title: Unfolding temporal dynamics: predicting social media popularity using multi-scale temporal decomposition

abstract: Time information plays a crucial role on social media popularity. Existing research on popularity prediction, effective though, ignores temporal information which is highly related to user-item associations and thus often results in limited success. An essential way is to consider all these factors (user, item, and time), which capture the dynamic nature of photo popularity. In this paper, we present a novel approach to factorize the popularity into user-item context and time-sensitive context for exploring the mechanism of dynamic popularity. The user-item context provides a holistic view of popularity, while the time-sensitive context captures the temporal dynamics nature of popularity. Accordingly, we develop two kinds of time-sensitive features, including user activeness variability and photo prevalence variability. To predict photo popularity, we propose a novel framework named Multi-scale Temporal Decomposition (MTD), which decomposes the popularity matrix in latent spaces based on contextual associations. Specifically, the proposed MTD models time-sensitive context on different time scales, which is beneficial to automatically learn temporal patterns. Based on the experiments conducted on a real-world dataset with 1.29M photos from Flickr, our proposed MTD can achieve the prediction accuracy of 79.8% and outperform the best three state-of-the-art methods with a relative improvement of 9.6% on average.


title: How intermittent breaks in interaction improve collective intelligence

abstract: People influence each other when they interact to solve problems. Such social influence introduces both benefits (higher average solution quality due to exploitation of existing answers through social learning) and costs (lower maximum solution quality due to a reduction in individual exploration for novel answers) relative to independent problem solving. In contrast to prior work, which has focused on how the presence and network structure of social influence affect performance, here we investigate the effects of time. We show that when social influence is intermittent it provides the benefits of constant social influence without the costs. Human subjects solved the canonical traveling salesperson problem in groups of three, randomized into treatments with constant social influence, intermittent social influence, or no social influence. Groups in the intermittent social-influence treatment found the optimum solution frequently (like groups without influence) but had a high mean performance (like groups with constant influence); they learned from each other, while maintaining a high level of exploration. Solutions improved most on rounds with social influence after a period of separation. We also show that storing subjects' best solutions so that they could be reloaded and possibly modified in subsequent rounds-a ubiquitous feature of personal productivity software-is similar to constant social influence: It increases mean performance but decreases exploration.


title: Practical measurement-based spectral rendering of human skin

abstract: Realistic appearance modeling of human skin is an important research topic with a variety of application in computer graphics. Various diffusion based BSSRDF models [Jensen et al. 2001, Donner and Jensen 2005, Donner and Jensen 2006] have been introduced in graphics to efficiently simulate subsurface scattering in skin including modeling its layered structure. These models however assume homogeneous subsurface scattering parameters and produce spatial color variation using an albedo map. In this work, we build upon the spectral scattering model of [Donner and Jensen 2006] and target a practical measurement-based rendering approach for such a spectral BSSRDF. The model assumes scattering in the two primary layers of skin (epidermis and dermis respectively) can be modeled with relative melanin and hemoglobin chromophore concentrations respectively. To drive this model for realistic rendering, we employ measurements of skin patches using an off-the-shelf Miravex Antera 3D camera which provides spatially varying maps of these chromophore concentrations as well as corresponding 3D surface geometry (see Figure 1) using a custom imaging setup.


title: Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks

abstract: Inferring latent attributes of people online is an important social computing task, but requires integrating the many heterogeneous sources of information available on the web. We propose learning individual representations of people using neural nets to integrate rich linguistic and network evidence gathered from social media. The algorithm is able to combine diverse cues, such as the text a person writes, their attributes (e.g. gender, employer, education, location) and social relations to other people. We show that by integrating both textual and network evidence, these representations offer improved performance at four important tasks in social media inference on Twitter: predicting (1) gender, (2) occupation, (3) location, and (4) friendships for users. Our approach scales to large datasets and the learned representations can be used as general features in and have the potential to benefit a large number of downstream tasks including link prediction, community detection, or probabilistic reasoning over social networks.


title: Approximation schemes for covering and packing problems in image processing and VLSI

abstract: A unified and powerful approach is presented for devising polynomial approximation schemes for many strongly NP-complete problems. Such schemes consist of families of approximation algorithms for each desired performance bound on the relative error e > O, with running time that is polynomial when e is fixed. Though the polynomiality of these algorithms depends on the degree of approximation e being fixed, they cannot be improved, owing to a negative result stating that there are no fully polynomial approximation schemes for strongly NP-complete problems unless NP = P. The unified technique that is introduced here, referred to as the shifting strategy, is applicable to numerous geometric covering and packing problems. The method of using the technique and how it varies with problem parameters are illustrated. A similar technique, independently devised by B. S. Baker, was shown to be applicable for covering and packing problems on planar graphs.


title: Link prediction based on temporal similarity metrics using continuous action set learning automata

abstract: Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link prediction calculates a chosen similarity metric for each non-connected link and outputs the links with higher similarity scores as the prediction result. In this paper, we propose a new link prediction method based on temporal similarity metrics and Continuous Action set Learning Automata (CALA). The proposed method takes advantage of using different similarity metrics as well as different time periods. In the proposed algorithm, we try to model the link prediction problem as a noisy optimization problem and use a team of CALAs to solve the noisy optimization problem. CALA is a reinforcement based optimization tool which tries to learn the optimal behavior from the environment feedbacks. To determine the importance of different periods and similarity metrics on the prediction result, we define a coefficient for each of different periods and similarity metrics and use a CALA for each coefficient. Each CALA tries to learn the true value of the corresponding coefficient. Final link prediction is obtained from a combination of different similarity metrics in different times based on the obtained coefficients. The link prediction results reported here show satisfactory of the proposed method for some social network data sets.


Tencent Neural Machine Translation Systems for WMT18


title: Generalizable Data-Free Objective for Crafting Universal Adversarial Perturbations

abstract: Machine learning models are susceptible to adversarial perturbations: small changes to input that can cause large changes in output. It is also demonstrated that there exist input-agnostic perturbations, called universal adversarial perturbations, which can change the inference of target model on most of the data samples. However, existing methods to craft universal perturbations are (i) task specific, (ii) require samples from the training data distribution, and (iii) perform complex optimizations. Additionally, because of the data dependence, fooling ability of the crafted perturbations is proportional to the available training data. In this paper, we present a novel, generalizable and data-free approach for crafting universal adversarial perturbations. Independent of the underlying task, our objective achieves fooling via corrupting the extracted features at multiple layers. Therefore, the proposed objective is generalizable to craft image-agnostic perturbations across multiple vision tasks such as object recognition, semantic segmentation, and depth estimation. In the practical setting of black-box attack scenario (when the attacker does not have access to the target model and it's training data), we show that our objective outperforms the data dependent objectives to fool the learned models. Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations. Significant fooling rates achieved by our objective emphasize that the current deep learning models are now at an increased risk, since our objective generalizes across multiple tasks without the requirement of training data for crafting the perturbations. To encourage reproducible research, we have released the codes for our proposed algorithm.1


Evaluation of the trauma and bone metabolism after internal absorbable screw and metal screw fixation treatment of tibia-fibula fracture


title: Adaptive Sampling Rate Assignment for Block Compressed Sensing ofImages Using Wavelet Transform

abstract: Compressed sensing theory breaks through the limit that two times the bandwidth of the signal sampling rate in Nyquist theorem, providing a guideline for new methods for image acquisition and compression. For still images, block compressed sensing (BCS) has been designed to reduce the size of sensing matrix and the complexity of sampling and re- construction. However, BCS algorithm assigns the same sampling rate for all image blocks without considering the struc- tures of the blocks. In this paper, we present an adaptive sampling rate assignment method for BCS of images using wave- let transform. Wavelet coefficients of an image can reflect the structure information. Therefore, adaptive sampling rates are calculated and assigned to image blocks based on their wavelet coefficients. Several standard test images are em- ployed to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algo- rithm provides superior performance on both the reconstructed image quality and the visual effect.


title: Online Metric Learning and Fast Similarity Search

abstract: Metric learning algorithms can provide useful distance functions for a variety of domains, and recent work has shown good accuracy for problems where the learner can access all distance constraints at once. However, in many real applications, constraints are only available incrementally, thus necessitating methods that can perform online updates to the learned metric. Existing online algorithms offer bounds on worst-case performance, but typically do not perform well in practice as compared to their offline counterparts. We present a new online metric learning algorithm that updates a learned Mahalanobis metric based on LogDet regularization and gradient descent. We prove theoretical worst-case performance bounds, and empirically compare the proposed method against existing online metric learning algorithms. To further boost the practicality of our approach, we develop an online locality-sensitive hashing scheme which leads to efficient updates to data structures used for fast approximate similarity search. We demonstrate our algorithm on multiple datasets and show that it outperforms relevant baselines.


