title: Development of IoT based smart security and monitoring devices for agriculture

abstract: Agriculture sector being the backbone of the Indian economy deserves security. Security not in terms of resources only but also agricultural products needs security and protection at very initial stage, like protection from attacks of rodents or insects, in fields or grain stores. Such challenges should also be taken into consideration. Security systems which are being used now a days are not smart enough to provide real time notification after sensing the problem. The integration of traditional methodology with latest technologies as Internet of Things and Wireless Sensor Networks can lead to agricultural modernization. Keeping this scenario in our mind we have designed, tested and analyzed an 'Internet of Things' based device which is capable of analyzing the sensed information and then transmitting it to the user. This device can be controlled and monitored from remote location and it can be implemented in agricultural fields, grain stores and cold stores for security purpose. This paper is oriented to accentuate the methods to solve such problems like identification of rodents, threats to crops and delivering real time notification based on information analysis and processing without human intervention. In this device, mentioned sensors and electronic devices are integrated using Python scripts. Based on attempted test cases, we were able to achieve success in 84.8% test cases.


title: An Adaptive Edge Router Enabling Internet of Things

abstract: The vision of future networking is that not only people but also all things, services, and media will be connected and integrated, creating an Internet of Everything (IoE). Internet-of-Things (IoT) systems aim to connect and scale billions of devices in various domains such as transportation, industry, smart home/city, medical services, and energy systems. Different wireless and wired technologies link sensors and systems together, through wireless access points, gateways, and routers that in turn connect to the web and cloud-based intelligence. IoT architectures make great demands on network control methods for the efficient management of massive amounts of nodes and data. Therefore, some of the cloud’s management tasks should be distributed around the edges of networked systems, utilizing fog computing to control and manage, e.g., network resources, quality, traffic prioritizations, and security. In this work, we present adaptive edge computing solutions based on regressive admission control (REAC) and fuzzy weighted queueing (FWQ) that monitor and react to network quality-of-service (QoS) changes within heterogeneous networks, and in a vehicular use case scenario utilizing IEEE 802.11p technology. These adaptive solutions are providing more stable network performance and optimizing the network path and resources.


Net!Works European Technology Platform Expert Working Group on Smart Cities Applications and Requirements Chair: List of Contributors Contributors Company/Institute CTTC, ES Carles Fernándes-Prades CTTC, ES EMCANTA, ES SIT Fraunhofer, DE Krzysztof Nyka Pomerania Region ICT Cluster, PL IBBT/VUB, BE


title: Automatic question generation for literature review writing support

abstract: This paper presents a novel Automatic Question Generation (AQG) approach that generates trigger questions as a form of support for students' learning through writing. The approach first automatically extracts citations from students' compositions together with key content elements. Next, the citations are classified using a rule-based approach and questions are generated based on a set of templates and the content elements. A pilot study using the Bystander Turing Test investigated differences in writers' perception between questions generated by our AQG system and humans (Human Tutor, Lecturer, or Generic Question). It is found that the human evaluators have moderate difficulties distinguishing questions generated by the proposed system from those produced by human (F-score=0.43). Moreover, further results show that our system significantly outscores Generic Question on overall quality measures.


title: Pull the Plug? Predicting If Computers or Humans Should Segment Images

abstract: Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need of practical solutions. We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods. The framework is based on a proposed prediction module that estimates the quality of given algorithm-drawn segmentations. We demonstrate the value of the framework for two novel tasks related to "pulling the plug" on computer and human annotators. Specifically, we implement two systems that automatically decide, for a batch of images, when to replace 1) humans with computers to create coarse segmentations required to initialize segmentation tools and 2) computers with humans to create final, fine-grained segmentations. Experiments demonstrate the advantage of relying on a mix of human and computer efforts over relying on either resource alone for segmenting objects in three diverse datasets representing visible, phase contrast microscopy, and fluorescence microscopy images.


Climbing and the Stoic Conception of Freedom


title: Real-coded evolutionary algorithms with parent-centric recombination

abstract: Due to an increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have developed a number of real-parameter genetic algorithms (GAs) in the recent past. In such studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create offspring. Some operators emphasize solutions at the center of mass of parents and some around the parents. We propose a generic parent-centric recombination operator (PCX) and compare its performance with a couple of commonly-used mean-centric recombination operators (UNDX and SPX). With the help of a steady-state, elite-preserving, and computationally fast EA model, simulation results show the superiority of PCX on three test problems.


Introduction to Deep Learning


title: A multi-channel/multi-speaker interactive 3D audio-visual speech corpus in Mandarin

abstract: This paper presents a multi-channel/multi-speaker 3D audio-visual corpus for Mandarin continuous speech recognition and other fields, such as speech visualization and speech synthesis. This corpus consists of 24 speakers with about 18k utterances, about 20 hours in total. For each utterance, the audio streams were recorded by two professional microphones in near-field and far-field respectively, while a marker-based 3D facial motion capturing system with six infrared cameras was used to acquire the 3D video streams. Besides, the corresponding 2D video streams were captured by an additional camera as a supplement. A data process is described in this paper for synchronizing audio and video streams, detecting and correcting outliers, and removing head motions during recording. Finally, results about data process are also discussed. As so far, this corpus is the largest 3D audio-visual corpus for Mandarin.


title: Matrix register file and extended subwords: two techniques for embedded media processors

abstract: In this paper we employ two techniques suitable for embedded media processors. The first technique, extended subwords, uses four extra bits for every byte in a media register. This allows many SIMD operations to be performed without overflow and avoids packing/unpacking conversion overhead because of mismatch between storage and computational formats. The second technique, the Matrix Register File (MRF), allows flexible row-wise as well as column-wise access to the register file. It is useful for many block-based multimedia kernels such as (I)DCT, 2x2 Haar Transform, and pixel padding. In addition, we propose a few new media instructions. We employ Modified MMX (MMMX), MMX with extended subwords, to evaluate these techniques. Our results show that MMMX combined with an MRF reduces the dynamic number of instructions by up to 80% compared to other multimedia extensions such as MMX


title: Illegal, Inappropriate, And Unethical Behavior In An Information Technology Context: A Study To Explain Influences

abstract: A recent study by Banerjee et al. (1998) proposed and tested an information technology (IT) ethics model. They found that personal normative beliefs, organizational ethical climate, and organization-scenario were significant indicators of ethical behavioral intention. Moreover, they found that factors affecting ethical intention are situational and depend upon the ethical dilemma. Further research was suggested and recommended, among other things, replications with different samples. The present study furthers the development/validation of the IT ethical model by utilizing a large sample of students in the same organizational climate (a university).


title: Weighted PageRank algorithm

abstract: With the rapid growth of the Web, users easily get lost in the rich hyper structure. Providing the relevant information to users to cater to their needs is the primary goal of Website owners. Therefore, finding the content of the Web and retrieving the users' interests and needs from their behavior have become increasingly important. Web mining is used to categorize users and pages by analyzing user behavior, the content of the pages, and the order of the URLs that tend to be accessed. Web structure mining plays an important role in this approach. Two page ranking algorithms, HITS and PageRank, are commonly used in Web structure mining. Both algorithms treat all links equally when distributing rank scores. Several algorithms have been developed to improve the performance of these methods. The weighted PageRank algorithm (WPR), an extension to the standard PageRank algorithm, is introduced. WPR takes into account the importance of both the inlinks and the outlinks of the pages and distributes rank scores based on the popularity of the pages. The results of our simulation studies show that WPR performs better than the conventional PageRank algorithm in terms of returning a larger number of relevant pages to a given query.


title: Sleep stage classification using unsupervised feature learning

abstract: Most attempts at training computers for the difficult and time-consuming task of sleep stage classification involve a feature extraction step. Due to the complexity of multimodal sleep data, the size of the feature space can grow to the extent that it is also necessary to include a feature selection step. In this paper, we propose the use of an unsupervised feature learning architecture called deep belief nets (DBNs) and show how to apply it to sleep data in order to eliminate the use of handmade features. Using a postprocessing step of hidden Markov model (HMM) to accurately capture sleep stage switching, we compare our results to a feature-based approach. A study of anomaly detection with the application to home environment data collection is also presented. The results using raw data with a deep architecture, such as the DBN, were comparable to a feature-based approach when validated on clinical datasets.


