title: Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling

abstract: The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-the-art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-the-art, with a performance gain of 27.4%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.


title: Hybrid Memristor-CMOS (MeMOS) based Logic Gates and Adder Circuits

abstract: Practical memristor came into picture just few years back and instantly became the topic of interest for researchers and scientists. Memristor is the fourth basic two-terminal passive circuit element apart from well known resistor, capacitor and inductor. Recently, memristor based architectures has been proposed by many researchers. In this paper, we have designed a hybrid Memristor-CMOS (MeMOS) logic based adder circuit that can be used in numerous logic computational architectures. We have also analyzed the transient response of logic gates designed using MeMOS logic circuits. MeMOS use CMOS 180 nm process with memristor to compute boolean logic operations. Various parameters including speed, ares, delay and power dissipation are computed and compared with standard CMOS 180 nm logic design. The proposed logic shows better area utilization and excellent results from existing CMOS logic circuits at standard 1.8 V operating voltage.


THE COMBINATION OF HMAX AND HOGS IN AN ATTENTION GUIDED FRAMEWORK FOR OBJECT LOCALIZATION


title: How to read a paper: Statistics for the non-statistician. I: Different types of data need different statistical tests

abstract: As medicine leans increasingly on mathematics no clinician can afford to leave the statistical aspects of a paper to the “experts.” If you are numerate, try the “Basic Statistics for Clinicians” series in the Canadian Medical Association Journal 1 2 3 4 or a more mainstream statistical textbook.5 If, on the other hand, you find statistics impossibly difficult, this article and the next in this series give a checklist of preliminary questions to help you appraise the statistical validity of a paper. ::: ::: ### Have they determined whether their groups are comparable, and, if necessary, adjusted for baseline differences? ::: ::: Most comparative clinical trials include either a table or a paragraph in the text showing the baseline characteristics of the groups being studied. Such a table should show that the intervention and control groups are similar in terms of age and sex distribution and key prognostic variables (such as the average size of a cancerous lump). Important differences in these characteristics, even if due to chance, can pose a challenge to your interpretation of results. In this situation, adjustments can be made to allow for these differences and hence strengthen the argument.6 ::: ::: #### Summary points ::: ::: In assessing the choice of statistical tests in a paper, first consider whether groups were analysed for their comparability at baseline ::: ::: Does the test chosen reflect the type of data analysed (parametric or non-parametric, paired or unpaired)? ::: ::: Has a two tailed test been performed whenever the effect of an intervention could conceivably be a negative one? ::: ::: Have the data been analysed according to the original study protocol? ::: ::: If obscure tests have been used, do the authors justify their choice and provide a reference? ::: ::: ### What sort of data have they got, and have they used appropriate statistical tests? ::: ::: Numbers are often used to label the properties of things. We can assign a number to represent our height, weight, and so on. For properties like these, the measurements can be treated as actual numbers. We can, for example, calculate the …


title: Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

abstract: Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer. For most users, however, using them as tools can be a challenging task due to their unpredictable behavior that goes against common intuitions. This paper introduces a novel concept to augment such generative architectures with semantic annotations, either by manually authoring pixel labels or using existing solutions for semantic segmentation. The result is a content-aware generative algorithm that offers meaningful control over the outcome. Thus, we increase the quality of images generated by avoiding common glitches, make the results look significantly more plausible, and extend the functional range of these algorithms---whether for portraits or landscapes, etc. Applications include semantic style transfer and turning doodles with few colors into masterful paintings!


title: Correlation between resistor characteristics and cohesive property of carbon black in resin

abstract: Carbon black (CB)-reson composites have been studied through resistivity parameters (resistivity), ( rho ), temperature coefficient of resistance (TCR), a parameter of the dielectric relaxation time, and equivalent circuit parameters (R/sup s/, R/sup c/, C/sup c/) using two kinds of CBs. High-structure CB results in smaller rho and TCR than does low-structure CB, suggesting that the former CB easily forms a mechanically stronger aggregation of CB structures, leading to more conductive paths bridging electrodes. There is a difference between the dielectric relaxation types on the Cole-Cole plot. The high-structure CB shows a Cole-Cole type relaxation, whereas the low-structure CB shows a Davidson-Cole one. The parameters giving a measure of the distribution of the relaxation time increase with loading. It is noted that branches of conductive paths become uniform with higher loading. Though the difference of C/sup c/ between the two CBs is not clear, the high-structure CB shows smaller R/sup s/ and R/sup c/ than the low-structure one at a given loading of CB. >


title: A modified two-section UWB Wilkinson power divider

abstract: A novel Wilkinson power divider with modification of the traditional two-section Wilkinson power divider by adding one open stub on each branch is proposed. This design method is validated by building a divider working in the UWB band (3.1 GHz - 10.6 GHz). It is better in performance, but is about 10% smaller than the traditional three-section power divider and spares one resistor. The simulation and measurement results of the modified divider show equal power split, a good insertion loss of less than 1.1 dB, a good return loss of all ports of less than - 10 dB, and a good isolation is less than -11 dB over the UWB band.


title: Blockchain: Solving the privacy and research availability tradeoff for EHR data: A new disruptive technology in health data management

abstract: A blockchain powered Health information ecosystem can solve a frequently discussed problem of the lifelong recorded patient health data, which seriously could hurdle the privacy of the patients and the growing data hunger of the research and policy maker institutions. On one side the general availability of the data is vital in emergency situations and supports heavily the different research, population health management and development activities, on the other side using the same data can lead to serious social and ethical problems caused by malicious actors. Currently, the regulation of the privacy data varies all over the world, however underlying principles are always defensive and protective towards patient privacy against general availability. The protective principles cause a defensive, data hiding attitude of the health system developers to avoid breaching the overall law regulations. It makes the policy makers and different — primarily drug — developers to find ways to treat data such a way that lead to ethical and political debates. In our paper we introduce how the blockchain technology can help solving the problem of secure data storing and ensuring data availability at the same time. We use the basic principles of the American HIPAA regulation, which defines the public availability criteria of health data, however the different local regulations may differ significantly. Blockchain's decentralized, intermediary-free, cryptographically secured attributes offer a new way of storing patient data securely and at the same time publicly available in a regulated way, where a well-designed distributed peer-to-peer network incentivize the smooth operation of a full-featured EHR system.


title: S-Surge: Novel Portable Surgical Robot with Multiaxis Force-Sensing Capability for Minimally Invasive Surgery

abstract: To achieve a compact and lightweight surgical robot with force-sensing capability, in this paper, we propose a surgical robot called “S-surge,” which is developed for robot-assisted minimally invasive surgery, focusing mainly on its mechanical design and force-sensing system. The robot consists of a 4-degree-of-freedom (DOF) surgical instrument and a 3-DOF remote center-of-motion manipulator. The manipulator is designed by adopting a double-parallelogram mechanism and spherical parallel mechanism to provide advantages such as compactness, simplicity, improved accuracy, and high stiffness. Kinematic analysis was performed in order to optimize workspace. The surgical instrument enables multiaxis force sensing including a three-axis pulling force and single-axis grasping force. In this study, it will be verified that it is feasible to carry the entire robot around thanks to its light weight (4.7 kg); therefore, allowing the robot to be applicable for telesurgery in remote areas. Finally, it will be explained how we experimented with the performance of the robot and conducted tissue manipulating task using the motion and force sensing capability of the robot in a simulated surgical setting.


title: Detecting sim box fraud using neural network

abstract: One of the most severe threats to revenue and quality of service in telecom providers is fraud. The advent of new technologies has provided fraudsters new techniques to commit fraud. SIM box fraud is one of such fraud that has emerged with the use of VOIP technologies. In this work, a total of nine features found to be useful in identifying SIM box fraud subscriber are derived from the attributes of the Customer Database Record (CDR). Artificial Neural Networks (ANN) has shown promising solutions in classification problems due to their generalization capabilities. Therefore, supervised learning method was applied using Multi layer perceptron (MLP) as a classifier. Dataset obtained from real mobile communication company was used for the experiments. ANN had shown classification accuracy of 98.71 %.