title: The immunology of the allergy epidemic and the hygiene hypothesis

abstract: The immunology of the hygiene hypothesis of allergy is complex and involves the loss of cellular and humoral immunoregulatory pathways as a result of the adoption of a Western lifestyle and the disappearance of chronic infectious diseases. The influence of diet and reduced microbiome diversity now forms the foundation of scientific thinking on how the allergy epidemic occurred, although clear mechanistic insights into the process in humans are still lacking. Here we propose that barrier epithelial cells are heavily influenced by environmental factors and by microbiome-derived danger signals and metabolites, and thus act as important rheostats for immunoregulation, particularly during early postnatal development. Preventive strategies based on this new knowledge could exploit the diversity of the microbial world and the way humans react to it, and possibly restore old symbiotic relationships that have been lost in recent times, without causing disease or requiring a return to an unhygienic life style.


title: A domain-specific approach to heterogeneous parallelism

abstract: Exploiting heterogeneous parallel hardware currently requires mapping application code to multiple disparate programming models. Unfortunately, general-purpose programming models available today can yield high performance but are too low-level to be accessible to the average programmer. We propose leveraging domain-specific languages (DSLs) to map high-level application code to heterogeneous devices. To demonstrate the potential of this approach we present OptiML, a DSL for machine learning. OptiML programs are implicitly parallel and can achieve high performance on heterogeneous hardware with no modification required to the source code. For such a DSL-based approach to be tractable at large scales, better tools are required for DSL authors to simplify language creation and parallelization. To address this concern, we introduce Delite, a system designed specifically for DSLs that is both a framework for creating an implicitly parallel DSL as well as a dynamic runtime providing automated targeting to heterogeneous parallel hardware. We show that OptiML running on Delite achieves single-threaded, parallel, and GPU performance superior to explicitly parallelized MATLAB code in nearly all cases.


title: Flowchart recognition for non-textual information retrieval in patent search

abstract: Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.


title: So close yet so far: Motor anomalies impacting on social functioning in autism spectrum disorder

abstract: Abstract Difficulties in the social domain and motor anomalies have been widely investigated in Autism Spectrum Disorder (ASD). However, they have been generally considered as independent, and therefore tackled separately. Recent advances in neuroscience have hypothesized that the cortical motor system can play a role not only as a controller of elementary physical features of movement, but also in a complex domain as social cognition. Here, going beyond previous studies on ASD that described difficulties in the motor and in the social domain separately, we focus on the impact of motor mechanisms anomalies on social functioning. We consider behavioral, electrophysiological and neuroimaging findings supporting the idea that motor cognition is a critical “intermediate phenotype” for ASD. Motor cognition anomalies in ASD affect the processes of extraction, codification and subsequent translation of “external” social information into the motor system. Intriguingly, this alternative “motor” approach to the social domain difficulties in ASD may be promising to bridge the gap between recent experimental findings and clinical practice, potentially leading to refined preventive approaches and successful treatments.


GENETIC ALGORITHM APPLIED TO OPTIMIZATION OF THE SHIP HULL FORM WITH RESPECT TO SEAKEEPING PERFORMANCE


title: An Efficient K-Means Clustering Algorithm

abstract: In this paper, we present a novel algorithm for performing k-means clustering. It organizes all the patterns in a k-d tree structure such that one can find all the patterns which are closest to a given prototype efficiently. The main intuition behind our approach is as follows. All the prototypes are potential candidates for the closest prototype at the root level. However, for the children of the root node, we may be able to prune the candidate set by using simple geometrical constraints. This approach can be applied recursively until the size of the candidate set is one for each node. Our experimental results demonstrate that our scheme can improve the computational speed of the direct k-means algorithm by an order to two orders of magnitude in the total number of distance calculations and the overall time of computation.


title: Static Detection of Logic Vulnerabilities in Java Web Applications

abstract: This paper concerns about logic vulnerabilities that result from faulty logic of a web application. Logic vulnerabilities typically accompany with the exposure of unexpected functionalities and lead to the bypass of the intended constraints. From a semantic perspective, logic vulnerabilities occur when mistakes arise in the control flows guarding the processes of invoking critical functionalities. In this paper, we propose the first lightweight static analysis approach to automatically detect logic vulnerabilities in Java web applications. Logic errors in our approach are characterized as erroneous invocations of functionalities. Program-slicing technique has been leveraged to capture the processes of invoking critical functionalities. A back-tracing algorithm is originally designed to extract control flows guarding functionality-invocation processes. Finally, logic vulnerability detection is transformed into mining abnormal functionality-invocation processes in a cluster of similar ones by comparing these processes' control flows. We implemented our approach in a prototype tool named logic vulnerability detector and evaluated it on seven real-world applications scaled from thousands to million lines of code. The evaluation results show that our approach achieves bigger coverage with acceptable cost and better scalability than previous approaches. Copyright © 2013 John Wiley & Sons, Ltd.


title: Concept-Oriented Deep Learning: Generative Concept Representations

abstract: Generative concept representations have three major advantages over discriminative ones: they can represent uncertainty, they support integration of learning and reasoning, and they are good for unsupervised and semi-supervised learning. We discuss probabilistic and generative deep learning, which generative concept representations are based on, and the use of variational autoencoders and generative adversarial networks for learning generative concept representations, particularly for concepts whose data are sequences, structured data or graphs.


title: A tool-supported compliance process for software systems

abstract: Laws and regulations impact the design of software systems, as they may introduce additional requirements and possible conflicts with pre-existing requirements. We propose a systematic, tool-supported process for establishing compliance of a software system with a given law. The process elicits new requirements from the law, compares them with existing ones and manages conflicts, exploiting a set of heuristics, partially supported by a tool. We illustrate our proposal through an exploratory study using the Italian Privacy Law. We also present results of a preliminary empirical study that indicates that adoption of the process improves compliance analysis for a simple compliance scenario.


title: Treatment of Pseudomonas aeruginosa Biofilm–Infected Wounds with Clinical Wound Care Strategies

abstract: BACKGROUND ::: Bacterial biofilm is recognized as a major detriment to wound healing. The efficacy of traditional wound care against biofilm has never been studied. The authors evaluated the effect of clinical strategies against biofilm-infected wounds in a quantitative, in vivo model. ::: ::: ::: METHODS ::: Using a rabbit ear biofilm model, wounds were inoculated with Pseudomonas aeruginosa or left as uninfected controls. Inoculated wounds acted as untreated controls or underwent treatment: every-other-day sharp débridement (I), lavage (II), Silvadene (III), or lavage and Silvadene (IV), or initial débridement with daily lavage and Silvadene (V). Wounds were harvested on days 12 and 18. Histological wound healing parameters and viable bacterial counts were measured. Biofilm structure was studied with scanning electron microscopy. ::: ::: ::: RESULTS ::: Uninfected controls healed better than P. aeruginosa biofilm-infected wounds across all parameters (p = 0.01). Groups IV and V demonstrated improved healing (p = 0.05) and decreased bacterial count (p = 0.05) compared with untreated P. aeruginosa biofilm, whereas groups I through III showed no differences in either. Scanning electron microscopy following a group V treatment showed temporary disruption of biofilm structure, which reformed in 24 hours. ::: ::: ::: CONCLUSIONS ::: Pseudomonal biofilm markedly impairs wound healing, shown quantitatively using our in vivo model. Despite common practice, wound care strategies cannot restore biofilm wounds to a healing phenotype when used alone or infrequently. The durability of biofilm extends nonhealing wound chronicity, thus requiring aggressive, multimodal therapy aimed at reducing bacterial burden. The authors' novel, rigorous study validates critical principles applicable to all clinical wound care.


title: Pursuing Differentiated Services in a SDN-Based IoT-Oriented Pub/Sub System

abstract: Software-defined networking (SDN) is a promising future network architecture which introduces new dimensionsin flexibility and adaptability to cope with different Quality of service (QoS) metrics, such as latency constraint, loss rate, etc. In this paper, we provide differentiated Services for flows of a topic-oriented publish/subscribe system to address some of the QoS guarantee issues, by leveraging multi-priority queue functions of SDN. Flows are bifurcated into multi-priority queues in terms of packet properties such as delay and reliability requirements. An active feedback-based queue management mechanism is responsible for ensuring bounded queueing delays for those higher priority queues, and a mapping method accounts for mapping time-constrained flows to different priority queues. The Experiments verify the effectiveness of the queue mechanism and flow scheduling.


