title: Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos

abstract: Deep learning has been demonstrated to achieve excellent results for image classification and object detection. However, the impact of deep learning on video analysis (e.g. action detection and recognition) has been limited due to complexity of video data and lack of annotations. Previous convolutional neural networks (CNN) based video action detection approaches usually consist of two major steps: frame-level action proposal detection and association of proposals across frames. Also, these methods employ two-stream CNN framework to handle spatial and temporal feature separately. In this paper, we propose an end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for action detection in videos. The proposed architecture is a unified network that is able to recognize and localize action based on 3D convolution features. A video is first divided into equal length clips and for each clip a set of tube proposals are generated next based on 3D Convolutional Network (ConvNet) features. Finally, the tube proposals of different clips are linked together employing network flow and spatio-temporal action detection is performed using these linked video proposals. Extensive experiments on several video datasets demonstrate the superior performance of T-CNN for classifying and localizing actions in both trimmed and untrimmed videos compared to state-of-the-arts.


title: A survey of software refactoring

abstract: We provide an extensive overview of existing research in the field of software refactoring. This research is compared and discussed based on a number of different criteria: the refactoring activities that are supported, the specific techniques and formalisms that are used for supporting these activities, the types of software artifacts that are being refactored, the important issues that need to be taken into account when building refactoring tool support, and the effect of refactoring on the software process. A running example is used to explain and illustrate the main concepts.


title: Permanent-magnet-free-synchronous motor with self-excited wound-field technique utilizing space harmonics

abstract: This paper describes a permanent-magnet-free synchronous motor where space harmonic power is utilized for the field magnetization instead of permanent magnets. The stator has a three-phase concentrated winding structure, and the rotor has two types of windings, i.e., an induction coil that retrieves mainly the second space harmonic and a field coil for the field magnetization. The two coils are connected via a center-tapped full bridge diode rectifying circuit. The principle of self-excitation utilizing space harmonics power is conducted, and the mathematical model is discussed. In addition, experimental test results are demonstrated compared with interior-permanent-magnet synchronous motor for HEV application.


title: Discrete model predictive frequency and voltage control of isolated micro-grid in smart grid scenario

abstract: In an isolated micro-grid electronically interfaced DG (EI-DG) units should also participate in frequency and voltage control along with synchronous generator based DG (SG-DG) units to ensure power quality and stability of the micro-grid. The conventional way of controlling frequency and voltage is to balance active power and reactive power of the system by implementing droop characteristics (P-f, Q-V) for EI-DG units with PI controllers and automatic generation control (AGC) and automatic voltage regulator (AVR) for SG-DG units. With the advent of smart micro-grids, a centralized control for both frequency and voltage using model predictive controller (MPC) is an alternative to the conventional controllers. The optimization capabilities of MPC make it suitable for frequency and voltage control of micro-grids equipped with fast and reliable communication. This paper investigates the performance of a centralized discrete model predictive controller (DMPC) in an isolated micro-grid with photovoltaic (PV) and diesel generators. The small signal dynamic models of the PV converter and SG-DG are considered in the design of the DMPC. The micro-grid network and loads are represented using steady state power balance equations. The performance of the DMPC is tested using MATLAB software package.


title: Constructing All Composition Series of a Finite Group

abstract: This paper describes an effective method for enumerating all composition series of a finite group, possibly up to action of a group of automorphisms. By building the series in an ascending way it only requires a very easy case of complement computation and can avoid the need to fuse subspace chains in vector spaces. As a by-product it also enumerates all subnormal subgroups.


title: ScanMe mobile: a cloud-based Android malware analysis service

abstract: As mobile malware have increased in number and sophistication, it has become pertinent for users to have tools that can inform them of potentially malicious applications. To fulfill this need, we develop a cloud-based malware analysis service called ScanMe Mobile, for the Android platform. The objective of this service is to provide users with detailed information about Android Application Package (APK) files before installing them on their devices. With ScanMe Mobile, users are able to upload APK files from their device SD card, scan the APK in the malware detection system that could be deployed in the cloud, compile a comprehensive report, and store or share the report by publishing it to the website. ScanMe Mobile works by running the APK in a virtual sandbox to generate permission data, and analyzes the result in the machine learning detection system. Through our experimental results, we demonstrate that the proposed system can effectively detect malware on the Android platform.


title: A Simple Picosecond Pulse Generator Based on a Pair of Step Recovery Diodes

abstract: A picosecond pulse generator based on a pair of step recovery diodes (SRD), leveraging the transient response of the SRD PN junction and controlling the pulse width by a resistor, is proposed. We first explain the operation principle of the device, decomposing the pulse generation into different phases, and then demonstrate an experimental prototype with two different resistance, and hence pulse width, values.


title: Family A and B DNA Polymerases in Cancer: Opportunities for Therapeutic Interventions

abstract: DNA polymerases are essential for genome replication, DNA repair and translesion DNA synthesis (TLS). Broadly, these enzymes belong to two groups: replicative and non-replicative DNA polymerases. A considerable body of data suggests that both groups of DNA polymerases are associated with cancer. Many mutations in cancer cells are either the result of error-prone DNA synthesis by non-replicative polymerases, or the inability of replicative DNA polymerases to proofread mismatched nucleotides due to mutations in 3′-5′ exonuclease activity. Moreover, non-replicative, TLS-capable DNA polymerases can negatively impact cancer treatment by synthesizing DNA past lesions generated from treatments such as cisplatin, oxaliplatin, etoposide, bleomycin, and radiotherapy. Hence, the inhibition of DNA polymerases in tumor cells has the potential to enhance treatment outcomes. Here, we review the association of DNA polymerases in cancer from the A and B families, which participate in lesion bypass, and conduct gene replication. We also discuss possible therapeutic interventions that could be used to maneuver the role of these enzymes in tumorigenesis.


title: A micro-video recommendation system based on big data

abstract: With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favorite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, this paper proposes a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Bid Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, this paper improves the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system.


title: Apparecium: Revealing Data Flows in Android Applications

abstract: With Android applications processing not only personal but also business-critical data, efficient and precise data flow analysis has become a major technique to detect apps handling critical data in unwanted ways. Although data flow analysis in general is a thoroughly researched topic, the event-driven lifecycle model of Android has its own challenges and practical application requires for reliable and efficient analysis techniques. In this paper we present Apparecium, a tool to reveal data flows in Android applications. Apparecium has conceptual differences to other techniques, and can be used to find arbitrary data flows inside Android applications. Details about the used techniques and the differences to existing data flow analysis tools are presented, as well as an evaluation against the data flow analysis framework Flow Droid.


title: Building Energy Load Forecasting using Deep Neural Networks

abstract: Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.


title: Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills

abstract: The best method of training for laparoscopic surgical skills is controversial. Some advocate observation in the operating room, while others promote animal and simulated models or a combination of surgery-related tasks. A crucial process in surgical education is to evaluate the level of surgical skills. For laparoscopic surgery, skill evaluation is traditionally performed subjectively by experts grading a video of a procedure performed by a student. By its nature, this process uses fuzzy criteria. The objective of the current study was to develop and assess a skill scale using Markov models (MMs). Ten surgeons [five novice surgeons (NS); five expert surgeons (ES)] performed a cholecystectomy and Nissen fundoplication in a porcine model. An instrumented laparoscopic grasper equipped with a three-axis force/torque (F/T) sensor was used to measure the forces/torques at the hand/tool interface synchronized with a video of the tool operative maneuvers. A synthesis of frame-by-frame video analysis and a vector quantization algorithm, allowed to define F/T signatures associated with 14 different types of tool/tissue interactions. The magnitude of F/T applied by NS and ES were significantly different (p<0.05) and varied based on the task being performed. High F/T magnitudes were applied by NS compared with ES while performing tissue manipulation and vice versa in tasks involved tissue dissection. From each step of the surgical procedures, two MMs were developed representing the performance of three surgeons out of the five in the ES and NS groups. The data obtained by the remaining two surgeons in each group were used for evaluating the performance scale. The final result was a surgical performance index which represented a ratio of statistical similarity between the examined surgeon's MM and the MM of NS and ES. The difference between the performance index value, for a surgeon under study, and the NS/ES boundary, indicated the level of expertise in the surgeon's own group. Preliminary data suggest that a performance index based on MM and F/T signatures provides an objective means of distinguishing NS from ES. In addition, this methodology can be further applied to evaluate haptic virtual reality surgical simulators for improving realism in surgical education.