title: Fast-Write Resistive RAM (RRAM) for Embedded Applications

abstract: Especially for microcontroller and mobile applications, embedded nonvolatile memory is an important technology offering to reduce power and provide local persistent storage. This article describes a new resistive RAM device with fast write operation to improve the speed of embedded nonvolatile memories.


title: Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems

abstract: Classification in imbalanced domains is a recent challenge in data mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. One of the most used techniques to tackle this problem consists in preprocessing the data previously to the learning process. This preprocessing could be done through under-sampling; removing examples, mainly belonging to the majority class; and over-sampling, by means of replicating or generating new minority examples. In this paper, we propose an under-sampling procedure guided by evolutionary algorithms to perform a training set selection for enhancing the decision trees obtained by the C4.5 algorithm and the rule sets obtained by PART rule induction algorithm. The proposal has been compared with other under-sampling and over-sampling techniques and the results indicate that the new approach is very competitive in terms of accuracy when comparing with over-sampling and it outperforms standard under-sampling. Moreover, the obtained models are smaller in terms of number of leaves or rules generated and they can considered more interpretable. The results have been contrasted through non-parametric statistical tests over multiple data sets.


title: Data-driven light field depth estimation using deep Convolutional Neural Networks

abstract: This paper presents a data-driven approach to estimate the object depths from light field data using Convolutional Neural Networks (CNN). By exploring the relationship between the epipolar-plane images (EPI) and the corresponding depth map, we propose an enhanced EPI feature that encodes the depth information of each physical point in the light field and obtains the disparity map of the whole scene in a supervised manner. This work covers two major contributions, namely the extraction of the enhanced EPI features and the light field depth estimation with CNN. The proposed features augment the depth information of the corresponding points in the light field, and then our CNN architecture differentiates them into different depth layers. Forward propagation step of the CNN model allows rapid recognition of the disparity map of the test light field data. In the experiments, we apply our method on the HCI (Heidelberg Col-laboratory for Image Processing) benchmark dataset and demonstrate that it is significantly faster than the state-of-the-art light field depth estimation approaches while achieving satisfactory performance.


title: Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen

abstract: The concepts of health and health care are moving towards the notion of personalized preventive health maintenance and away from an exclusive focus on the cure of disease. This is against the backdrop of contemporary public health challenges that include increasing costs, worsening outcomes, 'diabesity' epidemics, and anticipated physician shortages. Personalized preventive medicine could be critical to solving public health challenges at their causal root. This paper sets forth a vision and plan for the realization of preventive medicine by 2050 and examines efforts already underway such as participatory health initiatives, the era of big health data, and qualitative shifts in mindset.


title: The state of the art in distributed query processing

abstract: Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.


title: Spoor counts as indices of large carnivore populations: the relationship between spoor frequency, sampling effort and true density

abstract: 1. Studies of large carnivore populations and, particularly, reliable estimates of population density are necessary for effective conservation management. However, these animals are difficult to study, and direct methods of assessing population size and density are often expensive and time-consuming. ::: ::: ::: ::: 2. Indirect sampling, by counting spoor, could provide repeatable and inexpensive measures of some population parameters. The relationship between true population density and indirect sampling results has seldom been described in large carnivore studies. ::: ::: ::: ::: 3. In northern Namibia the population densities of leopards, lions and wild dogs were measured through recognition of individuals and groups. Spoor counts were then conducted independently, to assess the relationship between true density and the distribution of spoor. ::: ::: ::: ::: 4. Sampling effort, both in terms of the number of roads and total road distance in a sample zone, and the intensity of sampling, had a marked effect on the accuracy and precision of spoor frequency calculations. ::: ::: ::: ::: 5. In a homogeneous habitat, leopard spoor were evenly spread along different roads and spoor frequency was independent of road length. Taking into account very low sample sizes, the spoor density of leopards, lions and wild dogs showed a strong linear correlation with true density. The slope of the regression for leopards was different to that of lions and wild dogs.


title: Face Recognition and Verification Using Photometric Stereo: The Photoface Database and a Comprehensive Evaluation

abstract: This paper presents a new database suitable for both 2-D and 3-D face recognition based on photometric stereo (PS): the Photoface database. The database was collected using a custom-made four-source PS device designed to enable data capture with minimal interaction necessary from the subjects. The device, which automatically detects the presence of a subject using ultrasound, was placed at the entrance to a busy workplace and captured 1839 sessions of face images with natural pose and expression. This meant that the acquired data is more realistic for everyday use than existing databases and is, therefore, an invaluable test bed for state-of-the-art recognition algorithms. The paper also presents experiments of various face recognition and verification algorithms using the albedo, surface normals, and recovered depth maps. Finally, we have conducted experiments in order to demonstrate how different methods in the pipeline of PS (i.e., normal field computation and depth map reconstruction) affect recognition and verification performance. These experiments help to 1) demonstrate the usefulness of PS, and our device in particular, for minimal-interaction face recognition, and 2) highlight the optimal reconstruction and recognition algorithms for use with natural-expression PS data. The database can be downloaded from http://www.uwe.ac.uk/research/Photoface.


title: PyThinSearch: A Simple Web Search Engine

abstract: We describe a simple, functioning web search engine for indexing and searching online documents using python programming language. Python was chosen because it is an elegant language with simple syntax, is easy to learn and debug, and supports main operating systems almost evenly. The remarkable characteristics of this program are an adjustable search function that allows users to rank documents with several combinations of score functions and the focus on anchor text analysis as we provide four additional schemes to calculate scores based on anchor text. We also provide an additional ranking algorithm based on link addition process in network motivated by PageRank and HITS as an experimental tool. This algorithm is the original contribution of this paper.


title: Exact Synthesis and Implementation of New High-Order Wideband Marchand Baluns

abstract: New ultra-wideband high-order Marchand baluns with one microstrip unbalanced port and two microstrip balanced ports are proposed in this paper. The proposed Marchand baluns are synthesized based on an -plane high-pass prototype using the Richards' transformation. The responses of the synthesized high-order Marchand baluns are exactly predicted at all real frequencies. All circuit elements are commensurate, which means the electrical lengths of all transmission line elements are a quarter-wavelength long at the center frequency. Two fifth-order Marchand baluns with reflection coefficients of 20.53 and 21.71 dB corresponding to 131% and 152% bandwidth, respectively, are synthesized and realized using the combinations of microstrip lines, slotlines, and coplanar striplines. Simulated and measured results are presented.


title: Understanding Human-Machine Networks: A Cross-Disciplinary Survey

abstract: In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.


title: Activity Recognition using Actigraph Sensor

abstract: accelerometers are being extensively used in the recognition of simple ambulatory activities. Using wearable sensors for activity recognition is the latest topic of interest in smart home research. We use an Actigraph watch with an embedded accelerometer sensor to recognize real-life activities done in a home. Real-life activities include the set of Activities of Daily Living (ADL). ADLs are the crucial activities we perform everyday in our homes. Actigraph watches have been profusely used in sleep studies to determine the sleep/wake cycles and also the quality of sleep. In this paper, we investigate the possibility of using Actigraph watches to recognize activities. The data collected from an Actigraph watch was analyzed to predict ADLs (Activities of Daily Living). We apply machine learning algorithms to the Actigraph data to predict the ADLs. Also, a comparative study of activity prediction accuracy obtained from four machine learning algorithms is discussed.


title: Teachers' pedagogical designs for technology-supported collective inquiry: a national case study

abstract: The aim of the present study was to analyze teachers' pedagogical designs, plans of organized technology-supported, collective student inquiry. Ten teachers in Finland designed and implemented eight, inquiry-learning units ('designs') in 12 primary and secondary level classrooms in various subject domains. The guiding principles behind the designs were the objectives of progressive inquiry, such as facilitation of question-and explanation-driven learning, and the use of collaborative technology to support the sharing of knowledge. The participating teachers received substantial pedagogical training on these issues before the classroom implementations. The present study concentrated on examining three aspects in the teachers' pedagogical designs: solutions for supporting students' inquiry efforts, organization of collaboration, and the role given to the web-based Collaborative Learning Environment (CLE). The teachers experienced the use of CLE as a valuable new possibility to foster collaboration in classroom work, but there was much variation in the ways that the affordances of the system were utilized. The results indicated that it was a challenge for the teachers, especially in secondary level, to find appropriate methods for supporting students' inquiry efforts. The most difficult aim to achieve appeared to be the promotion of real collaborative knowledge building; the social arrangements of many designs still relied on rather individualistic ways of working.