title: Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture–on-chip (4C)

abstract: The spatial organization of DNA in the cell nucleus is an emerging key contributor to genomic function. We developed 4C technology (chromosome conformation capture (3C)-on-chip), which allows for an unbiased genome-wide search for DNA loci that contact a given locus in the nuclear space. We demonstrate here that active and inactive genes are engaged in many long-range intrachromosomal interactions and can also form interchromosomal contacts. The active beta-globin locus in fetal liver preferentially contacts transcribed, but not necessarily tissue-specific, loci elsewhere on chromosome 7, whereas the inactive locus in fetal brain contacts different transcriptionally silent loci. A housekeeping gene in a gene-dense region on chromosome 8 forms long-range contacts predominantly with other active gene clusters, both in cis and in trans, and many of these intra- and interchromosomal interactions are conserved between the tissues analyzed. Our data demonstrate that chromosomes fold into areas of active chromatin and areas of inactive chromatin and establish 4C technology as a powerful tool to study nuclear architecture.


title: Relationships between compulsive exercise, quality of life, psychological distress and motivation to change in adults with anorexia nervosa

abstract: Background ::: For people with anorexia nervosa (AN), compulsive exercise is characterized by extreme concerns about the perceived negative consequences of stopping/reducing exercise, dysregulation of affect, and inflexible exercise routines. It is associated with increased eating disorder psychopathology and poor clinical outcome. However, its relationships with two important clinical issues, quality of life (QoL) and motivation to change, are currently unknown. This study aimed to assess the cross-sectional relationships between compulsive exercise, QoL, psychological distress (anxiety and depressive symptoms, and obsessive-compulsive traits) and motivation to change in patients with AN.


title: AES and the cryptonite crypto processor

abstract: CRYPTONITE is a programmable processor tailored to the needs of crypto algorithms. The design of CRYPTONITE was based on an in-depth application analysis in which standard crypto algorithms (AES, DES, MD5, SHA-1, etc) were distilled down to their core functionality. We describe this methodology and use AES as a central example. Starting with a functional description of AES, we give a high level account of how to implement AES efficiently in hardware, and present several novel optimizations (which are independent of CRYPTONITE).We then describe the CRYPTONITE architecture, highlighting how AES implementation issues influenced the design of the processor and its instruction set. CRYPTONITE is designed to run at high clock rates and be easy to implement in silicon while providing a significantly better performance/area/power tradeoff than general purpose processors.


title: Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning

abstract: Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution, the classic GCN can not take full advantage of unlabeled data, especially when the unlabeled node is far from labeled ones. To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning. SEGCN contains a student model and a teacher model. As a student, it not only learns to correctly classify the labeled nodes, but also tries to be consistent with the teacher on unlabeled nodes in more challenging situations, such as a high dropout rate and graph collapse. As a teacher, it averages the student model weights and generates more accurate predictions to lead the student. In such a mutual-promoting process, both labeled and unlabeled samples can be fully utilized for backpropagating effective gradients to train GCN. In three article classification tasks, i.e. Citeseer, Cora and Pubmed, we validate that the proposed method matches the state of the arts in the classification accuracy.


title: Efficient Constellation-Based Map-Merging for Semantic SLAM

abstract: Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable “duplicate” landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loopclosure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of “best” hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.


title: Individualization of Information Systems - Analyzing Antecedents of IT Consumerization Behavior

abstract: Recently, much attention in research and practice has been devoted to the topic of IT consumerization, referring to the adoption of private consumer IT in the workplace. However, research lacks an analysis of possible antecedents of the trend on an individual level. To close this gap, we derive a theoretical model for IT consumerization behavior based on the theory of planned behavior and perform a quantitative analysis. Our investigation shows that it is foremost determined by normative pressures, specifically the behavior of friends, co*workers an d direct supervisors. In addition, behavioral beliefs and control beliefs were found to affect the intention to use non* corporate IT. With respect to the former, we found expected performance improvements and an increase in ease of use to be two of the key determinants. As for the latter, especially monetary costs and installation knowledge were correlated with IT consumerization intention.


title: No-cost distance estimation using standard WSN radios

abstract: Being able to determine the location of a node is of great advantage in many IoT and WSN applications. For example, in health care scenarios or for autonomous configuration of IoT setups this information can be useful. One of the key challenges in localization is to estimate the distance between nodes. Most present indoor localization systems require additional hardware for this estimation which is costly in terms of money and energy consumption. To overcome this disadvantage, we developed a system which is able to perform distance measurements without adding any extra hardware and costs. It is based on phase measurements performed by the IEEE 802.15.4 transceiver chip that is normally solely used to realize communication. In the evaluation we investigate the performance of our system in different real world environments that are typical for IoT and WSN setups.


title: Key Factors for Determining Student Satisfaction in Distance Learning Courses: A Study of Allama Iqbal Open University (AIOU) Islamabad, Pakistan

abstract: In this paper, the primary objective of the research team was to find out the relationship between student satisfaction and the following variables of the distance learning environment: instructors' performance, course evaluation and studentinstructor interaction. The sample consisted of 245 students of Allama Iqbal Open University of Pakistan. The purpose of this study was to address the most recent problem of AIOU students relevant to their distance learning. The problem was that most of the people in Pakistan perceived distance learning as poorer quality. Therefore, the researchers conducted this study to find out whether it's only people perception or there is any thing in reality about the poorer outcome of the distance learning students as compare to traditional students. By using correlation, regression and descriptive analysis, it was found that just like the traditional education, in distance learning education at AIOU, enough interaction take place between students and their instructors, courses are up to date and well designed, instructors are devoted, motivated and equipped with the required skill and knowledge. Moreover, the faculty at AIOU is delivering distance learning courses that meet the students' needs in regard to students-instructor interaction, instructor performance and course evaluation.


title: nuts-flow/ml: data pre-processing for deep learning

abstract: Data preprocessing is a fundamental part of any machine learning application and frequently the most time-consuming aspect when developing a machine learning solution. Preprocessing for deep learning is characterized by pipelines that lazily load data and perform data transformation, augmentation, batching and logging. Many of these functions are common across applications but require different arrangements for training, testing or inference. Here we introduce a novel software framework named nuts-flow/ml that encapsulates common preprocessing operations as components, which can be flexibly arranged to rapidly construct efficient preprocessing pipelines for deep learning.


title: Scriptless attacks: stealing the pie without touching the sill

abstract: Due to their high practical impact, Cross-Site Scripting (XSS) attacks have attracted a lot of attention from the security community members. In the same way, a plethora of more or less effective defense techniques have been proposed, addressing the causes and effects of XSS vulnerabilities. NoScript, and disabling scripting code in non-browser applications such as e-mail clients or instant messengers. As a result, an adversary often can no longer inject or even execute arbitrary scripting code in several real-life scenarios. In this paper, we examine the attack surface that remains after XSS and similar scripting attacks are supposedly mitigated by preventing an attacker from executing JavaScript code. We address the question of whether an attacker really needs JavaScript or similar functionality to perform attacks aiming for information theft. The surprising result is that an attacker can also abuse Cascading Style Sheets (CSS) in combination with other Web techniques like plain HTML, inactive SVG images or font files. Through several case studies, we introduce the so called scriptless attacks and demonstrate that an adversary might not need to execute code to preserve his ability to extract sensitive information from well protected websites. More precisely, we show that an attacker can use seemingly benign features to build side channel attacks that measure and exfiltrate almost arbitrary data displayed on a given website. We conclude this paper with a discussion of potential mitigation techniques against this class of attacks. In addition, we have implemented a browser patch that enables a website to make a vital determination as to being loaded in a detached view or pop-up window. This approach proves useful for prevention of certain types of attacks we here discuss.


title: Analysis of Respiratory Sounds: State of the Art

abstract: Objective: This paper describes state of the art, scientific publications and ongoing research related to the methods of analysis of respiratory sounds. ::: ::: Methods and material: Review of the current medical and technological literature using Pubmed and personal experience. ::: ::: Results: The study includes a description of the various techniques that are being used to collect auscultation sounds, a physical description of known pathologic sounds for which automatic detection tools were developed. Modern tools are based on artificial intelligence and on technics such as artificial neural networks, fuzzy systems, and genetic algorithms… ::: ::: Conclusion: The next step will consist in finding new markers so as to increase the efficiency of decision aid algorithms and tools.