title: Why Frankenstein is a Stigma Among Scientists

abstract: As one of the best known science narratives about the consequences of creating life, Mary Shelley’s Frankenstein; or, The Modern Prometheus (1818) is an enduring tale that people know and understand with an almost instinctive familiarity. It has become a myth reflecting people’s ambivalent feelings about emerging science: they are curious about science, but they are also afraid of what science can do to them. In this essay, we argue that the Frankenstein myth has evolved into a stigma attached to scientists that focalizes the public’s as well as the scientific community’s negative reactions towards certain sciences and scientific practices. This stigma produces ambivalent reactions towards scientific artifacts and it leads to negative connotations because it implies that some sciences are dangerous and harmful. We argue that understanding the Frankenstein stigma can empower scientists by helping them revisit their own biases as well as responding effectively to people’s expectations for, and attitudes towards, scientists and scientific artifacts. Debunking the Frankenstein stigma could also allow scientists to reshape their professional identities so they can better show the public what ethical and moral values guide their research enterprises.


title: Cranial Impalement of a Falling Fence Spike in a Child: A Case Report

abstract: Cranial impalement injuries are rare. They occur from a variety of objects, and via different mechanisms. We describe the case of a 5-year old boy who suffered cranial impalement injury via a unique mechanism. He presented to our centre with an impacted 17.8cm long metallic rod (a fence spike) in the vertex of his cranium, just off the midline. The spike penetrated his head and broke off its supporting frame as the frame was falling off a collapsing brick fence. He was transported as soon as possible to the hospital by relatives, without any attempt to remove the impaled spike. An urgent cranial computerized tomogram was done, and the object was removed under general anaesthesia in the operating theatre. The patient had complete recovery and was subsequently discharged from the hospital, with no residual neurological deficit. This case demonstrates a rare mechanism of cranial impalement. It also highlights the importance of following basic principles in the management of such injuries.


title: Kalman filter-based localization for Internet of Things LoRaWAN™ end points

abstract: This paper addresses the problem of estimating the location of Internet of Things (IoT) Long Range Wide Area Networks (LoRaWAN) devices from time of arrival differences measured at gateways. An Extended Kalman Filter (EKF) based approach is considered to aggregate the measurements obtained at different time instants. Particular attention is paid to the processing of outliers. Based on experimental data obtained from field measurements conducted on a real LoRaWAN™ network an insight into the realistic localization accuracy of the considered localization approach is provided.


title: Learning Locally-Adaptive Decision Functions for Person Verification

abstract: This paper considers the person verification problem in modern surveillance and video retrieval systems. The problem is to identify whether a pair of face or human body images is about the same person, even if the person is not seen before. Traditional methods usually look for a distance (or similarity) measure between images (e.g., by metric learning algorithms), and make decisions based on a fixed threshold. We show that this is nevertheless insufficient and sub-optimal for the verification problem. This paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule. We further formulate the inference on our decision function as a second-order large-margin regularization problem, and provide an efficient algorithm in its dual from. We evaluate our algorithm on both human body verification and face verification problems. Our method outperforms not only the classical metric learning algorithm including LMNN and ITML, but also the state-of-the-art in the computer vision community.


title: Budget Analysis of RF Transceiver Used in 5.8GHz RFID Reader Based on the ETC-DSRC National Specifications of China

abstract: Electronic Toll Collection system, developed and spread rapidly in recent years, is a kind of modern traffic toll collection means based on RFID technology. In this paper, some parameters of the physical layer defined by the national specifications of China on ETC&DSRC, GB/T20851-2007, are listed out and then a budget analysis is made to estimate the gain of antenna, the radiation power and the noise feature of the reader. Based on the analysis, a kind of concrete structure of ETC reader transceiver is proposed here and the detailed performance and parameters of every circuit modules in the RF transceiver are given.


title: Highly cited articles in the Education and Educational Research category in the Social Science Citation Index: a bibliometric analysis

abstract: AbstractThis paper presents a bibliometric analysis of highly cited articles published in the Web of Science category “Education and Educational Research” in the Social Science Citation Index (SSCI). A total of 2091 journal articles published in this category and cited more than 100 times up to the end of the year 2016 were retrieved as highly cited articles. Distributions of highly cited articles per publication year, journals, institutions and countries were analysed, as well as the citation life cycle of the top-cited articles. The USA, its institutions and researchers are the absolute leaders in the category of Education and Educational Research according to the results of the conducted analysis.


title: A Vacuum-Based Bonding Mechanism for Modular Robotics

abstract: We explore vacuum as bonding force for modular robotics. Vacuubes are a set of modules that propagate vacuum across their interfaces in order to generate adhesive forces to form and hold structures. We use analog circuits to simulate vacuum transients and understand critical design parameters and then validate these insights in experiments. A 49-module structure that employs vacuum bonding is demonstrated. We conclude that vacuum offers a relatively strong, simple, reliable, and power-efficient connection principle.


title: Extracting Patterns and Relations from the World Wide Web

abstract: The World Wide Web is a vast resource for information. At the same time it is extremely distributed. A particular type of data such as restaurant lists may be scattered across thousands of independent information sources in many different formats. In this paper, we consider the problem of extracting a relation for such a data type from all of these sources automatically. We present a technique which exploits the duality between sets of patterns and relations to grow the target relation starting from a small sample. To test our technique we use it to extract a relation of (author,title) pairs from the World Wide Web.


title: Semantic Annotation of Papers: Interface & Enrichment Tool (SAPIENT)

abstract: In this paper we introduce a web application (SAPIENT) for sentence based annotation of full papers with semantic information. SAPIENT enables experts to annotate scientific papers sentence by sentence and also to link related sentences together, thus forming spans of interesting regions, which can facilitate text mining applications. As part of the system, we developed an XML-aware sentence splitter (SSSplit) which preserves XML markup and identifies sentences through the addition of in-line markup. SAPIENT has been used in a systematic study for the annotation of scientific papers with concepts representing the Core Information about Scientific Papers (CISP) to create a corpus of 225 annotated papers.


title: A Survey of Arabic Named Entity Recognition and Classification

abstract: As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition NER is an Information Extraction task that has become an integral part of many other Natural Language Processing NLP tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification.


title: Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models

abstract: We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. samples generated from the model. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established information-theoretic lower-bound. Our statistical estimator has a physical interpretation in terms of"interaction screening". The estimator is consistent and is efficiently implemented using convex optimization. We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.


title: FPGA implementation of Shaped Offset QPSK modulator

abstract: This paper presents a novel architecture for the modulation scheme Shaped Offset QPSK (SOQPSK), which belongs to the class of Continuous Phase Modulations (CPMs). These waveforms have recently regained remarkable popularity, due to the increasing demand of highly energy and spectral efficient communication systems for deep-space, telemetry and Unmanned Aerial Vehicle (UAV) links, for example. To the best of the authors' knowledge, this is the first SOQPSK modulator architecture and implementation reported in literature. The modulation task itself is divided into a SOQPSK-specific precoding part, with minimal hardware complexity, as well as an optimized quadrature CPM modulator. With a proof-of-concept FPGA implementation, the feasibility of the proposed architecture is shown together with its close to optimal algorithmic performance.


title: Perceiving emotion in crowds: the role of dynamic body postures on the perception of emotion in crowded scenes

abstract: Although the perception of emotion in individuals is an important social skill, very little is known about how emotion is determined from a crowd of individuals. We investigated the perception of emotion in scenes of crowds populated by dynamic characters each expressing an emotion. Facial expressions were masked in these characters and emotion was conveyed using body motion and posture only. We systematically varied the proportion of characters in each scene depicting one of two emotions and participants were required to categorise the overall emotion of the crowd. In Experiment 1, we found that the perception of emotions in a crowd is efficient even with relatively brief exposures of the crowd stimuli. Furthermore, the emotion of a crowd was generally determined by the relative proportions of characters conveying it, although we also found that some emotions dominated perception. In Experiment 2, we found that an increase in crowd size was not associated with a relative decrease in the efficiency with which the emotion was categorised. Our findings suggest that body motion is an important social cue in perceiving the emotion of crowds and have implications for our understanding of how we perceive social information from groups.