title: Digital Forensic Research: The Good, the Bad and the Unaddressed

abstract: Digital forensics is a relatively new scientific discipline, but one that has matured greatly over the past decade. In any field of human endeavor, it is important to periodically pause and review the state of the discipline. This paper examines where the discipline of digital forensics is at this point in time and what has been accomplished in order to critically analyze what has been done well and what ought to be done better. The paper also takes stock of what is known, what is not known and what needs to be known. It is a compilation of the author’s opinion and the viewpoints of twenty-one other practitioners and researchers, many of whom are leaders in the field. In synthesizing these professional opinions, several consensus views emerge that provide valuable insights into the “state of the discipline.”


title: Aesthetic-based Clothing Recommendation

abstract: Recently, product images gain increasing concern in clothing recommendation since the visual appearance of the items have a significant impact on consumers’ decision. Existing models usually extract conventional features, such as convolutional neural network (CNN) features, scale-invariant feature transform (SIFT) features, and color histograms, to represent item image characters and capture user visual preferences. However, one important feature, aesthetic feature, is typically ignored. It is vital in recommendation since users’ decision depends largely on if the clothing is in line with their aesthetic while the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce aesthetic information which is more related with users’ preference into the field of clothing recommender system. To do so, we first present the aesthetic features extracted by an pre-trained neural network, which is a brain inspired deep structure trained for aesthetic assessment task. Considering the aesthetic preference shows diversity with different people and time, we propose a novel tensor factorization model as a basic model and then incorporate the aesthetic features into it. Finally, extensive experiments on real-world datasets demonstrate that our approach can capture the aesthetic preference of consumers and outperform several state-of-the-art models significantly.


title: Learning from one’s own errors and those of others

abstract: Three experiments investigated the effects of making errors oneself, as compared to just hearing the correct answer without error generation, hearing another person make an error, or being “on-the-hook,” that is, possibly but not necessarily being the person who would be “called-on” to give a response. In all three experiments, generating either an error of commission or generating the correct response, oneself, out loud, compared to being a person who heard another's commission errors (or correct responses), was beneficial for later recall of the correct answer. Experiment 1 suggested that the decrement in recall from self- to other-generation could be partially offset by being “on-the-hook.” However, this benefit was fragile and did not hold up either at a delay or when the presence of the other participants was downplayed. The beneficial effect of self-generation, both of correct responses and of errors of commission is consistent with reconsolidation theory. That theory holds that retrieval has a special status as a memory process that renders the retrieved traces labile. If the person was correct, reconsolidating the correct trace results in strengthening. If wrong, the malleability of the retrieved trace implied by reconsolidation theory makes it open to enhanced modification and correction. If the person was not the agent who retrieved, though, such as when someone else retrieves information, or when nothing is retrieved as is the case with omission errors (which we argue is truly how the term “unsuccessful retrieval” should be used), the benefit conferred by the special malleability entailed by the postulated reconsolidation process does not obtain.


title: Judges ’ Awareness, Understanding, and Application of Digital Evidence

abstract: As digital evidence grows in both volume and importance in criminal and civil courts, judges need to fairly and justly evaluate the merits of the offered evidence. To do so, judges need a general understanding of the underlying technologies and applications from which digital evidence is derived. Due to the relative newness of the computer forensics field, there have been few studies on the use of digital forensic evidence and none about judges’ relationship with digital evidence. ::: This study addressed judges’ awareness, knowledge, and perceptions of digital evidence, using grounded theory methods. The interaction of judges with digital evidence has a social aspect that makes a study of this relationship well suited to grounded theory. This study gathered data via a written survey distributed to judges in the American Bar Association and National Judicial College, followed by interviews with judges from Massachusetts and Vermont. ::: The results indicated that judges generally recognize the importance of evidence derived from digital sources, although they are not necessarily aware of all such sources. They believe that digital evidence needs to be authenticated just like any type of evidence and that it is the role of attorneys rather than of judges to mount challenges to that evidence, as appropriate. Judges are appropriately wary of digital evidence, recognizing how easy it is to alter or misinterpret such evidence. Less technically aware judges appear even more wary of digital evidence than their more knowledgeable peers. ::: Judges recognize that they need additional training in computer and Internet technology as the computer forensics process and digital evidence, citing a lack of availability of such training. This training would enable judges to better understand the arguments presented by lawyers, testimony offered by technical witnesses, and judicial opinions forming the basis of decisional law. A framework for such training is provided in this report. ::: This study is the first in the U.S. to analyze judges and digital forensics, thus opening up a new avenue of research. It is the second time that grounded theory has been employed in a digital forensics study, demonstrating the applicability of that methodology to this discipline.


A Variational Feature Encoding Method of 3 D Object for Probabilistic Semantic SLAM


title: Attribute-based fine-grained access control with efficient revocation in cloud storage systems

abstract: A cloud storage service allows data owner to outsource their data to the cloud and through which provide the data access to the users. Because the cloud server and the data owner are not in the same trust domain, the semi-trusted cloud server cannot be relied to enforce the access policy. To address this challenge, traditional methods usually require the data owner to encrypt the data and deliver decryption keys to authorized users. These methods, however, normally involve complicated key management and high overhead on data owner. In this paper, we design an access control framework for cloud storage systems that achieves fine-grained access control based on an adapted Ciphertext-Policy Attribute-based Encryption (CP-ABE) approach. In the proposed scheme, an efficient attribute revocation method is proposed to cope with the dynamic changes of users' access privileges in large-scale systems. The analysis shows that the proposed access control scheme is provably secure in the random oracle model and efficient to be applied into practice.


title: A Survey on Metamorphic Testing

abstract: A test oracle determines whether a test execution reveals a fault, often by comparing the observed program output to the expected output. This is not always practical, for example when a program's input-output relation is complex and difficult to capture formally. Metamorphic testing provides an alternative, where correctness is not determined by checking an individual concrete output, but by applying a transformation to a test input and observing how the program output “morphs” into a different one as a result. Since the introduction of such metamorphic relations in 1998, many contributions on metamorphic testing have been made, and the technique has seen successful applications in a variety of domains, ranging from web services to computer graphics. This article provides a comprehensive survey on metamorphic testing: It summarises the research results and application areas, and analyses common practice in empirical studies of metamorphic testing as well as the main open challenges.


title: Consistency-based service level agreements for cloud storage

abstract: Choosing a cloud storage system and specific operations for reading and writing data requires developers to make decisions that trade off consistency for availability and performance. Applications may be locked into a choice that is not ideal for all clients and changing conditions. Pileus is a replicated key-value store that allows applications to declare their consistency and latency priorities via consistency-based service level agreements (SLAs). It dynamically selects which servers to access in order to deliver the best service given the current configuration and system conditions. In application-specific SLAs, developers can request both strong and eventual consistency as well as intermediate guarantees such as read-my-writes. Evaluations running on a worldwide test bed with geo-replicated data show that the system adapts to varying client-server latencies to provide service that matches or exceeds the best static consistency choice and server selection scheme.


title: Training Neural Networks with Very Little Data -- A Draft

abstract: Deep neural networks are complex architectures composed of many layers of nodes, resulting in a large number of parameters including weights and biases that must be estimated through training the network. Larger and more complex networks typically require more training data for adequate convergence than their more simple counterparts. The data available to train these networks is often limited or imbalanced. We propose the radial transform in polar coordinate space for image augmentation to facilitate the training of neural networks from limited source data. Pixel-wise coordinate transforms provide representations of the original image in the polar coordinate system and both augment data as well as increase the diversity of poorly represented classes. Experiments performed on MNIST and a set of multimodal medical images using the AlexNet and GoogLeNet neural network models show high classification accuracy using the proposed method.