title: Forecasting of Commercial Sales with Large Scale Gaussian Processes

abstract: This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.


title: Maximum Likelihood Linear Transformations for HMM-Based Speech Recognition

abstract: Abstract This paper examines the application of linear transformations for speaker and environmental adaptation in an HMM-based speech recognition system. In particular, transformations that are trained in a maximum likelihood sense on adaptation data are investigated. Only model-based linear transforms are considered, since, for linear transforms, they subsume the appropriate feature–space transforms. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii) constrained, which requires the variance transform to have the same form as the mean transform. Re-estimation formulae for all appropriate cases of transform are given. This includes a new and efficient full variance transform and the extension of the constrained model–space transform from the simple diagonal case to the full or block–diagonal case. The constrained and unconstrained transforms are evaluated in terms of computational cost, recognition time efficiency, and use for speaker adaptive training. The recognition performance of the two model–space transforms on a large vocabulary speech recognition task using incremental adaptation is investigated. In addition, initial experiments using the constrained model–space transform for speaker adaptive training are detailed.


title: Annotated Control Flow Graph for Metamorphic Malware Detection

abstract: Metamorphism is a technique that mutates the binary code using different obfuscations and never keeps the same sequence of opcodes in the memory. This stealth technique provides the capability to a malware for evading detection by simple signature-based (such as instruction sequences, byte sequences and string signatures) anti-malware programs. In this paper, we present a new scheme named Annotated Control Flow Graph (ACFG) to efficiently detect such kinds of malware. ACFG is built by annotating CFG of a binary program and is used for graph and pattern matching to analyse and detect metamorphic malware. We also optimize the runtime of malware detection through parallelization and ACFG reduction, maintaining the same accuracy (without ACFG reduction) for malware detection. ACFG proposed in this paper: (i) captures the control flow semantics of a program; (ii) provides a faster matching of ACFGs and can handle malware with smaller CFGs, compared with other such techniques, without compromising the accuracy; (iii) contains more information and hence provides more accuracy than a CFG. Experimental evaluation of the proposed scheme using an existing dataset yields malware detection rate of 98.9% and false positive rate of 4.5%.


title: Are We One Hop Away from a Better Internet?

abstract: The Internet suffers from well-known performance, reliability, and security problems. However, proposed improvements have seen little adoption due to the difficulties of Internet-wide deployment. We observe that, instead of trying to solve these problems in the general case, it may be possible to make substantial progress by focusing on solutions tailored to the paths between popular content providers and their clients, which carry a large share of Internet traffic. In this paper, we identify one property of these paths that may provide a foothold for deployable solutions: they are often very short. Our measurements show that Google connects directly to networks hosting more than 60% of end-user prefixes, and that other large content providers have similar connectivity. These direct paths open the possibility of solutions that sidestep the headache of Internet-wide deployability, and we sketch approaches one might take to improve performance and security in this setting.


title: Collaborative job prediction based on Naïve Bayes Classifier using python platform

abstract: The paper aims to implement recommendation system based on collaborative filtering technique for job portals. The system is designed to suggest the jobs to the user depending upon his profile and by calculating a similarity index using Euclidian distance of two skill sets and then ranking them according to their naive Bayes algorithm. The recommendation system has been implemented in python.


title: On Combining Crowdsourcing, Sensing and Open Data for an Accessible Smart City

abstract: This work presents a novel geospatial mapping service, based on OpenStreetMap, which has been designed and developed in order to provide personalized path to users with special needs. This system gathers data related to barriers and facilities of the urban environment via crowd sourcing and sensing done by users. It also considers open data provided by bus operating companies to identify the actual accessibility feature and the real time of arrival at the stops of the buses. The resulting service supports citizens with reduced mobility (users with disabilities and/or elderly people) suggesting urban paths accessible to them and providing information related to travelling time, which are tailored to their abilities to move and to the bus arrival time. The manuscript demonstrates the effectiveness of the approach by means of a case study focusing on the differences between the solutions provided by our system and the ones computed by main stream geospatial mapping services.


title: Customer churn prediction using a hybrid genetic programming approach

abstract: A churn consumer can be defined as a customer who transfers from one service provider to another service provider. Recently, business operators have investigated many techniques that identify the customer churn since churn rates leads to serious business loss. In this paper, a hybrid technique has been used which combines K-means clustering with Genetic Programming to predict churners in telecommunication companies. First, K-means clustering is used to filter the training dataset from outliers and non representative customer behaviors then Genetic Programming is applied in order to build classification trees that are able to classify customers into churners and non churners. The proposed approach is evaluated and compared with other common classification approaches. Experimental results show that K-means clustering with Genetic Programming has promising


title: Multi camera image tracking

abstract: Abstract This paper presents a method for multi-camera image tracking in the context of image surveillance. The approach differs from most methods in that we exploit multiple camera views to resolve object occlusion. Moving objects are detected by using background subtraction. Viewpoint correspondence between the detected objects is then established by using the ground plane homography constraint. The Kalman Filter is then used to facilitate the tracking of objects in 3D. Tracking in 3D offers benefits in terms of allowing multiple views to be combined to generate a network field of view (FOV), i.e. the FOV of all the cameras combined. In addition, tracking the objects location in 3D allows us to use the Linear Kalman Filter, which is less cumbersome to implement than the Extended Kalman Filter (EKF). The current system is capable of tracking moving objects within three camera views


title: Learning Representations Using Complex-Valued Nets

abstract: Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals. It is common in signal processing to transform sequences of real values to the complex domain via a set of complex basis functions, such as the Fourier transform. We show how CVNNs can be used to learn complex representations of real valued time-series data. We present methods and results using a framework that can compose holomorphic and non-holomorphic functions in a multi-layer network using a theoretical result called the Wirtinger derivative. We test our methods on a representation learning task for real-valued signals, recurrent complex-valued networks and their real-valued counterparts. Our results show that recurrent complex-valued networks can perform as well as their real-valued counterparts while learning filters that are representative of the domain of the data.


title: Active Plasmonics: Surface Plasmon Interaction With Optical Emitters

abstract: The interaction between surface plasmons and optical emitters is fundamentally important for engineering applications, especially surface plasmon amplification and controlled spontaneous emission. We investigate these phenomena in an active planar metal-film system comprising InGaN/GaN quantum wells and a silver film. First, we present a detailed study of the propagation and amplification of surface plasmon polaritons (SPPs) at visible frequencies. In doing so, we propose a multiple quantum well structure and present quantum well gain coefficient calculations accounting for SPP polarization, line broadening due to exciton damping, and particularly, the effects of finite temperature. Second, we show that the emission of an optical emitter into various channels (surface plasmons, lossy surface waves, and free radiation) can be precisely controlled by strategically positioning the emitters. Together, these could provide a range of photonic devices (for example, surface plasmon amplifiers, nanolasers, nanoemitters, plasmonic cavities) and a foundation for the study of cavity quantum electrodynamics associated with surface plasmons.


title: Dealing with the evaluation of supervised classification algorithms

abstract: Performance assessment of a learning method related to its prediction ability on independent data is extremely important in supervised classification. This process provides the information to evaluate the quality of a classification model and to choose the most appropriate technique to solve the specific supervised classification problem at hand. This paper aims to review the most important aspects of the evaluation process of supervised classification algorithms. Thus the overall evaluation process is put in perspective to lead the reader to a deep understanding of it. Additionally, different recommendations about their use and limitations as well as a critical view of the reviewed methods are presented according to the specific characteristics of the supervised classification problem scenario.


