title: Entrepreneurship Development, Business Ownership and WomenEmpowerment in Nigeria

abstract: Entrepreneurship plays an important role in economic empowerment of the perceived disadvantaged segment of the population of which women form a larger part in the Nigerian context. Over time, what determines women participation in entrepreneurship activities remain an important research issue. Consequently, this study investigated the factor(s) that motivates women to be entrepreneurial and also identified the characteristics of women business owners in Nigeria. The study sampled 200 trained women entrepreneurs who have participated in entrepreneurship training at the Central Bank of Nigeria (CBN) Entrepreneurship Development Centre (EDC), Lagos. The study also generates data from Network of Entrepreneurial Women (NNEW), a gender-oriented network of the Nigeria Employers’ Consultative Association (NECA) to compliment its 200 sample size. The essence of this was to draw a comparison from the findings since EDC and NECA have same goal of promoting entrepreneurial activities. The data were analyzed using descriptive statistics. The findings from the 200 trainee’s sampled shows that the need for independence is the major reason women start a business. Furthermore, the study showed that majority of the respondents used their personal fund to start their business and operate home based and unregistered business that could hardly pay levy for the operation of their business. Contrary to the traditional buying and selling businesses women did in the past, businesses in the service sector is gradually taking over. The result from NECA’s data also validates the later.


title: Application of SVM and KNN to Duval Pentagon 1 for transformer oil diagnosis

abstract: The carried out investigations deal with the application of machine learning algorithms to Duval Pentagon 1 graphical method for the diagnosis of transformer oil. In fact, combined to graphical methods, pattern recognition aims to may complement. For this purpose, we have used the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) algorithms combined to the Duval method. The SVM parameters have been optimized with the Particle Swarm Optimization (PSO). Inspired from IEC and IEEE, five classes namely PD, D1, D2, T1&T2, and T3 have been adopted. The combined algorithms were verified using 155 samples from IEC TC 10 and related databases. We found that KNN, SVM may complement the Duval Pentagon 1 diagnosis method.


Priorities in Educational Technology : A Delphi Study


title: Leveraging ARM TrustZone and Verifiable Computing to Provide Auditable Mobile Functions

abstract: The increase of personal data on mobile devices has been followed by legislation that forces service providers to process and maintain users' data under strict data protection policies. In this paper, we propose a new primitive for mobile applications called auditable mobile function (AMF) to help service providers enforcing such policies by enabling them to process sensitive data within users' devices and collecting proofs of function execution integrity. We present SafeChecker, a computation verification system that provides mobile application support for AMFs, and evaluate the practicality of different usage scenario AMFs on TrustZone-enabled hardware.


title: Pulsed Eddy-Current Based Giant Magnetoresistive System for the Inspection of Aircraft Structures

abstract: Research in nondestructive evaluation is constantly increasing the sensitivity of detection of small cracks embedded deep in layered aircraft structures. Pulsed eddy-current (PEC) techniques using coil probes have shown considerable promise in detection and characterization of buried cracks in multilayered structures. In this paper, we describe the design and development of a nondestructive inspection system that uses pulse excitation of a planar multiline coil to generate a transient field that is detected via a giant magnetoresistive (GMR) field sensor. An analysis algorithm using features in time and frequency domain processes the experimentally measured signals for automatic detection of small cracks under fasteners in multilayered structures at a depth of up to 10 mm.


title: The FREMM architecture: A first step towards innovation

abstract: Once delineated the criteria followed to define main electrical distribution and propulsion systems configurations for the FREMM frigates, will be described the prerogatives of the peculiar and innovative architecture chosen in relation to ship operative profile, and will be outlined the system engineering activities to be carried out to guarantee a safe and efficient plant operation and to benefit from technical innovations adopted.


title: Intelligent Cloud Resource Management with Deep Reinforcement Learning

abstract: The cloud provides low-cost and flexible IT resources (hardware and software) across the Internet. As more cloud providers seek to drive greater business outcomes and the environments of the cloud become more complicated, it is evident that the era of the intelligent cloud has arrived. The intelligent cloud faces several challenges, including optimizing the economic cloud service configuration and adaptively allocating resources. In particular, there is a growing trend toward using machine learning to improve the intelligence of cloud management. This article discusses an architecture of intelligent cloud resource management with deep reinforcement learning. The deep reinforcement learning makes clouds automatically and efficiently negotiate the most appropriate configuration, directly from complicated cloud environments. Finally, we give an example to evaluate and conclude the remarkable ability of the intelligent cloud with deep reinforcement learning.


title: Fast and memory-efficient regular expression matching for deep packet inspection

abstract: Packet content scanning at high speed has become extremely important due to its applications in network security, network monitoring, HTTP load balancing, etc. In content scanning, the packet payload is compared against a set of patterns specified as regular expressions. In this paper, we first show that memory requirements using traditional methods are prohibitively high for many patterns used in packet scanning applications. We then propose regular expression rewrite techniques that can effectively reduce memory usage. Further, we develop a grouping scheme that can strategically compile a set of regular expressions into several engines, resulting in remarkable improvement of regular expression matching speed without much increase in memory usage. We implement a new DFA-based packet scanner using the above techniques. Our experimental results using real-world traffic and patterns show that our implementation achieves a factor of 12 to 42 performance improvement over a commonly used DFA- based scanner. Compared to the state-of-art NFA-based implementation, our DFA-based packet scanner achieves 50 to 700 times speedup.


title: Lane-level route planning based on a multi-layer map model

abstract: The demand for high precision navigation services for human drivers and even intelligent vehicles cannot be satisfied by traditional road-level maps, and on the other hand, current enhanced digital maps with abundant details for vehicle localization and navigation simultaneously bring about practical difficulties for lane-level route planning. In this paper, aiming at achieving lane-level route planning, a three-layer map model combining the traditional road-level data with high precision lane-level data is created, in which an intermediate layer serves as a bridge between road and lane level. Then, based on the lane-level map model, a hierarchical route planning framework is established, including road-level route planning, node contraction according to the intermediate layer, and lane-level route planning with the cost model in lane level which takes the lane change and turn into account. A simulation result is implemented to reveal the validation of this hierarchical routing algorithm based on this novel lane-level map model.


title: All robots are not created equal: the design and perception of humanoid robot heads

abstract: This paper presents design research conducted as part of a larger project on human-robot interaction. The primary goal of this study was to come to an initial understanding of what features and dimensions of a humanoid robot's face most dramatically contribute to people's perception of its humanness. To answer this question we analyzed 48 robots and conducted surveys to measure people's perception of each robot's humanness. Through our research we found that the presence of certain features, the dimensions of the head, and the total number of facial features heavily influence the perception of humanness in robot heads. This paper presents our findings and initial guidelines for the design of humanoid robot heads.


title: Automatic , fast , online calibration between depth and color cameras

abstract: HighlightsAutomatic, online calibration between a color camera and a depth camera.No special equipment; just a square, checkered target.Calibration occurs very quickly and with minimal user interaction.Online calibration allows user to see results immediately.Rigorous ground truth validation places this algorithm at leading edge. Automatic camera calibration has remained a hard topic in computer vision since its inception due to its reliance on the image correspondence problem. This problem becomes even more pronounced when calibrating a depth image with a color image due to a lack of simple correspondences between the two modalities. In this work, we develop a completely automatic, very fast, online algorithm that demonstrates how a consumer-grade depth camera can be calibrated with a color camera with minimal user interaction.


title: A mobile agent platform for distributed network and systems management

abstract: The mobile agent (MA) technology has been proposed for the management of networks and distributed systems as an answer to the scalability problems of the centralized paradigm. Management tasks may be assigned to an agent, which delegates and executes management logic in a distributed and autonomous fashion. MA-based management has been a subject of intense research in the past few years, reflected on the proliferation of MA platforms (MAPs) expressly oriented to distributed management. However, most of these platforms impose considerable burden on network and system resources and also lack of essential functionality, such as security mechanisms, fault tolerance, strategies for building network-aware MA itineraries and support for user-friendly customization of MA-based management tasks. In this paper, we discuss the design considerations and implementation details of a complete MAP research prototype that sufficiently addresses all the aforementioned issues. Our MAP has been implemented in Java and optimized for network and systems management applications. The paper also presents the evaluation results of our prototype in real and simulated networking environments.