title: Unsupervised Keyphrase Extraction Based on Outlier Detection

abstract: We propose a novel unsupervised keyphrase extraction approach based on outlier detection. Our approach starts by training word embeddings on the target document to capture semantic regularities among the words. It then uses the minimum covariance determinant estimator to model the distribution of non-keyphrase word vectors, under the assumption that these vectors come from the same distribution, indicative of their irrelevance to the semantics expresses by the dimensions of the learned vector representation. Candidate keyphrases are based on words that are outliers of this dominant distribution. Empirical results show that our approach outperforms state-of-the-art unsupervised keyphrase extraction methods.


title: Pricing in Network Effect Markets

abstract: According to literature, penetration pricing is the dominant pricing strategy for network effect markets. In this paper we show that diffusion of products in a network effect market does not only vary with the set of pricing strategies chosen by competing vendors but also strongly depends on the topological structure of the customers' network. This stresses the inappropriateness of classical "installed base" models (abstracting from this structure). Our simulations show that although competitive prices tend to be significantly higher in close topology markets, they lead to lower total profit and lower concentration of vendors' profit in these markets.


title: Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU Acceleration

abstract: The fast filtering of massive point cloud data from light detection and ranging (LiDAR) systems is important for many applications, such as the automatic extraction of digital elevation models in urban areas. We propose a simple scan-line-based algorithm that detects local lowest points first and treats them as the seeds to grow into ground segments by using slope and elevation. The scan line segmentation algorithm can be naturally accelerated by parallel computing due to the independent processing of each line. Furthermore, modern graphics processing units (GPUs) can be used to speed up the parallel process significantly. Using a strip of a LiDAR point cloud, with up to 48 million points, we test the algorithm in terms of both error rate and time performance. The tests show that the method can produce satisfactory results in less than 0.6 s of processing time using the GPU acceleration.


title: Syntactic Theory: A Formal Introduction

abstract: This second edition of "Syntactic Theory: A Formal Introduction" expands and improves upon a truly unique introductory syntax textbook. Like the first edition its focus is on the development of precisely formulated grammars whose empirical predictions can be directly tested. There is also considerable emphasis on the prediction and evaluation of grammatical hypotheses, as well as on integrating syntactic hypotheses with matters of semantic analysis. The book covers the core areas of English syntax from the last quarter century, including complementation, control, "raising constructions", passives, the auxiliary system, and the analysis of long distance dependency constructions. "Syntactic Theory's" step-by-step introduction to a consistent grammar in these core areas is complemented by extensive problem sets drawing from a variety of languages. The book's theoretical perspective is presented in the context of current models of language processing, and the practical value of the constraint-based, lexicalist grammatical architecture proposed has already been demonstrated in computer language processing applications. This thoroughly reworked second edition includes revised and extended problems sets, updated analyses, additional examples and more detailed exposition throughout.


title: Tree-Structured Variational Autoencoder

abstract: Many kinds of variable-sized data we would like to model contain an internal hierarchical structure in the form of a tree, including source code, formal logical statements, and natural language sentences with parse trees. For such data it is natural to consider a model with matching computational structure. In this work, we introduce a variational autoencoder-based generative model for tree-structured data. We evaluate our model on a synthetic dataset, and a dataset with applications to automated theorem proving. By learning a latent representation over trees, our model can achieve similar test log likelihood to a standard autoregressive decoder, but with the number of sequentially dependent computations proportional to the depth of the tree instead of the number of nodes in the tree.


title: Environmental Applications of Semiconductor Photocatalysis

abstract: The civilian, commercial, and defense sectors of most advanced industrialized nations are faced with a tremendous set of environmental problems related to the remediation of hazardous wastes, contaminated groundwaters, and the control of toxic air contaminants. For example, the slow pace of hazardous waste remediation at military installations around the world is causing a serious delay in conversion of many of these facilities to civilian uses. Over the last 10 years problems related to hazardous waste remediation have emerged as a high national and international priority.


title: MAD Skills: New Analysis Practices for Big Data

abstract: As massive data acquisition and storage becomes increasingly affordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this paper we highlight the emerging practice of Magnetic, Agile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intelligence. We present our design philosophy, techniques and experience providing MAD analytics for one of the world's largest advertising networks at Fox Audience Network, using the Greenplum parallel database system. We describe database design methodologies that support the agile working style of analysts in these settings. We present dataparallel algorithms for sophisticated statistical techniques, with a focus on density methods. Finally, we reflect on database system features that enable agile design and flexible algorithm development using both SQL and MapReduce interfaces over a variety of storage mechanisms.


title: High coercive Zn-bonded Sm-Fe-N magnets prepared using fine Zn particles with low oxygen content

abstract: Abstract To improve the coercivity of Zn-bonded Sm-Fe-N magnets, fine Zn particles with low oxygen content were fabricated by the hydrogen plasma-metal reaction (HPMR), and Zn-bonded Sm-Fe-N magnets were prepared using the Zn particles. The primary and secondary average Zn particle sizes were 0.23 and 0.93 μm, respectively, and the oxygen content was 0.068 wt%. The oxygen content in the Zn-bonded Sm-Fe-N magnets prepared using the Zn particles also decreased, and the coercivity and energy products of the 15 wt% Zn-bonded Sm-Fe-N magnets were 2.66 MA·m−1 and 53.1 kJ·m−3, respectively, at room temperature. The 10 wt% Zn-bonded Sm-Fe-N magnet was also a high coercivity value of 2.41 MA·m−1, and the energy product was 56.1 kJ·m−3. The coercivity strongly depended on the oxygen content rather than the particle size of Zn, and decreasing the oxygen content in the starting material improved the magnetic properties of Zn-bonded Sm-Fe-N magnets. The coercivity of the 15 wt% Zn magnet measured at 180 and 200 °C was 1.23 and 1.10 MA·m−1, respectively, and the temperature coefficient of coercivity was −0.32%·°C−1.


title: Recommendation for Repeat Consumption from User Implicit Feedback

abstract: Recommender system has been studied as a useful tool to discover novel items for users while fitting their personalized interest. Thus, the previously consumed items are usually out of consideration due to the “lack” of novelty. However, as time elapses, people may forget those previously consumed and preferred items which could become “novel” again. Meanwhile, repeat consumption accounts for a major portion of people's observed activities; examples include: eating regularly at a same restaurant, or repeatedly listening to the same songs. Therefore, we believe that recommending repeat consumption will have a real utility at certain times. In this paper, we formulate the problem of recommendation for repeat consumption with user implicit feedback. A time-sensitive personalized pairwise ranking ( TS-PPR ) method based on user behavioral features is proposed to address this problem. The proposed method factorizes the temporal user-item interactions via learning the mappings from the behavioral features in observable space to the preference features in latent space, and combines users’ static and dynamic preferences together in recommendation. An empirical study on real-world data sets shows encouraging results.


title: MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification

abstract: Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.


title: A Concurrent SOM-Based Chan-Vese Model for Image Segmentation

abstract: Concurrent Self Organizing Maps (CSOMs) deal with the pattern classification problem in a parallel processing way, aiming to minimize a suitable objective function. Similarly, Active Contour Models (ACMs) (e.g., the Chan-Vese (CV) model) deal with the image segmentation problem as an optimization problem by minimizing a suitable energy functional. The effectiveness of ACMs is a real challenge in many computer vision applications. In this paper, we propose a novel regional ACM, which relies on a CSOM to approximate the foreground and background image intensity distributions in a supervised way, and to drive the active-contour evolution accordingly. We term our model Concurrent Self Organizing Map-based Chan-Vese (CSOM-CV) model. Its main idea is to concurrently integrate the global information extracted by a CSOM from a few supervised pixels into the level-set framework of the CV model to build an effective ACM. Experimental results show the effectiveness of CSOM-CV in segmenting synthetic and real images, when compared with the stand-alone CV and CSOM models.