title: DeepCD: Learning Deep Complementary Descriptors for Patch Representations

abstract: This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for image patch representation by employing deep learning techniques. It can be achieved by taking any descriptor learning architecture for learning a leading descriptor and augmenting the architecture with an additional network stream for learning a complementary descriptor. To enforce the complementary property, a new network layer, called data-dependent modulation (DDM) layer, is introduced for adaptively learning the augmented network stream with the emphasis on the training data that are not well handled by the leading stream. By optimizing the proposed joint loss function with late fusion, the obtained descriptors are complementary to each other and their fusion improves performance. Experiments on several problems and datasets show that the proposed method1 is simple yet effective, outperforming state-of-the-art methods.


title: Machine learning, statistical learning and the future of biological research in psychiatry

abstract: Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.


title: Comparing business competition positions based on Web co-link data: The global market vs. the Chinese market

abstract: Based on the findings from earlier studies which showed that links to business Websites contain useful business information, we examined the feasibility of using Web co-link data to compare business competitive positions. We hypothesized that the number of co-links to a pair of business Websites is a measure of the similarity between the two companies. Since similar or related businesses are competing businesses, the co-link data can be used to map business competitive positions. We selected 32 telecommunications companies for the study and collected co-link data to these companies from Yahoo!. Multidimensional scaling (MDS) analysis on the co-link data correctly mapped these companies into telecommunications industry sectors. This proved our hypothesis and further confirmed the theory that links to business Websites can be objects for Web data mining. We collected data in a way that would reflect two markets, the global market and the Chinese market. Results from the two data sets revealed the competitive positions of the companies in the two markets. We propose that regular data collection and analysis based on this method can be used to monitor the business competitive environment and trigger early warnings on the change of the competitive landscape.


title: Internet Traffic Measurement

abstract: The Internet's evolution over the past 30 years (1971-2001), has been accompanied by the development of various network applications. These applications range from early text-based utilities such as file transfer and remote login to the more recent advent of the Web, electronic commerce, and multimedia streaming. For most users, the Internet is simply a connection to these applications. They are shielded from the details of how the Internet works, through the-information-hiding principles of the Internet protocol stack, which dictates how user-level data is transformed into network packets for transport across the network and put back together for delivery at the receiving application. For many networking researchers however, the protocols themselves are of interest. Using specialized network measurement hardware or software, these researchers collect information about network packet transmissions. With detailed packet-level measurements and some knowledge of the IP stack, they can use reverse engineering to gather significant information about both the application structure and user behavior, which can be applied to a variety of tasks like network troubleshooting, protocol debugging, workload characterization, and performance evaluation and improvement. Traffic measurement technologies have scaled up to provide insight into fundamental behavior properties of the Internet, its protocols, and its users. The author introduces the tools and methods for measuring Internet traffic and offers highlights from research results.


title: A Novel Slotless Halbach-Array Permanent-Magnet Brushless DC Motor for Spacecraft Applications

abstract: This paper presents the design and analysis of a novel machine family-the enclosed-rotor Halbach-array permanent-magnet brushless dc motors for spacecraft applications. The initial design, selection of major parameters, and air-gap magnetic flux density are estimated using the analytical model of the machine. The proportion of the Halbach array in the machine is optimized using finite element analysis to obtain a near-trapezoidal flux pattern. The machine is found to provide uniform air-gap flux density along the radius, thus avoiding circulating currents in stator conductors and thereby reducing torque ripple. Furthermore, the design is validated with experimental results on a fabricated machine and is found to suit the design requirements of critical spacecraft applications.


title: Parallel graph-cuts by adaptive bottom-up merging

abstract: Graph-cuts optimization is prevalent in vision and graphics problems. It is thus of great practical importance to parallelize the graph-cuts optimization using today's ubiquitous multi-core machines. However, the current best serial algorithm by Boykov and Kolmogorov (called the BK algorithm) still has the superior empirical performance. It is non-trivial to parallelize as expensive synchronization overhead easily offsets the advantage of parallelism. In this paper, we propose a novel adaptive bottom-up approach to parallelize the BK algorithm. We first uniformly partition the graph into a number of regularly-shaped disjoint subgraphs and process them in parallel, then we incrementally merge the subgraphs in an adaptive way to obtain the global optimum. The new algorithm has three benefits: 1) it is more cache-friendly within smaller subgraphs; 2) it keeps balanced workloads among computing cores; 3) it causes little overhead and is adaptable to the number of available cores. Extensive experiments in common applications such as 2D/3D image segmentations and 3D surface fitting demonstrate the effectiveness of our approach.


title: A novel approach for dynamic traffic lights management based on Wireless Sensor Networks and multiple fuzzy logic controllers

abstract: HighlightsFlexible, scalable traffic lights dynamic control system for isolated intersections.Multiple parallel fuzzy controllers dynamically manage both the phase and green time.Outperforms other works in terms of vehicle waiting time and balancing between phases.Very effective under heavy traffic and when phases have unbalanced arrival rates.Lightweight effective implementable on COTS devices: broad practical impact expected. This paper proposes a novel approach to dynamically manage the traffic lights cycles and phases in an isolated intersection. The target of the work is a system that, comparing with previous solutions, offers improved performance, is flexible and can be implemented on off-the-shelf components. The challenge here is to find an effective design that achieves the target while avoiding complex and computationally expensive solutions, which would not be appropriate for the problem at hand and would impair the practical applicability of the approach in real scenarios. The proposed solution is a traffic lights dynamic control system that combines an IEEE 802.15.4 Wireless Sensor Network (WSN) for real-time traffic monitoring with multiple fuzzy logic controllers, one for each phase, that work in parallel. Each fuzzy controller addresses vehicles turning movements and dynamically manages both the phase and the green time of traffic lights. The proposed system combines the advantages of the WSN, such as easy deployment and maintenance, flexibility, low cost, noninvasiveness, and scalability, with the benefits of using four parallel fuzzy controllers, i.e., better performance, fault-tolerance, and support for phase-specific management. Simulation results show that the proposed system outperforms other solutions in the literature, significantly reducing the vehicles waiting times. A proof-of-concept implementation on an off-the-shelf device proves that the proposed controller does not require powerful hardware and can be easily implemented on a low-cost device, thus paving the way for extensive usage in practice.


title: Road-Condition Recognition Using 24-GHz Automotive Radar

abstract: This paper studies 24-GHz automotive radar technology for detecting low-friction spots caused by water, ice, or snow on asphalt. The backscattering properties of asphalt in different conditions are studied in both laboratory and field experiments. In addition, the effect of water on the backscattering properties of asphalt is studied with a surface scattering model. The results suggest that low-friction spots could be detected with a radar by comparing backscattered signals at different polarizations. The requirements for the radar are considered, and a 24-GHz radar for road-condition recognition is found to be feasible.


title: A Realistic Dataset for Performance Evaluation of Document Layout Analysis

abstract: There is a significant need for a realistic dataset on which to evaluate layout analysis methods and examine their performance in detail. This paper presents a new dataset (and the methodology used to create it) based on a wide range of contemporary documents. Strong emphasis is placed on comprehensive and detailed representation of both complex and simple layouts, and on colour originals. In-depth information is recorded both at the page and region level. Ground truth is efficiently created using a new semi-automated tool and stored in a new comprehensive XML representation, the PAGE format. The dataset can be browsed and searched via a web-based front end to the underlying database and suitable subsets (relevant to specific evaluation goals) can be selected and downloaded.


title: Prescribed performance control for signal temporal logic specifications

abstract: Motivated by the recent interest in formal methods-based control for dynamic robots, we discuss the applicability of prescribed performance control to nonlinear systems subject to signal temporal logic specifications. Prescribed performance control imposes a desired transient behavior on the system trajectories that is leveraged to satisfy atomic signal temporal logic specifications. A hybrid control strategy is then used to satisfy a finite set of these atomic specifications. Simulations of a multi-agent system, using consensus dynamics, show that a wide range of specifications, i.e., formation, sequencing, and dispersion, can be robustly satisfied.