title: Fundamental of Content Based Image Retrieval

abstract: The aim of this paper is to review the present state of the art in Content-Based Image Retrieval (CBIR), a technique for retrieving images on the basis of automatically-derived features like color, texture and shape. Our findings are based both on a review of the relevant literature and on discussions with researchers in the field. There is need to find a desired image from a collection is shared by many professional groups, including journalists, design engineers and art historians. During the requirements of image users can vary considerably, it can be useful to illustrate image queries into three levels of abstraction first is primitive features such as color or shape, second is logical features such as the identity of objects shown and last is abstract attributes such as the significance of the scenes depicted. While CBIR systems currently operate well only at the lowest of these levels, most users demand higher levels of retrieval.


title: A service computing manifesto: the next 10 years

abstract: Mapping out the challenges and strategies for the widespread adoption of service computing.


title: Towards Continuous Integration and Continuous Delivery in the Automotive Industry

abstract: Development cycles are getting shorter and Continuous Integration and Delivery are being established in the automotive industry. We give an overview of the peculiarities in an automotive deployment pipeline, introduce technologies used and analyze Tesla's deliveries as a state-of-the-art showcase.


title: Textural Features for Image Classification

abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.


title: Developing SWOT Analysis from Customer Satisfaction Surveys

abstract: Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is one of the most commonly used tools for strategic planning. It helps organizations gain a better insight of their internal and external business environment when making strategic plans and decisions. Although SWOT analysis has been widely accepted as a tool for strategic planning, the traditional method of conducting SWOT analysis does not prioritize and is likely to hold subjective views that may result in an improper strategic action. One way to make better use of the SWOT framework is to take the customer's perspective in to account which helps the companies to ensure that their perceived capabilities are also recognized and valued by their customer. This work proposes a combination of Back Propagation Neural Network based Importance-Performance Analysis (BPNN-IPA) and SWOT analysis. The 'Importance' is derived by applying BPNN on a customer satisfaction survey and 'Performance' is computed as the mean value of customer's satisfaction rating. The intersection of these two components creates a two-dimensional matrix termed, IPA matrix, which can be identified as strengths and weaknesses of the company. Base on the same principle, opportunities and threats are obtained through the IPA matrix of the company's competitor. The BPNN-IPA yields significant improvement in estimating of attribute importance compared to conventional IPA and regression-based IPA approach, thereby generating effective SWOT analysis. It is expected that this approach will provide more accurate information for strategic planning as it generates prioritized SWOT factors based on the customer's perception.


title: Digital Forensic Approaches for Amazon Alexa Ecosystem

abstract: Internet of Things devices such as the Amazon Echo are undoubtedly great sources of potential digital evidence due to their ubiquitous use and their always on mode of operation, constituting a human life black box. The Amazon Echo in particular plays a centric role for the cloud based intelligent virtual assistant Alexa developed by Amazon Lab126. The Alexa enabled wireless smart speaker is the gateway for all voice commands submitted to Alexa. Moreover, the IVA interacts with a plethora of compatible IoT devices and third party applications that leverage cloud resources. Understanding the complex cloud ecosystem that allows ubiquitous use of Alexa is paramount on supporting digital investigations when need raises. This paper discusses methods for digital forensics pertaining to the IVA Alexa ecosystem. The primary contribution of this paper consists of a new efficient approach of combining cloud native forensics with client side forensics, to support practical digital investigations. Based on a deep understanding of the targeted ecosystem, we propose a proof of concept tool, CIFT, that supports identification, acquisition and analysis of both native artifacts from the cloud and client centric artifacts from local devices.


title: Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards

abstract: This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning. The algorithm uses a Multi- Armed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10,000 unique 3D object models and 2.5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, to provide a similarity metric between objects, and the Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing experiment runtime by up to three orders of magnitude. Experiments suggest that correlated bandit techniques can use a cloud-based network of object models to significantly reduce the number of samples required for robust grasp planning. We report on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction. Code and updated information is available at http://berkeleyautomation.github.io/dex-net/.


title: Design and Research on Private Cloud Computing Architecture to Support Smart Grid

abstract: In recent years, State Grid Corporation of China has been vigorously promoting smart grid construction, and cloud computing is developing rapidly. Trend of the electric power enterprise informatization construction will be the private cloud computing, which will become the comprehensive platform of smart grid. Comparing private cloud with public cloud, this paper lists differences between them and puts forward an architecture of private cloud computing to support smart gird, expounds structure of each layer, and presents concept of private cloud computing operating system and network virtualization. It provides the theoretical reference to build the private cloud computing, thus promotes the construction of the smart grid.


title: Embracing Diversity: Enhanced DSP Blocks for Low-Precision Deep Learning on FPGAs

abstract: Use of reduced precisions in Deep Learning (DL) inference tasks has recently been shown to significantly improve accelerator performance and greatly reduce both model memory footprint and the required external memory bandwidth. With appropriate network retuning, reduced precision networks can achieve accuracy close or equal to that of full-precision floating-point models. Given the wide spectrum of precisions used in DL inference, FPGAs' ability to create custom bit-width datapaths gives them an advantage over other acceleration platforms in this domain. However, the embedded DSP blocks in the latest Intel and Xilinx FPGAs do not natively support precisions below 18-bit and thus can not efficiently pack low-precision multiplications, leaving the DSP blocks under-utilized. In this work, we present an enhanced DSP block that can efficiently pack 2× as many 9-bit and 4× as many 4-bit multiplications compared to the baseline Arria-10-like DSP block at the cost of 12% block area overhead which leads to only 0.6% total FPGA core area increase. We quantify the performance gains of using this enhanced DSP block in two state-of-the-art convolutional neural network accelerators on three different models: AlexNet, VGG-16, and ResNet-50. On average, the new DSP block enhanced the computational performance of the 8-bit and 4-bit accelerators by 1.32× and 1.6× and at the same time reduced the utilized chip area by 15% and 30% respectively.


title: Training and Inference with Integers in Deep Neural Networks

abstract: Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring both training and inference processes to low-bitwidth integers has not been demonstrated simultaneously. In this work, we develop a new method termed as "WAGE" to discretize both training and inference, where weights (W), activations (A), gradients (G) and errors (E) among layers are shifted and linearly constrained to low-bitwidth integers. To perform pure discrete dataflow for fixed-point devices, we further replace batch normalization by a constant scaling layer and simplify other components that are arduous for integer implementation. Improved accuracies can be obtained on multiple datasets, which indicates that WAGE somehow acts as a type of regularization. Empirically, we demonstrate the potential to deploy training in hardware systems such as integer-based deep learning accelerators and neuromorphic chips with comparable accuracy and higher energy efficiency, which is crucial to future AI applications in variable scenarios with transfer and continual learning demands.


title: A review on consensus algorithm of blockchain

abstract: Blockchain is the basic technology of bitcoin. With the value appreciation and stable operation of bitcoin, blockchain is attracting more and more attention in many areas. Blockchain has the characteristics of decentralization, stability, security, and non-modifiability. It has the potential to change the network architecture. The consensus algorithm plays a crucial role in maintaining the safety and efficiency of blockchain. Using a right algorithm may bring a significant increase to the performance of blockchain application. In this paper, we reviewed the basic principles and characteristics of the consensus algorithms and analyzed the performance and application scenarios of different consensus mechanisms. We also gave a technical guidance of selecting a suitable consensus algorithm and summarized the limitations and future development of blockchain technology.


title: From Queries to Cards: Re-ranking Proactive Card Recommendations Based on Reactive Search History

abstract: The growing accessibility of mobile devices has substantially reformed the way users access information. While the reactive search by query remains as common as before, recent years have witnessed the emergence of various proactive systems such as Google Now and Microsoft Cortana. In these systems, relevant content is presented to users based on their context without a query. Interestingly, despite the increasing popularity of such services, there is very little known about how users interact with them. In this paper, we present the first study on user interactions with information cards. We demonstrate that the usage patterns of these cards vary depending on time and location. We also show that while overall different topics are clicked by users on proactive and reactive platforms, the topics of the clicked documents by the same user tend to be consistent cross-platform. Furthermore, we propose a supervised framework for re-ranking proactive cards based on the user's context and past history. To train our models, we use the viewport duration and clicks to infer pseudo-relevance labels for the cards. Our results suggest that the quality of card ranking can be significantly improved particularly when the user's reactive search history is %leveraged and matched against the proactive data about the cards.


title: Local trilateral upsampling for thermal image

abstract: This paper presents an image upsampling method. Joint-bilateral filtering has been successfully applied to this problem that upsample “target” images using high-resolutional “control” images. In this filtering, the kernel is a product of weights representing spatial proximity and color (or intensity) proximity of the “control” image. However, when the “target” image involves textures that are invisible in the “control” image, these textures are damaged by this upsampling. In the case of thermal target image and visible-light control image, we can often find such textures. For solving this problem, we first propose trilateral upsampling, which is a simple extension of joint-bilateral upsampling incorporating color (or intensity) proximity weight in the “target” image. For improving this method so as to increase the spatial resolution, we introduce weight control based on local mutual information between target and control images. We confirmed that resulted images have higher spatial resolution and lower noise.