title: CRUDE: Combining Resource Usage Data and Error Logs for Accurate Error Detection in Large-Scale Distributed Systems

abstract: The use of console logs for error detection in large scale distributed systems has proven to be useful to system administrators. However, such logs are typically redundant and incomplete, making accurate detection very difficult. In an attempt to increase this accuracy, we complement these incomplete console logs with resource usage data, which captures the resource utilisation of every job in the system. We then develop a novel error detection methodology, the CRUDE approach, that makes use of both the resource usage data and console logs. We thus make the following specific technical contributions: we develop (i) a clustering algorithm to group nodes with similar behaviour, (ii) an anomaly detection algorithm to identify jobs with anomalous resource usage, (iii) an algorithm that links jobs with anomalous resource usage with erroneous nodes. We then evaluate our approach using console logs and resource usage data from the Ranger Supercomputer. Our results are positive: (i) our approach detects errors with a true positive rate of about 80%, and (ii) when compared with the well-known Nodeinfo error detection algorithm, our algorithm provides an average improvement of around 85% over Nodeinfo, with a best-case improvement of 250%.


title: Wireless and Portable EOG-Based Interface for Assisting Disabled People

abstract: This paper describes a new portable and wireless interface based on electrooculography (EOG) aimed at people with severe motor disorders. This interface allows us detecting the movement of the eyes measuring the potential between the cornea and the retina. The interface uses five electrodes placed around the eyes of the user in order to register this potential. A processing algorithm of the EOG signals has been developed in order to detect the movement of the eyes. This interface has many advantages in comparison to commercial devices. It is a cheap and small sized device with USB compatibility. It does not need power supply from the network as it works with batteries and USB supply. Several experiments have been done to test the electronics of the interface. A first set of experiments has been performed to obtain the movement of the eyes of the user processing the signals provided by the interface. In addition, the interface has been used to control a real robot arm. The accuracy and time taken have been measured showing that the user is capable of controlling the robot using only his/her eyes with satisfactory results.


title: Design of 8-bit SAR-ADC CMOS

abstract: Successive approximation analog-to-digital converter (ADC) implemented in a conventional 0.18μm CMOS technology with low voltage. The SAR composite of sample-and-hold dummy switch compensation was employed, comparator is low-voltage latched and realized based on current-mode approach, control logic circuit and digital-to-analog conversion consists of binary weighted capacitor arrays for the differential inputs. The ADC has INL and DNL of 0.45 LSB for supply voltage 1.8V, at sampling rate 200 KS/S and signal to noise ratio distortion is 58.5 dB. This design is suitable for standard CMOS technology with low-power low-cost VLSI implementation. It is well applied when embedded into system-on-chip (SOC) circuit designs.


Evaluating Network Embeddings : Node 2 Vec vs Spectral Clustering vs GCN


title: A single target voting scheme for traffic sign detection

abstract: Traffic sign detection and recognition is an important part of advanced driver assistance systems. Many prototype solutions for this task have been developed, and first commercial systems have just become available. Their image processing chain can be devided into three steps, preprocessing, detection, and recognition. Albeit several reliable sign recognition algorithms exist by now sign detection under real-world conditions is still unstable. Therefore, we address the first two steps of the processing chain presenting an analysis of widely used detectors, namely Hough-like methods. We evaluate several preprocessing steps and tweaks to increase their performance. Hence, the detectors are applied to a large, publicly available set of images from real-life traffic scenes. As main result we establish a new probabilistic measure for traffic sign colour detection and, based on the findings in our analysis, propose a novel Hough-like algorithm for detecting circular and triangular shapes. These improvements significantly increased detection performance in our experiments.


title: Influence of fiber nonlinearities in the performance of an all-optical fiber-based demultiplexer

abstract: The four wave mixing (FWM) process in nonlinear media can be used for all optical demultiplexing of high bit-rate optical time division multiplexed channels. Interaction of the optical time division multiplexed signal and a pulsed pump with a repetition frequency equal to the desired output bit-rate, results in generation of a new wave at a new wavelength which carries the information of one of the de-multiplexed channels. Dispersion shifted fiber (DSF) is an adequate nonlinear medium in which the four wave mixing process takes place. In this work a detailed theoretical study of an all-optical demultiplexer based on four wave mixing in dispersion shifted fibers is presented for different demultiplexing input/output bit-rates. The four wave mixing process in dispersion shifted fibers is studied through numerical simulation of the non- linear Schroedinger equation, taking into account all fiber nonlinearities. The performance of the demultiplexer is characterized in terms of efficiency, Q-factor, suppression of adjacent channels and eye pattern for each de-multiplexed channel. These characteristics are studied for different fiber lengths, pulsewidths, powers, etc. This detailed characterization of the operational conditions of the demultiplexer will reveal its limitations and hints for its optimal design will be proposed.


Toward Probabilistic Natural Logic for Syllogistic Reasoning


title: SimRT: an automated framework to support regression testing for data races

abstract: Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.


title: Predicting customer behavior in telecommunications

abstract: The wireless service subscriber calls a customer service representative to complain about dropped calls. During the conversation with the customer, the CSR views a display that shows this customer's probability of churn-switching from this service provider to another-as well as the most probable reasons to churn and the best strategy to retain this customer. The CSR then quickly responds to the subscriber according to the system's recommendation. This is an intelligent customer-care system designed to predict customer behavior. Predicting customer churn is a component in the decision framework for retaining customers and maximizing profitability. Companies can use these probability and revenue estimates in a decision-theoretic framework to determine a churn intervention strategy and a profitability optimization strategy. Predicting customer behavior helps service providers build customer loyalty and maximize profitability. For the success of a project, data preparation is often a critical part of the predictive algorithm.


title: Cyber Security Incidents on Critical Infrastructure and Industrial Networks

abstract: National critical infrastructure and industrial processes are heavily reliant on automation, monitoring and control technologies, including the widely used Supervisory Control and Data Acquisition (SCADA) systems. The growing interconnection of these systems with corporate networks exposes them to cyber attacks, with several security incidents reported over the last few decades. This study provides a classification scheme for categorising security incidents related to critical infrastructure and industrial control systems. The classification scheme is applied to analyse 242 security incidents on critical infrastructure and industrial control networks, which were reported between 1982 and 2014. The results show interesting patterns, with key points highlighted for the purpose of improving the way we plan for and direct efforts toward protecting critical infrastructure and industrial networks.


title: Concrete Crack Detection by Multiple Sequential Image Filtering

abstract: This article presents a new robust automated image processing method for detecting cracks in surface images of concrete structures. This method involves two steps: (1) development of an image filter for detecting major cracks using genetic programming (GP), and (2) elimination of residual noise after filtering and detection of indistinct cracks by iterative applications of the image filter to the local regions surrounding the cracks. The proposed method can be used for the accurate detection of cracks in surface images recorded under various conditions. Moreover, the widths of the detected cracks can be quantified on the basis of the spatial derivatives of the brightness patterns. The estimated crack widths are in good agreement with those measured manually. The paper shows how research on structural health monitoring of bridges and structures has received significant attention in recent years.


title: In Defense of Locality-Sensitive Hashing

abstract: Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search. We claim in this paper that the true merit of LSH lies in transforming the semantic labels to obtain the binary codes, resulting in an effective and efficient two-step hashing framework. Specifically, we developed the locality-sensitive two-step hashing (LS-TSH) that generates the binary codes through LSH rather than any complex optimization technique. Theoretically, with proper assumption, LS-TSH is actually a useful LSH scheme, so that it preserves the label-based semantic similarity and possesses sublinear query complexity for hash lookup. Experimentally, LS-TSH could obtain comparable retrieval accuracy with state of the arts with two to three orders of magnitudes faster training speed.