title: Traffic sign recognition based on color, shape, and pictogram classification using support vector machines

abstract: Traffic sign recognition is the second part of traffic sign detection and recognition systems. It plays a crucial role in driver assistance systems and provides drivers with crucial safety and precaution information. In this study, the recognition of the TS is performed based on its border color, shape, and pictogram information. This technique breaks down the recognition system into small parts, which makes it efficient and accurate. Moreover, this makes it easy to understand TS components. The proposed technique is composed of three independent stages. The first stage involves extracting the border colors using an adaptive image segmentation technique that is based on learning vector quantization. Then, the shape of the TS is detected using a fast and simple matching technique based on the logical exclusive OR operator. Finally, the pictogram is extracted and classified using a support vector machines classifier model. The proposed technique is applied on the German traffic sign recognition benchmark and achieves an overall recognition rate of 98.23%, with an average computational speed of 30 ms.


title: The grit effect: predicting retention in the military, the workplace, school and marriage

abstract: Remaining committed to goals is necessary (albeit not sufficient) to attaining them, but very little is known about domain-general individual differences that contribute to sustained goal commitment. The current investigation examines the association between grit, defined as passion and perseverance for long-term goals, other individual difference variables, and retention in four different contexts: the military, workplace sales, high school, and marriage. Grit predicted retention over and beyond established context-specific predictors of retention (e.g. intelligence, physical aptitude, Big Five personality traits, job tenure) and demographic variables in each setting. Grittier soldiers were more likely to complete an Army Special Operations Forces (ARSOF) selection course, grittier sales employees were more likely to keep their jobs, grittier students were more likely to graduate from high school, and grittier men were more likely to stay married. The relative predictive validity of grit compared to other traditional predictors of retention is examined in each of the four studies. These findings suggest that in addition to domain-specific influences, there may be domain-general individual differences which influence commitment to diverse life goals over time.


title: Cross-Modal Scene Networks

abstract: People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.


abstract: HUTCHINSON M and JACKSON D. Nursing Inquiry 2013; 20: 11–22 ::: Transformational leadership in nursing: towards a more critical interpretation ::: ::: Effective nurse leadership is positioned as an essential factor in achieving optimal patient outcomes and workplace enhancement. Over the last two decades, writing and research on nursing leadership has been dominated by one conceptual theory, that of transformational leadership. This theoretical framework has provided insight into various leader characteristics, with research findings presented as persuasive evidence. While elsewhere there has been robust debate on the merits of the transformational model of leadership, in the nursing literature, there has been little critical review of the model and the commonly used assessment instruments. In this article, we critically review more than a decade of nursing scholarship on the transformational model of leadership and its empirical evidence. Applying a critical lens to the literature, the conceptual and methodological weaknesses of much nursing research on this topic, we question whether the uncritical adoption of the transformational model has resulted in a limited interpretation of nursing leadership. Given the limitations of the model, we advocate embracing new ways of thinking about nursing leadership.


title: Exploring Home and Work Locations in a City from Mobile Phone Data

abstract: Recently, mobile phone call detail records (CDR) have been used to study mobility patterns in cities. Since home and workplace are the most important places in people's lives and define the structure and activity patterns of the city, identifying home and work locations and home-work commuting patterns are of much interest. However, due to decreasing usage of voice calls and SMS, and low usage while people staying at home, identifying home and work locations are getting more difficult. In this paper, we develop a method to exploit daily-aggregated internet usage data (G-CDR) in addition to CDR for identifying work locations. We also develop a method for identifying home locations based on detecting sleeping time. This method can significantly increase home detection success rate and accuracy. From the identified home and work locations, we have explored the population distribution and commuting patterns of people in Bangkok.


A resources perspective on when and how proactive work behaviour leads to employee withdrawal


title: A Lambda Calculus for Quantum Computation

abstract: The classical lambda calculus may be regarded both as a programming language and as a formal algebraic system for reasoning about computation. It provides a computational model equivalent to the Turing machine and continues to be of enormous benefit in the classical theory of computation. We propose that quantum computation, like its classical counterpart, may benefit from a version of the lambda calculus suitable for expressing and reasoning about quantum algorithms. In this paper we develop a quantum lambda calculus as an alternative model of quantum computation, which combines some of the benefits of both the quantum Turing machine and the quantum circuit models. The calculus turns out to be closely related to the linear lambda calculi used in the study of linear logic. We set up a computational model and an equational proof system for this calculus, and we argue that it is equivalent to the quantum Turing machine.


title: Wearable Sensor Systems for Infants

abstract: Continuous health status monitoring of infants is achieved with the development and fusion of wearable sensing technologies, wireless communication techniques and a low energy-consumption microprocessor with high performance data processing algorithms. As a clinical tool applied in the constant monitoring of physiological parameters of infants, wearable sensor systems for infants are able to transmit the information obtained inside an infant's body to clinicians or parents. Moreover, such systems with integrated sensors can perceive external threats such as falling or drowning and warn parents immediately. Firstly, the paper reviews some available wearable sensor systems for infants; secondly, we introduce the different modules of the framework in the sensor systems; lastly, the methods and techniques applied in the wearable sensor systems are summarized and discussed. The latest research and achievements have been highlighted in this paper and the meaningful applications in healthcare and behavior analysis are also presented. Moreover, we give a lucid perspective of the development of wearable sensor systems for infants in the future.


title: A Formal Theory of Inductive Inference . Part II

abstract: 1. Summary In Part I, four ostensibly different theoretical models of induction are presented, in which the problem dealt with is the extrapolation of a very long sequence of symbols—presumably containing all of the information to be used in the induction. Almost all, if not all problems in induction can be put in this form. Some strong heuristic arguments have been obtained for the equivalence of the last three models. One of these models is equivalent to a Bayes formulation, in which a priori probabilities are assigned to sequences of symbols on the basis of the lengths of inputs to a universal Turing machine that are required to produce the sequence of interest as output. Though it seems likely, it is not certain whether the first of the four models is equivalent to the other three. Few rigorous results are presented. Informal investigations are made of the properties of these models. There are discussions of their consistency and meaningfulness, of their degree of independence of the exact nature of the Turing machine used, and of the accuracy of their predictions in comparison to those of other induction methods. In Part II these models are applied to the solution of three problems—prediction of the Bernoulli sequence, extrapolation of a certain kind of Markov chain, and the use of phrase structure grammars for induction. Though some approximations are used, the first of these problems is treated most rigorously. The result is Laplace's rule of succession. The solution to the second problem uses less certain approximations, but the properties of the solution that are discussed, are fairly independent of these approximations. The third application, using phrase structure grammars, is least exact of the three. First a formal solution is presented. Though it appears to have certain deficiencies, it is hoped that presentation of this admittedly inadequate model will suggest acceptable improvements in it. This formal solution is then applied in an approximate way to the determination of the “optimum” phrase structure grammar for a given set of strings. The results that are obtained are plausible, but subject to the uncertainties of the approximation used.


title: SimMed: combining simulation and interactive tabletops for medical education

abstract: A large body of work asserts that interactive tabletops are well suited for group work, and numerous studies have examined these devices in educational contexts. However, few of the described systems support simulations for collaborative learning, and none of them explicitly address immersion. We present SimMed, a system allowing medical students to collaboratively diagnose and treat a virtual patient using an interactive tabletop. The hybrid user interface combines elements of virtual reality with multitouch input. The paper delineates the development process of the system and rationale behind a range of interface design decisions. Thereby, the role of realism in gaining procedural knowledge is discussed - in particular, the interplay between realism, immersion and training goals. We implemented several medical test cases and evaluated our approach with a user study that suggests the great potential of the system. Results show a high level of immersion, cooperation and engagement by the students.


title: Bayesian models of cognition

abstract: For over 200 years, philosophers and mathematicians have be en using probability theory to describe human cognition. While the theory of prob abilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty (Gigerenzer et al., 1989; Hacking, 1975). Our goal in this ch apter is to illustrate the kinds of computational models of cognition that we can build if we assume that human learning and inference approximately follow the principles of Bayesian probabilistic inference, and to explain some of the mathematical ideas and techniques underlying those models


title: Design of a novel broadband power divider based on oversized-coaxial and radial waveguide

abstract: This paper proposes an improved 1 to 4 power divider based on oversized-coaxial and radial waveguide. To widen the bandwidth of the divider/combiner, an axially symmetric oversized coaxial waveguide is used to transform input to the radial waveguide, and at the bottom of the radial waveguide, stepped-impedance technology is used to realize the impedance match. According to EM simulations, the simulated −20dB return loss bandwidth is approximately 44.25% (ranging from 22 to 34.5GHz), and good amplitude imbalance and phase imbalance are observed in the band. This structure has properties of not only a wide band, low insertion loss and high output power, but also a small size, a high power combining efficiency and efficient heat sinking, and has potential for broadband high power combination.