title: Inversion for seismic anisotropy using genetic algorithms

abstract: Seismic anisotropy is a complicated non-linear phenomenon in which quasi compressional(qP) and two quasi shear waves(qS1 and qS2) propagate in most directions isolated from singularity effects (Crampin 1991). Problems arise in inverting shear wave data due to the presente of singularities and the deviation of group from phase velocities so that linearization approaches are invalid (Chapman and Pratt 1992). Genetic Algorithms (GA) are non-linear optimization methods which are receiving increasing attention in the geophysics community (Sen & Stoffa 1991, Sambridge & Drijkoningen 1992) due to their robustness, efficiency and wide range of applicability. Just as simulated annealing is analogous to crystal annealing so are GA's analogous to the evolutionary processes of reproduction, crossover and mutation in nature. GA's use a population of models which are combined in such a way that more successful parameter combinations receive an increasing sampling rate. Previous attempts to invert shear wave data by MacBeth (1991) used a systematic search of a database of anisotropic materials. This approach is computationally intensive and is restricted by the precalculated database size. GA's appear to offeramore flexible and efficient anisotropic roversion scheme.


title: Unsupervised Learning from Narrated Instruction Videos

abstract: We address the problem of automatically learning the main steps to complete a certain task, such as changing a car tire, from a set of narrated instruction videos. The contributions of this paper are three-fold. First, we develop a new unsupervised learning approach that takes advantage of the complementary nature of the input video and the associated narration. The method solves two clustering problems, one in text and one in video, applied one after each other and linked by joint constraints to obtain a single coherent sequence of steps in both modalities. Second, we collect and annotate a new challenging dataset of real-world instruction videos from the Internet. The dataset contains about 800,000 frames for five different tasks that include complex interactions between people and objects, and are captured in a variety of indoor and outdoor settings. Third, we experimentally demonstrate that the proposed method can automatically discover, in an unsupervised manner, the main steps to achieve the task and locate the steps in the input videos.


title: AirCloud: a cloud-based air-quality monitoring system for everyone

abstract: We present the design, implementation, and evaluation of AirCloud -- a novel client-cloud system for pervasive and personal air-quality monitoring at low cost. At the frontend, we create two types of Internet-connected particulate matter (PM2:5) monitors -- AQM and miniAQM, with carefully designed mechanical structures for optimal air-flow. On the cloud-side, we create an air-quality analytics engine that learn and create models of air-quality based on a fusion of sensor data. This engine is used to calibrate AQMs and mini-AQMs in real-time, and infer PM2:5 concentrations. We evaluate AirCloud using 5 months of data and 2 month of continuous deployment, and show that AirCloud is able to achieve good accuracies at much lower cost than previous solutions. We also show three real applications built on top of AirCloud by 3rd party developers to further demonstrate the value of our system.


title: Operational semantics of UML activity diagram: An application in project management

abstract: With its recent adoption by the International Organization for Standardization, we foresee that UML will be systematically used for object-oriented modeling in industry. UML activity diagrams have been typically used to model software and business processes. Due to its semi-formal semantics and high complexity, its advanced constructs such as expansion regions, interruptible regions, object nodes, time events, and compound activities are rarely used in practice. There has been significant work on formalizing UML activity diagrams in terms of its semantic domain: Petri net. However, none address the recent advanced constructs it offers. In this paper, we define the semantics of UML activity diagram using a rule-based model transformation. Verification and validation of the UML activity diagram model is then achieved by simulating and analyzing the Petri net model. We illustrate our technique by using an extension of UML activity diagram to facilitate project management tasks such as scheduling, cost estimation, and resource allocation.


title: Automatic reading of domestic electric meter: an intelligent device based on image processing and ZigBee/Ethernet communication

abstract: In undeveloped areas around the world, many traditional meters need to be upgraded. Compared with replacing the mounted meters with high-cost modern ones, it is a better choice to upgrade them with new technologies. In this paper, an automatic reading system of the traditional household meter is designed on the basis of image processing and advanced DSP system. To identify the meter reading accurately, a regional average method is proposed to implement the image-scaling in order to avoid the distortion. In the image-filtering process, we raise an average-product method which is verified to attain good effects. For image segmentation, a new union thresholding method, based on the grayscale transformation, is proposed to enhance the adaptability of uneven luminance. Iterative rejection is applied to decrease the errors during character localization. Then, a training sample library with 1,400 characters is designed and collected for the training of the BP neural network. For data transmission, NAT technology is introduced to build data connection between the remote server and the data collectors working in the local area network. According to the field test, the proposed system can obtain a recognition rate of 99.7 % under normal environment, with the identification period below 2 s, while the resulting data can be transferred reliably through 1---3 walls in ordinary buildings.


title: Random Register Renaming to Foil DPA

abstract: Techniques such as DPA and SPA can be used to find the secret keys stored in smart-cards. These techniques have caused concern for they can allow people to recharge their stored value smartcards (in effect printing money), or illegally use phone or digital TV services. We propose an addition to current processors which will counteract these techniques. By randomising register usage, we can hide the secret key stored in a smartcard. The extension we propose can be added to existing processors, and is transparent to the algorithm.


title: Predicting the Popularity of GitHub Repositories

abstract: GitHub is the largest source code repository in the world. It provides a git-based source code management platform and also many features inspired by social networks. For example, GitHub users can show appreciation to projects by adding stars to them. Therefore, the number of stars of a repository is a direct measure of its popularity. In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories. These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market. In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months. Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars. Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories. We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).


title: Virtual Community Success: A Uses and Gratifications Perspective

abstract: In this study we report the findings of a survey on a virtual community of knowledge and interest for end-users. We approach a community participant both as a member and a user, who is also a virtual value initiator for other members. The central issue raised in this study is: what factors motivate a member to participate in a virtual community of knowledge? We utilize uses and gratifications theoretical framework which is grounded in the functionalist and communication research paradigm to develop scales to be empirically tested in the virtual community environment. We use member need satisfaction as a proxy measure for virtual community success. With the help of a community organizer, we posted our survey to develop the constructs and test the relationship with user satisfaction in a virtual community.


title: Metamorphic Testing: A Review of Challenges and Opportunities

abstract: Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.


Making strategy: the journey of strategic management


Active Road Noise Cancelation—Off-the-Shelf Digital Audio Amplifier Platform


How can we investigate citation behavior? A study of reasons for citing literature in communication


title: A novel method of fingerprint minutiae extraction based on Gabor phase

abstract: In this paper, a novel method of fingerprint minutiae extraction on grey-scale images is proposed based on the Gabor phase field. The novelty of our approach is that the proposed algorithm is performed on the transform domain, i.e. the Gabor phase field of the fingerprint image. This is different from most existing minutiae extraction methods, in which the minutiae are usually extracted from the binarized and thinned fingerprint image. Experimental results on benchmark data sets demonstrate that the proposed algorithm has promising performances.


title: Sympathetic ANS modulation of pupil diameter in emotional scene perception: Effects of hedonic content, brightness, and contrast

abstract: A series of studies investigated the effects of hedonic content, brightness, and contrast on pupil diameter during free viewing of natural scenes, assessing the amplitude of the initial light reflex and subsequent sustained pupil diameter change. Hedonic picture content varied from highly arousing scenes of erotica and violence to scenes depicting nature, babies, loss, contamination, food, and more. Despite equivalent overall picture brightness and contrast, pupil diameter still varied as a function of the local brightness of central vision at fixation. Statistical (Experiment 1) and methodological (Experiment 2, 3) solutions produced complementary data indicating that scenes of erotica and violence reliably attenuate the amplitude of the initial light reflex and prompt enhanced late diameter pupil changes, compared to other scene contents. A principal components analysis supported the hypothesis that a single sympathetically mediated process enhances pupil dilation during picture viewing, modulating both initial constriction and late diameter changes. Rather than being a subtle index of “liking,” pupil diameter is primarily sensitive to events that reliably elicit measurable sympathetic nervous system activity.


