title: Getting Everyone on Board: The Role of Inspirational Leadership in Geographically Dispersed Teams

abstract: A rich body of research in the area of leadership has examined the influence of transformational/charismatic forms of leadership on employees' motivation, attitudes, and behaviors. This research is based on the assumption that leaders are able to influence followers based on close, sustained, and personalized contact with them. However, new organizational realities are challenging this assumption. Drawing on the intersections between social identity theory and leadership research, this study highlights the importance of inspirational leaders who, by developing socialized relationships with team members, can foster attitudes that are critical for team effectiveness in geographically dispersed settings. Findings support the role of this form of leadership in dispersed settings. Inspirational leadership emerged as a significant predictor of individuals' trust in team members and commitment to the team. Further, the positive relationship between inspirational leadership and individuals' commitment to the team and trust in team members was strengthened in teams that were more dispersed suggesting that inspirational leaders are important in all contexts but that their importance is underscored in highly dispersed contexts. Finally, shared perceptions of trust and commitment predicted performance at the team level.


title: An objective comparison of languages for teaching introductory programming

abstract: The question of which language to use in introductory programming has been cause for protracted debate, often based on emotive opinions. Several studies on the benefits of individual languages or comparisons between two languages have been conducted, but there is still a lack of objective data used to inform these comparisons. This paper presents a list of criteria based on design decisions used by prominent teaching-language creators. The criteria, once justified, are then used to compare eleven languages which are currently used in introductory programming courses. Recommendations are made on how these criteria can be used or adapted for different situations.


Performance Measurement for Knowledge Management : Designing a Reference Model


title: A new Hough transform mapping for ellipse detection

abstract: Detecting geometric primitives in images is one of the basic tasks of computer vision. The Hough transform (HT) and its extensions constitute a popular method for extracting geometric shapes. Primitives on the HT are represented by parametric curves with a number of free parameters. The principal concept of the HT is to define a mapping between an image space and a parameter space. Each edge point in an image is transformed by the mapping to determine cells in the parameter space whose associated parameters are such that the defined primitive passes through the data point. The chosen cells are accumulated and after all the points in an image have been considered, local maxima in the accumulator correspond to the parameters of the specified shape. Because a curve with n parameters requires an n-dimensional parameter space, many applications of the HT concern line and circle detection. In order to overcome the excessive time and space requirements for ellipse extraction, proposed techniques (Yip et al., Pao et al., Yoo and Sethi, Wu and Wang, Ho and Chen) decompose the five dimensional parameter space into several sub spaces of fewer dimensions. The decomposition is achieved by using geometric features which define constraints in the organization of edge data. These constraints include distance and angular relationships which define relative positions between a set of edge points. Hence, the parameters are computed after labelling the points which satisfy the constraints in a computational intensive approach. Here, we show how it is possible to decompose the parameter space, based on the polar definition of an ellipse. Angular information, defined from the variation of a position function, represents local change in the curvature of border points. This information is used to formulate expressions which define an ellipse by including local shape properties. We show that in order to achieve a parameter decomposition (due to the ellipse anisotropy) it is necessary to consider the angular change of the second derivative (tangent angle of the second directional derivative). We compute angular information by taking a pair of points and their gradient direction. This avoids the constraints which define relative position, as required by other approach


Statistical Methods for Reliability Data Using SAS R Software


An Eye Tracking Study of How Font Size and Type Influence Online Reading


title: Hash Function Vulnerability Index and Hash Chain Attacks

abstract: A hash chain is constructed by repeated hashing from an initial value. While it finds applications for network protocol design it also poses threats to hash function one-way and collision-free properties. We investigate the complexity of breaking hash function security properties by hash chain attacks using probabilistic algorithms. We show that each hash function has a vulnerability index that measures its inherent vulnerability against hash chains attacks. The vulnerability index is invariant with respect to different types of hash chain attacks using probabilistic algorithms with or without an oracle. It provides a criterion for the evaluation of the prevalent hash functions and also be used as a guide for the design of new hash functions. We analyze the properties of the vulnerability indices and estimate their values of the commonly used hash functions: MD5, SHA1, RIPEMD128 and RIPEMD160. Preliminary experiments indicate that their vulnerability indices are rather low; that is, it is hard to break their security properties by hash chain attacks with probabilistic algorithms.