title: Coverage Analysis for Low-Altitude UAV Networks in Urban Environments

abstract: Wireless access points on unmanned aerial vehicles (UAVs) are being considered for mobile service provisioning in commercial networks. To be able to efficiently use these devices in cellular networks it is necessary to first have a qualitative and quantitative understanding of how their design parameters reflect on the service quality experienced by the end user. In this paper we set up a scenario where a network of UAVs operating at a certain height above ground provide wireless service within coverage areas shaped by their directional antennas. We provide an analytical expression for the coverage probability experienced by a typical user as a function of the UAV parameters and demonstrate the performance trade-offs that occur as these parameters are varied.


title: A review of predictive coding algorithms

abstract: Abstract Predictive coding is a leading theory of how the brain performs probabilistic inference. However, there are a number of distinct algorithms which are described by the term “predictive coding”. This article provides a concise review of these different predictive coding algorithms, highlighting their similarities and differences. Five algorithms are covered: linear predictive coding which has a long and influential history in the signal processing literature; the first neuroscience-related application of predictive coding to explaining the function of the retina; and three versions of predictive coding that have been proposed to model cortical function. While all these algorithms aim to fit a generative model to sensory data, they differ in the type of generative model they employ, in the process used to optimise the fit between the model and sensory data, and in the way that they are related to neurobiology.


title: Success model of project management information system in construction

abstract: Abstract Among various IT solutions, the internet-based (or web-based) PMIS has been highlighted because of its strong advantages. While not sufficient to insure project success, using PMIS to manage projects has thus become a necessity. Establishing a success model of a specific information system is critical to understand the mechanism of IS success, the various dimensions of IS performance, and the factors and their causal relations in IS success. As one of the key IT applications, the project management information system (PMIS) has played a significant role in construction management processes. While not sufficient to insure project success, using PMIS to manage projects has thus become a necessity. However, research that attempts to establish or apply an IS success model have relatively recently begun to emerge and not many have been carried out as yet. Therefore, the main propose of this study is to develop and validate the ASP-PMIS success model based on the DeLone and McLean (2003) IS success model. A questionnaire instrument was remitted to experienced users (CMs and constructors), and 253 completed questionnaires were retrieved. Using AMOS 18.0, we used Structural Equation Modeling for hypothesis testing. The validated ASP-based PMIS success model can serve as a foundation for positioning and comparing PMIS success research, and can provide users with a useful framework for evaluating PMIS success.


title: Computer game-flow design

abstract: Computer games are a rapidly growing segment of the entertainment industry. Design and development of modern computer games can be a complex activity involving many participants from a variety of disciplines. However, computer game design approaches typically appear to be less formalised than those used for other types of software systems. In this article we describe an approach to computer game-flow design intended for the design of individual game levels within a computer game and demonstrate its application in practice.


title: DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving

abstract: Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations.


title: Learning Recurrent Neural Networks with Hessian-Free Optimization

abstract: In this work we resolve the long-outstanding problem of how to effectively train recurrent neural networks (RNNs) on complex and difficult sequence modeling problems which may contain long-term data dependencies. Utilizing recent advances in the Hessian-free optimization approach (Martens, 2010), together with a novel damping scheme, we successfully train RNNs on two sets of challenging problems. First, a collection of pathological synthetic datasets which are known to be impossible for standard optimization approaches (due to their extremely long-term dependencies), and second, on three natural and highly complex real-world sequence datasets where we find that our method significantly outperforms the previous state-of-the-art method for training neural sequence models: the Long Short-term Memory approach of Hochreiter and Schmidhuber (1997). Additionally, we offer a new interpretation of the generalized Gauss-Newton matrix of Schraudolph (2002) which is used within the HF approach of Martens.


title: Hair for brain trade-off, a metabolic bypass for encephalization

abstract: Hair loss in humans is perplexing and raises many hypothetical explanations. This paper suggests that hair loss in humans is metabolically related to encephalization; and that hair covered hominids would have been unable to evolve large brains because of a dietary restriction of several amino acids which are essential for hair and brain development. We use simulations to imply that hair loss must have preceded increase in brain size & volume. In this respect we see hair loss as a major force in human evolution. We assume that hair reduction required favorable climatic conditions and must have been quick. Using evolutionary and ecological time scales, we pinpoint hair loss to a period around 2.2-2.4 million years ago. The dating is further supported by a rapid selection at that time of the sialic acid deletion mutation which may have protected growing human brains against calcium ion flux. In summary we view encephalization, in part, as a metabolic trade-off between hair and brain. Other biochemical changes may have intervened in the process too; and the deletion mutation of sialic acid hydroxylation may have been involved as well.


title: Using Augmented Reality as a Medium to Assist Teaching in Higher Education

abstract: In this paper we describe the use of a high-level augmented reality ::: (AR) interface for the construction of collaborative educational applications ::: that can be used in practice to enhance current teaching ::: methods. A combination of multimedia information including spatial ::: three-dimensional models, images, textual information, video, ::: animations and sound, can be superimposed in a student-friendly ::: manner into the learning environment. In several case studies different ::: learning scenarios have been carefully designed based on ::: human-computer interaction principles so that meaningful virtual ::: information is presented in an interactive and compelling way. Collaboration ::: between the participants is achieved through use of a ::: tangible AR interface that uses marker cards as well as an immersive ::: AR environment which is based on software user interfaces ::: (UIs) and hardware devices. The interactive AR interface has been ::: piloted in the classroom at two UK universities in departments of ::: Informatics and Information Science.


title: An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications

abstract: This study investigated the impact of Bluetooth Low Energy devices in advertising/beaconing mode on fingerprint-based indoor positioning schemes. Early experimentation demonstrated that the low bandwidth of BLE signals compared to WiFi is the cause of significant measurement error when coupled with the use of three BLE advertising channels. The physics underlying this behaviour is verified in simulation. A multipath mitigation scheme is proposed and tested. It is determined that the optimal positioning performance is provided by 10Hz beaconing and a 1 second multipath mitigation processing window size. It is determined that a steady increase in positioning performance with fingerprint size occurs up to 7 ±1, above this there is no clear benefit to extra beacon coverage.


title: Resistive memory device requirements for a neural algorithm accelerator

abstract: Resistive memories enable dramatic energy reductions for neural algorithms. We propose a general purpose neural architecture that can accelerate many different algorithms and determine the device properties that will be needed to run backpropagation on the neural architecture. To maintain high accuracy, the read noise standard deviation should be less than 5% of the weight range. The write noise standard deviation should be less than 0.4% of the weight range and up to 300% of a characteristic update (for the datasets tested). Asymmetric nonlinearities in the change in conductance vs pulse cause weight decay and significantly reduce the accuracy, while moderate symmetric nonlinearities do not have an effect. In order to allow for parallel reads and writes the write current should be less than 100 nA as well.


title: Fingerprint Template Protection: From Theory to Practice

abstract: One of the potential vulnerabilities in a biometric system is the leakage of biometric template information, which may lead to serious security and privacy threats. Most of the available template protection techniques fail to meet all the desired requirements of a practical biometric system like revocability, security, privacy, and high matching accuracy. In particular, protecting the fingerprint templates has been a difficult problem due to large intra-user variations (e.g., rotation, translation, nonlinear deformation, and partial prints). There are two fundamental challenges in any fingerprint template protection scheme. First, we need to select an appropriate representation scheme that captures most of the discriminatory information, but is sufficiently invariant to changes in finger placement and can be secured using available template protection algorithms. Secondly, we need to automatically align or register the fingerprints obtained during enrollment and matching without using any information that could reveal the features, which uniquely characterize a fingerprint. This chapter analyzes how these two challenges are being addressed in practice and how the design choices affect the trade-off between the security and matching accuracy. Though much progress has been made over the last decade, we believe that fingerprint template protection algorithms are still not sufficiently robust to be incorporated into practical fingerprint recognition systems.