title: CNN+CNN: Convolutional Decoders for Image Captioning

abstract: Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field. However, RNNs or LSTMs cannot be calculated in parallel and ignore the underlying hierarchical structure of a sentence. In this paper, we propose a framework that only employs convolutional neural networks (CNNs) to generate captions. Owing to parallel computing, our basic model is around 3 times faster than NIC (an LSTM-based model) during training time, while also providing better results. We conduct extensive experiments on MSCOCO and investigate the influence of the model width and depth. Compared with LSTM-based models that apply similar attention mechanisms, our proposed models achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of CIDEr. We also test our model on the paragraph annotation dataset, and get higher CIDEr score compared with hierarchical LSTMs


title: Deep Learning in Microscopy Image Analysis: A Survey

abstract: Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerging as a leading machine learning tool in computer vision and has attracted considerable attention in biomedical image analysis. In this paper, we provide a snapshot of this fast-growing field, specifically for microscopy image analysis. We briefly introduce the popular deep neural networks and summarize current deep learning achievements in various tasks, such as detection, segmentation, and classification in microscopy image analysis. In particular, we explain the architectures and the principles of convolutional neural networks, fully convolutional networks, recurrent neural networks, stacked autoencoders, and deep belief networks, and interpret their formulations or modelings for specific tasks on various microscopy images. In addition, we discuss the open challenges and the potential trends of future research in microscopy image analysis using deep learning.


title: The appropriate use of proton pump inhibitors (PPIs): Need for a reappraisal.

abstract: The advent of powerful acid-suppressive drugs, such as proton pump inhibitors (PPIs), has revolutionized the management of acid-related diseases and has minimized the role of surgery. The major and universally recognized indications for their use are represented by treatment of gastro-esophageal reflux disease, eradication of Helicobacter pylori infection in combination with antibiotics, therapy of H. pylori-negative peptic ulcers, healing and prophylaxis of non-steroidal anti-inflammatory drug-associated gastric ulcers and control of several acid hypersecretory conditions. However, in the last decade, we have witnessed an almost continuous growth of their use and this phenomenon cannot be only explained by the simple substitution of the previous H2-receptor antagonists, but also by an inappropriate prescription of these drugs. This endless increase of PPI utilization has created an important problem for many regulatory authorities in terms of increased costs and greater potential risk of adverse events. The main reasons for this overuse of PPIs are the prevention of gastro-duodenal ulcers in low-risk patients or the stress ulcer prophylaxis in non-intensive care units, steroid therapy alone, anticoagulant treatment without risk factors for gastro-duodenal injury, the overtreatment of functional dyspepsia and a wrong diagnosis of acid-related disorder. The cost for this inappropriate use of PPIs has become alarming and requires to be controlled. We believe that gastroenterologists together with the scientific societies and the regulatory authorities should plan educational initiatives to guide both primary care physicians and specialists to the correct use of PPIs in their daily clinical practice, according to the worldwide published guidelines.


title: ESD self-protection design on 2.4-GHz T/R switch for RF application in CMOS process

abstract: An RF transceiver front-end for 2.4GHz applications realized by a fully integrated T/R switch with ESD self-protection capability is presented in this work. Experimental results show that the proposed design without using any additional ESD protection device can provide enough ESD self-protection capability with good RF performances.


title: A simple intrusion detection method for controller area network

abstract: The Controller Area Network (CAN) is established as the main communication channel inside vehicles. CAN relies on frame broadcast to share data between different microcontrollers managing critical or comfort functions such as cruise control or air conditioning. CAN is distinguished by its simplicity, its real-time application compatibility and its low deployment cost. However, CAN major drawback is its lack of security support. That is, CAN fails to provide protections against attacks such as intrusion, denial of service or impersonation. We propose in this work a simple intrusion detection method for CAN. Our main idea is to make each microcontroller monitor the CAN bus to detect malicious frames.


title: Information Theory: A Tutorial Introduction

abstract: Shannon's mathematical theory of communication defines fundamental limits on how much information can be transmitted between the different components of any man-made or biological system. This paper is an informal but rigorous introduction to the main ideas implicit in Shannon's theory. An annotated reading list is provided for further reading.


title: Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data

abstract: A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations


title: Research through design fiction: narrative in real and imaginary abstracts

abstract: This paper reflects on the uses of prototypes in "Research through Design" and considers "Design Fiction" as a technique for exploring the potential value of new design work. It begins with an analysis of Research through Design abstracts in the ACM digital library and identifies an emerging language and structure of papers in this emerging field. The abstracts: frame a problem space, introduce a study, often involving the deployment of a prototype, and conclude with considerations, reflections and discussion. This format is then pastiched in a series of design fictions written for a project investigating new and emerging forms of reproduction in Art. The fictions take the form of "imaginary abstracts" which summarize findings of papers that have not been written about prototypes that do not exist. It is argued that framing concept designs as fictional studies can provide a space for research focused critique and development.


title: Urban Computing: Concepts, Methodologies, and Applications

abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.


title: A Classification and Characterization of Security Threats in Cloud Computing

abstract: Security and privacy are the most critical issues that need to be ::: addressed in designing a computing environment that is reliable and ::: trustworthy. Like all other computing paradigms, Cloud Computing is no ::: different. Since data and storage are outsourced to third party ::: service providers, users lose direct control of data management and ::: have to depend solely on the providers who may not always be ::: dependable. This distinctive feature of Cloud Computing makes it ::: susceptible to several security threats and vulnerabilities. Although some of the security issues such as network and virtualization security, authentication, access control, confidentiality, and integrity are not new to computing, the effect of such issues is exacerbated in cloud environment because of the unique features (e.g., multi-tenancy, data and resource sharing, virtualization, etc.) it possesses. In this paper, we classify and characterize the various security and privacy challenges associated with Cloud Computing.


title: The breakingNews dataset

abstract: We present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (e.g. GPS coordinates and popularity metrics). The tenuous connection between the images and text in news data is appropriate to take work at the intersection of Computer Vision and Natural Language Processing to the next step, hence we hope this dataset will help spur progress in the field.


title: IMP: indirect memory prefetcher

abstract: Machine learning, graph analytics and sparse linear algebra-based applications are dominated by irregular memory accesses resulting from following edges in a graph or non-zero elements in a sparse matrix. These accesses have little temporal or spatial locality, and thus incur long memory stalls and large bandwidth requirements. A traditional streaming or striding prefetcher cannot capture these irregular access patterns. A majority of these irregular accesses come from indirect patterns of the form A[B[i]]. We propose an efficient hardware indirect memory prefetcher (IMP) to capture this access pattern and hide latency. We also propose a partial cacheline accessing mechanism for these prefetches to reduce the network and DRAM bandwidth pressure from the lack of spatial locality. Evaluated on 7 applications, IMP shows 56% speedup on average (up to 2.3×) compared to a baseline 64 core system with streaming prefetchers. This is within 23% of an idealized system. With partial cacheline accessing, we see another 9.4% speedup on average (up to 46.6%).


title: Hiding images in plain sight: deep steganography

abstract: Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image. In this study, we attempt to place a full size color image within another image of the same size. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions. Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image's representation across all of the available bits.


title: The Theory Of Matter in Indian Physics

abstract: This paper is the second in series of the Indian physics of the Nyaya-Vaisheshika school. It is significant that the school defines matter not in terms of something gross that is anchored to the commonsensical notion of an object, but rather in terms of something that has attributes associated with it. Matter, or padartha, is whatever is knowable within the overarching complex of space and time, each of which is taken to be continuous and infinite. The significant concepts discussed here include how to localize an object, and that of nothingness, that is, vacuum.


title: Towards the discovery of scientific revolutions in scientometric data

abstract: Paradigms and revolutions are popular concepts in science studies and beyond, yet their meaning is notoriously vague and their existence is widely disputed. Drawing on recent developments in agent-based modeling and scientometric data, this paper offers a precise conceptualization of paradigms and their dynamics, as well as a number of hypotheses that could in principle be used to test for the existence of scientific revolutions in scientometric data.