title: Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets

abstract: Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, which scales up to thousands of variables. Through extensive experiments we show that it consistently yields higher-scoring structures than its competitors on complete data sets. We then consider the problem of structure learning from incomplete data sets. This can be addressed by structural EM, which however is computationally very demanding. We thus adopt the novel k-MAX algorithm in the maximization step of structural EM, obtaining an efficient computation of the expected sufficient statistics. We test the resulting structural EM method on the task of imputing missing data, comparing it against the state-of-the-art approach based on random forests. Our approach achieves the same imputation accuracy of the competitors, but in about one tenth of the time. Furthermore we show that it has worst-case complexity linear in the input size, and that it is easily parallelizable. We present a novel anytime algorithm (k-MAX) method for learning a Bayesian networks with bounded treewidth.k-MAX is able to scale up to datasets with thousands of variables.k-MAX consistently yields higher-scoring structures than its competitors on complete data sets.We present a novel algorithm (SEM-kMAX) method for imputing missing data.SEM-kMAX achieves the same imputation accuracy of the state-of-the-art approach, but in about one tenth of the time.


title: Learning Real-World Robot Policies by Dreaming

abstract: Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take significant time. In this paper, we focus on learning a realistic world model capturing the dynamics of scene changes conditioned on robot actions. Our dreaming model can emulate samples equivalent to a sequence of images from the actual environment, technically by learning an action-conditioned future representation/scene regressor. This allows the agent to learn action policies (i.e., visuomotor policies) by interacting with the dreaming model rather than the real-world. We experimentally confirm that our dreaming model enables robot learning of policies that transfer to the real-world.


title: Etiquette, empathy and trust in communities of practice: Stepping stones to social capital

abstract: Creating online communities of practice involves much more than creating software. Software houses online communities of practice activities but social interactions also depend on who is involved, what their goals are, their personalities and the community's norms and policies. By paying attention to these sociability issues, community members can influence how their community develops. Norms that lead to good online etiquette are a stepping-stone to social capital.


title: Building sustainable parking lots with the Web of Things

abstract: Peak-time traffic woes create considerable amount of stress and environmental pollution resulting in an economic loss. Research innovations in areas such as the Web of Things are able to curtail some of these issues by creating scalable and sustainable environments like parking lots, which provide motorists with access to convenient parking spots. We present a scalable parking lot network infrastructure that exposes parking management operations through a judicious mashup of physical things' services within a parking lot. Our system uses service-oriented architecture, allowing motorists to reserve parking spots in advance. In doing so, our proposed system leverages the use of HTTP and Wi-Fi for the Web enablement and interoperability of things within a parking spot and elevates it as a Smart Parking Spot on the Web. Our suggested semantic Web-based structure for representing things makes it possible to query physical things' states and services depending on their capabilities and other relevant parking-related parameters. Our performance evaluation reveals that a maximum of 40 % time is saved to find parking spots and also 40 % reduction in air pollution is observed.


title: Model Predictive Control of Buoy Type Wave Energy Converter

abstract: Abstract The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible. The straight forward solution to this maximization problem is achieved by maximizing the instantaneous range of motion of the buoy. The buoy as a single degree of freedom oscillator will undergo its maximum movements when it is in resonance with the sea state. Hence the best solution to the problem is achieved by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement. This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller.


title: A Three-Phase Current-Fed Push–Pull DC–DC Converter

abstract: In this paper, a new three-phase current-fed push-pull DC-DC converter is proposed. This converter uses a high-frequency three-phase transformer that provides galvanic isolation between the power source and the load. The three active switches are connected to the same reference, which simplifies the gate drive circuitry. Reduction of the input current ripple and the output voltage ripple is achieved by means of an inductor and a capacitor, whose volumes are smaller than in equivalent single-phase topologies. The three-phase DC-DC conversion also helps in loss distribution, allowing the use of lower cost switches. These characteristics make this converter suitable for applications where low-voltage power sources are used and the associated currents are high, such as in fuel cells, photovoltaic arrays, and batteries. The theoretical analysis, a simplified design example, and the experimental results for a 1-kW prototype will be presented for two operation regions. The prototype was designed for a switching frequency of 40 kHz, an input voltage of 120 V, and an output voltage of 400 V.


title: A review of presented mathematical models in Parkinson’s disease: black- and gray-box models

abstract: Parkinson’s disease (PD), one of the most common movement disorders, is caused by damage to the central nervous system. Despite all of the studies on PD, the formation mechanism of its symptoms remained unknown. It is still not obvious why damage only to the substantia nigra pars compacta, a small part of the brain, causes a wide range of symptoms. Moreover, the causes of brain damages remain to be fully elucidated. Exact understanding of the brain function seems to be impossible. On the other hand, some engineering tools are trying to understand the behavior and performance of complex systems. Modeling is one of the most important tools in this regard. Developing quantitative models for this disease has begun in recent decades. They are very effective not only in better understanding of the disease, offering new therapies, and its prediction and control, but also in its early diagnosis. Modeling studies include two main groups: black-box models and gray-box models. Generally, in the black-box modeling, regardless of the system information, the symptom is only considered as the output. Such models, besides the quantitative analysis studies, increase our knowledge of the disorders behavior and the disease symptoms. The gray-box models consider the involved structures in the symptoms appearance as well as the final disease symptoms. These models can effectively save time and be cost-effective for the researchers and help them select appropriate treatment mechanisms among all possible options. In this review paper, first, efforts are made to investigate some studies on PD quantitative analysis. Then, PD quantitative models will be reviewed. Finally, the results of using such models are presented to some extent.


title: Cephalic Positioning of the Lateral Crura

abstract: Objective To apply a mathematical model to determine the relative effectiveness of various tip-plasty maneuvers while the lateral crura are in cephalic position compared with orthotopic position. Methods A Matlab (MathWorks, Natick, Massachusetts) computer program, called the Tip-Plasty Simulator, was developed to model the medial and lateral crura of the tripod concept in order to estimate the change in projection, rotation, and nasal length yielded by changes in crural length. The following rhinoplasty techniques were modeled in the software program: columellar strut graft/tongue-in-groove, lateral crural steal, lateral crural overlay, medial/intermediate crural overlay, hinge release with alar strut graft, and lateral crural repositioning. Results Using the Tip-Plasty Simulator, the directionality of the change in projection, rotation, and nasal length produced by the various tip-plasty maneuvers, as shown by our mathematical model, is largely the same as that expected and observed clinically. Notably, cephalically positioned lateral crura affected the results of the rhinoplasty maneuvers studied. Conclusions By demonstrating a difference in the magnitude of change resulting from various rhinoplasty maneuvers, the results of this study enhance the ability of the rhinoplasty surgeon to predict the effects of various tip-plasty maneuvers, given the variable range in alar cartilage orientation that he or she is likely to encounter.


title: Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks

abstract: Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language—a set of logical rules that we call confidence rules —and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural–symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.


title: Analysis of PROFIBUS-DP network delay and its influence on the performance of control systems

abstract: This paper analyzes PROFIBUS-DP network delay in detail and presents the calculational formula of its maximum time-delay, which is significant to the research of PROFIBUS-DP. At the same time, the paper puts forward a method of simplifying the network-induced time-varying indeterminate system according to the features of the network. Through the analysis of a pump-control-motor system which is composed of PROFIBUS-DP network, it illustrates the network’s influence on the performance of control systems. This method helps to design and analyze the network’s influence on the performance of control systems, which is of considerable practical value in a time when network control system is widely used.