title: Frequent itemset mining on hadoop

abstract: One of the most important problems in data mining is frequent itemset mining. It requires very large computation and I/O traffic capacity. For that reason several parallel and distributed mining algorithms were developed. Recently the mapreduce distributed data processing paradigm is unavoidable and porting the current algorithms to mapreduce is in focus. In this paper a substantial frequent itemset mining algorithms and their mapreduce implementations are introduced and investi-gated. An algorithm improvement is also proposed and analyzed.


title: Was the Gender Identity Disorder of Childhood Diagnosis Introduced into DSM-III as a Backdoor Maneuver to Replace Homosexuality? A Historical Note

abstract: Over the years, the DSM diagnosis of gender identity disorder (and its predecessors gender identity disorder of childhood [GIDC] and transsexualism) has attracted controversy as a mental disorder, for its diagnostic criteria, as a target of therapeutic intervention, and for its relationship to a homosexual sexual orientation. Another point of controversy is the claim that the diagnosis of GIDC was introduced into the DSM-III in 1980 as a kind of “backdoor maneuver” to replace homosexuality, which was deleted from the DSM-II in 1973. In this article, we challenge this historical interpretation and provide an alternative account of how the GIDC diagnosis (and transsexualism) became part of psychiatric nosology in the DSM-III. We argue that GIDC was included as a psychiatric diagnosis because it met the generally accepted criteria used by the framers of DSM-III for inclusion (for example, clinical utility, acceptablility to clinicians of various theoretical persuasions, and an empirical database to propose e...


title: Exploiting the circulant structure of tracking-by-detection with kernels

abstract: Recent years have seen greater interest in the use of discriminative classifiers in tracking systems, owing to their success in object detection. They are trained online with samples collected during tracking. Unfortunately, the potentially large number of samples becomes a computational burden, which directly conflicts with real-time requirements. On the other hand, limiting the samples may sacrifice performance. ::: ::: Interestingly, we observed that, as we add more and more samples, the problem acquires circulant structure. Using the well-established theory of Circulant matrices, we provide a link to Fourier analysis that opens up the possibility of extremely fast learning and detection with the Fast Fourier Transform. This can be done in the dual space of kernel machines as fast as with linear classifiers. We derive closed-form solutions for training and detection with several types of kernels, including the popular Gaussian and polynomial kernels. The resulting tracker achieves performance competitive with the state-of-the-art, can be implemented with only a few lines of code and runs at hundreds of frames-per-second. MATLAB code is provided in the paper (see Algorithm 1).


title: Antioxidant Potential of Pea Beans (Phaseolus vulgaris L.)

abstract: : Four bean varieties (Phaseolus vulgaris L.) (white kidney, red pinto, Swedish brown, and black kidney) and their hull fractions were extracted with 80% acetone and evaluated for their phenolic contents and antiradical activities. Total phenolic content of bean hulls and whole seed extracts ranged from 6.7 to 270 and 4.9 to 93.6 mg/g extract as catechin equivalents, respectively. Trolox equivalent antioxidant capacity (TEAC) assay revealed that the antioxidant capacity of red, brown, and black whole seed extracts was in the same order of magnitude with little variation. TEAC values of red and brown whole seed extracts were superior to that of black whole seed extract. On the basis of the total phenolic content and TEAC values, it can be deduced that colored beans possess superior antioxidative activity compared with white beans. The hydrogen peroxide scavenging capacity of different bean extracts ranged from 58% to 67% at 50 ppm and 65% to 76% at 100 ppm. The corresponding superoxide radical scavenging capacity was 24% to 29% at 50 ppm and 53% to 60% at 100 ppm. The 2,2–diphenyl-1–picrylhydrazyl (DPPH) radical scavenging capacity of black bean whole seed extracts was 22% at 50 ppm, whereas the other extracts showed 100% scavenging of this radical at both 50 and 100 ppm levels. The hydroxyl radical scavenging capacities of the bean extracts at 50 and 100 ppm were 12% to 29% and 32% to 49%, respectively. All extracts used prevented human low-density lipoprotein (LDL) cholesterol oxidation by 61.4% to 99.9% at 2 to 50 ppm level as catechin equivalents.


title: Semantic data provisioning and reasoning for the Internet of Things

abstract: Semantic technologies could facilitate realizing features like interoperability and reasoning for Internet of Things (IoT). However, the dynamic and heterogeneous nature of IoT data, constrained resources, and real-time requirements set challenges for applying these technologies. In this paper, we study approaches for delivering semantic data from IoT nodes to distributed reasoning engines and reasoning over such data. We perform experiments to evaluate the scalability of these approaches and also study how reasoning is affected by different data aggregation strategies.


title: Training Deep Neural Networks via Direct Loss Minimization

abstract: Supervised training of deep neural nets typically relies on minimizing cross-entropy. However, in many domains, we are interested in performing well on metrics specific to the application. In this paper we propose a direct loss minimization approach to train deep neural networks, which provably minimizes the application-specific loss function. This is often non-trivial, since these functions are neither smooth nor decomposable and thus are not amenable to optimization with standard gradient-based methods. We demonstrate the effectiveness of our approach in the context of maximizing average precision for ranking problems. Towards this goal, we develop a novel dynamic programming algorithm that can efficiently compute the weight updates. Our approach proves superior to a variety of baselines in the context of action classification and object detection, especially in the presence of label noise.


title: Self-Test and Diagnosis for Self-Aware Systems

abstract: Editor’s note: Self-testing hardware has a long tradition as a complement to manufacturing testing based on test stimuli and response analysis. Today, it is a mature field and many complex SoCs have self-testing structures built-in (BIST). For self-aware SoCs this is a key technology, allowing the system to distinguish between correct and erroneous behavior. This survey article reviews the state of the art and shows how these techniques are to be generalized to facilitate self-awareness. — Axel Jantsch , TU Wien — Nikil Dutt, University of California at Irvine


title: Learning Semantic Composition to Detect Non-compositionality of Multiword Expressions

abstract: Non-compositionality of multiword expressions is an intriguing problem that can be the source of error in a variety of NLP tasks such as language generation, machine translation and word sense disambiguation. We present methods of non-compositionality detection for English noun compounds using the unsupervised learning of a semantic composition function. Compounds which are not well modeled by the learned semantic composition function are considered noncompositional. We explore a range of distributional vector-space models for semantic composition, empirically evaluate these models, and propose additional methods which improve results further. We show that a complex function such as polynomial projection can learn semantic composition and identify non-compositionality in an unsupervised way, beating all other baselines ranging from simple to complex. We show that enforcing sparsity is a useful regularizer in learning complex composition functions. We show further improvements by training a decomposition function in addition to the composition function. Finally, we propose an EM algorithm over latent compositionality annotations that also improves the performance.


title: Classification of cotton oil in the semi-refining process using image processing techniques: Image processing for industrial applications

abstract: This paper presents a steps model classification in the cotton oil semi-refining process by image processing. A simple system of efficient analysis was obtained, allowing its use on the factory floor, assisting in the process with respect to the assertiveness in the desired semi-refined state and its quality control. Two algorithms with different recognition patterns were analyzed: K-Nearest Neighbor and Quadratic Discriminant Analysis, with a validation strategy Leave-one-out and the neural network Extreme Learning Machine using K-fold validation technique. Both algorithms reached success rates of more than 90%, and the best result was obtained through the applied neural network, with more than 95% of accuracy.


title: Radar Cross Section Reduction of a Microstrip Antenna Based on Polarization Conversion Metamaterial

abstract: A novel method aimed at reducing radar cross section (RCS) under incident waves with both $x$ - and $y$ -polarizations, with the radiation characteristics of the antenna preserved, is presented and investigated. The goal is accomplished by the implementation of the polarization conversion metamaterial (PCM) and the principle of passive cancellation. As a test case, a microstrip patch antenna is simulated and experimentally measured to demonstrate the proposed strategy for dramatic radar cross section reduction (RCSR). Results exhibit that in-band RCSR is as much as 16 dB compared to the reference antenna. In addition, the PCM has a contribution to a maximum RCSR value of 14 dB out of the operating band. With significant RCSR and unobvious effect on the radiation performance of the antenna, the proposed method has a wide application for the design of other antennas with a requirement of RCS control.