title: Quantitatively measuring a large-scale agile transformation

abstract: We provide a quantitative metrics model to evaluate the impact of an agile and lean transformation.We propose eight rigorously described metrics within that model.We establish and apply the model in a large international telecommunication organization with 350 employees in two sites.Our findings show significant improvement in six of the eight metrics whereas one metric showed deteriorated results. Context: Agile software development continues to grow in popularity and is being adopted by more and more organizations. However, there is a need for empirical evidence on the impact, benefits and drawbacks of an agile transformation in an organization since the cost for such a transformation in terms of money, disrupted working routines and quality of development can become considerable. Currently, such evidence exists in the form of success stores and case studies, mostly of qualitative nature.Objective: Provide a metrics model to quantitatively measure the impact of an agile transformation in a software development organization.Method: The metrics model was elicited with the use of the Goal Question Metric approach.Results: A quantitative metrics model containing eight rigorously described metrics is presented and followed by its application to evaluate an agile and lean transformation in a large international telecommunication organization with 350 employees in two sites.Conclusions: The metrics model was sensitive to the changes that occurred in the organization and revealed significant improvements in six of the eight metrics and a deterioration in one of the metrics.


title: Challenges and Opportunities with Big Data

abstract: The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of "Big Data," including the recent announcement from the White House about new funding initiatives across different agencies, that target research for Big Data. While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is no clear consensus on what is Big Data. In fact, there have been many controversial statements about Big Data, such as "Size is the only thing that matters." In this panel we will try to explore the controversies and debunk the myths surrounding Big Data.


title: A 480-MHz to 1-GHz sub-picosecond clock generator with a fast and accurate automatic frequency calibration in 0.13-µm CMOS

abstract: In this paper, an ultra-low jitter clock generator that employs a novel automatic frequency calibration (AFC) technique is presented. To achieve low jitter, the clock generator uses an LC-VCO with S-bit switched tuning scheme. The clock output is taken from the output of a multi-modulus divider, which increases the output frequency range with small variation in the loop bandwidth. The capacitor array of the the VCO is controlled by a novel AFC technique that performs binary search for fast calibration and fine search to select an optimum tuning curve. A prototype chip implemented in 0.13-mum CMOS process achieves 480 MHz to 1 GHz of output frequency while consuming 22 mW from a 1.2 V supply. The measured tins jitter and calibration time of the proposed clock generator are 940 fs at 600 MM/, and 350 ns, respectively. These numbers are the fastest calibration time and one of the lowest jitter that have been reported in a clock generator.


title: Effects of Therapy Cushions on Classroom Behaviors of Children With Autism Spectrum Disorder

abstract: OBJECTIVE. We investigated the effects of therapy cushions on the in-seat and on-task behaviors of 2 kindergarten students with autism spectrum disorder during math activities. ::: ::: METHOD. We used a single-subject A�B�A�B�C design across 2 male participants who used chairs during baseline phases (A) and cushions during intervention phases (B). We included a choice phase (C) to determine participant seating preferences. Social validity was addressed by assessing teacher and participant seating preferences. Data were graphed and visually analyzed for functionally relevant changes between phases. ::: ::: RESULTS. No clinically relevant changes in the in-seat or on-task behaviors of either participant were observed with cushion use. ::: ::: CONCLUSION. Therapy cushions may not impose sufficient postural demands or provide adequate sensory input to produce behavioral changes. Continued research in this area is needed.


title: Obstruction-free synchronization: double-ended queues as an example

abstract: We introduce obstruction-freedom, a new nonblocking property for shared data structure implementations. This property is strong enough to avoid the problems associated with locks, but it is weaker than previous nonblocking properties-specifically lock-freedom and wait-freedom-allowing greater flexibility in the design of efficient implementations. Obstruction-freedom admits substantially simpler implementations, and we believe that in practice it provides the benefits of wait-free and lock-free implementations. To illustrate the benefits of obstruction-freedom, we present two obstruction-free CAS-based implementations of double-ended queues (deques); the first is implemented on a linear array, the second on a circular array. To our knowledge, all previous nonblocking deque implementations are based on unrealistic assumptions about hardware support for synchronization, have restricted functionality, or have operations that interfere with operations at the opposite end of the deque even when the deque has many elements in it. Our obstruction-free implementations have none of these drawbacks, and thus suggest that it is much easier to design obstruction-free implementations than lock-free and wait-free ones. We also briefly discuss other obstruction-free data structures and operations that we have implemented.


title: Fog Computing for Sustainable Smart Cities: A Survey

abstract: The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities.


Multi-Graph Learning with Positive and Unlabeled Bags


title: Contour Model-Based Hand-Gesture Recognition Using the Kinect Sensor

abstract: In RGB-D sensor-based pose estimation, training data collection is often a challenging task. In this paper, we propose a new hand motion capture procedure for establishing the real gesture data set. A 14-patch hand partition scheme is designed for color-based semiautomatic labeling. This method is integrated into a vision-based hand gesture recognition framework for developing desktop applications. We use the Kinect sensor to achieve more reliable and accurate tracking under unconstrained conditions. Moreover, a hand contour model is proposed to simplify the gesture matching process, which can reduce the computational complexity of gesture matching. This framework allows tracking hand gestures in 3-D space and matching gestures with simple contour model, and thus supports complex real-time interactions. The experimental evaluations and a real-world demo of hand gesture interaction demonstrate the effectiveness of this framework.


title: Optimal design of a novel 2-DOF compliant parallel micromanipulator for nanomanipulation

abstract: A new two-degrees-of-freedom (2-DOF) compliant parallel micromanipulator (CPM) utilizing flexure joints has been proposed for two-dimensional (2-D) nanomanipulation in this paper. The system is developed by a careful design and proper selection of electrical and mechanical components. Based upon the developed PRB model, both the position and velocity kinematic modelings have been performed in details, and the CPM's workspace area is determined analytically in view of the physical constraints imposed by pizeo-actuators and flexure hinges. Moreover, in order to achieve a maximum workspace subjected to the given dexterity indices, kinematic optimization of the design parameters has been carried out, which leads to a manipulator satisfying the requirement of this work. Simulation results reveal that the designed CPM can perform a high dexterous manipulation within its workspace.


title: Buffer-based smooth rate adaptation for dynamic HTTP streaming

abstract: Recently, Dynamic Streaming over HTTP (DASH) has been widely deployed in the Internet. However, it is still a challenge to play back video smoothly with high quality in the time-varying Internet. In this paper, we propose a buffer based rate adaptation scheme, which is able to smooth bandwidth variations and provide a continuous video playback. Through analysis, we show that simply preventing buffer underflow/overflow in the greedy rate adaptation method may incur serious rate oscillations, which is poor quality-of-experience for users. To improve it, we present a novel control-theoretic approach to control buffering size and rate adaptation. We modify the buffered video time model by adding two thresholds: an overflow threshold and an underflow threshold, to filter the effect of short-term network bandwidth variations while keeping playback smooth. However, the modified rate adaptation system is nonlinear. By choosing operating point properly, we linearize the rate control system. By a Proportional-Derivative (PD) controller, we are able to adapt video rate with high responsiveness and stability. We carefully design the parameters for the PD controller. Moreover, we show that reserving a small positive/negative bandwidth margin can greatly decrease the opportunities of buffer underflow/overflow incurred by the bandwidth prediction error. At last, we demonstrate that our proposed control-theoretic approach are highly efficient through real network trace.


title: A $\mu $ -Controller-Based System for Interfacing Selectorless RRAM Crossbar Arrays

abstract: Selectorless crossbar arrays of resistive random-access memory (RRAM), also known as memristors, conduct large sneak currents during operation, which can significantly corrupt the accuracy of cross-point analog resistance ( $M_{t}$ ) measurements. In order to mitigate this issue, we have designed, built, and tested a memristor characterization and testing (mCAT) instrument that forces redistribution of sneak currents within the crossbar array, dramatically increasing $M_{t}$ measurement accuracy. We calibrated the mCAT using a custom-made $32 \times 32$ discrete resistive crossbar array, and subsequently demonstrated its functionality on solid-state TiO2- x RRAM arrays, on wafer and packaged, of the same size. Our platform can measure standalone $M_{t}$ in the range of 1 $\text{k}\Omega $ to 1 $\text{M}\Omega $ with <1% error. For our custom resistive crossbar, 90% of devices of the same resistance range were measured with <10% error. The platform’s limitations have been quantified using large-scale nonideal crossbar simulations.


title: Robust Incremental Neural Semantic Graph Parsing

abstract: Parsing sentences to linguistically-expressive semantic representations is a key goal of Natural Language Processing. Yet statistical parsing has focused almost exclusively on bilexical dependencies or domain-specific logical forms. We propose a neural encoder-decoder transition-based parser which is the first full-coverage semantic graph parser for Minimal Recursion Semantics (MRS). The model architecture uses stack-based embedding features, predicting graphs jointly with unlexicalized predicates and their token alignments. Our parser is more accurate than attention-based baselines on MRS, and on an additional Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes it an order of magnitude faster than a high-precision grammar-based parser. Further, the 86.69% Smatch score of our MRS parser is higher than the upper-bound on AMR parsing, making MRS an attractive choice as a semantic representation.