Introduction to Algorithms, 3rd Edition


title: Safety helmet wearing detection based on image processing and machine learning

abstract: Safety helmet wearing detection is very essential in power substation. This paper proposed a innovative and practical safety helmet wearing detection method based on image processing and machine learning. At first, the ViBe background modelling algorithm is exploited to detect motion object under a view of fix surveillant camera in power substation. After obtaining the motion region of interest, the Histogram of Oriented Gradient (HOG) feature is extracted to describe inner human. And then, based on the result of HOG feature extraction, the Support Vector Machine (SVM) is trained to classify pedestrians. Finally, the safety helmet detection will be implemented by color feature recognition. Compelling experimental results demonstrated the correctness and effectiveness of our proposed method.


title: The adoption of crowdsourcing platforms in South Africa

abstract: Crowdsourcing has increasingly become a popular phenomenon where organisations solicit the help of the public to accomplish activities that are usually performed by employees. These activities can range from scientific problems to menial tasks that are sometimes too mundane for employees. A lot of organisations in different countries have adopted and embraced this phenomenon with gusto. South Africa being the African continent's most developed economy is also embracing crowdsourcing. However, the adoption of crowdsourcing initiatives has been slow, especially amongst public organisations, due to various reasons, such as lack of awareness. This research paper reports on the investigation conducted pertaining to the adoption of crowdsourcing platforms in South Africa. The primary research methods used for the study included a systematic literature review and document analysis. The results from the study suggest that the most prominent crowdsourcing platforms in South Africa deal with funding. However, there is still a lack of information pertaining to the status and number of users benefiting from the adopted and/or deployed platforms.


title: Systematic variation in gene expression patterns in human cancer cell lines

abstract: We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.


title: Machine Teaching: A New Paradigm for Building Machine Learning Systems

abstract: The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in order to meet this growing demand for machine learning systems we must significantly increase the number of individuals that can teach machines. We postulate that we can achieve this goal by making the process of teaching machines easy, fast and above all, universally accessible. ::: While machine learning focuses on creating new algorithms and improving the accuracy of "learners", the machine teaching discipline focuses on the efficacy of the "teachers". Machine teaching as a discipline is a paradigm shift that follows and extends principles of software engineering and programming languages. We put a strong emphasis on the teacher and the teacher's interaction with data, as well as crucial components such as techniques and design principles of interaction and visualization. ::: In this paper, we present our position regarding the discipline of machine teaching and articulate fundamental machine teaching principles. We also describe how, by decoupling knowledge about machine learning algorithms from the process of teaching, we can accelerate innovation and empower millions of new uses for machine learning models.


title: Recognizing indoor scenes

abstract: Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g, bookstores) are better characterized by the objects they contain. More generally, to address the indoor scenes recognition problem we need a model that can exploit local and global discriminative information. In this paper we propose a prototype based model that can successfully combine both sources of information. To test our approach we created a dataset of 67 indoor scenes categories (the largest available) covering a wide range of domains. The results show that our approach can significantly outperform a state of the art classifier for the task.


title: Speech enhancement and recognition using multi-task learning of long short-term memory recurrent neural networks

abstract: Long Short-Term Memory (LSTM) recurrent neural network has ::: proven effective in modeling speech and has achieved outstanding ::: performance in both speech enhancement (SE) and automatic ::: speech recognition (ASR). To further improve the performance of ::: noise-robust speech recognition, a combination of speech enhancement ::: and recognition was shown to be promising in earlier work. ::: This paper aims to explore options for consistent integration of SE ::: and ASR using LSTM networks. Since SE and ASR have different ::: objective criteria, it is not clear what kind of integration would ::: finally lead to the best word error rate for noise-robust ASR tasks. ::: In this work, several integration architectures are proposed and ::: tested, including: (1) a pipeline architecture of LSTM-based SE and ::: ASR with sequence training, (2) an alternating estimation architecture, ::: and (3) a multi-task hybrid LSTM network architecture. ::: The proposed models were evaluated on the 2nd CHiME speech ::: separation and recognition challenge task, and show significant ::: improvements relative to prior results.


title: I Am 'Totally' Human: Bypassing the reCaptcha

abstract: In recent years, website administrators have searched for a tool to prevent automated “bots” from using their sites' resources, hindering legitimate user access. By offering a “challenge” in the form of analyzing a picture for certain features, matching like symbols from a selection, or listening to an audio clip and transcribing the text, these tasks are intended to be easy for humans to solve but impossible for a program to overcome. More recently, there have been many successful attempts at using modern technology to automate passing these challenges. In this paper, we propose a simple approach which makes use of open-source tools (reCaptcha Widget, Selenium – a proxy server pool and a public Speech to Text API) to circumvent the reCaptcha tool utilized by Google. Preliminary results demonstrate an approximately 35% success rate which can be easily improved with minor modification. However, the focal point is the simplicity of the proposed approach to highlight the vulnerability of Google's reCaptcha tool against Speech to Text API attack.


title: A practical tool for maximal information coefficient analysis

abstract: Background ::: The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual information-based measures of association are particularly promising, in particular after the recent introduction of the TICe and MICe estimators, which combine computational efficiency with superior bias/variance properties. An open-source software implementation of these two measures providing a complete procedure to test their significance would be extremely useful. ::: ::: ::: Findings ::: Here, we present MICtools, a comprehensive and effective pipeline that combines TICe and MICe into a multistep procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. ::: ::: ::: Conclusions ::: We show that MICtools, combining TICe and MICe, is able to highlight associations that would not be captured by conventional strategies.


STICK-BREAKING VARIATIONAL AUTOENCODERS


title: Design of the EROS trusted window system

abstract: Window systems are the primary mediator of user input and output in modern computing systems. They are also a commonly used interprocess communication mechanism. As a result, they play a key role in the enforcement of security policies and the protection of sensitive information. A user typing a password or passphrase must be assured that it is disclosed exclusively to the intended program. In highly secure systems, global policies concerning information flow restrictions must be honored. Most window systems today, including X11 and Microsoft Windows, have carried forward the presumptive trust assumptions of the Xerox Alto from which they were conceptually derived. These assumptions are inappropriate for modern computing environments. ::: ::: In this paper, we present the design of a new trusted window system for the EROS capability-based operating system. The EROS Window System (EWS) provides robust traceability of user volition and is capable (with extension) of enforcing mandatory access controls. The entire implementation of EWS is less than 4,500 lines, which is a factor of ten smaller than previous trusted window systems such as Trusted X, and well within the range of what can feasibly be evaluated for high assurance.


title: An Improved Soft-Switching Buck Converter With Coupled Inductor

abstract: This letter presents a novel topology for a buck dc–dc converter with soft-switching capability, which operates under a zero-current-switching condition at turn on and a zero-voltage-switching condition at turn off. In order to realize soft switching, based on a basic buck converter, the proposed converter added a small inductor, a diode, and an inductor coupled with the main inductor. Because of soft switching, the proposed converter can obtain a high efficiency under heavy load conditions. Moreover, a high efficiency is also achieved under light load conditions, which is significantly different from other soft-switching buck converters. The detailed theoretical analyses of steady-state operation modes are presented, and the detailed design methods and some simulation results are also given. Finally, a 600 W prototype is built to validate the theoretical principles. The switching waveforms and the efficiencies are also measured to validate the proposed topology.


title: Reconfigurable Antennas for Wireless and Space Applications

abstract: Reconfigurable antennas, with the ability to radiate more than one pattern at different frequencies and polarizations, are necessary in modern telecommunication systems. The requirements for increased functionality (e.g., direction finding, beam steering, radar, control, and command) within a confined volume place a greater burden on today's transmitting and receiving systems. Reconfigurable antennas are a solution to this problem. This paper discusses the different reconfigurable components that can be used in an antenna to modify its structure and function. These reconfiguration techniques are either based on the integration of radio-frequency microelectromechanical systems (RF-MEMS), PIN diodes, varactors, photoconductive elements, or on the physical alteration of the antenna radiating structure, or on the use of smart materials such as ferrites and liquid crystals. Various activation mechanisms that can be used in each different reconfigurable implementation to achieve optimum performance are presented and discussed. Several examples of reconfigurable antennas for both terrestrial and space applications are highlighted, such as cognitive radio, multiple-input-multiple-output (MIMO) systems, and satellite communication.