Leveraging Social Capital to Obtain Top Management Support in Complex, Cross-Functional IT Projects


title: Determination of factors influencing the achievement of the first-year university students using data mining methods

abstract: Academic failure among first-year university students has long fuelled a large number of debates. Many educational psychologists have tried to understand and then explain it. Many statisticians have tried to foresee it. Our research aims to classify, as early in the academic year as possible, students into three groups: the ‘low risk' students, who have a high probability of succeeding, the ‘medium risk' students, who may succeed thanks to the measures taken by the university, and the ‘high risk' students, who have a high probability of failing (or dropping out). This article describes our methodology and provides the most significant variables correlated to academic success among all the questions asked to 533 first-year university students during the month of November of academic year 2003-04. Finally, it presents the results of the application of discriminant analysis, neural networks and decision trees aimed at predicting those students' academic success.


title: A novel approach for automatic acoustic novelty detection using a denoising autoencoder with bidirectional LSTM neural networks

abstract: Acoustic novelty detection aims at identifying abnormal/novel acoustic signals which differ from the reference/normal data that the system was trained with. In this paper we present a novel unsupervised approach based on a denoising autoencoder. In our approach auditory spectral features are processed by a denoising autoencoder with bidirectional Long Short-Term Memory recurrent neural networks. We use the reconstruction error between the input and the output of the autoencoder as activation signal to detect novel events. The autoencoder is trained on a public database which contains recordings of typical in-home situations such as talking, watching television, playing and eating. The evaluation was performed on more than 260 different abnormal events. We compare results with state-of-theart methods and we conclude that our novel approach significantly outperforms existing methods by achieving up to 93.4% F-Measure.


title: Generating Dialogue Agents via Automated Planning

abstract: Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced applications such as career coaching or planning a trip require a much more complex multi-turn dialogue. Current limitations of conversational systems have made it difficult to support applications that require personalization, customization and context dependent interactions. We tackle this challenging problem by using domain-independent AI planning to automatically create dialogue plans, customized to guide a dialogue towards achieving a given goal. The input includes a library of atomic dialogue actions, an initial state of the dialogue, and a goal. Dialogue plans are plugged into a dialogue system capable to orchestrate their execution. Use cases demonstrate the viability of the approach. Our work on dialogue planning has been integrated into a product, and it is in the process of being deployed into another.


title: Internet of Things: A Comprehensive Study of Security Issues and Defense Mechanisms

abstract: The Internet of Things (IoT) is an evolving global trend in Web-based information architecture aiding in the exchange of services and goods over a network without necessitating human-to-human or human-to-computer interaction. It has the potential to revolutionize physical world interaction of individuals and the organizations. The application of IoT can be recognized significantly in many areas such as in healthcare, resource management, learning, knowledge processing, and many more. The practical realization of IoT is met with a plethora of security and privacy challenges that need to be tackled for IoT's successful deployment on a commercially viable large scale. This paper analyzes the security issues related to IoT networks through an analysis of the existing empirical researches to get an insight on the security requirements of the IoT networks. The findings of the study revealed that security threats are one of the biggest and ever-growing challenges for IoT, and it is essential to substantially mitigate them for the success of this platform.


Development of IMAGINE: a three-pillar student initiative to promote social accountability and interprofessional education.


title: Using JavaScript as a Real Programming Language

abstract: With the increasing popularity of the World Wide Web, scripting languages and other dynamic languages are currently experiencing a renaissance. A whole new generation of programmers are growing up with languages such as JavaScript, Perl, PHP, Python and Ruby. The attention that dynamic languages are receiving is remarkable, and is something that has not occurred since the early days of personal computers and the BASIC programming language in the late 1970s and early 1980s. ::: ::: At the same time, the web is becoming the de facto target platform for advanced software applications, including social networking systems, games, productivity applications, and so on. Software systems that were conventionally written using static programming languages such as C, C++ or Java™, are now built with dynamic languages that were originally designed for scripting rather than full-scale application development. ::: ::: At Sun Labs, we have created a new, highly dynamic web programming environment called the Lively Kernel that is built entirely around JavaScript. As part of this effort, we have written a lot of JavaScript code and applications that exercise the JavaScript language in a different fashion than the typical JavaScript programs found on commercial web sites. Among other things, we have used JavaScript as a systems programming language to write the Lively Kernel itself. ::: ::: In this paper we summarize our experiences using JavaScript, focusing especially on its use as a real, general-purpose programming language.


title: A Survey: Soft Computing in Intelligent Information Retrieval Systems

abstract: this paper provides an in-depth survey of challenges in the design of intelligent information retrieval systems, pointing out some similarities and differences in the core data mining and web based search operations. The procedures for evaluation of search engine performance and implicit feedback of the user with respect to a search result are studied with references to the different algorithms. We have proposed a novel neural satisfaction based feedback vector in contrast to the existing activity pattern based feedback as a future research direction in Intelligent IR. We addressed the select research work in the area of soft information retrieval using fuzzy sets, artificial neural networks, genetic algorithms and probabilistic information retrieval. As an instance of information retrieval, web mining is a set of operations to retrieve relevant documents from preprocessed, crawled and indexed web, and it can be categorized into more specialized tasks of web content mining, web structure mining and web usage mining. We have given a survey of the important reviews on topic of web mining and its associated tasks. On the basis of Identified challenges in information retrieval in general, and web mining in particular, we have concentrated on applicability of soft computing techniques and their hybrids in web mining, the performance related issues of select solutions, future of web mining and the next generation user's expectations from a search engine.


title: Analysis of Convolutional Neural Networks for Document Image Classification

abstract: Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks. However, many of these approaches rely on parameters and architectures designed for classifying natural images, which differ from document images. We question whether this is appropriate and conduct a large empirical study to find what aspects of CNNs most affect performance on document images. Among other results, we exceed the state-of-the-art on the RVL-CDIP dataset by using shear transform data augmentation and an architecture designed for a larger input image. Additionally, we analyze the learned features and find evidence that CNNs trained on RVL-CDIP learn region-specific layout features.


title: Electrodes for transcutaneous (surface) electrical stimulation

abstract: In therapeutic and functional applications transcutaneous electrical stimulation (TES) is still the most frequently applied technique for muscle and nerve activation despite the huge efforts made to improve implantable technologies. Stimulation electrodes play the important role in interfacing the tissue with the stimulation unit. Between the electrode and the excitable tissue there are a number of obstacles in form of tissue resistivities and permittivities that can only be circumvented by magnetic fields but not by electric fields and currents. However, the generation of magnetic fields needed for the activation of excitable tissues in the human body requires large and bulky equipment. TES devices on the other hand can be built cheap, small and light weight. The weak part in TES is the electrode that cannot be brought close enough to the excitable tissue and has to fulfill a number of requirements to be able to act as efficient as possible. The present review article summarizes the most important factors that influence efficient TES, presents and discusses currently used electrode materials, designs and configurations, and points out findings that have been obtained through modeling, simulation and testing.


abstract: Web advertising (Online advertising) is a form of promotion that uses the World Wide Web for the expressed purpose of delivering marketing messages to attract customers. This paper addresses the mechanism of Content-Oriented advertising (Contextual advertising), which refers to the assignment of relevant ads within the content of a generic web page, e.g. blogs. As blogs become a platform for expressing personal opinion, they naturally contain various kinds of expressions, including both facts and comments of both a positive and negative nature. In this paper, we propose the utilization of sentiment detection to improve Web-based contextual advertising. The proposed SOCA (Sentiment-Oriented Contextual Advertising) framework aims to combine contextual advertising matching with sentiment analysis to select ads that are related to the positive (and neutral) aspects of a blog and rank them according to their relevance. We experimentally validate our approach using a set of data that includes both real ads and actual blog pages. The results clearly indicate that our proposed method can effectively identify those ads that are positively correlated with the given blog pages.


