelling; most participants of our study were not comfortable in English and hence they write their text messages in Roman Urdu; and the choice of language adopted by the participants sometimes varies according to who the message is being sent. Moreover we found that many young students send text messages(SMS) of intimate nature.


title: The Value Function Polytope in Reinforcement Learning

abstract: We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective to introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.