title: Smart ambulance system using IoT

abstract: Traffic jams is one of the crucial issue in India due to which ambulance services get affected on large amount, due to delay in ambulance service, patient may lose his life and number of these scenarios are getting increased day by day. ‘Green Corridor’ is the concept by which patient will get needed treatment on time. In smart ambulance different sensors like heart rate sensor, blood pressure, ECG will be judging status of vital parameters, the status of these parameters will be send to hospital's database simultaneously traffic signals will be operated by using GPRS message through cloud. After getting status of vital parameters, hospital authorities will plan accordingly. As the smart ambulance will reach within range of 100m, signal will be turned to green if it is red, the communication between smart ambulances will be done by GPRS through cloud. Our system ensures quick response for emergency situations by automatically controlling traffic signals on the path of the ambulances.


title: Design of a PCB Rogowski Coil based on the PEEC Method

abstract: In order to measure currents with high di/dt, Rogowski coils are usually used. This work studies the design of a PCB coil by means of electromagnetic field simulation. The PEEC method has been used to extract the parameters of the equivalent circuit of the coil geometry. Different winding arrangements were analyzed with respect to measurement signal strength and noise immunity. The magnetic coupling between the current sensor and the busbar is investigated for the used test setup. Finally, a comparison of the PEEC simulation results with measurements, applying a 400 A current pulse concludes this paper. The results show that the PEEC method allows a simulation of the coil including the setup in which it is used with a reasonable computational effort.


title: Assessing patent value through advanced text analysis

abstract: We developed a patent analysis tool, named COA (Claim Originality Analysis) at IBM Research. The system and the methods are discussed.


title: Design of chatbot with 3D avatar, voice interface, and facial expression

abstract: A chatbot is a conventional agent that is able to interact with users in a given subject by using natural language. The conversations in most chatbot are still using a keyboard as the input. Keyboard input is considered ineffective as the conversation is not natural without any saying and a conversation is not just about words. Therefore, this paper propose a design of a chatbot with avatar and voice interaction to make a conversation more alive. This proposed approach method will come from using several API and using its output as another input to next API. It would take speech recognition to take input from user, then proceed it to chatbot API to receive the chatbot reply in a text form. The reply will be processed to text-to-speech recognition and created a spoken, audio version of the reply. Last, the computer will render an avatar whose gesture and lips are sync with the audio reply. This design would make every customer service or any service with human interaction can use it to make interaction more natural. This design can be further explored with additional tool such as web camera to make the agent can analyze the user's emotion and reaction.


title: Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks

abstract: Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.


title: Decoding EEG and LFP signals using deep learning: heading TrueNorth

abstract: Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms. Furthermore, novel cognitive computing platforms such as IBM's recently introduced neuromorphic TrueNorth chip allow for deploying deep learning techniques in an ultra-low power environment with a minimum device footprint. Merging deep learning and TrueNorth technologies for real-time analysis of brain-activity data at the point of sensing will create the next generation of wearables at the intersection of neurobionics and artificial intelligence.


title: 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data

abstract: Recently, deep learning has demonstrated great success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined to solving 2D problems, with a few limited exceptions that treated the 3D space as a composition of 2D orthogonal planes. The challenge of 3D deep learning is due to a much larger input vector, compared to 2D, which dramatically increases the computation time and the chance of over-fitting, especially when combined with limited training samples hundreds to thousands, typical for medical imaging applications. To address this challenge, we propose an efficient and robust deep learning algorithm capable of full 3D detection in volumetric data. A two-step approach is exploited for efficient detection. A shallow network with one hidden layer is used for the initial testing of all voxels to obtain a small number of promising candidates, followed by more accurate classification with a deep network. In addition, we propose two approaches, i.e., separable filter decomposition and network sparsification, to speed up the evaluation of a network. To mitigate the over-fitting issue, thereby increasing detection robustness, we extract small 3D patches from a multi-resolution image pyramid. The deeply learned image features are further combined with Haar wavelet features to increase the detection accuracy. The proposed method has been quantitatively evaluated for carotid artery bifurcation detection on a head-neck CT dataset from 455 patients. Compared to the state-of-the-art, the mean error is reduced by more than half, from 5.97 mm to 2.64 mm, with a detection speed of less than 1 s/volume.


title: Kolmogorov's theorem and multilayer neural networks

abstract: Abstract Taking advantage of techniques developed by Kolmogorov, we give a direct proof of the universal approximation capabilities of perceptron type networks with two hidden layers. From our proof, we derive estimates of numbers of hidden units based on properties of the function being approximated and the accuracy of its approximation.


title: An Efficient Tag Search Protocol in Large-Scale RFID Systems With Noisy Channel

abstract: Radio frequency identification (RFID) technology has many applications in inventory management, supply chain, product tracking, transportation and logistics. One research issue of practical importance is to search for a particular group of tags in a large-scale RFID system. Time efficiency is a core factor that must be taken into consideration when designing a tag search protocol to ensure scalability. In this paper, we design a new technique called filtering vector, which can significantly reduce transmission overhead during search process, thereby shortening search time. Based on this technique, we propose an iterative tag search protocol. In each round, we filter out some tags and eventually terminate the search process when the search result meets the accuracy requirement. The simulation results demonstrate that our protocol performs much better than the best existing ones.


title: Structured Matching Pursuit for Reconstruction of Dynamic Sparse Channels

abstract: In this paper, by exploiting the special features of temporal correlations of dynamic sparse channels that path delays change slowly over time but path gains evolve faster, we propose the structured matching pursuit (SMP) algorithm to realize the reconstruction of dynamic sparse channels. Specifically, the SMP algorithm divides the path delays of dynamic sparse channels into two different parts to be considered separately, i.e., the common channel taps and the dynamic channel taps. Based on this separation, the proposed SMP algorithm simultaneously detects the common channel taps of dynamic sparse channels in all time slots at first, and then tracks the dynamic channel taps in each single time slot individually. Theoretical analysis of the proposed SMP algorithm provides a guarantee that the common channel taps can be successfully detected with a high probability, and the reconstruction distortion of dynamic sparse channels is linearly upper bounded by the noise power. Simulation results demonstrate that the proposed SMP algorithm has excellent reconstruction performance with competitive computational complexity compared with conventional reconstruction algorithms.


title: A Handbook for Building an Approximate Query Engine

abstract: There has been much research on various aspects of Approximate Query Processing (AQP), such as different sampling strategies, error estimation mechanisms, and various types of data synopses. However, many subtle challenges arise when building an actual AQP engine that can be deployed and used by real world applications. These subtleties are often ignored (or at least not elaborated) by the theoretical literature and academic prototypes alike. For the first time to the best of our knowledge, in this article, we focus on these subtle challenges that one must address when designing an AQP system. Our intention for this article is to serve as a handbook listing critical design choices that database practitioners must be aware of when building or using an AQP system, not to prescribe a specific solution to each challenge.


title: The modular structure of complex systems

abstract: This paper discusses the organization of software that is inherently complex because of very many arbitrary details that must be precisely right for the software to be correct. We show how the software design technique known as information hiding, or abstraction, can be supplemented by a hierarchically structured document, which we call a module guide. The guide is intended to allow both designers and maintainers to identify easily the parts of the software that they must understand, without reading irrelevant details about other parts of the software. The paper includes an extract from a software module guide to illustrate our proposals.


title: Ensemble Semantics for Large-scale Unsupervised Relation Extraction

abstract: Discovering significant types of relations from the web is challenging because of its open nature. Unsupervised algorithms are developed to extract relations from a corpus without knowing the relations in advance, but most of them rely on tagging arguments of predefined types. Recently, a new algorithm was proposed to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar ("synonymous") relation instances because of the sparseness of features. In this paper, we present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which we will show to be very effective for unsupervised extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the web.


title: Inferring land use from mobile phone activity

abstract: Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations.


title: Polyploidy and genome evolution in plants.

abstract: Genome doubling (polyploidy) has been and continues to be a pervasive force in plant evolution. Modern plant genomes harbor evidence of multiple rounds of past polyploidization events, often followed by massive silencing and elimination of duplicated genes. Recent studies have refined our inferences of the number and timing of polyploidy events and the impact of these events on genome structure. Many polyploids experience extensive and rapid genomic alterations, some arising with the onset of polyploidy. Survivorship of duplicated genes are differential across gene classes, with some duplicate genes more prone to retention than others. Recent theory is now supported by evidence showing that genes that are retained in duplicate typically diversify in function or undergo subfunctionalization. Polyploidy has extensive effects on gene expression, with gene silencing accompanying polyploid formation and continuing over evolutionary time.


