title: The impact of animated interface agents: a review of empirical research

abstract: Abstract Over the last years, the animation of interface agents has been the target of increasing interest. Largely, this increase in attention is fuelled by speculated effects on human motivation and cognition. However, empirical investigations on the effect of animated agents are still small in number and differ with regard to the measured effects. Our aim is two-fold. First, we provide a comprehensive and systematic overview of the empirical studies conducted so far in order to investigate effects of animated agents on the user's experience, behaviour and performance. Second, by discussing both implications and limitations of the existing studies, we identify some general requirements and suggestions for future studies.


title: Low Profile Spiral on a Thin Ferrite Ground Plane for 220–500 MHz Operation

abstract: A small size, low profile spiral antenna (2" thick and 14" in aperture) is described that is backed by a thin ferrite ground plane. The antenna is intended to cover Satellite (UHF TacSat earth to LEO: 243-318 MHz) and enhance Radio (PRC-148, PRC-117F, PSC-5D: 243-512 MHz) communications. We carry out a step-by-step design to achieve the challenging goals of RHCP gain >0 dBi at θ = 0°; Bandwidth 220-500 MHz; VSWR <;2:1; Low LHCP (cross-pol) gain (<; -10 dBi); Low profile (2") and desired aperture size 14". Ground plane ferrite treatment, inductive loading, resistive termination and impedance matching techniques are adopted to raise the gain between 200-300 MHz and achieve the stated goals. As the aperture size is about λ/3.5 at 220 MHz and only λ/30 in height, achieving 0 dBi (from 220-500 MHZ) is quite challenging. Measured and simulated data are presented in the last section leading to impressive conclusions about the capability of the magnetic material coating to overcome the perennial issue associated with the ground plane's negative effects.


title: DeepMind Control Suite

abstract: The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents. The tasks are written in Python and powered by the MuJoCo physics engine, making them easy to use and modify. We include benchmarks for several learning algorithms. The Control Suite is publicly available at this https URL A video summary of all tasks is available at this http URL .


An Overview of Association Rule Mining Algorithms


title: Permanent magnet generator for counter-rotating vertical axis micro-wind turbine

abstract: This paper approaches the design using two optimization techniques for a permanent magnet generator used in a micro-wind turbine, with a major importance on the overall performances of the energy conversion system. The proposed micro-wind generator has two armatures and both are rotating in opposite directions.


title: Energy Efficient Scientific Computing on FPGAs using OpenCL

abstract: An indispensable part of our modern life is scientific computing which is used in large-scale high-performance systems as well as in low-power smart cyber-physical systems. Hence, accelerators for scientific computing need to be fast and energy efficient. Therefore, partial differential equations (PDEs), as an integral component of many scientific computing tasks, require efficient implementation. In this regard, FPGAs are well suited for data-parallel computations as they occur in PDE solvers. However, including FPGAs in the programming flow is not trivial, as hardware description languages (HDLs) have to be exploited, which requires detailed knowledge of the underlying hardware. This issue is tackled by OpenCL, which allows to write standardized code in a C-like fashion, rendering experience with HDLs unnecessary. Yet, hiding the underlying hardware from the developer makes it challenging to implement solvers that exploit the full FPGA potential. Therefore, we propose in this work a comprehensive set of generic and specific optimization techniques for PDE solvers using OpenCL that improve the FPGA performance and energy efficiency by orders of magnitude. Based on these optimizations, our study shows that, despite the high abstraction level of OpenCL, very energy efficient PDE accelerators on the FPGA fabric can be designed, making the FPGA an ideal solution for power-constrained applications.


title: Induction of Interpretable Possibilistic Logic Theories from Relational Data

abstract: The field of Statistical Relational Learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which make them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.


title: Activation of Akt kinase accompanies increased cardiac resistance to ischemia/reperfusion in rats after short-term feeding with lard-based high-fat diet and increased sucrose intake.

abstract: High-fat or high-carbohydrate food consumption contributes to changes in myocardial tolerance to ischemia. However, with respect to experimental models, most studies used diets with very high doses of cholesterol, saturated fatty acids, or fructose. In our study, we fed rats a high-fat diet based on lard in combination with administration of a sweet beverage (30% sucrose solution) (high-fat sucrose diet [HFS]). This diet was used to simulate the unhealthy dietary habit typical for developed countries. We hypothesized that the application of HFS diet for 48 days might initiate progression of pathologic changes in the heart associated with myocardial remodeling and activation of adaptive mechanisms. We investigated the influence of HFS diet on cardiac function and vulnerability to ischemia-reperfusion (I/R) injury in Langendorff-perfused rat hearts subjected to 30-minute global ischemia and 120-minute reperfusion as well as on Akt kinase and matrix metalloproteinases. We found lower food consumption in HFS group compared with controls, but a significant increase in visceral fat mass and concentrations of triacylglycerol, low-density lipoprotein, and very low-density lipoprotein cholesterol. Baseline heart functional parameters and their postischemic recovery were not affected by HFS diet. On the other hand, hearts of HFS group were more resistant to lethal I/R injury manifested by significantly smaller infarct size. In addition, there was lower content of collagen I and III in the left ventricle associated with Akt kinase activation and matrix metalloproteinase 9 up-regulation. In conclusion, feeding rats with HFS diet resulted in heart remodeling associated with activation of some adaptive mechanisms, which can contribute to modulation of myocardial resistance to I/R injury.


title: A Planar Bandpass Filter Implemented With a Hybrid Structure of Substrate Integrated Waveguide and Coplanar Waveguide

abstract: In this paper, a novel planar bandpass filter is proposed, designed, and implemented with a hybrid structure of substrate integrated waveguide (SIW) and coplanar waveguide (CPW), which has the advantages of good passband and stopband performance inherited from SIW and miniaturized size accompanying with the CPW. Additional design flexibility is introduced by the hybrid structure for efficiently controlling the mixed electric and magnetic coupling, and then planar bandpass filters with controllable transmission zeros and quasi-elliptic response can be achieved. Several prototypes with single and dual SIW cavities are fabricated. The measured results verified the performance of the proposed planar bandpass filters, such as low passband insertion loss, sharp roll-off characteristics at transition band, etc.


title: A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients

abstract: Policy-gradient-based actor-critic algorithms are amongst the most popular algorithms in the reinforcement learning framework. Their advantage of being able to search for optimal policies using low-variance gradient estimates has made them useful in several real-life applications, such as robotics, power control, and finance. Although general surveys on reinforcement learning techniques already exist, no survey is specifically dedicated to actor-critic algorithms in particular. This paper, therefore, describes the state of the art of actor-critic algorithms, with a focus on methods that can work in an online setting and use function approximation in order to deal with continuous state and action spaces. After starting with a discussion on the concepts of reinforcement learning and the origins of actor-critic algorithms, this paper describes the workings of the natural gradient, which has made its way into many actor-critic algorithms over the past few years. A review of several standard and natural actor-critic algorithms is given, and the paper concludes with an overview of application areas and a discussion on open issues.


title: VSD, a public dataset for the detection of violent scenes in movies: design, annotation, analysis and evaluation

abstract: Content-based analysis to find where violence appears in multimedia content has several applications, from parental control and children protection to surveillance. This paper presents the design and annotation of the Violent Scene Detection dataset, a corpus targeting the detection of physical violence in Hollywood movies. We discuss definitions of physical violence and provide a simple and objective definition which was used to annotate a set of 18 movies, thus resulting in the largest freely-available dataset for such a task. We discuss borderline cases and compare with annotations based on a subjective definition which requires multiple annotators. We provide a detailed analysis of the corpus, in particular regarding the relationship between violence and a set of key audio and visual concepts which were also annotated. The VSD dataset results from two years of benchmarking in the framework of the MediaEval initiative. We provide results from the 2011 and 2012 benchmarks as a validation of the dataset and as a state-of-the-art baseline. The VSD dataset is freely available at the address: http://www.technicolor.com/en/innovation/research-innovation/scientific-data-sharing/violent-scenes-dataset..


title: Routing in multi-radio, multi-hop wireless mesh networks

abstract: We present a new metric for routing in multi-radio, multi-hop wireless networks. We focus on wireless networks with stationary nodes, such as community wireless networks.The goal of the metric is to choose a high-throughput path between a source and a destination. Our metric assigns weights to individual links based on the Expected Transmission Time (ETT) of a packet over the link. The ETT is a function of the loss rate and the bandwidth of the link. The individual link weights are combined into a path metric called Weighted Cumulative ETT (WCETT) that explicitly accounts for the interference among links that use the same channel. The WCETT metric is incorporated into a routing protocol that we call Multi-Radio Link-Quality Source Routing.We studied the performance of our metric by implementing it in a wireless testbed consisting of 23 nodes, each equipped with two 802.11 wireless cards. We find that in a multi-radio environment, our metric significantly outperforms previously-proposed routing metrics by making judicious use of the second radio.