title: Information-centric networking for connected vehicles: a survey and future perspectives

abstract: In the connected vehicle ecosystem, a high volume of information-rich and safety-critical data will be exchanged by roadside units and onboard transceivers to improve the driving and traveling experience. However, poor-quality wireless links and the mobility of vehicles highly challenge data delivery. The IP address-centric model of the current Internet barely works in such extremely dynamic environments and poorly matches the localized nature of the majority of vehicular communications, which typically target specific road areas (e.g., in the proximity of a hazard or a point of interest) regardless of the identity/address of a single vehicle passing by. Therefore, a paradigm shift is advocated from traditional IP-based networking toward the groundbreaking information- centric networking. In this article, we scrutinize the applicability of this paradigm in vehicular environments by reviewing its core functionalities and the related work. The analysis shows that, thanks to features like named content retrieval, innate multicast support, and in-network data caching, information-centric networking is positioned to meet the challenging demands of vehicular networks and their evolution. Interoperability with the standard architectures for vehicular applications along with synergies with emerging computing and networking paradigms are debated as future research perspectives.


title: Sustainable competitive advantage: towards a dynamic resource‐based strategy

abstract: This paper introduces the concept of strategic advantage and distinguishes it from competitive advantage. This concept helps to explain the full nature of sustainable competitive advantage through uncovering the dynamics of resource‐based strategy. A new classification of resources emerges, demonstrating that rents are more relevant than profits in the analysis of sustainable competitive advantage.


title: Adolescent Behavioral, Emotional, and Cognitive Engagement Trajectories in School and Their Differential Relations to Educational Success

abstract: The current study used a multidimensional approach to examine developmental trajectories of three dimension of school engagement (school participation, sense of school belonging, and self-regulated learning) from grades 7 to 11 and their relationships to changes in adolescents’ academic outcomes over time. The sample includes 1,148 African American and European American adolescents (52% females, 56% black, 34% white, and 10% others). As expected, the downward trajectories of change in school participation, sense of belonging to school, and self-regulated learning differed as did their predictive relationships with academic performance and educational aspiration, with school belonging declining most markedly, but being least predictive of changes in grade point average.


title: Papyrus swamp drainage and the conservation status of their avifauna

abstract: Papyrus Cyperus papyrus swamps sustain the livelihoods of millions of people, but threats to this habitat have never been quantified formally. Birds are useful indicators of threats that cannot be measured directly. Using satellite imagery classification and habitat associated modelling, we quantify drainage and present International Union for Conservation of Nature (IUCN) Red List assessments for the eight taxa most closely associated with this habitat. We show that, between 1984–1987 and 1999–2001 the areal extent of papyrus declined by 6.7 % from 1,643 to 1,532 km2. Papyrus-specialist avifauna has undergone much greater declines, due in part to fragmentation effects and in part to geographical overlap between areas of highest population densities and areas that have experienced greatest habitat loss. Our assessment does not alter the IUCN Red List status of any full species, but it improves current knowledge of the drivers of their extinction risk. Papyrus gonolek Laniarius mufumbiri should remain at least near threatened, but on the basis of population decline rather than a purported small and decreasing range size, and papyrus yellow warbler Chloropeta gracilirostris should remain vulnerable, but again due to declines rather than on the basis of previous under-estimates of population size. Other species should remain listed as least concern. However, taxonomically, likely specifically, distinct populations of papyrus yellow warbler in Zambia and Kenya are highly threatened and should be listed as endangered and critically endangered respectively. We propose several conservation priorities and discuss means of achieving these in a manner consistent with maintaining the livelihoods of people.


title: BareDroid: Large-Scale Analysis of Android Apps on Real Devices

abstract: To protect Android users, researchers have been analyzing unknown, potentially-malicious applications by using systems based on emulators, such as the Google's Bouncer and Andrubis. Emulators are the go-to choice because of their convenience: they can scale horizontally over multiple hosts, and can be reverted to a known, clean state in a matter of seconds. Emulators, however, are fundamentally different from real devices, and previous research has shown how it is possible to automatically develop heuristics to identify an emulated environment, ranging from simple flag checks and unrealistic sensor input, to fingerprinting the hypervisor's handling of basic blocks of instructions. Aware of this aspect, malware authors are starting to exploit this fundamental weakness to evade current detection systems. Unfortunately, analyzing apps directly on bare metal at scale has been so far unfeasible, because the time to restore a device to a clean snapshot is prohibitive: with the same budget, one can analyze an order of magnitude less apps on a physical device than on an emulator. In this paper, we propose BareDroid, a system that makes bare-metal analysis of Android apps feasible by quickly restoring real devices to a clean snapshot. We show how BareDroid is not detected as an emulated analysis environment by emulator-aware malware or by heuristics from prior research, allowing BareDroid to observe more potentially malicious activity generated by apps. Moreover, we provide a cost analysis, which shows that replacing emulators with BareDroid requires a financial investment of less than twice the cost of the servers that would be running the emulators. Finally, we release BareDroid as an open source project, in the hope it can be useful to other researchers to strengthen their analysis systems.


title: Developing reflective practice in LIS education: The SEA-change model of reflection

abstract: This paper presents the SEAchange model of reflection. It was developed to support the growing interest in reflective practice within the library domain. The model was developed from experience gained teaching and training reflective writing to students and practitioners within librarianship. The model was tested using data gathered from the reflective journals of 22 MA Librarianship students, who submitted 116 reflective journal entries from October 2007 toMay 2008. The model has three core process elements: ::: a consideration of the situation (S); consideration of the evidence used during the practice of reflection (E); and action (A) needed as a result of what has been learnt from the reflective process. An earlier study identified significant benefits that could be gained from engaging in reflective writing, particularly in relation to student learning outcomes, selfdevelopment and empowerment. These outcomes, identified as change or the need for change are integrated into the SEAchange model. Reflection is discussed ::: in relation to teachers or mentors applying a progressive “descaffolding” approach to learning support ::: in order to facilitate student autonomy. Further validation of the model is recommended in a range of different settings. The model provides a framework for the teaching, support and guidance of reflective practice.


title: Integrating information from speech and physiological signals to achieve emotional sensitivity

abstract: Recently, there has been a significant amount of work on the recognition of emotions from speech and biosignals. Most approaches to emotion recognition so far concentrate on a single modality and do not take advantage of the fact that an integrated multimodal analysis may help to resolve ambiguities and compensate for errors. In this paper, we describe various methods for fusing physiological and voice data at the feature-level and the decision-level as well as a hybrid integration scheme. The results of the integrated recognition approach are then compared with the individual recognition results from each modality.


title: Blockchain in government: Benefits and implications of distributed ledger technology for information sharing

abstract: Blockchain refers to a range of general purpose technologies to exchange information and transact digital assets in distributed networks. The core question addressed in this paper is whether blockchain technology will lead to innovation and transformation of governmental processes. To address this question we present a critical assessment of the often exaggerated benefits of blockchain technology found in the literature and discuss their implications for governmental organizations and processes. We plea for a shift from a technology-driven to need-driven approach in which blockchain applications are customized to ensure a fit with requirements of administrative processes and in which the administrative processes are changed to benefit from the technology. Having sound governance models are found to be a condition for realizing benefits. Based on a critical assessment we offer directions for further research into the potential benefits of BC applications in e-government and the role of governance of BC architectures and applications to comply with societal needs and public values.