Trading Bitcoin and Online Time Series Prediction


title: Adoption of mobile Internet services: An exploratory study of mobile commerce early adopters

abstract: The extent to which investment in information technology (IT) is related to organizational performance and productivity remains an open question in the minds of managers responsible for such investment decisions. Most past research into this relation has been based on cross-sectional analyses of 1-year periods. Attempts to determine relations between IT investment and the associated effect of that investment in such short periods have resulted in mostly mixed or negative findings. This study is thought to be the first to apply a multiyear, cross-sectional analysis. In the research, covering a 3-year period and involving firm-level data for a broad cross-section of U.S. industry, we found positive relations between higher levels of IT investment and selected measures representing organizational performance and productivity. These results, being based on multiyear analysis, are thought to be an important contribution in view of the fact that much of the existing literature in the area has failed to explain ...


title: Multitask Compressive Sensing

abstract: Compressive sensing (CS) is a framework whereby one performs N nonadaptive measurements to constitute a vector v isin RN used to recover an approximation u isin RM desired signal u isin RM with N 1 sets of compressive measurements {vi}i=1,L are performed, each of the associated {ui}i=1,Lare recovered one at a time, independently. In many applications the L ldquotasksrdquo defined by the mappings virarrui are not statistically independent, and it may be possible to improve the performance of the inversion if statistical interrelationships are exploited. In this paper, we address this problem within a multitask learning setting, wherein the mapping vrarru for each task corresponds to inferring the parameters (here, wavelet coefficients) associated with the desired signal vi, and a shared prior is placed across all of the L tasks. Under this hierarchical Bayesian modeling, data from all L tasks contribute toward inferring a posterior on the hyperparameters, and once the shared prior is thereby inferred, the data from each of the L individual tasks is then employed to estimate the task-dependent wavelet coefficients. An empirical Bayesian procedure for the estimation of hyperparameters is considered; two fast inference algorithms extending the relevance vector machine (RVM) are developed. Example results on several data sets demonstrate the effectiveness and robustness of the proposed algorithms.


title: An Algorithm for Learning Shape and Appearance Models without Annotations

abstract: This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate shape and appearance model leads to more accurate image registration, which in turn leads to a more accurate shape and appearance model. This leads naturally to an iterative scheme, which is based on a probabilistic generative model that is fit using Gauss-Newton updates within an EM-like framework. It was developed with the aim of enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle ``missing data'', which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1,900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets.


title: Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics

abstract: Widely used in news, business, and educational media, infographics are handcrafted to effectively communicate messages about complex and often abstract topics including `ways to conserve the environment' and `understanding the financial crisis'. Composed of stylistically and semantically diverse visual and textual elements, infographics pose new challenges for computer vision. While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'. To bridge this representation gap, we propose a synthetic data generation strategy: we augment background patches in infographics from our Visually29K dataset with Internet-scraped icons which we use as training data for an icon proposal mechanism. On a test set of 1K annotated infographics, icons are located with 38% precision and 34% recall (the best model trained with natural images achieves 14% precision and 7% recall). Combining our icon proposals with icon classification and text extraction, we present a multi-modal summarization application. Our application takes an infographic as input and automatically produces text tags and visual hashtags that are textually and visually representative of the infographic's topics respectively.


title: Named entity recognition for Amharic using deep learning

abstract: The paper describes a named entity recognition system for Amharic, an under-resourced language, using a recurrent neural network, a bi-directional long short term memory model to identify and classify tokens into six predefined classes: Person, Location, Organization, Time, Title, and Other (non-named entity tokens). Word vectors based on semantic information are built for all tokens using an unsupervised learning algorithm, word2vec. The word vectors were merged with a set of specifically developed language independent features and together fed to the neural network model to predict the classes of the words. When evaluated by 10-fold cross-validation, the created Amharic named entity recogniser achieved good average precision (77.2%), but did worse on recall (63.4%), for a 69.7% F1-score.


title: Empathy decline and its reasons: a systematic review of studies with medical students and residents.

abstract: PURPOSE ::: Empathy is a key element of patient-physician communication; it is relevant to and positively influences patients' health. The authors systematically reviewed the literature to investigate changes in trainee empathy and reasons for those changes during medical school and residency. ::: ::: ::: METHOD ::: The authors conducted a systematic search of studies concerning trainee empathy published from January 1990 to January 2010, using manual methods and the PubMed, EMBASE, and PsycINFO databases. They independently reviewed and selected quantitative and qualitative studies for inclusion. Intervention studies, those that evaluated psychometric properties of self-assessment tools, and those with a sample size <30 were excluded. ::: ::: ::: RESULTS ::: Eighteen studies met the inclusion criteria: 11 on medical students and 7 on residents. Three longitudinal and six cross-sectional studies of medical students demonstrated a significant decrease in empathy during medical school; one cross-sectional study found a tendency toward a decrease, and another suggested stable scores. The five longitudinal and two cross-sectional studies of residents showed a decrease in empathy during residency. The studies pointed to the clinical practice phase of training and the distress produced by aspects of the "hidden," "formal," and "informal" curricula as main reasons for empathy decline. ::: ::: ::: CONCLUSIONS ::: The results of the reviewed studies, especially those with longitudinal data, suggest that empathy decline during medical school and residency compromises striving toward professionalism and may threaten health care quality. Theory-based investigations of the factors that contribute to empathy decline among trainees and improvement of the validity of self-assessment methods are necessary for further research.


title: A GPU-based Streaming Algorithm for High-Resolution Cloth Simulation

abstract: We present a GPU-based streaming algorithm to perform high-resolution and accurate cloth simulation. We map all the components of cloth simulation pipeline, including time integration, collision detection, collision response, and velocity updating to GPU-based kernels and data structures. Our algorithm perform intra-object and inter-object collisions, handles contacts and friction, and is able to accurately simulate folds and wrinkles. We describe the streaming pipeline and address many issues in terms of obtaining high throughput on many-core GPUs. In practice, our algorithm can perform high-fidelity simulation on a cloth mesh with 2M triangles using 3GB of GPU memory. We highlight the parallel performance of our algorithm on three different generations of GPUs. On a high-end NVIDIA Tesla K20c, we observe up to two orders of magnitude performance improvement as compared to a single-threaded CPU-based algorithm, and about one order of magnitude improvement over a 16-core CPU-based parallel implementation.


title: Pre-training Long Short-term Memory Neural Networks for Efficient Regression in Artificial Speech Postfiltering

abstract: Several attempts to enhance statistical parametric speech synthesis have contemplated deep-learning-based postfil-ters, which learn to perform a mapping of the synthetic speech parameters to the natural ones, reducing the gap between them. In this paper, we introduce a new pre-training approach for neural networks, applied in LSTM-based postfilters for speech synthesis, with the objective of enhancing the quality of the synthesized speech in a more efficient manner. Our approach begins with an auto-regressive training of one LSTM network, whose is used as an initialization for postfilters based on a denoising autoencoder architecture. We show the advantages of this initialization on a set of multi-stream postfilters, which encompass a collection of denoising autoencoders for the set of MFCC and fundamental frequency parameters of the artificial voice. Results show that the initialization succeeds in lowering the training time of the LSTM networks and achieves better results in enhancing the statistical parametric speech in most cases, when compared to the common random-initialized approach of the networks.