BEYOND CONTEXTUALIZATION : USING CONTEXT THEORIES TO NARROW THE MICRO-MACRO GAP


title: Online and Offline Domain Adaptation for Reducing BCI Calibration Effort

abstract: Many real-world brain–computer interface (BCI) applications rely on single-trial classification of event-related potentials (ERPs) in EEG signals. However, because different subjects have different neural responses to even the same stimulus, it is very difficult to build a generic ERP classifier whose parameters fit all subjects. The classifier needs to be calibrated for each individual subject, using some labeled subject-specific data. This paper proposes both online and offline weighted adaptation regularization (wAR) algorithms to reduce this calibration effort, i.e., to minimize the amount of labeled subject-specific EEG data required in BCI calibration, and hence to increase the utility of the BCI system. We demonstrate using a visually evoked potential oddball task and three different EEG headsets that both online and offline wAR algorithms significantly outperform several other algorithms. Moreover, through source domain selection, we can reduce their computational cost by about $\text{50}\%$ , making them more suitable for real-time applications.


title: Drug addiction: Pathways to the disease and pathophysiological perspectives

abstract: Drug addiction is a medical condition, a chronic relapsing disease. As in other domains of experimental medicine, appropriate experimental investigations are needed in order to better understand the disease. However, to understand the diverse facets of drug effects and of the underlying pathophysiology it is necessary to keep in mind the complexity of the psychopathological processes. The main symptoms that characterize addiction correspond to expressions of dysfunctions within specific circuits and regions. Pathways to addiction are numerous and comorbidity and in the real world poly-drug use are common. Some of these aspects will be examined as well as the role of life events and stress. Theoretical considerations will be proposed [see also: Koob, G.F., & Le Moal, M.. 2005a. Neurobiology of Addiction. Elsevier. 570 pp] to account for the stages of the disease from impulse control disorder to compulsive disorders, for affective dynamics and for the relations between the symptoms and pathophysiology.


title: A Hybrid Approach for Credit Card Fraud Detection using Rough Set and Decision Tree Technique

abstract: To make the business accessible to a large number of customers worldwide, many companies small and big have put up their presence on the internet. Online businesses gave birth to e-commerce platforms which in turn use digital modes of transaction such as credit-card, debit card etc. This kind of digital transaction attracted millions of users to transact on the internet. Along came the risk of online credit card frauds. Hence the need to have secure payment transactions arose and many techniques based on Neural Network, Decision Tree, Artificial Intelligence, Artificial Immune System, Fuzzy based systems, Nearest neighbor algorithm, Support Vector Machines, Genetic Algorithm were developed to detect the fraudulent online credit card transactions. This paper presents hybrid Approach for Credit Card Fraud detection using Rough Set and Decision Tree Technique which can be used in credit card fraud detection mechanisms. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.


Rapid k-d Tree Construction for Sparse Volume Data


title: AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy

abstract: Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: 1) a new encoding scheme to allow auto-segmentation on whole-volume CT images instead of local patches or subsets of slices, 2) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and 3) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep-learning-based HaN segmentation: a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and b) training with inconsistent data annotations with missing ground truth for some anatomical structures. ::: Results: We collected 261 HaN CT images to train AnatomyNet, and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 seconds to fully segment a head and neck CT image of dimension 178 x 302 x 225, significantly faster than previous methods. In addition, the model is able to process whole-volume CT images and delineate all OARs in one pass, requiring little pre- or post-processing. this https URL.


title: Identification of power distribution network topology via voltage correlation analysis

abstract: We consider the problem of reconstructing the topology of a portion of the power distribution network, given a dataset of voltage measurements. By using an approximate model for the grid voltage magnitudes, we show that these signals exhibit some specific correlation properties, that can be described via a sparse Markov random field. By specializing the tools available for the identification of graphical models, we propose an algorithm for the reconstruction of the grid topology. Via simulations, we show how the algorithm performs well also when an exact nonlinear model of the grid voltages is adopted, when realistic power demand profiles are considered, and when the voltage measurements are affected by measurement noise.


title: Blood pressure estimation from photoplethysmogram using latent parameters

abstract: Non-invasive cuff-less Blood Pressure (BP) estimation from Photoplethysmogram (PPG) is a well known challenge in the field of affordable healthcare. This paper presents a set of improvements over an existing method that estimates BP using 2-element Windkessel model from PPG signal. A noisy PPG corpus is collected using fingertip pulse oximeter, from two different locations in India. Exhaustive pre-processing techniques, such as filtering, baseline and topline correction are performed on the noisy PPG signals, followed by the selection of consistent cycles. Subsequently, the most relevant PPG features and demographic features are selected through Maximal Information Coefficient (MIC) score for learning the latent parameters controlling BP. Experimental results reveal that overall error in estimating BP lies within 10% of a commercially available digital BP monitoring device. Also, use of alternative latent parameters that incorporate the variation in cardiac output, shows a better trend following for abnormally low and high BP.


abstract: In this study, we present Dice's coefficient on trigram profiles as metric for language similarity. As testbed, we focused on eight Philippine languages. No known language similarity value for these languages exists. Documents containing transcribed audio recordings, news articles, religious and literary texts were taken from an online corpus and used as training data. Character trigram profiles were then generated using an n-gram generator and language similarity was computed. The results were matched against those reported in the literature and against the language family tree. To evaluate the metric, it was applied to five languages with known similarity values. The results were then compared with an existing lexical similarity metric. The average difference is 27%. Analyses of the results reveal that phonetic spelling play an important role in language similarity. As future work, the metric can be used on phonetic transcriptions.


title: Rapid breeding of parthenocarpic tomato plants using CRISPR/Cas9

abstract: Parthenocarpy in horticultural crop plants is an important trait with agricultural value for various industrial purposes as well as direct eating quality. Here, we demonstrate a breeding strategy to generate parthenocarpic tomato plants using the CRISPR/Cas9 system. We optimized the CRISPR/Cas9 system to introduce somatic mutations effectively into SlIAA9—a key gene controlling parthenocarpy—with mutation rates of up to 100% in the T0 generation. Furthermore, analysis of off-target mutations using deep sequencing indicated that our customized gRNAs induced no additional mutations in the host genome. Regenerated mutants exhibited morphological changes in leaf shape and seedless fruit—a characteristic of parthenocarpic tomato. And the segregated next generation (T1) also showed a severe phenotype associated with the homozygous mutated genome. The system developed here could be applied to produce parthenocarpic tomato in a wide variety of cultivars, as well as other major horticultural crops, using this precise and rapid breeding technique.


title: Switched-capacitor DC-DC converters for low-power on-chip applications

abstract: The paper describes switched-capacitor DC-DC power converters (charge pumps) suitable for on-chip, low-power applications. The proposed configurations are based on connecting two identical but opposite-phase SC converters in parallel, thus eliminating the need for separate bootstrap gate drivers. The authors focus on emerging very low-power VLSI applications such as battery-powered or self-powered signal processors where high power conversion efficiency is important and where power levels are in the milliwatt range. Conduction and switching losses are considered to allow design optimization in terms of switching frequency and component sizes. Open-loop and closed-loop operation of an experimental, fully integrated, 10 MHz voltage doubler is described. The doubler has 2 V or 3 V input and generates 3.3 V or 5 V output at up to 5 mW load. The converter circuit fabricated in a standard 1.2 /spl mu/ CMOS technology takes 0.7 mm/sup 2/ of the chip area.