title: A benchmark dataset for outdoor foreground/background extraction

abstract: Most of video-surveillance based applications use a foreground extraction algorithm to detect interest objects from videos provided by static cameras. This paper presents a benchmark dataset and evaluation process built from both synthetic and real videos, used in the BMC workshop (Background Models Challenge). This dataset focuses on outdoor situations with weather variations such as wind, sun or rain. Moreover, we propose some evaluation criteria and an associated free software to compute them from several challenging testing videos. The evaluation process has been applied for several state of the art algorithms like gaussian mixture models or codebooks.


title: Using grayscale images for object recognition with convolutional-recursive neural network

abstract: There is a common tendency in object recognition research to accumulate large volumes of image features to improve performance. However, whether using more information contributes to higher accuracy is still controversial given the increased computational cost. This work investigates the performance of grayscale images compared to RGB counterparts for visual object classification. A comparison between object recognition based on RGB images and RGB images converted to grayscale was conducted using a cascaded CNN-RNN neural network structure, and compared with other types of commonly used classifiers such as Random Forest, SVM and SP-HMP. Experimental results showed that classification with grayscale images resulted in higher accuracy classification than with RGB images across the different types of classifiers. Results also demonstrated that utilizing a small receptive field CNN and edgy feature selection on grayscale images can result in higher classification accuracy with the advantage of reduced computational cost.


title: Architecture and evaluation of a unified V2V and V2I communication system based on cellular networks

abstract: Vehicle communications are becoming the cornerstone in the future vehicle equipment. More specifically, vehicle to vehicle communications (V2V) are the main object of researching nowadays, because vehicle to infrastructure (V2I) approximations are already being developed as commercial solutions. Cellular networks (CN) are usually applied in V2I solutions, whereas ad hoc networks are practically the only technology considered in V2V communications. Due to fact that CN are currently a reality and the operators are continuously improving the network, this communication technology could be considered as a candidate to deal with V2V necessities as well. The present paper defends the applicability of CN in the V2V field, and presents a novel communication paradigm for vehicles which unifies both V2V and V2I paradigms into one system. A peer to peer network technology has been used over the CN basis to create a group-based communication infrastructure which enables the message propagation among vehicles and between the car and the road side infrastructure. The architecture has been implemented in both hardware and software terms, and multitude of field tests have been carried out, whose main performance results are shown in the paper.


title: Removal of artifacts from JPEG compressed document images

abstract: We present a segmentation-based post-processing method to remove compression artifacts from JPEG compressed document images. JPEG compressed images typically exhibit ringing and blocking artifacts, which can be objectionable to the viewer above certain compression levels. The ringing is more dominant around textual regions while the blocking is more visible in natural images. Despite extensive research, reducing these artifacts in an effective manner still remains challenging. Document images are often segmented for various reasons. As a result, the segmentation information in many instances is available without requiring additional computation. We have developed a low computational cost method to reduce ringing and blocking artifacts for segmented document images. The method assumes the textual parts and pictorial regions in the document have been separated from each other by an automatic segmentation technique. It performs simple image processing techniques to clean out ringing and blocking artifacts from these regions.


title: Natural language processing future

abstract: Natural Language Processing is a technique where machine can become more human and thereby reducing the distance between human being and the machine can be reduced. Therefore in simple sense NLP makes human to communicate with the machine easily. There are many applications developed in past few decades in NLP. Most of these are very useful in everyday life for example a machine that takes instructions by voice. There are lots of research groups working on this topic to develop more practical are useful systems. Natural Language Processing holds great promise for making computer interfaces that are easier to use for people, since people will hopefully be able to talk to the computer in their own language, rather than learn a specialized language of computer commands. For programming, however, the necessity of a formal programming language for communicating with a computer has always been taken for granted. We would like to challenge this assumption. We believe that modern Natural Language Processing techniques can make possible the use of natural language to express programming ideas, thus drastically increasing the accessibility of programming to non-expert users. To demonstrate the feasibility of Natural Language Programming, this paper tackles what are perceived to be some of the hardest cases: steps and loops.


Similarity Index based Link Prediction Algorithms in Social Networks: A Survey


title: Mushrooms: A Potential Natural Source of Anti-Inflammatory Compounds for Medical Applications

abstract: For centuries, macrofungi have been used as food and medicine in different parts of the world. This is mainly attributed to their nutritional value as a potential source of carbohydrates, proteins, amino acids, and minerals. In addition, they also include many bioactive metabolites which make mushrooms and truffles common components in folk medicine, especially in Africa, the Middle East, China, and Japan. The reported medicinal effects of mushrooms include anti-inflammatory effects, with anti-inflammatory compounds of mushrooms comprising a highly diversified group in terms of their chemical structure. They include polysaccharides, terpenoids, phenolic compounds, and many other low molecular weight molecules. The aims of this review are to report the different types of bioactive metabolites and their relevant producers, as well as the different mechanisms of action of mushroom compounds as potent anti-inflammatory agents.


title: Design of fuzzy controller for induction heating using DSP

abstract: This paper presents the development of fuzzy controller for induction heating temperature control. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) digital signal processor (DSP)-based inverter. DSP is used to store the required commands for generating the necessary waveforms to control the frequency of the inverter through proper design of switching pulses. The SPWM technique produces pure sinusoidal wave of voltage and current output. Fuzzy controller software has been developed in the C++ language. VisSim software is used to experiment with a DSP. The inverter is current-fed and SPWM is used for DC to AC conversion. SPWM normally cuts off high-order harmonic by low-pass filter to produce sine wave power. The LC low-pass filter with cutoff frequency at 300Hz is used. Since low frequency produces ripple to cause transformer and inductance magnetic saturation, the range of frequency is between 100 Hz and 300 Hz. The results of the experiment are presented and show the effectiveness and superiority of this temperature control system.


HDFS+: Erasure Coding Based Hadoop Distributed File System


title: Embracing an interdisciplinary approach to plastics pollution awareness and action

abstract: This paper considers how an interdisciplinary approach to the “wicked problem” of plastics pollution offers unique and important collaborative possibilities. Specially, the paper considers the approach of the Synthetic Collective, a group comprising artists, humanities scholars, and scientists. Considering first how artists and scientists might respond differently to tracking, mapping, understanding, and representing plastics pollution, we then look for potential points of commonality across disciplinary difference. In respect to the urgent and multifaceted problem of marine plastics pollution in the Great Lakes region, we ask what are some of the successes and pitfalls of bringing together diverse approaches and interests? The paper concludes with a clear strategy: a set of instructions geared towards building successful interdisciplinary collaborations. Ultimately, we conclude that a strong relationship amongst scientists and artists is possible, fruitful, and indeed warranted when shared goals are the driving principle of the group.


title: Age and gender-based human face reconstruction from single frontal image

abstract: We present an approach for the human face reconstruction from a single frontal image for the use in forensic anthropology when the subject’s age and gender is known. In our approach we build a database of several depth images per each age and gender group pair, marked with facial landmarks. To reconstruct a 3D facial model from an unknown frontal image we search the most similar face in the depth database based on the automatically detected landmarks and assign its depth to the model. In the evaluation part, we compared our approach to a recent automatic convolutional neural network based algorithm and a semi-automatic approach, where landmarks are required to be detected manually. In contrast to other tested approaches our algorithm can estimate all major components, such as eyes, nose and mouth, evenly. Thanks to the external depth database, it can also reconstruct human faces from images with partial facial occlusions and uneven lighting. Additionally, we have found that a single depth image provides a good approximation of the human face and a combination of multiple precomputed depth images has a little impact on the final 3D face reconstruction result. Speed measurements show that our algorithm provides a quick and a fully automatic way to reconstruct a human face from a single frontal image for the use in forensic anthropology.


title: Lizard: Cut off the Tail! // Practical Post-Quantum Public-Key Encryption from LWE and LWR

abstract: The LWE problem has been widely used in many constructions for post-quantum cryptography due to its reduction from the worst-case of lattice hard problems and the lightweight operations for generating its instances. The PKE schemes based on the LWE problem have a simple and fast decryption, but the encryption phase requires large parameter size for the leftover hash lemma or Gaussian samplings.