title: HDP code: A Horizontal-Diagonal Parity Code to Optimize I/O load balancing in RAID-6

abstract: With higher reliability requirements in clusters and data centers, RAID-6 has gained popularity due to its capability to tolerate concurrent failures of any two disks, which has been shown to be of increasing importance in large scale storage systems. Among various implementations of erasure codes in RAID-6, a typical set of codes known as Maximum Distance Separable (MDS) codes aim to offer data protection against disk failures with optimal storage efficiency. However, because of the limitation of horizontal parity or diagonal/anti-diagonal parities used in MDS codes, storage systems based on RAID-6 suffers from unbalanced I/O and thus low performance and reliability. To address this issue, in this paper, we propose a new parity called Horizontal-Diagonal Parity (HDP), which takes advantages of both horizontal and diagonal/anti-diagonal parities. The corresponding MDS code, called HDP code, distributes parity elements uniformly in each disk to balance the I/O workloads. HDP also achieves high reliability via speeding up the recovery under single or double disk failure. Our analysis shows that HDP provides better balanced I/O and higher reliability compared to other popular MDS codes.


title: Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation

abstract: Tracking multiple persons is a challenging task when persons move in groups and occlude each other. Existing group-based methods have extensively investigated how to make group division more accurately in a tracking-by-detection framework; however, few of them quantify the group dynamics from the perspective of targets’ spatial topology or consider the group in a dynamic view. Inspired by the sociological properties of pedestrians, we propose a novel socio-topology model with a topology-energy function to factor the group dynamics of moving persons and groups. In this model, minimizing the topology-energy-variance in a two-level energy form is expected to produce smooth topology transitions, stable group tracking, and accurate target association. To search for the strong minimum in energy variation, we design the discrete group-tracklet jump moves embedded in the gradient descent method, which ensures that the moves reduce the energy variation of group and trajectory alternately in the varying topology dimension. Experimental results on both RGB and RGB-D data sets show the superiority of our proposed model for multiple person tracking in crowd scenes.


title: Synthesis of Nanoparticles from Malleable and Ductile Metals Using Powder-Free, Reactant-Assisted Mechanical Attrition

abstract: A reactant-assisted mechanochemical method was used to produce copious nanoparticles from malleable/ductile metals, demonstrated here for aluminum, iron, and copper. The milling media is intentionally degraded via a reactant-accelerated wear process, where the reactant aids particle production by binding to the metal surfaces, enhancing particle production, and reducing the tendency toward mechanochemical (cold) welding. The mechanism is explored by comparing the effects of different types of solvents and solvent mixtures on the amount and type of particles produced. Particles were functionalized with oleic acid to aid in particle size separation, enhance dispersion in hydrocarbon solvents, and protect the particles from oxidation. For aluminum and iron, the result is air-stable particles, but for copper, the suspended particles are found to dissolve when exposed to air. Characterization was performed using electron microscopy, dynamic light scattering, Fourier transform infrared spectroscopy, solid state...


title: Fully Convolutional Multi-Class Multiple Instance Learning

abstract: Multiple instance learning (MIL) can reduce the need for costly annotation in tasks such as semantic segmentation by weakening the required degree of supervision. We propose a novel MIL formulation of multi-class semantic segmentation learning by a fully convolutional network. In this setting, we seek to learn a semantic segmentation model from just weak image-level labels. The model is trained end-to-end to jointly optimize the representation while disambiguating the pixel-image label assignment. Fully convolutional training accepts inputs of any size, does not need object proposal pre-processing, and offers a pixelwise loss map for selecting latent instances. Our multi-class MIL loss exploits the further supervision given by images with multiple labels. We evaluate this approach through preliminary experiments on the PASCAL VOC segmentation challenge.


title: Health chair: implicitly sensing heart and respiratory rate

abstract: People interact with chairs frequently, making them a potential location to perform implicit health sensing that requires no additional effort by users. We surveyed 550 participants to understand how people sit in chairs and inform the design of a chair that detects heart and respiratory rate from the armrests and backrests of the chair respectively. In a laboratory study with 18 participants, we evaluated a range of common sitting positions to determine when heart rate and respiratory rate detection was possible (32% of the time for heart rate, 52% for respiratory rate) and evaluate the accuracy of the detected rate (83% for heart rate, 73% for respiratory rate). We discuss the challenges of moving this sensing to the wild by evaluating an in-situ study totaling 40 hours with 11 participants. We show that, as an implicit sensor, the chair can collect vital signs data from its occupant through natural interaction with the chair.


title: Support vector machines and Word2vec for text classification with semantic features

abstract: With the rapid expansion of new available information presented to us online on a daily basis, text classification becomes imperative in order to classify and maintain it. Word2vec offers a unique perspective to the text mining community. By converting words and phrases into a vector representation, word2vec takes an entirely new approach on text classification. Based on the assumption that word2vec brings extra semantic features that helps in text classification, our work demonstrates the effectiveness of word2vec by showing that tf-idf and word2vec combined can outperform tf-idf because word2vec provides complementary features (e.g. semantics that tf-idf can't capture) to tf-idf. Our results show that the combination of word2vec weighted by tf-idf and tf-idf does not outperform tf-idf consistently. It is consistent enough to say the combination of the two can outperform either individually.


title: Core stability: The centerpiece of any training program

abstract: Core strengthening and stability exercises have become key components of training programs for athletes of all levels. The core muscles act as a bridge between upper and lower limbs, and force is transferred from the core, often called the powerhouse, to the limbs. Stability initially requires maintenance of a neutral spine but must progress beyond the neutral zone in a controlled manner. Some studies have demonstrated a relationship between core stability and increased incidence of injury. A training program should start with exercises that isolate specific core muscles but must progress to include complex movements and incorporate other training principles.


title: Structured Perceptron with Inexact Search

abstract: Most existing theory of structured prediction assumes exact inference, which is often intractable in many practical problems. This leads to the routine use of approximate inference such as beam search but there is not much theory behind it. Based on the structured perceptron, we propose a general framework of "violation-fixing" perceptrons for inexact search with a theoretical guarantee for convergence under new separability conditions. This framework subsumes and justifies the popular heuristic "early-update" for perceptron with beam search (Collins and Roark, 2004). We also propose several new update methods within this framework, among which the "max-violation" method dramatically reduces training time (by 3 fold as compared to early-update) on state-of-the-art part-of-speech tagging and incremental parsing systems.


title: Patient neglect in healthcare institutions: a systematic review and conceptual model

abstract: Patient neglect is an issue of increasing public concern in Europe and North America, yet remains poorly understood. This is the first systematic review on the nature, frequency and causes of patient neglect as distinct from patient safety topics such as medical error. The Pubmed, Science Direct, and Medline databases were searched in order to identify research studies investigating patient neglect. Ten articles and four government reports met the inclusion criteria of reporting primary data on the occurrence or causes of patient neglect. Qualitative and quantitative data extraction investigated (1) the definition of patient neglect, (2) the forms of behaviour associated with neglect, (3) the reported frequency of neglect, and (4) the causes of neglect. Patient neglect is found to have two aspects. First, procedure neglect, which refers to failures of healthcare staff to achieve objective standards of care. Second, caring neglect, which refers to behaviours that lead patients and observers to believe that staff have uncaring attitudes. The perceived frequency of neglectful behaviour varies by observer. Patients and their family members are more likely to report neglect than healthcare staff, and nurses are more likely to report on the neglectful behaviours of other nurses than on their own behaviour. The causes of patient neglect frequently relate to organisational factors (e.g. high workloads that constrain the behaviours of healthcare staff, burnout), and the relationship between carers and patients. A social psychology-based conceptual model is developed to explain the occurrence and nature of patient neglect. This model will facilitate investigations of i) differences between patients and healthcare staff in how they perceive neglect, ii) the association with patient neglect and health outcomes, iii) the relative importance of system and organisational factors in causing neglect, and iv) the design of interventions and health policy to reduce patient neglect.