A New Indoor Positioning System Architecture Using GPS Signals


title: In Your Right Mind: Right Hemisphere Contributions to Language Processing and Production

abstract: The verbal/nonverbal account of left and right hemisphere functionality is the prevailing dichotomy describing the cerebral lateralization of function. Yet the fact that the left hemisphere is the superior language processor does not necessarily imply that the right hemisphere is completely lacking linguistic ability. This paper reviews the growing body of research demonstrating that, far from being nonverbal, the right hemisphere has significant language processing strength. From prosodic and paralinguistic aspects of speech production, reception, and interpretation, to prelexical, lexical and postlexical components of visual word recognition; strong involvement of the right hemisphere is implicated. The evidence reviewed challenges the notion that language is solely a function of the “verbal” left hemisphere, indicating that the right cerebral hemisphere makes significant and meaningful contributions to normal language processing as well.


title: Securing balise-based train control systems using cryptographic random fountains

abstract: In modern train control systems, a moving train retrieves its location information through passive transponders called balises, which are placed on the sleepers of the track at regular intervals. When the train-borne antenna energizes them using tele-powering signals, balises backscatter preprogrammed telegrams, which carry information about the train's current location. Since the telegrams are static in the existing implementations, the uplink signals from the balises could be recorded by an adversary and then replayed at a different location of the track, leading to what is well-known as the replay attack. Such an attack, while the legitimate balise is still functional, introduces ambiguity to the train about its location, can impact the physical operations of the trains. For balise-to-train communication, we propose a new communication framework referred to as cryptographic random fountains (CRF), where each balise, instead of transmitting telegrams with fixed information, transmits telegrams containing random signals. A salient feature of CRF is the use of challenge-response based interaction between the train and the balise for communication integrity. We present a thorough security analysis of CRF to showcase its ability to mitigate sophisticated replay attacks. Finally, we also discuss the implementation aspects of our framework.


title: Factors Influencing Successful Achievement in Contrasting Design and Technology Activities in Higher Education.

abstract: The aim of the study was to investigate the relationship between such factors as learning style, gender, prior experience, and successful achievement in contrasting modules taken by a cohort of thirty design and technology trainee teachers during their degree programme at a University in the North East of England. Achievement data were collected from three design and three electronic modules at levels 1, 2 and 3. Data concerning appropriate, previous experience before starting the course was obtained through a short questionnaire. The learning style of each member of the sample was ascertained using the Cognitive Style Analysis test. The findings from the study indicated that the learning style groupings were not as expected. A positive relationship between achievement and past experience in both electronics and design activity was found, although improvement for those with no prior experience in comparison to those with previous experience was only evident in electronics. A concern arising out of the data was the differences in terms of achievement between male and female students and also the difference in achievement when learning style and gender were scrutinised. The implications of the findings in relation to the success of the trainees as impending teachers of design and technology were discussed. The problems associated with the small cell size caused by splitting the sample by the three variables was acknowledged and a suggestion was made that further study would be required to ascertain whether the gender and learning style differences witnessed in this study would be replicated in a larger sample.


title: Attention-based Deep Multiple Instance Learning

abstract: Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.


FORECASTING IONOSPHERIC TOTAL ELECTRON CONTENT MAPS WITH DEEP NEURAL NETWORKS


title: Depthwise Convolution is All You Need for Learning Multiple Visual Domains

abstract: There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.


title: Estimation of Tire Slip Angle and Friction Limits Using Steering Torque

abstract: Knowledge of the vehicle's lateral handling limits is important for vehicle control systems which aim to enhance vehicle handling and passenger safety. This paper presents a model-based estimation method that utilizes pneumatic trail information in steering torque to identify a vehicle's lateral handling limits, which are defined by the tire slip angle and peak lateral force limits. This method uses measurements available on many current production vehicles. Most importantly, it takes advantage of the early friction information encoded in the tire pneumatic trail. Pneumatic trail decreases as a function of the tire parameters even in the linear handling region, enabling early detection of the limits before they are reached. Experimental results on an independent front steering steer-by-wire research vehicle demonstrate the observer's ability to provide accurate real-time estimates of tire slip angle up to the limits of handling. Testing conditions include maneuvers performed on dry, flat paved road, as well as on lower-friction, dry gravel.


title: Optic-Flow Based Control of a 46g Quadrotor

abstract: We aim at developing autonomous miniature hov- ering flying robots capable of navigating in unstructured GPS- denied environments. A major challenge is the miniaturization of the embedded sensors and processors allowing such platforms to fly autonomously. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in real- time on a microcontroller. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional distance sensors or assumptions about the environment. Key to this method is the introduction of the translational optic-flow direction constraint (TOFDC), which does not use the optic-flow scale, but only its direction to correct for inertial sensor drift during changes of direction. This solution requires comparatively much simpler electronics and sensors and works in environments of any geometries. We demonstrate the implementation of this algorithm on a miniature 46g quadrotor for closed-loop position control.


title: Sensory substitution and the human-machine interface

abstract: Abstract Recent advances in the instrumentation technology of sensory substitution have presented new opportunities to develop systems for compensation of sensory loss. In sensory substitution (e.g. of sight or vestibular function), information from an artificial receptor is coupled to the brain via a human–machine interface. The brain is able to use this information in place of that usually transmitted from an intact sense organ. Both auditory and tactile systems show promise for practical sensory substitution interface sites. This research provides experimental tools for examining brain plasticity and has implications for perceptual and cognition studies more generally.


title: Comparative Study of Soft Motion for Motion Copying System with Environmental Variations

abstract: Technologies for the skill succession and machine automation are strongly demanded in some developed countries under the circumstance of declining working population due to the lower birth rate and the aged society. Motion copying system (MCS) is one of the methods for the skill succession and machine automation. MCS is able to play back the saved motion in the case that the environmental conditions between the saving and the loading phases are the same. Conventionally, the pure position controller, the pure force controller, or Hybrid Position and Force Control (HPFC) is applied to MCS. The reproduction performance is, however, degraded when the environmental variations happen. In such case, impedance control is applicable to MCS instead of HPFC. The purpose of this paper is to conduct the comparative study of some control strategies: position control, force control, HPFC, the Position-Based Impedance Control (PBIC), and the Force-Based Impedance Control (FBIC), and clarify which control strategy is appropriate for the tasks. Simulations were conducted for supporting the argument.


title: Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging

abstract: This paper is to explore applications of the blockchain technology to the concept of “proof of X” such as proof of identity, proof of property ownership, proof of specific transaction, proof of college degree, proof of medical records, proof of academic achievements, etc. It describes a novel approach of building a decentralized transparent immutable secure personal archive management and service system. Personal archive is defined as a collection of various artifacts that reflect personal portfolio as well as personal unique identifications. Personal portfolio is beyond of a statement of personal achievement. It is an evidentiary document designed to provide qualitative and quantitative chronically documents and examples. Subjects can tag their information with proof, that is, certified by trusted entities or organizations like universities. Such proofs are associated with confidentiality levels exposed in the public domains. Personal identifications include biometrics as well as other multi-factors such as something the subject “has”, the subject “knows” or the subject “acts”. Stack holders in a consortium oriented blockchain network serve as verifiers and /or miners that provide their trusted services the delegated proof of stake. Such personal archive based system can be exploited to various applications including professional network like Linkedin, instant credit approval like alipay or live human from social bots like internet social media. A prototype simulation shows that such personal portfolio management and service system is feasible and immune to many ID attacks.


title: Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks

abstract: The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks. Current approaches parallelize training onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network. Although easy to reason about, these approaches result in suboptimal runtime performance in large-scale distributed training, since different layers in a network may prefer different parallelization strategies. In this paper, we propose layer-wise parallelism that allows each layer in a network to use an individual parallelization strategy. We jointly optimize how each layer is parallelized by solving a graph search problem. Our evaluation shows that layer-wise parallelism outperforms state-of-the-art approaches by increasing training throughput, reducing communication costs, achieving better scalability to multiple GPUs, while maintaining original network accuracy.


title: Bioelectrical impedance as a discriminator of pressure ulcer risk.

abstract: In an effort to identify objective measures of tissue structure and function associated with pressure ulcer risk, bioelectrical impedance analysis (BIA) was measured in three progressively larger zones over the trochanter and coccyx in patients assessed at high risk for pressure ulcers (HR group) and age-matched normal controls (CONT group). Local BIA measures were significantly lower in the HR group for each individual site and zone (p < .01). Full-thickness tissue biopsies obtained from each measurement zone did not differ between groups. However, additional nutritional and metabolic parameters were significantly different (p < or = .05). These results suggest that local BIA may be useful in assessing pressure ulcer risk.