title: A low cost embedded color vision system

abstract: In this paper we describe a functioning low cost embedded vision system which can perform basic color blob tracking at 16.7 frames per second. This system utilizes a low cost CMOS color camera module and all image data is processed by a high speed, low cost microcontroller. This eliminates the need for a separate frame grabber and high speed host computer typically found in traditional vision systems. The resulting embedded system makes it possible to utilize simple color vision algorithms in applications like small mobile robotics where a traditional vision system would not be practical.


title: From SPARQL to rules (and back)

abstract: As the data and ontology layers of the Semantic Web stack have achieved a certain level of maturity in standard recommendations such as RDF and OWL, the current focus lies on two related aspects. On the one hand, the definition of a suitable query language for RDF, SPARQL, is close to recommendation status within the W3C. The establishment of the rules layer on top of the existing stack on the other hand marks the next step to be taken, where languages with their roots in Logic Programming and Deductive Databases are receiving considerable attention. The purpose of this paper is threefold. First, we discuss the formal semantics of SPARQLextending recent results in several ways. Second, weprovide translations from SPARQL to Datalog with negation as failure. Third, we propose some useful and easy to implement extensions of SPARQL, based on this translation. As it turns out, the combination serves for direct implementations of SPARQL on top of existing rules engines as well as a basis for more general rules and query languages on top of RDF.


title: Rendering for an interactive 360° light field display

abstract: We describe a set of rendering techniques for an autostereoscopic light field display able to present interactive 3D graphics to multiple simultaneous viewers 360 degrees around the display. The display consists of a high-speed video projector, a spinning mirror covered by a holographic diffuser, and FPGA circuitry to decode specially rendered DVI video signals. The display uses a standard programmable graphics card to render over 5,000 images per second of interactive 3D graphics, projecting 360-degree views with 1.25 degree separation up to 20 updates per second. We describe the system's projection geometry and its calibration process, and we present a multiple-center-of-projection rendering technique for creating perspective-correct images from arbitrary viewpoints around the display. Our projection technique allows correct vertical perspective and parallax to be rendered for any height and distance when these parameters are known, and we demonstrate this effect with interactive raster graphics using a tracking system to measure the viewer's height and distance. We further apply our projection technique to the display of photographed light fields with accurate horizontal and vertical parallax. We conclude with a discussion of the display's visual accommodation performance and discuss techniques for displaying color imagery.


title: Extracting and composing robust features with denoising autoencoders

abstract: Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite.


title: A Locomotion Intent Prediction System Based on Multi-Sensor Fusion

abstract: Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers.


title: Balancing performance measures for information security management

abstract: Purpose – To provide useful references for manufacturing industry which guide the linkage of business strategies and performance indicators for information security projects.Design/methodology/approach – This study uses balanced scorecard (BSC) framework to set up performance index for information security management in organizations. Moreover, BSC used is to strengthen the linkage between foundational performance indicators and progressive business strategy theme.Findings – The general model of information security management builds the strategy map with 12 strategy themes and 35 key performance indicators are established. The development of strategy map also express how to link strategy themes to key performance indicators.Research limitations/implications – The investigation of listed manufacturing companies in Taiwan may limit the application elsewhere.Practical implications – Traditional performance measurement system like return on investment, sales growth is not enough to describe and manage intang...


title: A "No Data Center" Solution to Cloud Computing

abstract: Current Cloud Computing is primarily based on proprietary data centers, where hundreds of thousands of dedicated servers are setup to host the cloud services. In addition to the huge number of dedicated servers deployed in data centers, there are billions of underutilized Personal Computers (PCs), usually used only for a few hours per day, owned by individuals and organizations worldwide. The vast untapped compute and storage capacities of the underutilized PCs can be consolidated as alternative cloud fabrics to provision broad cloud services, primarily infrastructure as a service. This approach, thus referred to as "no data center" approach, complements the data center based cloud provision model. In this paper, we present our opportunistic Cloud Computing system, called cuCloud, that runs on scavenged resources of underutilized PCs within an organization/community. Our system demonstrates that the "no data center" solution indeed works. Besides proving our concept, model, and philosophy, our experimental results are highly encouraging.


Face Anti-spoofing based on Deep Stack Generalization Networks


title: Low-Cost Soil Moisture Profile Probe Using Thin-Film Capacitors and a Capacitive Touch Sensor

abstract: Soil moisture is an important property for agriculture, but currently commercialized soil moisture sensors are too expensive for many farmers. The objective of this study is to develop a low-cost soil moisture sensor using capacitors on a film substrate and a capacitive touch integrated circuit. The performance of the sensor was evaluated in two field experiments: a grape field and a mizuna greenhouse field. The developed sensor captured dynamic changes in soil moisture at 10, 20, and 30 cm depth, with a period of 10–14 days required after sensor installation for the contact between capacitors and soil to settle down. The measured soil moisture showed the influence of individual sensor differences, and the influence masked minor differences of less than 0.05 m3·m−3 in the soil moisture at different locations. However, the developed sensor could detect large differences of more than 0.05 m3·m−3, as well as the different magnitude of changes, in soil moisture. The price of the developed sensor was reduced to 300 U.S. dollars and can be reduced even more by further improvements suggested in this study and by mass production. Therefore, the developed sensor will be made more affordable to farmers as it requires low financial investment, and it can be utilized for decision-making in irrigation.


title: KCT: a MATLAB toolbox for motion control of KUKA robot manipulators

abstract: The Kuka Control Toolbox (KCT) is a collection of MATLAB functions for motion control of KUKA robot manipulators, developed to offer an intuitive and high-level programming interface to the user. The toolbox, which is compatible with all 6 DOF small and low payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 30 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display and diagnostics. The flexibility, ease of use and reliability of the toolbox is demonstrated through two applicative examples.


title: Curved document image rectification

abstract: Digitization of documents has gained prominence in the recent past for data preserving. Paper documents can be converted to digital form by using various modes of acquisition techniques. In this paper processing of data captured using normal digital camera has been considered. The camera captured document images may contain warped document due to perspective and geometric distortions. Curvature of text lines is a prominent characteristic of such distortions, which can affect the recognition accuracy of Optical Characters for OCR (Optical Character Recognition). To improve the accuracy, rectification of perspective distortions plays an important role. Here, a rectification technique that detects and straightens text lines with curvature in document images captured using normal digital camera is proposed.


title: Combinatorial Testing of ACTS: A Case Study

abstract: In this paper we present a case study of applying combinatorial testing to test a combinatorial test generation tool called ACTS. The purpose of this study is two-fold. First, we want to gain experience and insights about how to apply combinatorial testing in practice. Second, we want to evaluate the effectiveness of combinatorial testing applied to a real-life system. ACTS has 24637 lines of uncommented code, and provides a command line interface and a fairly sophisticated graphic user interface. The main challenge of this study was to model the input space in terms of a set of parameters and values. Once the model was designed, we generated test cases using ACTS, which were then later used to test ACTS. The results of this study show that input space modeling can be a significant undertaking, and needs to be carefully managed. The results also show that combinatorial testing is effective in terms of achieving high code coverage and fault detection.


title: Linux kernel integrity measurement using contextual inspection

abstract: This paper introduces the Linux Kernel Integrity Monitor (LKIM) as an improvement over conventional methods of software integrity measurement. LKIM employs contextual inspection as a means to more completely characterize the operational integrity of a running kernel. In addition to cryptographically hashing static code and data in the kernel, dynamic data structures are examined to provide improved integrity measurement. The base approach examines structures that control the execution flow of the kernel through the use of function pointers as well as other data that affect the operation of the kernel. Such structures provide an efficient means of extending the kernel operations, but they are also a means of inserting malicious code without modifying the static parts. The LKIM implementation is discussed and initial performance data is presented to show that contextual inspection is practical


title: Broadband Class-E Power Amplifier for HF and VHF

abstract: The power amplifier described here uses a 50-V gemini MOSFET and operates in class E to produce up to 200 W from 1.8 to 128 MHz. The circuit incorporates a broadband input so that frequency changes require only retuning or switching of the output filter. The efficiency varies from 90 percent at 1.8 MHz to 70 percent at 128 MHz. The amplitude-modulation linearity is excellent, making it well suited for use in a Kahn-technique (EER) transmitter.