title: Deep Learning for Skin Lesion Classification

abstract: Melanoma, a malignant form of skin cancer is very threatening to life. Diagnosis of melanoma at an earlier stage is highly needed as it has a very high cure rate. Benign and malignant forms of skin cancer can be detected by analyzing the lesions present on the surface of the skin using dermoscopic images. In this work, an automated skin lesion detection system has been developed which learns the representation of the image using Google's pretrained CNN model known as Inception-v3 \cite{cnn}. After obtaining the representation vector for our input dermoscopic images we have trained two layer feed forward neural network to classify the images as malignant or benign. The system also classifies the images based on the cause of the cancer either due to melanocytic or non-melanocytic cells using a different neural network. These classification tasks are part of the challenge organized by International Skin Imaging Collaboration (ISIC) 2017. Our system learns to classify the images based on the model built using the training images given in the challenge and the experimental results were evaluated using validation and test sets. Our system has achieved an overall accuracy of 65.8\% for the validation set.


title: TAaaS: Trustworthy Authentication as a Service Based on Trusted Path

abstract: Authentication as a Service (AaaS) provides on-demand delivery of multi-factor authentication (MFA). However, current AaaS has left out of consideration the trustworthiness of user inputs at client evices and the risk of privacy exposure at the AaaS providers. To solve these concerns, we present TAaaS, Trustworthy Authentication as a Service, which offers a trusted path-based MFA service to the service provider in the cloud. TAaaS leverages the hypervisor-based trusted path to ensure the trustworthiness of user inputs, and addresses privacy concerns in the cloud by storing only the irreversible user account information. We implement two end-to-end prototypes and evaluate our work to show its feasibility and security.


title: Scalable Honeypot Architecture for Identifying Malicious Network Activities

abstract: Server honey pots are computer systems that hide in a network capturing attack packets. As the name goes, server honey pots are installed in server machines running a set of services. Enterprises and government organisations deploy these honey pots to know the extent of attacks on their network. Since, most of the recent attacks are advanced persistent attacks there is much research work going on in building better peripheral security measures. In this paper, the authors have deployed several honey pots in a virtualized environment to gather traces of malicious activities. The network infrastructure is resilient and provides much information about hacker's activities. It is cost-effective and can be easily deployed in any organisation without specialized hardware.


title: Development of an efficient fractal based texture analysis technique for improved classification of dermoscopic images

abstract: Dermoscopy, a non-invasive imaging technique has been significantly used by the doctors and radiologists for the early diagnosis of the various skin disorders. The characteristically similar nature of the melanocytic skin lesions specifically melanoma and dysplastic nevi make the diagnosis more subjective and time consuming, even for expert clinicians. Computer aided diagnostic system has a great impact on the notable discrimination of two closely similar classes of skin diseases by extracting a large number of effective features. In this reported work fractal geometry has been used for both skin lesion border irregularity measurement and texture features extraction. Here, in fractal based texture analysis technique the dermoscopic images have been decomposed into a set of binary images to extract more effective texture features from different grey regions of the image. From each of the image grey region, some statistical features have been extracted along with the fractal dimension measurement to quantify the intensity variation in that region. In this paper it has been established that the extraction of texture information from different small sub regions of the original image using fractal texture analysis increases the classification performance for both the classes. An analysis of the dependence of the performance of classifier using fractal based texture analysis, on the number of decomposed binary images, has been discussed. The highest classification sensitivity of 93.75% and 91.66% have been achieved for melanoma and dysplastic nevi respectively, using support vector machine (SVM) classifier by extracting fractal based texture features from forty grey scale regions of the dermoscopic images.


title: Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering

abstract: Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection. To fulfill the goal of jointly learning these two tasks, we propose a novel multi-task learning scheme that utilizes multi-view attention learned from various perspectives to enable these tasks to interact with each other as well as learn more comprehensive sentence representations. The experiments conducted on several real-world datasets demonstrate the effectiveness of the proposed method, and the performance of answer selection and KBQA is improved. Also, the multi-view attention scheme is proved to be effective in assembling attentive information from different representational perspectives.


title: Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training

abstract: Recently, several optimization methods have been successfully applied to the hyperparameter optimization of deep neural networks (DNNs). The methods work by modeling the joint distribution of hyperparameter values and corresponding error. Those methods become less practical when applied to modern DNNs whose training may take a few days and thus one cannot collect sufficient observations to accurately model the distribution. To address this challenging issue, we propose a method that learns to transfer optimal hyperparameter values for a small source dataset to hyperparameter values with comparable performance on a dataset of interest. As opposed to existing transfer learning methods, our proposed method does not use hand-designed features. Instead, it uses surrogates to model the hyperparameter-error distributions of the two datasets and trains a neural network to learn the transfer function. Extensive experiments on three CV benchmark datasets clearly demonstrate the efficiency of our method.


title: Basic Investigation on a Robust and Practical Plant Diagnostic System

abstract: Accurate plant diagnosis requires experts' knowledge but is usually expensive and time consuming. Therefore, it has become necessary to design an accurate, easy, and low-cost automated diagnostic system for plant diseases. In this paper, we propose a new practical plant-disease detection system. We use 7,520 cucumber leaf images comprising images of healthy leaves and those infected by almost all types of viral diseases. The leaves were photographed on site under only one requirement, that is, each image must contain a leaf roughly at its center, thus providing them with a large variety of appearances (i.e., parameters including distance, angle, background, and lighting condition were not uniform). Although half of the images used in this experiment were taken in bad conditions, our classification system based on convolutional neural networks attained an average of 82.3% accuracy under the 4-fold cross validation strategy.


title: Can we fight social engineering attacks by social means? Assessing social salience as a means to improve phish detection

abstract: Phishing continues to be a problem for both individuals and organisations, with billions of dollars lost every year. We propose the use of nudges – more specifically social saliency nudges that aim to highlight important information to the user when evaluating emails. We used a signal detection analysis to assess the effects of both sender saliency (highlighting important fields from the sender) and receiver saliency (showing numbers of other users in receipt of the same email). Sender saliency improved phish detection but did not introduce any unwanted response bias. Users were asked to rate their confidence in their own judgements and these confidence scores were poorly calibrated with actual performance, particularly for phishing (as opposed to genuine) emails. We also examined the role of impulsive behaviour on phish detection, concluding that those who score highly on dysfunctional impulsivity are less likely to detect the presence of phishing emails.


title: Assessing the impact of contemporary urbanization on bioclimatic features of historic architecture through a two-step simulation process

abstract: The aim of this paper is to provide a systematic understanding, through simulation-based assessment, of how contemporary urban planning affects the bioclimatic features of existing historic architecture. An emblematic early 20th century Brazilian building, the Casa das Rosas in Sao Paulo, has been chosen as a case study to see how the deep transformation of its surroundings has altered its indoor conditions. Taking into account both the original and the current urban and environmental conditions, a two-step assessment is conducted by moving between two levels of simulation: the urban- and building-scales. The urban-scale simulations characterize the microclimate parameters (temperature, humidity, and wind speed) that will represent the boundary conditions for the building-scale simulation. EnergyPlus and DIVA-for-Rhino were used to assess the bioclimatic features in terms of indoor thermal and visual comfort levels respectively. Despite the revival of passive design solutions derived from historic architecture, studies of the influence of contemporary urban settlements on their comfort behavior are still quite limited. Outcomes from our simulations show that urban planning can have a significant impact on the indoor light levels of historic buildings, but that the average temperature conditions are not significantly affected. We expect that the results would show a bigger difference if anthropogenic heat sources were taken into account, especially for outdoor comfort conditions.


Comparative Study of the Working Memory Scales of the WISC-IV and SB5 in Referred Students


title: A survey on routing protocols in Wireless Sensor Networks

abstract: This paper presents a survey of state-of-the-art routing techniques in Wireless Sensor Networks (WSNs). Compared with traditional wireless networks, WSNs are characterized with denser levels of node deployment, higher unreliability of sensor nodes and severe power, computation and memory constraints. Various design challenges such as energy efficiency, data delivery models, quality of service, overheads etc., for routing protocols in WSNs are highlighted. We addressed most of the proposed routing methods along with scheme designs, benefits and result analysis wherever possible. The routing protocols discussed are classified into seven categories such as Data centric routing, Hierarchical routing, Location based routing, Negotiation based routing, Multipath based routing, Quality of Service (QoS) routing and Mobility based routing. This paper also compares the routing protocols against parameters such as power consumption, scalability, mobility, optimal routing and data aggregation. The paper concludes with possible open research issues in WSNs.