title: Automatically improving accuracy for floating point expressions

abstract: Scientific and engineering applications depend on floating point arithmetic to approximate real arithmetic. This approximation introduces rounding error, which can accumulate to produce unacceptable results. While the numerical methods literature provides techniques to mitigate rounding error, applying these techniques requires manually rearranging expressions and understanding the finer details of floating point arithmetic. We introduce Herbie, a tool which automatically discovers the rewrites experts perform to improve accuracy. Herbie's heuristic search estimates and localizes rounding error using sampled points (rather than static error analysis), applies a database of rules to generate improvements, takes series expansions, and combines improvements for different input regions. We evaluated Herbie on examples from a classic numerical methods textbook, and found that Herbie was able to improve accuracy on each example, some by up to 60 bits, while imposing a median performance overhead of 40%. Colleagues in machine learning have used Herbie to significantly improve the results of a clustering algorithm, and a mathematical library has accepted two patches generated using Herbie.


title: Driver head pose estimation using efficient descriptor fusion

abstract: A great interest is focused on driver assistance systems using the head pose as an indicator of the visual focus of attention and the mental state. In fact, the head pose estimation is a technique allowing to deduce head orientation relatively to a view of camera and could be performed by model-based or appearance-based approaches. Model-based approaches use a face geometrical model usually obtained from facial features, whereas appearance-based techniques use the whole face image characterized by a descriptor and generally consider the pose estimation as a classification problem. Appearance-based methods are faster and more adapted to discrete pose estimation. However, their performance depends strongly on the head descriptor, which should be well chosen in order to reduce the information about identity and lighting contained in the face appearance. In this paper, we propose an appearance-based discrete head pose estimation aiming to determine the driver attention level from monocular visible spectrum images, even if the facial features are not visible. Explicitly, we first propose a novel descriptor resulting from the fusion of four most relevant orientation-based head descriptors, namely the steerable filters, the histogram of oriented gradients (HOG), the Haar features, and an adapted version of speeded up robust feature (SURF) descriptor. Second, in order to derive a compact, relevant, and consistent subset of descriptor’s features, a comparative study is conducted on some well-known feature selection algorithms. Finally, the obtained subset is subject to the classification process, performed by the support vector machine (SVM), to learn head pose variations. As we show in experiments with the public database (Pointing’04) as well as with our real-world sequence, our approach describes the head with a high accuracy and provides robust estimation of the head pose, compared to state-of-the-art methods.


title: Web Site Usability, Design, and Performance Metrics

abstract: Web sites provide the key interface for consumer use of the Internet. This research reports on a series of three studies that developand validate Web site usability, design and performance metrics, including download delay, navigability, site content, interactivity, and responsiveness. The performance metric that was developed includes the subconstructs user satisfaction, the likelihood of return, and the frequency of use.Data was collected in 1997, 1999, and 2000 from corporate Web sites via three methods, namely, a jury, third-party ratings, and a software agent. Significant associations between Web site design elements and Web site performance indicate that the constructs demonstrate good nomological validity. Together, the three studies provide a set of measures with acceptable validity and reliability. The findings also suggest lack of significant common methods biases across the jury-collected data, third-party data, and agent-collected data.Results suggest that Web site success is a first-order construct. Moreover, Web site success is significantly associated with Web site download delay (speed of access and display rate within the Web site), navigation (organization, arrangement, layout, and sequencing), content (amount and variety of product information), interactivity (customization and interactivity), and responsiveness (feedback options and FAQs).


title: Motivation and Transactional, Charismatic, and Transformational Leadership: A Test of Antecedents:

abstract: Relationships between leaders' motivation and their use of charismatic, transactional, and / or transformational leadership were examined in this study. One hundred eighty-six leaders and 759 direct reports from a variety of organizations were sampled. Leaders were administered the Motivation Sources Inventory (MSI) while followers reported leaders' full range leadership behaviors using the Multifactor Leadership Questionnaire (MLQ-rater version). Leaders were also administered the self-rating version of the Multi-factor Leadership Questionnaire (MLQ-rater version). The Motivation Sources Inventory subscales subsequently significantly correlated with leader self-reports of inspirational motivation, idealized influence (behavior) and individualized consideration (range, r = .10 to .29), as well as with raters' perceptions of inspirational motivation, idealized influence (behavior) and individualized consideration (range, r = .18 to .19). The Motivation Sources Inventory subscales significantly correlated w...


title: A SOFTWARE ARCHITECTURE FOR DISTRIBUTED VOLUME RENDERING ON HPC SYSTEMS

abstract: We present a scalable software architecture for distributed direct volume rendering on HPC systems. Our approach allows for generically replacing components along the distributed volume rendering pipeline. Renderer components range from highly specialized GPU renderers that implement state of the art features to more versatile remote renderers, that can make use of numerous distributed memory nodes to exploit sort-last parallel rendering, with each node running a generic renderer component itself. Renderer components that are designed to run on CPUs or GPUs respectively make our software architecture most useful for HPC systems. Using zero configuration networking, our system is able to scale at run time by introducing additional resources without having to reset the whole cluster. Generic I/O subsystems allow for various interprocess communication technologies to be used interchangeably, while generalizing the display phase and decoupling it from the rendering and I/O phases can be exploited to hide latency. We integrate our proposed software architecture into the freely available open source direct volume rendering library Virvo.


title: A 28-nm FD-SOI 115-fs Jitter PLL-Based LO System for 24–30-GHz Sliding-IF 5G Transceivers

abstract: A system for local oscillator (LO) signal generation in 5G millimeter-wave (mmW) multi-antenna transceivers is presented. The system is modular with one phase locked loop (PLL) per antenna element transceiver, and a test circuit implemented in 28-nm fully depleted silicon on insulator (FD-SOI) CMOS features two such PLLs and a 491.52 MHz crystal oscillator (XO) generating a common frequency reference. A fractional-N architecture is employed to achieve high-frequency resolution, and the quantization noise is reduced using a novel frequency divider, which achieves full integer resolution while still using a pre-scaler. The system covers the 3rd Generation Partnership Project (3GPP) bands n257 and n258, achieved by a digital coarse tuning of the voltage-controlled oscillator (VCO). The chip area of each PLL is 0.11 mm2, and 0.029 mm2 for the XO. The total power consumption of the system is 35 mW, where each PLL consumes 15.4 mW and the XO consumes 0.84 mW. The total rms jitter from 20-kHz to 500-MHz offset for a 26-GHz carrier is just 115 fs, corresponding to an FOM $_{\vphantom {R_{j}}j}$ of −244 dB, which is the best reported figure for a fractional-N PLL above 15 GHz. The error-vector magnitude (EVM) due to phase noise is −34.6 dBc using an orthogonal frequency-division multiplexing (OFDM) signal with 120-kHz sub-carrier spacing, sufficient to support 256 QAM.


title: A new deep convolutional neural network for fast hyperspectral image classification

abstract: Abstract Artificial neural networks (ANNs) have been widely used for the analysis of remotely sensed imagery. In particular, convolutional neural networks (CNNs) are gaining more and more attention in this field. CNNs have proved to be very effective in areas such as image recognition and classification, especially for the classification of large sets composed by two-dimensional images. However, their application to multispectral and hyperspectral images faces some challenges, especially related to the processing of the high-dimensional information contained in multidimensional data cubes. This results in a significant increase in computation time. In this paper, we present a new CNN architecture for the classification of hyperspectral images. The proposed CNN is a 3-D network that uses both spectral and spatial information. It also implements a border mirroring strategy to effectively process border areas in the image, and has been efficiently implemented using graphics processing units (GPUs). Our experimental results indicate that the proposed network performs accurately and efficiently, achieving a reduction of the computation time and increasing the accuracy in the classification of hyperspectral images when compared to other traditional ANN techniques.


title: Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

abstract: In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has already been used as ground truth data for training such networks, this abundant data source remains rarely exploited as an input information layer. In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images. Especially , we look into fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks. We illustrate how these methods can be successfully used on two public datasets: ISPRS Potsdam and DFC2017. We show that OpenStreetMap data can efficiently be integrated into the vision-based deep learning models and that it significantly improves both the accuracy performance and the convergence speed of the networks.


title: Significant Causes and Effects of Variation Orders in Construction Projects

abstract: Variation Order (VO) is a common phenomenon in construction projects. It involves an amendment of the original scope of work as in the contract. VO is caused from various factors. Variations often cause disputes and dissatisfactions among the parties involved in construction projects. Thus, it is very important to control VOs in a construction project. For this, the first step is to uncover and understand the causes and effects of VO. Hence, the aim of this study is to determine the significant causes and effects of VO in construction projects. Data collection involved the survey with a structured questionnaire consisting of 18 causes and 9 effects of variation orders identified through comprehensive literature review. Survey was carried out among client representatives, consultants and contractors involved in handling projects of Malaysian Public Works Department (PWD) known as Jabatan Kerja Raya Malaysia (JKR). A total of 101 completed questionnaire sets were collected against 200 questionnaires distributed among the practitioners. Collected questionnaires were analyzed with statistical software package SPSS and Average Index formula. The results of the study showed that in Malaysia’s JKR projects often variation orders are occurred; these VO’s are majorly caused because of unavailability of equipment, poor workmanship and design complexity. While most significant effects of VO on the projects are increased project cost, delay in completion and logistic delays. Early participation of professionals may be beneficial in reducing the occurrence of variations. Also, improved design and avoiding frequent design changes will be very effective in controlling the problem of variations.