title: A low-power 20-GHz 52-dB/spl Omega/ transimpedance amplifier in 80-nm CMOS

abstract: This paper describes the design of a transimpedance amplifier (TIA) for a low-power, short-distance, high-density fiber-optic interconnect communication system. The single-ended circuit has been designed in an 80-nm digital CMOS process and consumes only 2.2 mW from a 1-V supply. The measured results show a transimpedance gain of 52 dB/spl Omega/ and a large bandwidth of 20 GHz. This work presents the highest bandwidth at the lowest power consumption for CMOS transimpedance amplifiers reported to date.


title: IoT Based Embedded System for Vehicle Security and Driver Surveillance

abstract: Toady due to rapid increase in vehicles, there is an exponential increase in crime and accidents hence it has become challenge for governments to limit such crimes especially from professional thieves. This paper proposes designing and development of anti-theft as well as driver surveillance embedded system that uses biometric authentication to access the vehicle. This system contains camera which take the image of a person trying to get access of vehicle and compare with authorized person's image and then allowing or denying access. In The case of denial of vehicle access or even if there is an accident occurs, camera will capture the images and email it to the owner or authorizer. This will help to catch thieves, also allows the surveillance of driver and also the inner part of vehicle. The recent work on proposed embedded system is written in this paper. The system is designed and developed using raspberry pi, high resolution camera, vibration sensor and open source software.


title: Human Activity Recognition through Smartphone’s Tri-Axial Accelerometer using Time Domain Wave Analysis and Machine Learning

abstract: activity recognition aims at capturing current state of human in the immediate environment. This article poses a novel approach to predict the human activity by exploiting the data collected from smartphone's triaxial accelerometer sensor. This approach employs a time domain wave analysis on the collected data and extracts relevant features that intuitively distinguish various activities such as walking, standing, running, jogging and sitting. The method then classifies the extracted features using various Machine learning algorithms comprising of SVM, J48 and AdaBoost and Random Forest. This work attains a steadfast accuracy of 98.8283%. HAR has numerous uses, ranging from healthcare, HCI, ubiquitous networks, to entertainment. The need for ever-increasing accuracy levels is inevitable and this work serves the purpose.


title: Clock Synchronization in Distributed Real-Time Systems

abstract: The generation of a fault-tolerant global time base with known accuracy of synchronization is one of the important operating system functions in a distributed real-time system. Depending on the types and number of tolerated faults, this paper presents upper bounds on the achievable synchronization accuracy for external and internal synchronization in a distributed real-time system. The concept of continuous versus instantaneous synchronization is introduced in order to generate a uniform common time base for local, global, and external time measurements. In the last section, the functions of a VLSI clock synchronization unit, which improves the synchronization accuracy and reduces the CPU load, are described. With this unit, the CPU overhead and the network traffic for clock synchronization in state-of-the-art distributed real-time systems can be reduced to less than 1 percent.


title: The Movius Line controversy: the state of the debate

abstract: Abstract Patterns of Palaeolithic variability between eastern Asia and western portions of the Old World continue to engender controversial discussion. Most famously, debate has focused on variability in the absence/presence of ‘handaxes’ east and west of the so-called ‘Movius Line’. However, it is becoming equally apparent that cross-regional contrasts can be made using categories of data other than handaxe presence/absence alone. This, in turn, is leading to a reconfiguration of the archaeological patterns that demand explanation. Here, we outline the current state of the Movius Line controversy in these terms, and undertake a series of metric analyses of eastern and western biface samples. This leads us to highlight specific lines of enquiry that may be important for future attempts to address these enduring questions.


title: A Systematic Exploration of Diversity in Machine Translation

abstract: This paper addresses the problem of producing a diverse set of plausible translations. We present a simple procedure that can be used with any statistical machine translation (MT) system. We explore three ways of using diverse translations: (1) system combination, (2) discriminative reranking with rich features, and (3) a novel post-editing scenario in which multiple translations are presented to users. We find that diversity can improve performance on these tasks, especially for sentences that are difficult for MT.


title: Situated cognition and learning environments: implications for teachers on- and offline in the new digital media age

abstract: John Seely Brown suggested that learning environments should be spaces in which all work is public, is subject to iterative critique by instructors and peers, and in which social interaction is primary. In such spaces, students and teachers engage in a situated cognition approach to teaching and learning where “cognitive accomplishments rely in part on structures and processes outside the individual”. Here we describe a qualitative analysis of a socially situated learning setting that aimed to develop children who can design, analyze, critique, and transform media, subjecting existing social media, their designs, and their peers’ designs to public and iterative critique. In this setting, adult mentors supported children’s self-expression, self-reflection, and skillbuilding through authentic, socially situated reading, writing, and discussion, and media production. Creating and leveraging such spaces is essential for preparing all children for successful experiences in the new knowledge economy in formal and informal educational settings.


title: Predicting Vulnerabilities in Computer-Supported Inferential Analysis under Data Overload

abstract: Abstract: Data overload is a condition where a practitioner, supported by artefacts and other practitioners, finds it extremely challenging to focus in on, assemble and synthesise the significant subset of data for the problem context into a coherent situation assessment, where the subset is a small portion of a vast data field. In order to predict vulnerabilities in intelligence analysis that might arise when traditional strategies for coping with data overload are undermined, we conducted an observational study in a simulated setting. Ten professional intelligence analysts analysed the causes and impacts of the Ariane 501 accident. When study participants performed a time-pressured analysis outside their base of expertise based on sampling reports from a large set, some made inaccurate statements in verbal briefings. Participants that made no inaccurate statements spent more time during the analysis, read more documents, and relied on higher-quality documents than participants who made inaccurate statements. All participants missed potentially available relevant information and had difficulty detecting and resolving data conflicts. Sources of inaccurate statements were: (1) relying upon default assumptions, (2) incorporating inaccurate information and (3) incorporating information that was considered accurate at one point in time. These findings have design implications and point to evaluation criteria for systems designed to address the data overload problem in intelligence analysis.


title: Simultaneous multithreading on x86_64 systems: an energy efficiency evaluation

abstract: In recent years, power consumption has become one of the most important design criteria for microprocessors. CPUs are therefore no longer developed with a narrow focus on raw compute performance. This means that well-established processor features that have proven to increase compute performance now need to be re-evaluated with a new focus on energy efficiency. This paper presents an energy efficiency evaluation of the symmetric multithreading (SMT) feature on state-of-the-art x86_64 processors. We use a mature power measurement methodology to analyze highly sophisticated low-level microbenchmarks as well as a diverse set of application benchmarks. Our results show that--depending on the workload--SMT can be at the same time advantageous in terms of performance and disadvantageous in terms of energy efficiency. Moreover, we demonstrate how the SMT efficiency has advanced between two processor generations.


title: Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

abstract: Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief Networks (DBNs) represent the state-of-the-art for many machine learning and computer vision classification problems. To overcome the large computational cost of deep networks, spiking deep networks have recently been proposed, given the specialized hardware now available for spiking neural networks (SNNs). However, this has come at the cost of performance losses due to the conversion from analog neural networks (ANNs) without a notion of time, to sparsely firing, event-driven SNNs. Here we analyze the effects of converting deep ANNs into SNNs with respect to the choice of parameters for spiking neurons such as firing rates and thresholds. We present a set of optimization techniques to minimize performance loss in the conversion process for ConvNets and fully connected deep networks. These techniques yield networks that outperform all previous SNNs on the MNIST database to date, and many networks here are close to maximum performance after only 20 ms of simulated time. The techniques include using rectified linear units (ReLUs) with zero bias during training, and using a new weight normalization method to help regulate firing rates. Our method for converting an ANN into an SNN enables low-latency classification with high accuracies already after the first output spike, and compared with previous SNN approaches it yields improved performance without increased training time. The presented analysis and optimization techniques boost the value of spiking deep networks as an attractive framework for neuromorphic computing platforms aimed at fast and efficient pattern recognition.


title: Joint Extraction of Events and Entities within a Document Context

abstract: Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.


title: Online SOC and SOH estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer

abstract: This paper proposes an adaptive hybrid battery model-based high-fidelity state of charge (SOC) and state of health (SOH) estimation method for rechargeable multicell batteries. The hybrid battery model consists of an enhanced Coulomb counting algorithm for SOC estimation and an electrical circuit battery model. A variable-length sliding window least squares (VSWLS)-based online parameter identification algorithm is designed to estimate the electrical parameters of the electrical battery model, which are then used as the parameters of an adaptive discrete-time sliding-mode observer (ADSMO) for terminal and open-circuit voltage estimation of a battery cell. The error of the SOC estimated from the enhanced Coulomb counting algorithm is then corrected by using the SOC obtained from the ADSMO-estimated open-circuit voltage. This leads to an accurate, robust real-time SOC estimation. In addition, the maximum capacity of the cell is estimated to determine the SOH of the cell. The proposed method is validated by simulation and experimental results for a four-cell cylindrical lithium-ion battery pack.