title: Spotting Fake Reviewer Groups in Consumer Reviews

abstract: Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or demote some target products. For reviews to reflect genuine user experiences and opinions, such spam reviews should be detected. Prior works on opinion spam focused on detecting fake reviews and individual fake reviewers. However, a fake reviewer group (a group of reviewers who work collaboratively to write fake reviews) is even more damaging as they can take total control of the sentiment on the target product due to its size. This paper studies spam detection in the collaborative setting, i.e., to discover fake reviewer groups. The proposed method first uses a frequent itemset mining method to find a set of candidate groups. It then uses several behavioral models derived from the collusion phenomenon among fake reviewers and relation models based on the relationships among groups, individual reviewers, and products they reviewed to detect fake reviewer groups. Additionally, we also built a labeled dataset of fake reviewer groups. Although labeling individual fake reviews and reviewers is very hard, to our surprise labeling fake reviewer groups is much easier. We also note that the proposed technique departs from the traditional supervised learning approach for spam detection because of the inherent nature of our problem which makes the classic supervised learning approach less effective. Experimental results show that the proposed method outperforms multiple strong baselines including the state-of-the-art supervised classification, regression, and learning to rank algorithms.


title: Minimum Flying Capacitor for N-Level Capacitor DC/DC Boost Converter

abstract: This paper proposes a Flying Capacitor DC/DC Boost Converter (FCBC) with a small capacitance of the flying capacitor. As a result the size and weight of the proposed converter can be reduced. The capacitance of the flying capacitor is designed based on switching device voltage rating and regardless of the output voltage ripple influences. Moreover, the achieved maximum efficiency of the designed 3-level FCBC is 98.5% at the output power of 1 kW. Finally, the effectiveness of small capacitances of the flying capacitor in the 3-level and 5-level FCBCs are investigated. The characteristics of the distorted voltage across an input voltage source and an input inductor and the distorted current to the output side which is an output capacitor and a load are investigated with several capacitances of the flying capacitors for 3-level and 5-level FCBCs. As a result, it is experimentally confirmed that the distortion of the voltage across the input voltage source and the input inductor is drastically reduced by increasing the number of level. On the other hand, the distortion of the current to the output side which is the output capacitor and the load is almost same even the number of level is increased. Therefore, the experimental results show that small capacitance of the flying capacitor can be used in the n-level FCBC.


title: BadgerTrap: a tool to instrument x86-64 TLB misses

abstract: The overheads of memory management units (MMUs) have gained importance in today's systems. Detailed simulators may be too slow to gain insights into micro-architectural techniques that improve MMU efficiency. To address this issue, we propose a novel tool, BadgerTrap, which allows online instrumentation of TLB misses. It allows first-order analysis of new hardware techniques to improve MMU efficiency. The tool helps to create and analyze x86-64 TLB miss trace. We describe example studies to show various ways this tool can be applied to gain new research insights.


title: Addressing Limited Data for Textual Entailment Across Domains

abstract: We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains. To that end, we first create (for experimental purposes) an entailment dataset for the clinical domain, and a highly competitive supervised entailment system, ENT, that is effective (out of the box) on two domains. We then explore self-training and active learning strategies to address the lack of labeled data. With self-training, we successfully exploit unlabeled data to improve over ENT by 15% F-score on the newswire domain, and 13% F-score on clinical data. On the other hand, our active learning experiments demonstrate that we can match (and even beat) ENT using only 6.6% of the training data in the clinical domain, and only 5.8% of the training data in the newswire domain.


title: Revisiting congestion control for multipath TCP with shared bottleneck detection

abstract: Multipath TCP (MPTCP) enables the simultaneous use of multiple links for bandwidth aggregation, better resource utilization and improved reliability. Its coupled congestion control intends to reap the increased bandwidth of multiple links, while avoiding being more aggressive than regular TCP flows on every used link. We argue that this leads to a very conservative behavior when paths do not share a bottleneck. Therefore, in this paper, we first quantify the penalty of the coupled congestion control for links that do not share a bottleneck. Then, in order to overcome this penalty, we design and implement a practical shared bottleneck detection (SBD) algorithm for MPTCP, namely MPTCP-SBD. Through extensive emulations, we show that MPTCP-SBD outperforms all currently deployed MPTCP coupled congestion controls by accurately detecting bottlenecks. For the non-shared bottleneck scenario, we observe throughput gains of up to 40% with two subflows and the gains increase significantly as the number of subflows increase, reaching more than 100% for five subflows. Furthermore, for the shared bottleneck scenario, we show that MPTCP-SBD remains fair to TCP. We complement the emulation results with real-network experiments justifying its safeness for deployment.


title: Ontology-supported object and event extraction with a genetic algorithms approach for object classification

abstract: Current solutions are still far from reaching the ultimate goal, namely to enable users to retrieve the desired video clip among massive amounts of visual data in a semantically meaningful manner. With this study we propose a video database model (OVDAM) that provides automatic object, event and concept extraction. By using training sets and expert opinions, low-level feature values for objects and relations between objects are determined. N-Cut image segmentation algorithm is used to determine segments in video keyframes and the genetic algorithm-based classifier is used to make classification of segments (candidate objects) to objects. At the top level ontology of objects, events and concepts are used. Objects and/or events use all these information to generate events and concepts. The system has a reliable video data model, which gives the user the ability to make ontology-supported fuzzy querying. RDF is used to represent metadata. OWL is used to represent ontology and RDQL is used for querying. Queries containing objects, events, spatio-temporal clauses, concepts and low-level features are handled.


title: Development of SD environmental protection type power cable plastic protection tube (bush)

abstract: With the incessant pushing ahead of the retrofit projects of moving the urban electric power cable into underground space,the prevention of the heavy metal pollution problem has gradually become the key point concerned about by the environmental departments. The research, manufacture and development of the power cable plastic protection tube that is endowed with environmental protection behaviours appear especially urgent. It is explained what should be done during the successive parts of the research work such as the definition of the environmental protection type power cable plastic protection tube and its technical requirements, the ingredients of the product, the production technology and forming machinery to meet the requirements of the environmental protection standards. The concerned examination items and behavior parameters are also illustrated. This product reaches the advanced level of the overseas similar products.


title: Envisioning Future Playful Interactive Environments for Animals

abstract: Play stands as one of the most natural and inherent behavior among the majority of living species, specifically humans and animals. Human play has evolved significantly over the years, and so have done the artifacts which allow us to play: from children playing tag games without any tools other than their bodies, to modern video games using haptic and wearable devices to augment the playful experience. However, this ludic revolution has not been the same for the humans’ closest companions, our pets. Recently, a new discipline inside the human–computer interaction (HCI) community, called animal–computer interaction (ACI), has focused its attention on improving animals’ welfare using technology. Several works in the ACI field rely on playful interfaces to mediate this digital communication between animals and humans. Until now, the development of these interfaces only comprises a single goal or activity, and its adaptation to the animals’ needs requires the developers’ intervention. This work analyzes the existing approaches, proposing a more generic and autonomous system aimed at addressing several aspects of animal welfare at a time: Intelligent Playful Environments for Animals. The great potential of these systems is discussed, explaining how incorporating intelligent capabilities within playful environments could allow learning from the animals’ behavior and automatically adapt the game to the animals’ needs and preferences. The engaging playful activities created with these systems could serve different purposes and eventually improve animals’ quality of life.


title: Image Deblurring via Enhanced Low-Rank Prior

abstract: Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.


title: FlowTrApp: An SDN based architecture for DDoS attack detection and mitigation in data centers

abstract: Distributed Denial of Service attack (DDoS) is one of the severe security problems in data centers. In present times, data center operators adopt several hardware based dedicated measures for detection and mitigation of such attacks. It is a challenging task always to detect and mitigate DDoS attacks completely. Software Defined Network (SDN) provides a central control over the network which helps in getting the global view of the network. In this paper, we propose an SDN framework for data centers named FlowTrApp which performs DDoS detection and mitigation using some bounds on two per flow based traffic parameters i.e., flow rate and flow duration of a flow. It attempts to detect attack traffic ranging from low rate to high rate and long lived to short lived attacks using an SDN engine consisting of sFlow based flow analytics engine sFlow-RT and an OpenFlow controller. The proposed framework of FlowTrApp has been implemented in mininet emulator which outperforms an OpenFlow based QoS approach for DoS attack mitigation.


title: Intuitionistic fuzzy soft semigroups

abstract: Maji et al. introduced the concept of intuitionistic fuzzy soft sets, which is an extension to the soft set and intuitionistic fuzzy set. In this paper, we apply the concept of intuitionistic fuzzy soft sets to semigroup theory. The notion of intuitionistic fuzzy soft ideals over a semigroup is introduced and their basic properties are investigated. Some lattice structures of the set of all intuitionistic fuzzy soft ideals of a semigroup are derived. Mathematics Subject Classication: 20N20, 20N15, 20N25


title: Numba: a LLVM-based Python JIT compiler

abstract: Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].