title: FMA: A Dataset For Music Analysis

abstract: We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at this https URL


title: Conducted EMI Reduction in Power Converters by Means of Periodic Switching Frequency Modulation

abstract: Spread spectrum clock generation techniques were originally developed to reduce electromagnetic interference (EMI) in communications and microprocessor systems working in the range of hundreds of megahertz. Nowadays, the switching frequency of power converters has been increasing up to values that make worthy the application of such switching frequency modulation techniques to reduce EMI emissions in power converters. Although random modulations have been applied before to power converters, periodic patterns can provide some advantages. First, theoretical principles of frequency modulation using three periodic patterns for the modulating function are presented. The influence of some important modulation parameters on the EMI reduction is analyzed and some considerations about the EMI filters design are also presented. The effectiveness of such methods in terms of EMI reduction is demonstrated theoretically and confirmed with experimental results obtained from tests carried out on two converters. The first one is a 2.5 W buck converter that can be switched up to 1 MHz and the second one is a 600 W boost converter switching at 40 kHz. In both cases, attenuations obtained in conducted EMI are evaluated. Finally, special attention has been paid to input current and output voltage ripple in order to evaluate possible undesired side-effects produced by this technique.


title: Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

abstract: Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-channel CNNs have exhibited excellent performance for sentence classification tasks. We apply CNNs to large-scale authorship attribution, which aims to determine an unknown text's author among many candidate authors, motivated by their ability to process character-level signals and to differentiate between a large number of classes, while making fast predictions in comparison to state-of-the-art approaches. We extensively evaluate CNN-based approaches that leverage word and character channels and compare them against state-of-the-art methods for a large range of author numbers, shedding new light on traditional approaches. We show that character-level CNNs outperform the state-of-the-art on four out of five datasets in different domains. Additionally, we present the first application of authorship attribution to reddit.


A Review of Clinical Prediction Models


title: Tunable oxide-bypassed trench gate MOSFET: breaking the ideal superjunction MOSFET performance line at equal column width

abstract: The superjunction (SJ) MOSFET power device is recognized for its higher blocking capability and lower on-state resistance that break the conventional unipolar silicon limit. However, SJ devices below 100 V rating incur the constraint of unrealistically narrow column widths , and their performance is greatly handicapped due to difficulties in the formation of perfectly charge-balanced SJ p-n columns by current process technology. Based on an alternative approach of the tunable oxide-bypassed (TOB) SJ MOSFET concept, a TOB-UMOS device of 79 V rating has been successfully fabricated for the first time. Laboratory measurements indicate that the device breaks the ideal SJ MOSFET performance line at equal column width of 3.5 /spl mu/m, and potentially the ideal silicon limit as well.


title: A 7 DOF exoskeleton arm: Shoulder, elbow, wrist and hand mechanism for assistance to upper limb disabled individuals

abstract: This paper illustrates the mechanical structure's spherical motion, kinematic matrices and achievable workspace of an exoskeleton upper limb device. The purpose of this paper is to assist individuals that have lost their upper limb motor functions by creating an exoskeleton device that does not require an external support; but still provides a large workspace. This allows for movement according to the Activities of Daily Living (ADL).


title: Palmprint Recognition Using Deep Scattering Convolutional Network

abstract: Palmprint recognition has drawn a lot of attention during the recent years. Many algorithms have been proposed for palmprint recognition in the past, majority of them being based on features extracted from the transform domain. Many of these transform domain features are not translation or rotation invariant, and therefore a great deal of preprocessing is needed to align the images. In this paper, a powerful image representation, called scattering network/transform, is used for palmprint recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering network captures similar features to SIFT descriptors and the higher-layer features capture higher-frequency content of the signal which are lost in SIFT and other similar descriptors. After extraction of the scattering features, their dimensionality is reduced by applying principal component analysis (PCA) which reduces the computational complexity of the recognition task. Two different classifiers are used for recognition: multi-class SVM and minimum-distance classifier. The proposed scheme has been tested on a well-known palmprint database and achieved accuracy rate of 99.95% and 100% using minimum distance classifier and SVM respectively.


title: Re-Weighted Discriminatively Embedded $K$ -Means for Multi-View Clustering

abstract: Recent years, more and more multi-view data are widely used in many real world applications. This kind of data (such as image data) are high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called Re-weighted Discriminatively Embedded KMeans (RDEKM), for this task. The proposed method is a multiview least-absolute residual model which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme which utilizes Iterative Re-weighted Least Squares to solve leastabsolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared to several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark datasets, which demonstrates the superiority of the proposed work.


title: An insight into the pull requests of GitHub

abstract: Given the increasing number of unsuccessful pull requests in GitHub projects, insights into the success and failure of these requests are essential for the developers. In this paper, we provide a comparative study between successful and unsuccessful pull requests made to 78 GitHub base projects by 20,142 developers from 103,192 forked projects. In the study, we analyze pull request discussion texts, project specific information (e.g., domain, maturity), and developer specific information (e.g., experience) in order to report useful insights, and use them to contrast between successful and unsuccessful pull requests. We believe our study will help developers overcome the issues with pull requests in GitHub, and project administrators with informed decision making.


title: Human-Assisted Graph Search: It's Okay to Ask Questions

abstract: We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form "Is there a target node that is reachable from the current node?". This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowd-sourcing platforms such as Mechanical Turk.


title: Authorization framework for the Internet-of-Things

abstract: This paper describes a framework that allows fine-grained and flexible access control to connected devices with very limited processing power and memory. We propose a set of security and performance requirements for this setting and derive an authorization framework distributing processing costs between constrained devices and less constrained back-end servers while keeping message exchanges with the constrained devices at a minimum. As a proof of concept we present performance results from a prototype implementing the device part of the framework.


title: SSBC 2015: Sclera segmentation benchmarking competition

abstract: This article reports and summarizes the results of a competition on sclera segmentation benchmarking, called Sclera Segmentation Benchmarking Competition 2015 (SSBC 2015). It was organized in the context of the Seventh IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS 2015). The goal of this competition was to set a common platform for evaluation of the recent development in sclera segmentation and also to attract the attention of researchers to this new area. In this regard, we have prepared a Multi-Angle Sclera Dataset (MASD) version 1. It is comprised of 2624 RGB images taken from both the eyes of 82 identities. In other words, it consists of images of 164 (82*2) different eyes. Moreover, we have prepared manual segmentation masks of these images to create the baseline. We have, furthermore, adopted precision and recall-based statistical measures to evaluate the effectiveness and the ranks of the competing algorithms. To summarize, ten participants registered for the competition, and among them four participants submitted their algorithms/ systems. The results achieved by these algorithms reflect noteworthy developments in the literature of sclera segmentation employing cutting edge segmentation techniques. Along with the algorithm of the four competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website.


title: Improving Latent Factor Models via Personalized Feature Projection for One Class Recommendation

abstract: Latent Factor models, which transform both users and items into the same latent feature space, are one of the most successful and ubiquitous models in recommender systems. Most existing models in this paradigm define both users' and items' latent factors to be of the same size and use an inner product to represent a user's "compatibility" with an item. Intuitively, users' factors encode "preferences" while item factors encode "properties", so that the inner product encodes how well an item matches a user's preferences. However, a user's opinion of an item may be more complex, for example each dimension of each user's opinion may depend on a combination of multiple item factors simultaneously. Thus it may be better to view each dimension of a user's preference as a personalized projection of an item's properties so that the preference model can capture complex relationships between items' properties and users' preferences. Therefore, in this paper we propose a novel personalized feature projection method to model users' preferences over items. Specifically, for each user, we define a personalized projection matrix, which takes the place of user-specific factors from existing models. This matrix describes a mapping between items' factors and users' preferences in order to build personalized preference models for each user and item. The proposed personalized feature projection method is quite general and existing latent factor models, for example, can be cast as a special case. We present three objective functions to optimize predictions in the form of ranked lists of users' preferences over items, and demonstrate how each can be used to improve one-class recommendation performance. Experiments are conducted on four real-world datasets and our results show that our personalized feature projection method outperforms several state-of-the-art methods on various evaluation metrics.


title: Attack graph based evaluation of network security

abstract: The perspective directions in evaluating network security are simulating possible malefactor's actions, building the representation of these actions as attack graphs (trees, nets), the subsequent checking of various properties of these graphs, and determining security metrics which can explain possible ways to increase security level. The paper suggests a new approach to security evaluation based on comprehensive simulation of malefactor's actions, construction of attack graphs and computation of different security metrics. The approach is intended for using both at design and exploitation stages of computer networks. The implemented software system is described, and the examples of experiments for analysis of network security level are considered.