title: Sentiment Informed Cyberbullying Detection in Social Media

abstract: Cyberbullying is a phenomenon which negatively affects the individuals, the victims suffer from various mental issues, ranging from depression, loneliness, anxiety to low self-esteem. In parallel with the pervasive use of social media, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include the use of standards and guidelines, human moderators, and blacklists based on the profane words. However, these mechanisms fall short in social media and cannot scale well. Therefore, it is necessary to develop a principled learning framework to automatically detect cyberbullying behaviors. However, it is a challenging task due to short, noisy and unstructured content information and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behaviors and the correlation with emotions, we propose to leverage sentiment information to detect cyberbullying behaviors in social media by proposing a sentiment informed cyberbullying detection framework. Experimental results on two real-world, publicly available social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection.


title: Trap Design for Vibratory Bowl Feeders

abstract: The vibratory bowl feeder is the oldest and still most common approach to the automated feeding (orienting) of industrial parts. In this paper, the authors consider a class of vibratory bowl filters that can be described by removing polygonal sections from the track; this class of filters is referred to as traps. For an n-sided polygonal part and an m-sided polygonal trap, an O(n2m log n) algorithm is given to decide whether the part in a specific orientation will safely move across the trap or will fall through the trap and thus be filtered out. For an n-sided convex polygonal part and m-sided convex polygonal trap, this bound is improved to O((n+m) log n). Furthermore, the authors show how to design various trap shapes, ranging from simple traps to general polygons, which will filter out all but one of the different stable orientations of a given part. Although the runtimes of the design algorithms are exponential in the number of trap parameters, many industrial part feeders use few-parameter traps (ba...


2 The Role of Quantitative Models in Science


title: Academic procrastination : Frequency and cognitive-behavioral correlates

abstract: Analyse factorielle des raisons invoquees par 342 etudiants pour rendre compte de leur temporisation dans certaines tâches universitaires, apres un examen de la frequence d'un tel comportement


title: Actor-Critic Reinforcement Learning with Energy-Based Policies

abstract: We consider reinforcement learning in Markov decision processes with high dimensional state and action spaces. We parametrize policies using energy-based models (particularly restricted Boltzmann machines), and train them using policy gradient learning. Our approach builds upon Sallans and Hinton (2004), who parameterized value functions using energy-based models, trained using a non-linear variant of temporal-di!erence (TD) learning. Unfortunately, non-linear TD is known to diverge in theory and practice. We introduce the first sound and e"cient algorithm for training energy-based policies, based on an actorcritic architecture. Our algorithm is computationally e"cient, converges close to a local optimum, and outperforms Sallans and Hinton (2004) in several high dimensional domains.


title: A segmentation-free approach to Arabic and Urdu OCR

abstract: In this paper, we present a generic Optical Character Recognition system for Arabic script languages called ::: Nabocr. Nabocr uses OCR approaches specific for Arabic script recognition. Performing recognition on Arabic ::: script text is relatively more difficult than Latin text due to the nature of Arabic script, which is cursive and ::: context sensitive. Moreover, Arabic script has different writing styles that vary in complexity. Nabocr is initially ::: trained to recognize both Urdu Nastaleeq and Arabic Naskh fonts. However, it can be trained by users to be ::: used for other Arabic script languages. We have evaluated our system's performance for both Urdu and Arabic. ::: In order to evaluate Urdu recognition, we have generated a dataset of Urdu text called UPTI (Urdu Printed ::: Text Image Database), which measures different aspects of a recognition system. The performance of our system ::: for Urdu clean text is 91%. For Arabic clean text, the performance is 86%. Moreover, we have compared the ::: performance of our system against Tesseract's newly released Arabic recognition, and the performance of both ::: systems on clean images is almost the same.


title: Hyaluronic Acid/Chitosan-g-Poly(ethylene glycol) Nanoparticles for Gene Therapy: An Application for pDNA and siRNA Delivery

abstract: PURPOSE ::: To design hyaluronic acid (HA) and chitosan-g-poly(ethylene glycol) (CS-g-PEG) nanoparticles intended for a broad range of gene delivery applications. ::: ::: ::: METHODS ::: Nanoparticles formulated at different HA/CS-g-PEG mass ratios were developed to associate either pDNA or siRNA. The physico-chemical characteristics, morphology, association efficiency and nuclease protection ability of the nanocarriers were compared for these two molecules. Their biological performance, including transfection effciency, nanoparticle cellular uptake and citotoxicity, was assesed. ::: ::: ::: RESULTS ::: The resulting nanoparticles showed an adequate size (between 130 and 180 nm), and their surface charge could be modulated according to the nanoparticle composition (from +30 mV to -20 mV). All prototypes exhibited a greater association efficiency and nuclease protection for pDNA than for siRNA. However, cell culture experiments evidenced that HA/CS-g-PEG nanoparticles were effective carriers for the delivery of both, siRNA and pDNA, eliciting a biological response with minimal cytotoxicity. Moreover, experiments performed in the HEK-EGFP-Snail1 cell line showed the potential of the HA/CS-g-PEG nanoparticles to silence the expression of the Snail1 transcription factor, an important mediator in tumor progression. ::: ::: ::: CONCLUSIONS ::: HA/CS-g-PEG nanoparticles can be easily modulated for the delivery of different types of gene molecules, offering great potential for gene therapy applications, as evidenced by their biological performance.


title: Cross-lingual Dependency Parsing Based on Distributed Representations

abstract: This paper investigates the problem of cross-lingual dependency parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g. English). Existing approaches typically don’t include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is able to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms McDonald et al. (2013) augmented with projected cluster features on identical data.


title: In-Band RCS Reduction of Waveguide Slot Array Using Metasurface Bars

abstract: A novel approach aimed at reducing the in-band radar cross section (RCS) of a waveguide slot array while maintaining its radiation performance is presented. Anisotropic metasurface is developed and applied to cover half of the antenna structure. The RCS reduction is based on the energy cancelation from the metasurface and the metal ground. Both simulated and measured results show that after loading the metasurface, the in-band RCS is sufficiently reduced for both copolarized and cross-polarized incident waves while the radiation performance is well retained. The proposed method is simple, low-cost, and easily fabricated, providing a new way for in-band RCS reduction of antenna array.


title: Detecting Faces in Colored Images Using Multi-skin Color Models and Neural Network with Texture Analysis

abstract: This paper presents an efficient hybrid system to detect frontal faces in colored images regardless of scale, location, illumination, race, number of faces, and complex background. The general architecture of the proposed system encompasses three methods: skin color segmentation, rule-based geometric knowledge, and neural network-based classifier. In the proposed system, multi-skin color clustering models are applied to segment human skin regions, iterative merge stage creates a set of candidate face regions, then, the facial feature segmentation removes false alarms, caused by objects with the color that is similar to skin color. Furthermore, the rule-based geometric knowledge is employed to describe the human face in order to estimate the location of the “face center”. Then the Artificial Neural Network (ANN) face detector is applied only to the regions of the image, which are marked as candidate face regions. The ANN face detector must decide whether a given sub-window of an image contains a face or not. Partial face template is used, instead of the whole face, to reduce face variability, in order to make the training phase easier and to reduce misrecognition. To increase the accuracy of the system, twelve-texture descriptors are calculated and then attached as input data with each face image to train neural network to describe the content of sub-image window such as X-Y-Relieves, smoothness, ratio of darkness, etc. Training neural network is designed to be general with minimum customization. Comparisons with other face detection systems have revealed that our system shows better performance in terms of positive and negative detection rates.


title: The study of silicon passivation polymer adhesion to epoxy mold compound through button shear strength

abstract: When introducing new material, especially epoxy mold compound (EMC), there are always concerns on adhesion of the mold compound material to the interface of the silicon. Poor adhesion between the two interfaces will potentially cause reliability concerns such as delamination. In order to eliminate the reliability concerns, this study presents the effort to establish a quick and easy monitoring for JEDEC moisture sensitivity level (MSL) and multiple reflow tolerance between epoxy molding compound and silicon interface. For this study, silicon with passivation polymer surfaces is chosen as test vehicle. 2 different types of passivation and 3 different epoxy mold compounds were tested. We conducted MSL and multiple reflow tests with various test vehicles. Also button shear test was performed at various stages of moisture at elevated temperature. This is to simulate user environment in the fields. In this study we found different adhesion strength by mostly passivation type, also hint of relation between adhesion strength and real device reliability performance.