title: Prototypic Force Feedback Instrument for Minimally Invasive Robotic Surgery

abstract: In recent years the success of the daVinci robotic surgery system (Intuitive Surgical Inc., Sunnyvale, CA, USA) has demonstrated the advantages of a telerobotic approach in minimally invasive surgery (MIS). The worldwide need and acceptance of robotic assistance systems for minimally invasive surgery can be seen with more than 600 sold systems worldwide1. Still haptic feedback, important to surgeons who generally rely on the sense of touch in assessing tissue properties, is missing. This is due to the lack of suitable instruments capable of measuring the manipulation forces inside the human body on one hand and the lack of haptic displays for conveying this force information in a comprehensible way to the surgeon on the other hand. In this chapter we present a prototypic force feedback instrument as well as a surgeon workstation as part of a complete setup for minimally invasive robotic surgery (MIRS). The system serves as technology demonstrator showing the feasibility of integrating advanced manipulator technology, haptic feedback and (semi-) autonomous functionality in the context of MIRS. The system will be used to evaluate the impact and benefit of these technologies and hopefully help to improve the acceptance of advanced MIRS. A selection of surgical applications, notably suturing (anastomosis) of coronary vessels while following the motion of the beating heart (motion compensation), provide the requirements in terms of functionality and performance. In a first step, described in this chapter, components are built and the adherence to the required specifications is assured (objective performance measurement). In a future step the impact and benefit on the selected surgical tasks (subjective performance measurement) will be evaluated by defining relevant experiments and performance metrics. Over the course of the project emphasize is given to generic and modular concepts, as acceptance of MIRS technology will be improved by high usability and good integration into the clinical workflow. After a short introduction into MIS and MIRS (Section 1.1 and 1.2) the DLR scenario is introduced in Section 2 followed by a selection of related research in Section 3. Main focus lies on an instrument and a surgeon workstation providing haptic feedback, which are presented in Section 4, together with initial results. The chapter is concluded with a critical review of the contributions (Section 5) and closes with an outlook about future research (Section 6).


title: ImageSense: Towards contextual image advertising

abstract: The daunting volumes of community-contributed media contents on the Internet have become one of the primary sources for online advertising. However, conventional advertising treats image and video advertising as general text advertising by displaying relevant ads based on the contents of the Web page, without considering the inherent characteristics of visual contents. This article presents a contextual advertising system driven by images, which automatically associates relevant ads with an image rather than the entire text in a Web page and seamlessly inserts the ads in the nonintrusive areas within each individual image. The proposed system, called ImageSense, supports scalable advertising of, from root to node, Web sites, pages, and images. In ImageSense, the ads are selected based on not only textual relevance but also visual similarity, so that the ads yield contextual relevance to both the text in the Web page and the image content. The ad insertion positions are detected based on image salience, as well as face and text detection, to minimize intrusiveness to the user. We evaluate ImageSense on a large-scale real-world images and Web pages, and demonstrate the effectiveness of ImageSense for online image advertising.


title: Discrimination of focal and non-focal EEG signals using entropy-based features in EEMD and CEEMDAN domains

abstract: In this paper, a comprehensive analysis for the discrimination of the focal and non-focal electroencephalography (EEG) signals is carried out in the ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) domains. A number of spectral entropy-based features such as the Shannon entropy, log-energy entropy, Renyi entropy and Tsallis entropy are calculated in the EEMD and CEEMDAN domain of the EEG signals. In lieu of using the signals from the EEG channels, the differences between two adjacent EEG channels are used due to its robustness to noise and interference. The ability of the entropy-based features in separating the focal and non-focal EEG signals is explored utilizing the one-way ANOVA analysis and the box-whisker plots. The results reveal that among the entropy measures computed in the EEMD and CEEMDAN domains, the quadratic Renyi entropy and log-energy entropy measures are most promising in discriminating the focal and non-focal EEG signals. The analysis may encourage the researchers to develop improved algorithms to classify these signals and would be helpful in locating the epileptogenic zones.


title: Load Balancing Prioritized Tasks via Work-Stealing

abstract: Work-stealing schedulers focus on minimizing overhead in task scheduling. Consequently, they avoid features, such as task priorities, which can add overhead to the implementation. Thus in such schedulers, low priority tasks may be scheduled earlier, delaying the execution of higher priority tasks and possibly increasing overall execution time.


title: Self‐regulation strategies improve self‐discipline in adolescents: benefits of mental contrasting and implementation intentions

abstract: Adolescents struggle with setting and striving for goals that require sustained self‐discipline. Research on adults indicates that goal commitment is enhanced by mental contrasting (MC), a strategy involving the cognitive elaboration of a desired future with relevant obstacles of present reality. Implementation intentions (II), which identify the action one will take when a goal‐relevant opportunity arises, represent a strategy shown to increase goal attainment when commitment is high. This study tests the effect of mental contrasting combined with implementation intentions (MCII) on successful goal implementation in adolescents. Sixty‐six 2nd‐year high school students preparing to take a high‐stakes exam in the fall of their third year were randomly assigned to complete either a 30‐minute written mental contrasting with implementation intentions intervention or a placebo control writing exercise. Students in the intervention condition completed more than 60% more practice questions than did students in t...


title: 3DTI Amphitheater: Towards 3DTI Broadcasting

abstract: 3DTI Amphitheater is a live broadcasting system for dissemination of 3DTI (3D Tele-immersive) content. The virtual environment constructed by the system mimics an amphitheater in the real world, where performers interact with each other in the central circular stage, and the audience is placed in virtual seats that surround the stage. Users of the Amphitheater can be geographically dispersed and the streams created by the performer sites are disseminated in a P2P network among the participants. To deal with the high bandwidth demand and strict latency bound of the service, we identify the hierarchical priority of streams in construction of the content dissemination forest. Result shows that the Amphitheater outperforms prior 3DTI systems by boosting the application QoS by a factor of 2.8 while sustaining the same hundred-scale audience group.


title: Major requirements for building Smart Homes in Smart Cities based on Internet of Things technologies

abstract: The recent boom in the Internet of Things (IoT) will turn Smart Cities and Smart Homes (SH) from hype to reality. SH is the major building block for Smart Cities and have long been a dream for decades, hobbyists in the late 1970s made Home Automation (HA) possible when personal computers started invading home spaces. While SH can share most of the IoT technologies, there are unique characteristics that make SH special. From the result of a recent research survey on SH and IoT technologies, this paper defines the major requirements for building SH. Seven unique requirement recommendations are defined and classified according to the specific quality of the SH building blocks.


title: Uterus Didelphys with Obstructed Right Hemivagina, Ipsilateral Renal Agenesis and Right Pyocolpos: A Case Report

abstract: Uterus didelphys with obstructed hemivagina and ipsilateral renal agenesis (OHVIRA Syndrome) is a rare congenital anomaly of the female genital tract. Uterus didelphys occurs when the midline fusion of the mullerian ducts is arrested, either completely or incompletely. Women with didelphic uterus may be asymptomatic and unaware of having a double uterus. They may present with complaints of dysmenorrhoea and dyspareunia. A 25 year old woman attending the infertility clinic at Nizwa regional referral hospital presented with history of dysmenorrhoea and foul vaginal discharge with right cystic pelvic mass. She was diagnosed as a case of double uterus with obstructed right hemivagina and right pyocolpos with ipsilateral renal agenesis after routine ultrasonography in the clinic followed by MRI. Excision of the right vaginal septum with drainage of 200 ml of purulent discharge was performed. She was relieved of her symptoms and conceived promptly after the surgical excision of the partial vaginal septum.


title: Investigation on wafer warpage evolution and wafer asymmetric deformation in fan-out wafer level packaging processes

abstract: Fan-out wafer level packaging (FO-WLP) technology has been developed to shrink package size, to accommodate increasing I/O densities and to provide better electrical performance. However, wafer warpage induced during the process poses a threat to the yielding and reliability. In this paper, the warpage evolution during the FO-WLP process was investigated by theoretical calculation and finite element analysis (FEA). The effect of key parameters such as thickness ratio of die to total mold thickness was also studied to optimize the wafer warpage after the molding process. In 12-inch wafer process, the wafer often deforms in an asymmetric behavior such as cylindrical shape and this nonlinear or bifurcation behavior was also investigated by theoretical and FEA methods.


title: Nondestructive sub-surface microwave imaging of biomaterials

abstract: Microwave imaging is an emerging tool for medical diagnostics, potentially offering unique advantages such as the capability of providing quantitative images of the inspected tissues. However, this involves solving a challenging nonlinear and ill-posed electromagnetic inverse scattering problem. In this paper, design, fabrication, and characterization of a near-field microwave scanning probe for sub-surface imaging is presented. The focus is on an application of microwave biomaterials reflectometry imaging. Operating frequency is fixed at 18.5GHz. In order to obtain a 2D image, a network analyzer system monitors changes in the amplitude of the reflection coefficient (S11), quality factor (ΔQ), and resonant frequency (Δf). Results and findings which are presented here, are expected to have strong implications in a wide range of subsurface detection challenges including detection of biological anomalies in human tissue, and etc.


title: Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution

abstract: In the commonly employed regularization models of image restoration and super-resolution (SR), the norm determination is often challenging. This paper proposes a method to adaptively determine the optimal norms for both fidelity term and regularization term in the (SR) restoration model. Inspired by a generalized likelihood ratio test, a piecewise function is proposed to solve the norm of the fidelity term. This function can find the stable norm value in a certain number of iterations, regardless of whether the noise type is Gaussian, impulse, or mixed. For the regularization norm, the main advantage of the proposed method is that it is locally adaptive. Specifically, it assigns different norms for different pixel locations, according to the local activity measured by a structure tensor metric. The proposed method was tested using different types of images. The experimental results and error analyses verify the efficacy of the method.