title: Active motor babbling for sensorimotor learning

abstract: For a complex autonomous robotic system such as a humanoid robot, motor-babbling-based sensorimotor learning is considered an effective method to develop an internal model of the self-body and the environment autonomously. In this paper, we propose a method of sensorimotor learning and evaluate it performance in active learning. The proposed model is characterized by a function we call the “confidence”, and is a measure of the reliability of state prediction and control. The confidence for the state can be a good measure to bias the next exploration strategy of data sampling, and to direct its attention to areas in the state domain less reliably predicted and controlled. We consider the confidence function to be a first step toward an active behavior design for autonomous environment adaptation. The approach was experimentally validated using the humanoid robot James.


title: Breaking Fitness Records without Moving: Reverse Engineering and Spoofing Fitbit

abstract: Tens of millions of wearable fitness trackers are shipped yearly to consumers who routinely collect information about their exercising patterns. Smartphones push this health-related data to vendors' cloud platforms, enabling users to analyze summary statistics on-line and adjust their habits. Third-parties including health insurance providers now offer discounts and financial rewards in exchange for such private information and evidence of healthy lifestyles. Given the associated monetary value, the authenticity and correctness of the activity data collected becomes imperative. In this paper, we provide an in-depth security analysis of the operation of fitness trackers commercialized by Fitbit, the wearables market leader. We reveal an intricate security through obscurity approach implemented by the user activity synchronization protocol running on the devices we analyze. Although non-trivial to interpret, we reverse engineer the message semantics, demonstrate how falsified user activity reports can be injected, and argue that based on our discoveries, such attacks can be performed at scale to obtain financial gains. We further document a hardware attack vector that enables circumvention of the end-to-end protocol encryption present in the latest Fitbit firmware, leading to the spoofing of valid encrypted fitness data. Finally, we give guidelines for avoiding similar vulnerabilities in future system designs.


title: VERSE: Versatile Graph Embeddings from Similarity Measures

abstract: Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good results as the non-scalable full variant.


title: Quantum Blockchain using entanglement in time

abstract: We propose a conceptual design for a quantum blockchain. Our method involves encoding the blockchain into a temporal GHZ (Greenberger-Horne-Zeilinger) state of photons that do not simultaneously coexist. It is shown that the entanglement in time, as opposed to an entanglement in space, provides the crucial quantum advantage. All the subcomponents of this system have already been shown to be experimentally realized. Furthermore, our encoding procedure can be interpreted as nonclassically influencing the past.


title: Main-memory triangle computations for very large (sparse (power-law)) graphs

abstract: Finding, counting and/or listing triangles (three vertices with three edges) in massive graphs are natural fundamental problems, which have recently received much attention because of their importance in complex network analysis. Here we provide a detailed survey of proposed main-memory solutions to these problems, in a unified way. We note that previous authors have paid surprisingly little attention to space complexity of main-memory solutions, despite its both fundamental and practical interest. We therefore detail space complexities of known algorithms and discuss their implications. We also present new algorithms which are time optimal for triangle listing and beats previous algorithms concerning space needs. They have the additional advantage of performing better on power-law graphs, which we also detail. We finally show with an experimental study that these two algorithms perform very well in practice, allowing us to handle cases which were previously out of reach.


title: MRF-Based Multispectral Image Fusion Using an Adaptive Approach Based on Edge-Guided Interpolation

abstract: In interpretation of remote sensing images, it is possible that some images which are supplied by different sensors become understandable. For better visual perception of these images, it is essential to operate series of pre-processing and elementary corrections and then operate a series of main processing steps for more precise analysis on the images. There are several approaches for processing which are depended on the type of remote sensing images. The discussed approach in this article, i.e. image fusion, is the use of natural colors of an optical image for adding color to a grayscale satellite image which gives us the ability for better observation of the HR image of OLI sensor of Landsat-8. This process with emphasis on details of fusion technique has previously been performed; however, we are going to apply the concept of the interpolation process. In fact, we see many important software tools such as ENVI and ERDAS as the most famous remote sensing image processing tools have only classical interpolation techniques (such as bi-linear (BL) and bi-cubic/cubic convolution (CC)). Therefore, ENVI- and ERDAS-based researches in image fusion area and even other fusion researches often dont use new and better interpolators and are mainly concentrated on the fusion algorithms details for achieving a better quality, so we only focus on the interpolation impact on fusion quality in Landsat-8 multispectral images. The important feature of this approach is to use a statistical, adaptive, and edge-guided interpolation method for improving the color quality in the images in practice. Numerical simulations show selecting the suitable interpolation techniques in MRF-based images creates better quality than the classical interpolators.


title: Wireless adhoc sensor and actuator networks on the farm

abstract: Agriculture accounts for a significant portion of the GDP in most developed countries. However, managing farms, particularly large-scale extensive farming systems, is hindered by lack of data and increasing shortage of labour. We have deployed a large heterogeneous sensor network on a working farm to explore sensor network applications that can address some of the issues identified above. Our network is solar powered and has been running for over 6 months. The current deployment consists of over 40 moisture sensors that provide soil moisture profiles at varying depths, weight sensors to compute the amount of food and water consumed by animals, electronic tag readers, up to 40 sensors that can be used to track animal movement (consisting of GPS, compass and accelerometers), and 20 sensor/actuators that can be used to apply different stimuli (audio, vibration and mild electric shock) to the animal. The static part of the network is designed for 24/7 operation and is linked to the Internet via a dedicated high-gain radio link, also solar powered. The initial goals of the deployment are to provide a testbed for sensor network research in programmability and data handling while also being a vital tool for scientists to study animal behavior. Our longer term aim is to create a management system that completely transforms the way farms are managed.


Climate change and sustainable food security


title: A comprehensive simulation study of slotted CSMA/CA for IEEE 802.15.4 wireless sensor networks

abstract: In this paper, we analyze the performance limits of the slotted CSMA/CA mechanism of IEEE 802.15.4 in the beacon-enabled mode for broadcast transmissions in WSNs. The motivation for evaluating the beacon-enabled mode is due to its flexibility for WSN applications as compared to the non-beacon enabled mode. Our analysis is based on an accurate simulation model of the slotted CSMA/CA mechanism on top of a realistic physical layer, with respect to the IEEE 802.15.4 standard specification. The performance of the slotted CSMA/CA is evaluated and analyzed for different network settings to understand the impact of the protocol attributes (superframe order, beacon order and backoff exponent) on the network performance, namely in terms of throughput (S), average delay (D) and probability of success (Ps). We introduce the concept of utility (U) as a combination of two or more metrics, to determine the best offered load range for an optimal behavior of the network. We show that the optimal network performance using slotted CSMA/CA occurs in the range of 35% to 60% with respect to an utility function proportional to the network throughput (S) divided by the average delay (D).


title: Automatic tagging and retrieval of E-Commerce products based on visual features

abstract: This paper proposes an automatic tag assignment approach to various e-commerce products where tag allotment is done solely based on the visual features in the image. It then builds a tag based product retrieval system upon these allotted tags. The explosive growth of e-commerce products being sold online has made manual annotation infeasible. Without such tags it’s impossible for customers to be able to find these products. Hence a scalable approach catering to such large number of product images and allocating meaningful tags is essential and could be used to make an efficient tag based product retrieval system. In this paper we propose one such approach based on feature extraction using Deep Convolutional Neural Networks to learn descriptive semantic features from product images. Then we use inverse distance weighted K-nearest neighbours classifiers along with several other multi-label classification approaches to assign appropriate tags to our images. We demonstrate the functioning of our algorithm for the Amazon product dataset for various categories of products like clothing and apparel, electronics, sports equipment etc.


Robust Line-Based Calibration of Lens Distortion from a Single View


title: Automatically Labeled Data Generation for Large Scale Event Extraction

abstract: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences


title: Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems

abstract: Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.


title: Massive MIMO: Fundamentals and System Designs

abstract: The last ten years have seen a massive growth in the number of connected wireless devices. Billions of devices are connected and managed by wireless networks. At the same time, each device needs a ...