title: Quaternion-based Orientation Estimation Fusing a Camera and Inertial Sensors for a Hovering UAV

abstract: Orientation estimation in quadrotors is essential for low-level stability control and for high-levelnavigation and motion planning. This is usually carried out by fusing measurements from different sensorsincluding inertial sensor, magnetic compass, sonar, GPS, camera, etc. In indoor environments, the GPSsignal is not available and the Earth’s magnetic field is highly disturbed. In this work we present a newapproach for visual estimation of the yaw angle based on spectral features, and a fusion algorithm usingunit quaternions, both applied to a hovering quadrotor. The approach is based on an Inertial MeasurementUnit and a downward-looking camera, rigidly attached to the quadrotor. The fusion is performed by means of an Extended Kalman Filter with a double measurement update stage. The inertial sensors provideinformation for orientation estimation, mainly for roll and pitch angles, whereas the camera is used for measuring the yaw angle. A new method to integrate the yaw angle in the measurement update of the filteris also proposed, using an augmented measurement vector in order to avoid discontinuities in the filterinnovation vector. The proposed approach is evaluated with real data and compared with ground truthgiven by a Vicon system. Experimental results are presented for both the visual yaw angle estimationand the fusion with the inertial sensors, showing an improvement in orientation estimation.


title: Wireless Measurement of RFID IC Impedance

abstract: Accurate knowledge of the input impedance of a radio-frequency identification (RFID) integrated circuit (IC) at its wake-up power is valuable as it enables the design of a performance-optimized tag for a specific IC. However, since the IC impedance is power dependent, few methods exist to measure it without advanced equipment. We propose and demonstrate a wireless method, based on electromagnetic simulation and threshold power measurement, applicable to fully assembled RFID tags, to determine the mounted IC's input impedance in the absorbing state, including any parasitics arising from the packaging and the antenna-IC connection. The proposed method can be extended to measure the IC's input impedance in the modulating state as well.


title: Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing

abstract: Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. Smoothing approaches have been investigated as a viable alternative to extended Kalman filter (EKF)-based solutions to the problem. In particular, approaches have been looked at that factorize either the associated information matrix or the measurement Jacobian into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact; they can be used in either batch or incremental mode; are better equipped to deal with non-linear process and measurement models; and yield the entire robot trajectory, at lower cost for a large class of SLAM problems. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. This paper presents the theory underlying these methods, along with an interpretation of factorization in terms of the graphical model associated with the SLAM problem. Both simulation results and actual SLAM experiments in large-scale environments are presented that underscore the potential of these methods as an alternative to EKF-based approaches.


title: HyFlex: A Benchmark Framework for Cross-domain Heuristic Search

abstract: This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.


title: Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling

abstract: Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level language models offer a partial solution in that they can create word types not attested in the training corpus, they do not capture the "bursty" distribution of such words. In this paper, we augment a hierarchical LSTM language model that generates sequences of word tokens character by character with a caching mechanism that learns to reuse previously generated words. To validate our model we construct a new open-vocabulary language modeling corpus (the Multilingual Wikipedia Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse languages and demonstrate the effectiveness of our model across this range of languages.


title: Direct Evidence for a Motivation to See the Way Things Are as the Way They Should Be

abstract: How powerful is the status quo in determining people's social ideals? The authors propose (a) that people engage in injunctification, that is, a motivated tendency to construe the current status quo as the most desirable and reasonable state of affairs (i.e., as the most representative of how things should be); (b) that this tendency is driven, at least in part, by people's desire to justify their sociopolitical systems; and (c) that injunctification has profound implications for the maintenance of inequality and societal change. Four studies, across a variety of domains, provided supportive evidence. When the motivation to justify the sociopolitical system was experimentally heightened, participants injunctified extant (a) political power (Study 1), (b) public funding policies (Study 2), and (c) unequal gender demographics in the political and business spheres (Studies 3 and 4, respectively). It was also demonstrated that this motivated phenomenon increased derogation of those who act counter to the status quo (Study 4). Theoretical implications for system justification theory, stereotype formation, affirmative action, and the maintenance of inequality are discussed.


title: Selective control of gait subtasks in robotic gait training: foot clearance support in stroke survivors with a powered exoskeleton

abstract: BackgroundRobot-aided gait training is an emerging clinical tool for gait rehabilitation of neurological patients. This paper deals with a novel method of offering gait assistance, using an impedance controlled exoskeleton (LOPES). The provided assistance is based on a recent finding that, in the control of walking, different modules can be discerned that are associated with different subtasks. In this study, a Virtual Model Controller (VMC) for supporting one of these subtasks, namely the foot clearance, is presented and evaluated.MethodsThe developed VMC provides virtual support at the ankle, to increase foot clearance. Therefore, we first developed a new method to derive reference trajectories of the ankle position. These trajectories consist of splines between key events, which are dependent on walking speed and body height. Subsequently, the VMC was evaluated in twelve healthy subjects and six chronic stroke survivors. The impedance levels, of the support, were altered between trials to investigate whether the controller allowed gradual and selective support. Additionally, an adaptive algorithm was tested, that automatically shaped the amount of support to the subjects’ needs. Catch trials were introduced to determine whether the subjects tended to rely on the support. We also assessed the additional value of providing visual feedback.ResultsWith the VMC, the step height could be selectively and gradually influenced. The adaptive algorithm clearly shaped the support level to the specific needs of every stroke survivor. The provided support did not result in reliance on the support for both groups. All healthy subjects and most patients were able to utilize the visual feedback to increase their active participation.ConclusionThe presented approach can provide selective control on one of the essential subtasks of walking. This module is the first in a set of modules to control all subtasks. This enables the therapist to focus the support on the subtasks that are impaired, and leave the other subtasks up to the patient, encouraging him to participate more actively in the training. Additionally, the speed-dependent reference patterns provide the therapist with the tools to easily adapt the treadmill speed to the capabilities and progress of the patient.


title: Learning Distributed Representations for Multilingual Text Sequences

abstract: We propose a novel approach to learning distributed representations of variable-length text sequences in multiple languages simultaneously. Unlike previous work which often derive representations of multi-word sequences as weighted sums of individual word vectors, our model learns distributed representations for phrases and sentences as a whole. Our work is similar in spirit to the recent paragraph vector approach but extends to the bilingual context so as to efficiently encode meaning-equivalent text sequences of multiple languages in the same semantic space. Our learned embeddings achieve state-of-theart performance in the often used crosslingual document classification task (CLDC) with an accuracy of 92.7 for English to German and 91.5 for German to English. By learning text sequence representations as a whole, our model performs equally well in both classification directions in the CLDC task in which past work did not achieve.


title: A multimodal execution monitor with anomaly classification for robot-assisted feeding

abstract: Activities of daily living (ADLs) are important for quality of life. Robotic assistance offers the opportunity for people with disabilities to perform ADLs on their own. However, when a complex semi-autonomous system provides real-world assistance, occasional anomalies are likely to occur. Robots that can detect, classify and respond appropriately to common anomalies have the potential to provide more effective and safer assistance. We introduce a multimodal execution monitor to detect and classify anomalous executions when robots operate near humans. Our system builds on our past work on multimodal anomaly detection. Our new monitor classifies the type and cause of common anomalies using an artificial neural network. We implemented and evaluated our execution monitor in the context of robot-assisted feeding with a general-purpose mobile manipulator. In our evaluations, our monitor outperformed baseline methods from the literature. It succeeded in detecting 12 common anomalies from 8 able-bodied participants with 83% accuracy and classifying the types and causes of the detected anomalies with 90% and 81% accuracies, respectively. We then performed an in-home evaluation with Henry Evans, a person with severe quadriplegia. With our system, Henry successfully fed himself while the monitor detected, classified the types, and classified the causes of anomalies with 86%, 90%, and 54% accuracy, respectively.