An Open-Source Octave Toolbox for VTLN-Based Voice Conversion


title: Software Traceability: A Roadmap

abstract: Traceability of software artefacts has been recognised as an important factor for supporting various activities in the software system development process. In general, the objective of traceability is to improve the quality of software systems. More specifically, traceability information can be used to support the analysis of implications and integration of changes that occur software systems; the maintenance and evolution of software systems; the reuse of software system components by identifying and comparing requirements of new and existing systems; the testing of software system components; and system inspection, by indicating alternatives and compromises made during development. Traceability enables system acceptance by allowing users to better understand the system and contributes to a clear and consistent system documentation. Over the last few years, the software and system engineering communities have developed a large number of approaches and techniques to address various aspects of traceability. Research into software traceability has been mainly concerned with the study and definition of different types of traceability relations; support for the generation of traceability relations; development of architectures, tools, and environments for the representation and maintenance of traceability relations; and empirical investigations into organisational practices regarding the establishment and deployment of traceability relations in the software development life cycle. However, despite its importance and the work resulted from numerous years of research, empirical studies of traceability needs and practices in industrial organisations have indicated that traceability support is not always satisfactory. As a result, traceability is rarely established in existing industrial settings. In this article, we present a roadmap of research and practices related to software traceability and identify issues that are still open for further research. Our roadmap is organised according to the main topics that have been the focus of software traceability research.


title: AlterEgo: A Personalized Wearable Silent Speech Interface

abstract: We present a wearable interface that allows a user to silently converse with a computing device without any voice or any discernible movements - thereby enabling the user to communicate with devices, AI assistants, applications or other people in a silent, concealed and seamless manner. A user's intention to speak and internal speech is characterized by neuromuscular signals in internal speech articulators that are captured by the AlterEgo system to reconstruct this speech. We use this to facilitate a natural language user interface, where users can silently communicate in natural language and receive aural output (e.g - bone conduction headphones), thereby enabling a discreet, bi-directional interface with a computing device, and providing a seamless form of intelligence augmentation. The paper describes the architecture, design, implementation and operation of the entire system. We demonstrate robustness of the system through user studies and report 92% median word accuracy levels.


title: Ensemble Named Entity Recognition (NER): Evaluating NER Tools in the Identification of Place Names in Historical Corpora

abstract: The field of Spatial Humanities has advanced substantially in the past years. The identification and extraction of toponyms and spatial information mentioned in historical text collections has allowed its use in innovative ways, making possible the application of spatial analysis and the mapping of these places with geographic information systems. For instance, automated place name identification is possible with Named Entity Recognition (NER) systems. Statistical NER methods based on supervised learning, in particular, are highly successful with modern datasets. However, there are still major challenges to address when dealing with historical corpora. These challenges include language changes over time, spelling variations, transliterations, OCR errors, and sources written in multiple languages among others. In this article, considering a task of place name recognition over two collections of historical correspondence, we report an evaluation of five NER systems and an approach that combines these through a voting system. We found that although individual performance of each NER system was corpus dependent, the ensemble combination was able to achieve consistent measures of precision and recall, outperforming the individual NER systems. In addition, the results showed that these NER systems are not strongly dependent on preprocessing and translation to Modern English.


title: Applying Particle Swarm Optimization to Pairwise Testing

abstract: Combinatorial testing (also called interaction testing) is an effective specification-based test input generation technique. By now most of research work in combinatorial testing aims to propose novel approaches trying to generate test suites with minimum size that still cover all the pairwise, triple, or n-way combinations of factors. Since the difficulty of solving this problem is demonstrated to be NP-hard, existing approaches have been designed to generate optimal or near optimal combinatorial test suites in polynomial time. In this paper, we try to apply particle swarm optimization (PSO), a kind of meta-heuristic search technique, to pairwise testing (i.e. a special case of combinatorial testing aiming to cover all the pairwise combinations). To systematically build pairwise test suites, we propose two different PSO based algorithms. One algorithm is based on one-test-at-a-time strategy and the other is based on IPO-like strategy. In these two different algorithms, we use PSO to complete the construction of a single test. To successfully apply PSO to cover more uncovered pairwise combinations in this construction process, we provide a detailed description on how to formulate the search space, define the fitness function and set some heuristic settings. To verify the effectiveness of our approach, we implement these algorithms and choose some typical inputs. In our empirical study, we analyze the impact factors of our approach and compare our approach to other well-known approaches. Final empirical results show the effectiveness and efficiency of our approach.


title: Bayesian Auxiliary Variable Models for Binary and Multinomial Regression

abstract: A seat assembly that includes a seat bottom, a seat back, and a pivot mount for pivotally connecting the seat back to the seat bottom. The seat assembly further includes a guide member attaching the pivot mount to the vehicle, a leg link pivotally attaching the seat bottom to the vehicle, and an arm link pivotally attaching to the seat back to the vehicle. The leg link, the arm link, and the guide member all cooperate to guide the pivot mount, and hence the seat bottom and the seat back, in a downward and rearward direction from the in-use position to the stored position. Since the seat bottom moves in a downward direction from the in-use position to the stored position, the seat assembly forms a flat, uniform cargo area for increased cargo capabilities. Further, since the seat bottom and the seat back move in a rearward direction from the in-use position to the stored position, the seat assembly may include a longer seat back for increased passenger comfort without an increase in the fold-forward distance of the stored position.


title: The Hamburg Metaphor Database Project: Issues in Resource Creation

abstract: This paper concerns metaphor resource creation. It provides an account of methods used, problems discovered, and insights gained at the Hamburg Metaphor Database project, intended to inform similar resource creation initiatives, as well as future metaphor processing algorithms. After introducing the project, the theoretical underpinnings that motivate the subdivision of represented information into a conceptual and a lexical level are laid out. The acquisition of metaphor attestations from electronic corpora is explained, and annotation practices as well as database contents are evaluated. The paper concludes with an overview of related projects and an outline of possible future work.


title: Nontechnical skills assessment after simulation-based continuing medical education.

abstract: Introduction: Human factors have been identified as root causes of human error in medicine. The “Anesthetists' Non-Technical Skills (ANTS) system” evaluates the effect of simulation training and debriefing on nontechnical skills (NTS). Studies suggest that residents' NTS may improve after simulation training but the effect on NTS of practicing anesthesiologists is unclear. The purpose of this study was to determine whether high-fidelity simulation training and debriefing improved the NTS of practicing anesthesiologists using the ANTS tool. Methods: In a previous study, 67 practicing anesthesiologists managed a 45-minute standardized anesthetic case using high-fidelity simulation and returned 5 to 9 months later to manage a second case. After Research Ethics Board approval, two blinded video reviewers, trained in the use of the ANTS system, evaluated archived videotapes of the 59 subjects who completed both sessions. Results were analyzed with a mixed-design analysis of variance. Interrater reliability was calculated using the intraclass correlation coefficient. Results: Interrater reliability for the ANTS scoring was 0.436, P Conclusions: The relatively short simulation intervention, the length of time until the posttest was completed, well-developed NTS in practicing physicians, and a tool that might not be the optimal method of measurement may all account for the lack of improvement in NTS of practicing anesthesiologists as demonstrated in this study.


title: Understanding leadership in the environmental sciences

abstract: Leadership is often assumed, intuitively, to be an important driver of sustainable development. To understand how leadership is conceptualized and analyzed in the environmental sciences and to discover what this research says about leadership outcomes, we conducted a review of environmental leadership research over the last 10 years. We found that much of the environmental leadership literature focuses on a few key individuals and desirable leadership competencies. The literature also reports that leadership is one of the most important of a number of factors contributing to effective environmental governance. Only a subset of the literature highlights interacting sources of leadership, disaggregates leadership outcomes, or evaluates leadership processes in detail. We argue that the literature on environmental leadership is highly normative. Leadership is typically depicted as an unequivocal good, and its importance is often asserted rather than tested. We trace how leadership studies in the management sciences are evolving and argue that, taking into account the state of the art in environmental leadership research, more critical approaches to leadership research in environmental science can be developed.


title: Map-supervised road detection

abstract: We propose an approach to detect drivable road area in monocular images. It is a self-supervised approach which doesn't require any human road annotations on images to train the road detection algorithm. Our approach reduces human labeling effort and makes training scalable. We combine the best of both supervised and unsupervised methods in our approach. First, we automatically generate training road annotations for images using OpenStreetMap1, vehicle pose estimation sensors, and camera parameters. Next, we train a Convolutional Neural Network (CNN) for road detection using these annotations. We show that we are able to generate reasonably accurate training annotations in KITTI data-set [1]. We achieve state-of-the-art performance among the methods which do not require human annotation effort.


title: A review of the IT outsourcing literature: Insights for practice

abstract: This paper reviews research studies of information technology outsourcing (ITO) practice and provides substantial evidence that researchers have meaningfully and significantly addressed the call for academics to produce knowledge relevant to practitioners. Based on a review of 191 IT outsourcing articles, we extract the insights for practice on six key ITO topics relevant to practitioners. The first three topics relate to the early 1990s focus on determinants of IT outsourcing, IT outsourcing strategy, and mitigating IT outsourcing risks. A focus on best practices and client and supplier capabilities developed from the mid-1990s and is traced through to the late 2000s, while relationship management is shown to be a perennial and challenging issue throughout the nearly 20years under study. More recently studies have developed around offshore outsourcing, business process outsourcing and the rise, decline and resurrection of application service provision. The paper concludes by pointing to future challenges and developments.


Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques


Automatic Syllabification for Manipuri language


title: Internet addiction among Norwegian adults: A stratified probability sample study

abstract: Most Norwegians are Internet users. We conducted a stratified probability sample study (Norway, 2007, age-group 16–74 years, N= 3,399, response rate 35.3%, 87.1% Internet users) to assess the prevalence of Internet addiction and at-risk Internet use by the Young Diagnostic Questionnaire (YDQ). The prevalence of Internet addiction (YDQ score 5–8) was 1.0% and an additional 5.2% were at-risk Internet users (YDQ score 3–4). Internet addiction and at-risk Internet use was strongly dependent on gender and age with highest prevalences among young males (16–29 years 4.1% and 19.0%, 30–39 years 3.3% and 10.7%). Logistic regression showed that male gender, young age, university level education, and an unsatisfactory financial situation were factors positively associated with “problematic Internet use” (at-risk and addicted use combined). Time spent on the Internet and prevalence of self-reported sleeping disorders, depression, and other psychological impairments increased linearly with YDQ score. Problematic Internet use clearly affects the lives of many people.


title: Do humans integrate routes into a cognitive map? Map- versus landmark-based navigation of novel shortcuts

abstract: Do humans integrate experience on specific routes into metric survey knowledge of the environment, or do they depend on a simpler strategy of landmark navigation? The authors tested this question using a novel shortcut paradigm during walking in a virtual environment. The authors find that participants could not take successful shortcuts in a desert world but could do so with dispersed landmarks in a forest. On catch trials, participants were drawn toward the displaced landmarks whether the landmarks were clustered near the target location or along the shortcut route. However, when landmarks appeared unreliable, participants fell back on coarse survey knowledge. Like honeybees (F. C. Dyer, 1991), humans do not appear to derive accurate cognitive maps from path integration to guide navigation but, instead, depend on landmarks when they are available.


title: Evaluation of a pilot project on information and communication technology for rural education development: A Cofimvaba case study on the educational use of tablets

abstract: In an endeavour to find solutions to the country’s improving but ailing education system the South African government through some of its departments is attempting to find out if the introduction of technology in the class room has the potential to improve teaching and learning. The paper is based on a pilot study currently underway in Cofimvaba in Eastern Cape Province where tablets were introduced to teachers in eleven schools. The paper used the Cofimvaba ICT4RED initiative as a case study to ascertain how teachers accepted the introduction of tablets at their schools for teaching and learning. The main research methodology is qualitative multiple case study research with interpretivism as paradigm. Data were gathered though the use of questionnaires administered to teachers. The results showed that most teachers embraced tablets and were using them in the classroom, for their own professional development and personal use. Therefore, within the South African education system the introduction of tablets in learning and teaching is an important step towards improvement of education particularly in poorly resourced schools.


title: CLOCK-Pro: An effective improvement of the clock replacement

abstract: With the ever-growing performance gap between memory systems and disks, and rapidly improving CPU performance, virtual memory (VM) management becomes increasingly important for overall system performance. However, one of its critical components, the page replacement policy, is still dominated by CLOCK, a replacement policy developed almost 40 years ago. While pure LRU has an unaffordable cost in VM, CLOCK simulates the LRU replacement algorithm with a low cost acceptable in VM management. Over the last three decades, the inability of LRU as well as CLOCK to handle weak locality accesses has become increasingly serious, and an effective fix becomes increasingly desirable. ::: ::: Inspired by our I/O buffer cache replacement algorithm, LIRS [13], we propose an improved CLOCK replacement policy, called CLOCK-Pro. By additionally keeping track of a limited number of replaced pages, CLOCK-Pro works in a similar fashion as CLOCK with a VM-affordable cost. Furthermore, it brings all the much-needed performance advantages from LIRS into CLOCK. Measurements from an implementation of CLOCK-Pro in Linux Kernel 2.4.21 show that the execution times of some commonly used programs can be reduced by up to 47%.


title: Dermoscopy findings in tinea capitis: case report and literature review

abstract: Dermoscopy is a method of increasing importance in the diagnoses of cutaneous diseases. On the scalp, dermoscopic aspects have been described in psoriasis, lichen planus, seborrheic dermatitis and discoid lupus. We describe the "comma" and "corkscrew hair" dermoscopic aspects found in a child of skin type 4, with tinea capitis.


title: Optical Character Recognition Using Fuzzy Logic

abstract: Abstract This application note explains the design of an optical character recognition engine called the Optical Character Associator (OCA). Optical character recognition systems must classify optical inputs as specific letters, numbers, or other charcters, and are thus ideal candidates for fuzzy logic implementation. OCA is a classification engine that recognizes the set of 14 characters used by the US banking industry to encode account numbers along the lower edge of cheques. The engine is implemented using an MC68HC11E9 8-bit micro-controller, although it could have been implemented using devices from the M68HC05, M68HC16 or M68300 MCU families.


title: Stopping Criteria in Contrastive Divergence: Alternatives to the Reconstruction Error

abstract: Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used to decide whether the approximation provided by the CD algorithm is good enough, though several authors (Schulz et al., 2010; Fischer & Igel, 2010) have raised doubts concerning the feasibility of this procedure. However, not many alternatives to the reconstruction error have been used in the literature. In this manuscript we investigate simple alternatives to the reconstruction error in order to detect as soon as possible the decrease in the log-likelihood during learning.


title: Data-Driven Vehicle Trajectory Prediction

abstract: Vehicle trajectory or route prediction is useful in online, data-driven transportation simulation to predict future traffic patterns and congestion, among other uses. The various approaches to route prediction have varying degrees of data required to predict future vehicle trajectories. Three approaches to vehicle trajectory prediction, along with extensions, are examined to assess their accuracy on an urban road network. These include an approach based on the intuition that drivers attempt to reduce their travel time, an approach based on neural networks, and an approach based on Markov models. The T-Drive trajectory data set consisting of GPS trajectories of over ten thousand taxicabs and including 15 million data points in Beijing, China is used for this evaluation. These comparisons illustrate that using trajectory data from other vehicles can substantially improve the accuracy of forward trajectory prediction in the T-Drive data set. These results highlight the benefit of exploiting dynamic data to improve the accuracy of transportation simulation predictions.


title: Planning Collision-Free Trajectories for Reversing Multiply-Articulated Vehicles

abstract: A global path-planning strategy is proposed which defines the geometric path of the rear trailer axle of a multiply-articulated vehicle traveling in reverse. This is intended for use in conjunction with a path-tracking reversing controller. A general multiple-trailer vehicle model is derived and used to model a “B-double” heavy vehicle combination, which has two trailers. The curvature properties of a path for the rear trailer axle are investigated, and an empirical relationship is defined between the curvature and the length over which the curvature changes. This is used to generate a library of feasible path segments, and the vehicle model is used to calculate swept paths. A Dijkstra grid search algorithm is then used to connect the path segments together to generate a collision-free composite path. An example path generation is presented for the B-double.


title: Auto Correction Feedback for ripple suppression in a chopper amplifier

abstract: This paper proposes a local feedback, named Auto Correction Feedback (ACFB), used in a chopper amplifier to suppress its offset related ripple. It nulls out amplifier's offset in DC domain which would otherwise become modulated ripple at the chopper amplifier's output, instead of filtering the ripple by a post filter. The proposed chopper amplifier with the ACFB achieves 45dB ripple attenuation without introducing noise penalty, which has been proved by both simulation and measurement.


title: Legalization of Marijuana: Potential Impact on Youth

abstract: This technical report provides historical perspectives and comparisons of various approaches to the legal status of marijuana to aid in forming public policy. Information on the impact that decriminalization and legalization of marijuana could have on adolescents, in addition to concerns surrounding medicinal use of marijuana, are also addressed in this report. Recommendations are included in the accompanying policy statement.