MORTALITY FROM CORONARY HEART DISEASE IN SUBJECTS WITH TYPE 2 DIABETES AND IN NONDIABETIC SUBJECTS WITH AND WITHOUT PRIOR MYOCARDIAL INFARCTION


title: VerSum: Verifiable Computations over Large Public Logs

abstract: VerSum allows lightweight clients to outsource expensive computations over large and frequently changing data structures, such as the Bitcoin or Namecoin blockchains, or a Certificate Transparency log. VerSum clients ensure that the output is correct by comparing the outputs from multiple servers. VerSum assumes that at least one server is honest, and crucially, when servers disagree, VerSum uses an efficient conflict resolution protocol to determine which server(s) made a mistake and thus obtain the correct output. VerSum's contribution lies in achieving low server-side overhead for both incremental re-computation and conflict resolution, using three key ideas: (1) representing the computation as a functional program, which allows memoization of previous results; (2) recording the evaluation trace of the functional program in a carefully designed computation history to help clients determine which server made a mistake; and (3) introducing a new authenticated data structure for sequences, called SeqHash, that makes it efficient for servers to construct summaries of computation histories in the presence of incremental re-computation. Experimental results with an implementation of VerSum show that VerSum can be used for a variety of computations, that it can support many clients, and that it can easily keep up with Bitcoin's rate of new blocks with transactions.


title: SUAV:Q - An improved design for a transformable solar-powered UAV

abstract: Throughout the wide range of aerial robot related applications, selecting a particular airframe is often a trade-off. Fixed-wing small-scale unmanned aerial vehicles (UAVs) typically have difficulty surveying at low altitudes while quadrotor UAVs, having more maneuverability, suffer from limited flight time. Recent prior work [1] proposes a solar-powered small-scale aerial vehicle designed to transform between fixed-wing and quad-rotor configurations. Surplus energy collected and stored while in a fixed-wing configuration is utilized while in a quad-rotor configuration. This paper presents an improvement to the robot's design in [1] by pursuing a modular airframe, an optimization of the hybrid propulsion system, and solar power electronics. Two prototypes of the robot have been fabricated for independent testing of the airframe in fixed-wing and quad-rotor states. Validation of the solar power electronics and hybrid propulsion system designs were demonstrated through a combination of simulation and empirical data from prototype hardware.


title: Wearable Inkjet-Printed Wideband Antenna by Using Miniaturized AMC for Sub-GHz Applications

abstract: A novel compact wearable antenna printed on paper is presented. The antenna comprises a nonresonant coplanar monopole antenna placed on a miniaturized artificial magnetic conductor (AMC) (total size of 0.14 λ0 × 0.14 λ0 × 0.01 λ0 at 700 MHz). The manufacturing process only requires a commercial desktop inkjet printer and conductive nano-ink. The effectiveness of the proposed configuration (monopole above AMC) with respect to the unloaded monopole or to the monopole above a metallic ground plane is proved with on-body measurements in the sub-GHz band.


title: Perspectives and Limitations of Self-Organizing Maps in Blind Separation of Source Signals

abstract: |The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.


title: Characteristics of shift work and their impact on employee performance and wellbeing: A literature review.

abstract: OBJECTIVES ::: To identify the characteristics of shift work that have an effect on employee's performance (including job performance, productivity, safety, quality of care delivered, errors, adverse events and client satisfaction) and wellbeing (including burnout, job satisfaction, absenteeism, intention to leave the job) in all sectors including healthcare. ::: ::: ::: DESIGN ::: A scoping review of the literature was undertaken. ::: ::: ::: DATA SOURCES ::: We searched electronic databases (CINAHL, MEDLINE, PsychINFO, SCOPUS) to identify primary quantitative studies. ::: ::: ::: REVIEW METHODS ::: The search was conducted between January and March 2015. Studies were drawn from all occupational sectors (i.e. health and non health), meeting the inclusion criteria: involved participants aged ≥18 who have been working shifts or serve as control group for others working shifts, exploring the association of characteristics of shift work with at least one of the selected outcomes. Reference lists from retrieved studies were checked to identify any further studies. ::: ::: ::: RESULTS ::: 35 studies were included in the review; 25 studies were performed in the health sector. A variety of shift work characteristics are associated with compromised employee's performance and wellbeing. Findings from large multicentre studies highlight that shifts of 12h or longer are associated with jeopardised outcomes. Working more than 40h per week is associated with adverse events, while no conclusive evidence was found regarding working a 'Compressed Working Week'; working overtime was associated with decreased job performance. Working rotating shifts was associated with worse job performance outcomes, whilst fixed night shifts appeared to enable resynchronisation. However, job satisfaction of employees working fixed nights was reduced. Timely breaks had a positive impact on employee fatigue and alertness, whilst quick returns between shifts appeared to increase pathologic fatigue. The effect of shift work characteristics on outcomes in the studies reviewed is consistent across occupational sectors. ::: ::: ::: CONCLUSIONS ::: This review highlighted the complexity that encompasses shift work, but many studies do not account for this complexity. While some consistent associations emerge (e.g. 12h shifts and jeopardised outcomes), it is not always possible to conclude that results are not confounded by unmeasured factors.


title: Using crowdsourced data in location-based social networks to explore influence maximization

abstract: Online social networks have gained significant popularity recently. The problem of influence maximization in online social networks has been extensively studied. However, in prior works, influence propagation in the physical world, which is also an indispensable factor, is not considered. The Location-Based Social Networks (LBSNs) are a special kind of online social networks in which people can share location-embedded information. In this paper, we make use of mobile crowdsourced data obtained from location-based social network services to study influence maximization in LBSNs. A novel network model and an influence propagation model taking influence propagation in both online social networks and the physical world into consideration are proposed. An event activation position selection problem is formalized and a corresponding solution is provided. The experimental results indicate that the proposed influence propagation model is meaningful and the activation position selection algorithm has high performance.


title: A Method Based on Statistical Characteristics for Detection Malware Requests in Network Traffic

abstract: Network traffic inspection is an important method to discover the existence of malware when it bypasses security devices through polymorphic techniques or zero-day attacks. However, traditional network signature-based or IoC (Indicator of Compromise) detection could fail since the encryption and variability of threats has been increasing. It is well known that these methods are fragile and have difficulty dealing with new variants. This paper proposes a system designed to detect security threats based on the statistical characteristics of HTTP requests from malware. The corresponding method does not rely on signatures or the specific command and control (C&C) contents between bots and the botmaster and can be built given easily accessible information extracted from the HTTP data log, including HTTP headers and the URL. The results from millions of live traffic flows show correct detection with a precision exceeding 98.32% for malicious flows, and the recall reaches 98.70%. We believe this detector represents the main mechanism for discovering network threats in the future.


title: Uses of facial expressions of android head system according to gender and age

abstract: This paper analyzes emotional expressions of an android head system according to gender and age. We use an EveR-4 H33 controlled by thirty-three motors for facial expression. EveR-4 H33 is a head system for an android face consists of three layers: a mechanical layer, an inner cover layer and an outer cover layer. Facial expressions of robots are different from the purposes of robots. In addition, feeling of emotional expressions is also different from humans depending on age, gender, etc. Therefore, we find the appropriate uses of EveR-4 H33 in this paper. EveR-4 H33 shows her facial expressions about some emotions. Then, audiences of exhibition evaluate her facial expressions by enjoying a game of emotional correction. We analyze the results of the game according to gender and age, and decide appropriate uses of EveR-4 H33.


title: The Insecurity of the Elliptic Curve Digital Signature Algorithm with Partially Known Nonces

abstract: Nguyen and Shparlinski have recently presented a polynomial-time algorithm that provably recovers the signer’s secret DSA key when a few consecutive bits of the random nonces k (used at each signature generation) are known for a number of DSA signatures at most linear in log q (q denoting as usual the small prime of DSA), under a reasonable assumption on the hash function used in DSA. The number of required bits is about log1/2q, but can be decreased to log log q with a running time qO(1/log log q) subexponential in log q, and even further to two in polynomial time if one assumes access to ideal lattice basis reduction, namely an oracle for the lattice closest vector problem for the infinity norm. All previously known results were only heuristic, including those of Howgrave-Graham and Smart who introduced the topic. Here, we obtain similar results for the elliptic curve variant of DSA (ECDSA).