title: Thermal and saline tolerance of Antarctic krill Euphausia superba under controlled in-situ aquarium conditions

abstract: As a key species of the Southern Ocean ecosystem, the thermal and saline tolerances of Antarctic krill (Euphausia superba Dana) are relatively unknown because of the challenging environment and complicated situations needed for observation have inhibited in-situ experiments in the field. Hence, the thermal and saline tolerance of krill were examined under in-situ aquarium conditions with different controlled scenarios. According to the experiments, the critical lethal times of krill were 24 h, 2 h and 0.5 h under 9°C, 12°C, and 15°C, respectively, and the estimated 50% lethal times were about 17.1 h and 1.7 h under 12°C and 15°C, respectively. Additionally, the critical lethal times (the estimated 50% lethal times) of krill were approximately 14 h and 0.5 h (about 22.9 h and 1.7 h) of salinity under 19.7 and 15.9, respectively. The observed critical and 50% lethal times of krill were 0.5 h and approximately 1.4 h, respectively, salinity under 55.2. The critical and 50% lethal temperatures of krill were 13°C and approximately 14.2°C, respectively. Additionally, the critical and 50% lethal salinity was 19.6 and approximately 17.5 for the lower saline (below normal oceanic salinity [34.4]) environment and 50.3 and approximately 53.2 for the higher saline (above 34.4) environment, respectively. The upper thermal and saline preferences of krill can be considered 6°C and 26.8 to 41.2, respectively. These results can provide potential scenarios for predicting the possible fate of this key species in the Southern Ocean.


title: Spatial Reference in Linguistic Human-Robot Interaction: Iterative, Empirically Supported Development of a Model of Projective Relations

abstract: The aim of our research is to enable spontaneous and efficient spatial reference to objects in human-robot interaction situations. This paper presents the iterative, empirically based design of a robotic system that uses a computational model for identifying objects on the basis of a range of spatial reference systems. The efficiency of the system is evaluated by two successive empirical studies involving uninformed users. The linguistic analysis points to the striking variability in speakers' spontaneous strategies and preferences, and it motivates a number of modifications of the computational model.


CitePlag: A Citation-based Plagiarism Detection System Prototype


title: Characterizing Stylistic Elements in Syntactic Structure

abstract: Much of the writing styles recognized in rhetorical and composition theories involve deep syntactic elements. However, most previous research for computational stylometric analysis has relied on shallow lexico-syntactic patterns. Some very recent work has shown that PCFG models can detect distributional difference in syntactic styles, but without offering much insights into exactly what constitute salient stylistic elements in sentence structure characterizing each authorship. In this paper, we present a comprehensive exploration of syntactic elements in writing styles, with particular emphasis on interpretable characterization of stylistic elements. We present analytic insights with respect to the authorship attribution task in two different domains.


title: The decycling number of graphs

abstract: For a graph G and S ⊂ V (G), if G − S is acyclic, then S is said to be a decycling set of G. The size of a smallest decycling set of G is called the decycling number of G. The purpose of this paper is to provide a review of recent results and open problems on this parameter. Results to be reviewed include recent work on decycling numbers of cubes, grids and snakes and bounds on the decycling number of cubic graphs, and expected bounds on the decycling numbers of random regular graphs. A structural description of graphs with a fixed decycling number based on connectivity is also presented.


title: Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition

abstract: Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). In this paper, for emotion recognition from speech, we investigate DNN-HMMs with restricted Boltzmann Machine (RBM) based unsupervised pre-training, and DNN-HMMs with discriminative pre-training. Emotion recognition experiments are carried out on these two models on the eNTERFACE'05 database and Berlin database, respectively, and results are compared with those from the GMM-HMMs, the shallow-NN-HMMs with two layers, as well as the Multi-layer Perceptrons HMMs (MLP-HMMs). Experimental results show that when the numbers of the hidden layers as well hidden units are properly set, the DNN could extend the labeling ability of GMM-HMM. Among all the models, the DNN-HMMs with discriminative pre-training obtain the best results. For example, for the eNTERFACE'05 database, the recognition accuracy improves 12.22% from the DNN-HMMs with unsupervised pre-training, 11.67% from the GMM-HMMs, 10.56% from the MLP-HMMs, and even 17.22% from the shallow-NN-HMMs, respectively.


title: A survey of B-tree logging and recovery techniques

abstract: B-trees have been ubiquitous in database management systems for several decades, and they serve in many other storage systems as well. Their basic structure and their basic operations are well understood including search, insertion, and deletion. However, implementation of transactional guarantees such as all-or-nothing failure atomicity and durability in spite of media and system failures seems to be difficult. High-performance techniques such as pseudo-deleted records, allocation-only logging, and transaction processing during crash recovery are widely used in commercial B-tree implementations but not widely understood. This survey collects many of these techniques as a reference for students, researchers, system architects, and software developers. Central in this discussion are physical data independence, separation of logical database contents and physical representation, and the concepts of user transactions and system transactions. Many of the techniques discussed are applicable beyond B-trees.


title: Exploiting SCADA vulnerabilities using a Human Interface Device

abstract: SCADA (Supervisory Control and Data Acquisition) systems are used to control and monitor critical national infras-tructure functions like electricity, gas, water and railways. Field devices such as PLC’s (Programmable Logic Controllers) are one of the most critical components of a control system. Cyber-attacks usually target valuable infrastructures assets, taking advantage of architectural/technical vulnerabilities or even weaknesses in the defense systems. Even though novel intrusion detection systems are being implemented and used for defending cyber-attacks, certain vulnerabilities of SCADA systems can still be exploited. In this article we present an attack scenario based on a Human Interface Device (HID) device which is used as a means of communication/exploitation tool to compromise SCADA systems. The attack, which is a normal series of commands that are sent from the HID to the PLC cannot be detected through current intrusion detection mechanisms. Finaly we provide possible counter measures and defense mechanisms against this kind of cyber attacks.


title: Comparison of three classification techniques: CART, C4.5 and Multi-Layer Perceptrons

abstract: In this paper, after some introductory remarks into the classification problem as considered in various research communities, and some discussions concerning some of the reasons for ascertaining the performances of the three chosen algorithms, viz., CART (Classification and Regression Tree), C4.5 (one of the more recent versions of a popular induction tree technique known as ID3), and a multi-layer perceptron (MLP), it is proposed to compare the performances of these algorithms under two criteria: classification and generalisation. It is found that, in general, the MLP has better classification and generalisation accuracies compared with the other two algorithms.


title: DLR high altitude balloon launched experimental glider (HABLEG): System design, control and flight data analysis

abstract: In this paper the autopilot setup and the lessons learned from a successful balloon launched high altitude glider mission will be presented. Flying an unmanned aerial vehicle inside the stratosphere means operating it outside visual line of sight, in low pressures and cold temperatures. These environmental conditions pose many challenges to the UAV aerodynamic design, the autopilot system and the ground infrastructure. In May 2015 the HABLEG airplane was carried up to an altitude of 20km by a balloon. When the target altitude was reached it automatically transitioned to horizontal flight and landed back at the landing site where the balloon was released 145 minutes earlier. We will present and analyzed flight test result from every phase of the mission and summarize the lessons learned.


title: Deep Outdoor Illumination Estimation

abstract: We present a CNN-based technique to estimate high-dynamic range outdoor illumination from a single low dynamic range image. To train the CNN, we leverage a large dataset of outdoor panoramas. We fit a low-dimensional physically-based outdoor illumination model to the skies in these panoramas giving us a compact set of parameters (including sun position, atmospheric conditions, and camera parameters). We extract limited field-of-view images from the panoramas, and train a CNN with this large set of input image–output lighting parameter pairs. Given a test image, this network can be used to infer illumination parameters that can, in turn, be used to reconstruct an outdoor illumination environment map. We demonstrate that our approach allows the recovery of plausible illumination conditions and enables photorealistic virtual object insertion from a single image. An extensive evaluation on both the panorama dataset and captured HDR environment maps shows that our technique significantly outperforms previous solutions to this problem.


title: Arboricity and subgraph listing algorithms

abstract: In this paper we introduce a new simple strategy into edge-searching of a graph, which is useful to the various subgraph listing problems. Applying the strategy, we obtain the following four algorithms. The first one lists all the triangles in a graph G in $O(a(G)m)$ time, where m is the number of edges of G and $a(G)$ the arboricity of G. The second finds all the quadrangles in $O(a(G)m)$ time. Since $a(G)$ is at most three for a planar graph G, both run in linear time for a planar graph. The third lists all the complete subgraphs $K_l $ of order l in $O(la(G)^{l - 2} m)$ time. The fourth lists all the cliques in $O(a(G)m)$ time per clique. All the algorithms require linear space. We also establish an upper bound on $a(G)$ for a graph $G:a(G) \leqq \lceil (2m + n)^{1/2} \rceil $, where n is the number of vertices in G.