title: Gated Bi-directional CNN for Object Detection

abstract: The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. How to effectively integrate local and contextual visual cues from these regions has become a fundamental problem in object detection. Most existing works simply concatenated features or scores obtained from support regions. In this paper, we proposal a novel gated bi-directional CNN (GBD-Net) to pass messages between features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close iterations are modeled in a much more complex way. It is also shown that message passing is not always helpful depending on individual samples. Gated functions are further introduced to control message transmission and their on-and-off is controlled by extra visual evidence from the input sample. GBD-Net is implemented under the Fast RCNN detection framework. Its effectiveness is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.


title: Anteriorly displaced anus: an under-recognized cause of chronic constipation

abstract: Anterior displacement of a normal anus is recognized as a cause of severe constipation in some children. We have reviewed our experience with 13 children treated for this disorder over the past 8 years at the University of Virginia Health Sciences Center. Twelve of the patients were female and the age at the time of operation ranged between 12 months and 9 years. In all patients constipation began in the first 3 to 6 months of life. Symptoms were characterized by marked straining upon passage of stool, often accompanied by perineal pain. All patients had been unsuccessfully treated with agressive use of stool softeners and cathartics. The anus had a normal appearance, although it was located anterior to its normal position in every case. There was no evidence of anal stenosis on digital examination; in each case a large posterior rectal cul-de-sac could be palpated. A barium enema examination disclosed a prominent posterior shelf of the rectum, often with enormous dilation of the colon posteriorly. All patients underwent surgical treatment using a posterior anoplasty with advancement of the posterior rectal wall. In 10 patients a posterior rectal myomectomy was added to the procedure. Pathologic evaluation of the rectal muscle was normal in each instance. Complete relief of constipation was achieved in all patients, with follow-up extending for as long as 6 years. One patient continues to require occasional use of stool softeners. Anterior displacement of the anus is a frequently overlooked, although easily corrected, cause of severe constipation in childhood.


title: Male nurses and the protection of female patient dignity.

abstract: Nurses need to be aware of their professional, legal and ethical responsibilities towards patients. Male nurses in particular face problems in their practice as a result of their gender and the stereotypes associated with male nurses. Such stereotypes can act as a barrier to their duty of care. This article examines the challenges associated with male nurses carrying out intimate, physical care. It discusses the ethical, legal and professional issues that male nurses should consider in relation to maintenance of patient dignity during nursing care provision, particularly in relation to female patients.


title: 2-Hydroxy-3,4-naphthochalcone (2H-NC) inhibits TNFα-induced tumor invasion through the downregulation of NF-κB-mediated MMP-9 gene expression.

abstract: The control of tumor metastasis is important for the successful prevention and treatment of cancer. Emerging evidence indicates that various natural and synthetic chalcones exhibit antimetastatic activity through the inhibition of nuclear factor-κB (NF-κB), although the precise mechanism by which this occurs is currently unclear. In this study, 2-hydroxy-3,4-naphthochalcone (2H-NC) was found to reduce tumor necrosis factor alpha (TNFα)-induced MMP-9 mRNA expression and gelatinolytic enzyme activity. These actions were associated with inhibition of RelA/p65 NF-κB activity. In addition, 2H-NC inhibited TNFα-induced invasion of MDA-MB-231 breast cancer cells, as assessed using a three-dimensional spheroid invasion assay. Taken together, these data demonstrate that 2H-NC prevents TNFα-induced tumor cell invasion through downregulation of NF-κB-mediated MMP-9 gene expression, and thereby identify naphthochalcones as a potentially effective class of molecules to use as a platform for the development of antimetastatic agents.


title: A novel Multi-objective Modified Honey Bee Mating Optimization algorithm for Economic/Emission Dispatch

abstract: The main goal of Economic Dispatch (ED) in power systems is to distribute the total required generation between the generation units economically, while the equality and inequality constraints are satisfied. The conventional economic dispatch cannot satisfy the environmental protection requirements, since it only considers minimizing the total fuel cost. In this paper, a novel Multi-objective Modified Honey Bee Mating Optimization (MHBMO) algorithm to solve the Multi-objective Economic/Emission Dispatch (MEED) is presented. The MEED problem is formulated as a non-linear constrained multi-objective optimization problem. Therefore, in order to solve this problem, evolutionary methods because of independence on the type of objective function and constraints can be used. Honey Bee Mating Optimization (HBMO) is one of new evolutionary method in which presented by researchers. Original HBMO often converges to local optima, in order to avoid this shortcoming we propose a new method that improve the mating process. The proposed algorithm maintains a finite-sized repository of non-dominated solutions to generate Pareto-optimal set. Since the cost and emission functions are conflicting to each other, a fuzzy clustering technique is used to control the size of the repository. Numerical results for two standard IEEE test systems have been presented to illustrate the performance of the proposed method.


title: Domain Controlled Architecture - A New Approach for Large Scale Software Integrated Automotive Systems

abstract: Electric and electronic functionalities increase exponentially in every mobility domain. The automotive industry is confronted with a rising system complexity and several restricting requirements and standards (like AUTOSAR), in particular to design embedded software for electronic control units. To stand against rampant functionalities software units could be restructured according to their affiliation and should not be attached to a certain place. This can be effected by integration on single controllers. On the one hand the system wide amount of hardware controllers could such be limited. On the other hand the workload for integration CPUs will rise. To support this paradigm, multi-core systems can provide enough processing power in an efficient way. This paper shows a first approach to combine automotive functionality on such a single controller.


Behavioral / Systems / Cognitive Functional Connectivity and Brain Networks in Schizophrenia


title: A 30-MHz isolated push-pull VHF resonant converter

abstract: A new isolated push-pull very high frequency (VHF) resonant DC-DC converter is proposed. The primary side of the converter is a push-pull topology derived from the Class EF2 inverter. The secondary side is a class E based low dv/dt full-wave rectifier. A two-channel multi-stage resonant gate driver is applied to provide two complementary drive signals. The advantages of the converter are as follows: 1) the power isolation is achieved; 2) the MOSFETs and diodes are under soft-switching condition for high efficiency; 3) the voltage stress of the MOSFET is much reduced; 4) the parasitic inductance and capacitance can be absorbed. A 30~36 VDC input, 50-W/ 24-VDC output, 30-MHz prototype has been built to verify the functionality.


title: Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks

abstract: Vehicular ad-hoc networks (VANETs) have drawn much attention of researchers. The vehicles in VANETs frequently join and leave the networks, and therefore restructure the network dynamically and automatically. Forwarded messages in vehicular ad-hoc networks are primarily multimedia data, including structured data, plain text, sound, and video, which require access control with efficient privacy preservation. Ciphertext-policy attribute-based encryption (CP-ABE) is adopted to meet the requirements. However, solutions based on traditional CP-ABE suffer from challenges of the limited computational resources on-board units equipped in the vehicles, especially for the complex policies of encryption and decryption. In this paper, we propose a CP-ABE delegation scheme, which allows road side units (RSUs) to perform most of the computation, for the purpose of improving the decryption efficiency of the vehicles. By using decision tree to jointly optimize multiple factors, such as the distance from RSU, the communication and computational cost, the CP-ABE delegation scheme is adaptively activated based on the estimation of various vehicles decryption overhead. Experimental results thoroughly demonstrate that our scheme is effective and efficient for multimedia data forwarding in vehicular ad-hoc networks with privacy preservation.


title: The Neurometabolic Cascade of Concussion.

abstract: OBJECTIVE: To review the underlying pathophysiologic processes of concussive brain injury and relate these neurometabolic changes to clinical sports-related issues such as injury to the developing brain, overuse injury, and repeated concussion. DATA SOURCES: Over 100 articles from both basic science and clinical medical literature selected for relevance to concussive brain injury, postinjury pathophysiology, and recovery of function. DATA SYNTHESIS: The primary elements of the pathophysiologic cascade following concussive brain injury include abrupt neuronal depolarization, release of excitatory neurotransmitters, ionic shifts, changes in glucose metabolism, altered cerebral blood flow, and impaired axonal function. These alterations can be correlated with periods of postconcussion vulnerability and with neurobehavioral abnormalities. While the time course of these changes is well understood in experimental animal models, it is only beginning to be characterized following human concussion. CONCLUSIONS/RECOMMENDATIONS: Following concussion, cerebral pathophysiology can be adversely affected for days in animals and weeks in humans. Significant changes in cerebral glucose metabolism can exist even in head-injured patients with normal Glasgow Coma Scores, underscoring the need for in-depth clinical assessment in an effort to uncover neurocognitive correlates of altered cerebral physiology. Improved guidelines for clinical management of concussion may be formulated as the functional significance and duration of these postinjury neurometabolic derangements are better delineated.


title: BIRD: binary interpretation using runtime disassembly

abstract: The majority of security vulnerabilities published in the literature is due to software bugs. Many researchers have developed program transformation and analysis techniques to automatically detect or eliminate such vulnerabilities. So far, most of them cannot be applied to commercially distributed applications on the Windows/x86 platform, because it is almost impossible to disassemble a binary file with 100% accuracy and coverage on that platform. This paper presents the design, implementation, and evaluation of a binary analysis and instrumentation infrastructure for the Windows/x86 platform called BIRD (binary interpretation using runtime disassembly), which provides two services to developers of security-enhancing program transformation tools: converting binary code into assembly language instructions for further analysis, and inserting instrumentation code at specific places of a given binary without affecting its execution semantics. Instead of requiring a high-fidelity instruction set architectural emulator, BIRD combines static disassembly with an on-demand dynamic disassembly approach to guarantee that each instruction in a binary file is analyzed or transformed before it is executed. It takes 12 student months to develop the first BIRD prototype, which can successfully work for all applications in Microsoft office suite as well as Internet explorer and IIS Web server, including all DLLs that they use. Moreover, the additional throughput penalty of the BIRD prototype on production server applications such as Apache, IIS, and BIND is uniformly below 4%.


title: Structural Displacement Monitoring Using Smartphone Camera and Digital Image Correlation

abstract: Smartphones, which houses various onboard sensors and a camera, can be leveraged for structural health monitoring. In this paper, an iPhone’s camera coupled with a custom iOS app, D- Viewer , is employed for 3-D structural displacement monitoring. Based on the digital image correlation technique, the camera is used to track a circular target, and the app determines its in-plane, out-of-plane, and arbitrary direction displacements within the field of view. A series of static and dynamic experiments is conducted to validate the proposed technique.


title: Self-Normalizing Neural Networks

abstract: Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations. We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations. While batch normalization requires explicit normalization, neuron activations of SNNs automatically converge towards zero mean and unit variance. The activation function of SNNs are "scaled exponential linear units" (SELUs), which induce self-normalizing properties. Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero mean and unit variance -- even under the presence of noise and perturbations. This convergence property of SNNs allows to (1) train deep networks with many layers, (2) employ strong regularization, and (3) to make learning highly robust. Furthermore, for activations not close to unit variance, we prove an upper and lower bound on the variance, thus, vanishing and exploding gradients are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines. SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set. The winning SNN architectures are often very deep. Implementations are available at: github.com/bioinf-jku/SNNs.


title: Honey clinically stimulates granulation and epithelialization in chronic wounds: a report of two cases

abstract: Various studies have been widely reintroduced honey as a wound dressing in chronic wound treatment. However, its utilization in Indonesia is still very limited. This study reported the effect of honey in stimulating the epithelialization and granulation on chronic wound treatment in our center. Two chronic wound cases, diabetic foot ulcer patient and gangrenous extravasation due to sodium bicarbonate, were daily treated with honey applications. After six-week evaluation, clinically the tissue granulation and epithelization were seen at the edge of the observed wounds and decreased the wound size significantly with no reported side effects. Honey clinically stimulates the epithelialization and granulation on chronic wound treatment.


title: Cognitive science artificial intelligence: Simulating the human mind to achieve goals

abstract: This paper provides a general overview of the interdisciplinary study of cognitive science, specifically the area of the field involving artificial intelligence. In addition, the paper will elaborate on current research for cognitive science artificial intelligence, highlight the importance of this research by providing specific examples of its applications in present society, and briefly discuss future research opportunities for the overlapping fields of cognitive science and artificial intelligence.


AN EXPLORATORY STUDY OF FACTORS INDICATIVE OF AFFECTIVE STATES OF STUDENTS USING SQL-TUTOR


title: Head-and-face shape variations of U.S. civilian workers

abstract: The objective of this study was to quantify head-and-face shape variations of U.S. civilian workers using modern methods of shape analysis. The purpose of this study was based on previously highlighted changes in U.S. civilian worker head-and-face shape over the last few decades – touting the need for new and better fitting respirators – as well as the study's usefulness in designing more effective personal protective equipment (PPE) – specifically in the field of respirator design. The raw scan three-dimensional (3D) data for 1169 subjects were parameterized using geometry processing techniques. This process allowed the individual scans to be put in correspondence with each other in such a way that statistical shape analysis could be performed on a dense set of 3D points. This process also cleaned up the original scan data such that the noise was reduced and holes were filled in. The next step, statistical analysis of the variability of the head-and-face shape in the 3D database, was conducted using Principal Component Analysis (PCA) techniques. Through these analyses, it was shown that the space of the head-and-face shape was spanned by a small number of basis vectors. Less than 50 components explained more than 90% of the variability. Furthermore, the main mode of variations could be visualized through animating the shape changes along the PCA axes with computer software in executable form for Windows XP. The results from this study in turn could feed back into respirator design to achieve safer, more efficient product style and sizing. Future study is needed to determine the overall utility of the point cloud-based approach for the quantification of facial morphology variation and its relationship to respirator performance.


title: Scaling of Aharonov-Bohm couplings and the dynamical vacuum in

abstract: Recent results on the vacuum polarization induced by a thin string of magnetic flux lead us to suggest an analogue of the Copenhagen flux spaghetti'' QCD vacuum as a possible mechanism for avoiding the divergence of perturbative QED, thus permitting a consistent completion of the full, nonperturbative theory. The mechanism appears to operate for spinor, but not scalar, QED.


title: Authentication Protocol Using Quantum Superposition States

abstract: When it became known that quantum computers could break the RSA (named for its creators-Rivest. Shamir, and Adleman) encrypt ion algorithm within a polynomial-time, quantum cryptography began to he actively studied. Other classical cryptographic algorithms are only secure when malicious users do not have sufficient computational power to break security within a practical amount of time. Recently, many quantum authentication protocols sharing quantum entangled particles between commnunicators have been proposed. providing unconditional security. An issue caused by sharing quantum entangled particles is that it may not he simple to apply these protocols to authenticate a specific user in a group of many users. An authentication protocol using quantum superposition states instead of quantum entangled particles is proposed. The random number shared between a sender and a receiver can he used for classical encryption after the authentication has succeeded. The proposed protocol can he implemented with the current technologies we introduce in tins paper.


title: Large engineering project risk management using a Bayesian belief network

abstract: This paper presents a scheme for large engineering project risk management using a Bayesian belief network and applies it to the Korean shipbuilding industry. Twenty-six different risks were deduced from expert interviews and a literature review. A survey analysis was conducted on 252 experts from 11 major Korean shipbuilding companies in April 2007. The overall major risks were design change, design manpower, and raw material supply as internal risks, and exchange rate as external risk in both large-scale and medium-sized shipbuilding companies. Differences of project performance risks between large-scale and medium-sized shipbuilding companies were identified. Exceeding time schedule and specification discontent were more important to large-scale shipbuilding companies, while exceeding budget and exceeding time schedule were more important to medium-sized shipbuilding companies. The change of project performance risks was measured by risk reduction activities of quality management, and strikes at headquarters and subcontractors, in both large-scale and medium-sized shipbuilding companies. The research results should be valuable in enabling industrial participants to manage their large engineering project risks and in extending our understanding of Korean shipbuilding risks.


title: Learning Stereoscopic Visual Attention Model for 3D Video

abstract: Various saliency detection model have been proposed for the visual attention prediction in 2D images/videos. The rapid development of stereoscopic display techniques along with the emerging 3D applications, bring the depth information. Depth is an appealing vision feature since it provides instantaneous depth perception for the scene and can affect human's visual attention. In this paper, we propose to build a model of visual attention based on learning method for stereoscopic videos. The proposed method takes the eye tracking data to build up the ground truth saliency map. The 2D features and the depth information are integrated by the learning method. Experimental results demonstrate that our method is effective and reasonable.


title: Gestalt-based action segmentation for robot task learning

abstract: In programming by demonstration (PbD) systems, the problem of task segmentation and task decomposition has not been addressed with satisfactory attention. In this article we propose a method relying on psychological gestalt theories originally developed for visual perception and apply it to the domain of action segmentation. We propose a computational model for gestalt-based segmentation called competitive layer model (CLM). The CLM relies on features mutually supporting or inhibiting each other to form segments by competition. We analyze how gestalt laws for actions can be learned from human demonstrations and how they can be beneficial to the CLM segmentation method. We validate our approach with two reported experiments on action sequences and present the results obtained from those experiments.


title: Structure guided fusion for depth map inpainting

abstract: Depth acquisition becomes inexpensive after the revolutionary invention of Kinect. For computer vision applications, depth maps captured by Kinect require additional processing to fill up missing parts. However, conventional inpainting methods for color images cannot be applied directly to depth maps as there are not enough cues to make accurate inference about scene structures. In this paper, we propose a novel fusion based inpainting method to improve depth maps. The proposed fusion strategy integrates conventional inpainting with the recently developed non-local filtering scheme. The good balance between depth and color information guarantees an accurate inpainting result. Experimental results show the mean absolute error of the proposed method is about 20mm, which is comparable to the precision of the Kinect sensor.


title: Blockchain Standards for Compliance and Trust

abstract: Blockchain methods are emerging as practical tools for validation, record-keeping, and access control in addition to their early applications in cryptocurrency. This column explores the options for use of blockchains to enhance security, trust, and compliance in a variety of industry settings and explores the current state of blockchain standards.