title: LPV control of ICE torque ripple in hybrid electric vehicles

abstract: Abstract In this paper, a linear parameter varying (LPV) control strategy is implemented in order to reduce torque ripple in Hybrid Electric Vehicles (HEV). The Internal Combustion Engine (ICE) ripples are attenuated by a Permanent Magnet Synchronous Machine (PMSM). Because the ICE motor generates persistent disturbances which vary with the rotational speed, it is necessary to design a LPV controller. The control strategy uses the internal model principle. The controller is designed by several steps which includes linear matrix inequalities optimization (LMIs). A test bed simulator is used through a complete modeling of a hybrid powertrain propulsion. Simulation results show the effectiveness of the control.


title: Demographic Dialectal Variation in Social Media: A Case Study of African-American English

abstract: Though dialectal language is increasingly abundant on social media, few resources exist for developing NLP tools to handle such language. We conduct a case study of dialectal language in online conversational text by investigating African-American English (AAE) on Twitter. We propose a distantly supervised model to identify AAE-like language from demographics associated with geo-located messages, and we verify that this language follows well-known AAE linguistic phenomena. In addition, we analyze the quality of existing language identification and dependency parsing tools on AAE-like text, demonstrating that they perform poorly on such text compared to text associated with white speakers. We also provide an ensemble classifier for language identification which eliminates this disparity and release a new corpus of tweets containing AAE-like language.


title: A Pinch of Humor for Short-Text Conversation: An Information Retrieval Approach

abstract: The paper describes a work in progress on humorous response generation for short-text conversation using information retrieval approach. We gathered a large collection of funny tweets and implemented three baseline retrieval models: BM25, the query term reweighting model based on syntactic parsing and named entity recognition, and the doc2vec similarity model. We evaluated these models in two ways: in situ on a popular community question answering platform and in laboratory settings. The approach proved to be promising: even simple search techniques demonstrated satisfactory performance. The collection, test questions, evaluation protocol, and assessors’ judgments create a ground for future research towards more sophisticated models.


title: SIPS: Unsupervised Succinct Interest Points

abstract: Detecting interest points is a key component of vision-based estimation algorithms, such as visual odometry or visual SLAM. Classically, interest point detection has been done with methods such as Harris, FAST, or DoG. Recently, better detectors have been proposed based on Neural Networks. Traditionally, interest point detectors have been designed to maximize repeatability or matching score. Instead, we pursue another metric, which we call succinctness. This metric captures the minimum amount of interest points that need to be extracted in order to achieve accurate relative pose estimation. Extracting a minimum amount of interest points is attractive for many applications, because it reduces computational load, memory, and, potentially, data transmission. We propose a novel reinforcement- and ranking-based training framework, which uses a full relative pose estimation pipeline during training. It can be trained in an unsupervised manner, without pose or 3D point ground truth. Using this training framework, we present a detector which outperforms previous interest point detectors in terms of succinctness on a variety of publicly available datasets.


title: Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine

abstract: Advance in information and communication technologies, force us to keep most of the information electronically, consequently, the security of information has become a fundamental issue. The traditional intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper particle swarm optimization (PSO) is used to implement a feature selection, and support vector machine (SVMs) with the one-versus-rest method serve as a fitness function of PSO for classification problems from the literature. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods


title: Numerical evaluation of the first-order characteristic function of a filtered impulsive noise sample

abstract: Approximate expressions for the first-order characteristic function of a filtered impulsive noise process are derived herein. Such approximations are useful in the analysis of various non-Gaussian communication channels.


title: Class-E high-efficiency RF/microwave power amplifiers: Principles of operation, design procedures, and experimental verification,” in Analog Circuit Design: Scalable Analog Circuit Design

abstract: Class-E power amplifiers [1]–[6] achieve significantly higher efficiency than for conventional Class-B or -C. Class E operates the transistor as an on/off switch and shapes the voltage and current waveforms to prevent simultaneous high voltage and high current in the transistor; that minimizes the power dissipation, especially during the switching transitions. In the published low-order Class-E circuit, a transistor performs well at frequencies up to about 70% of its frequency of good Class-B operation (an unpublished higher-order Class-E circuit operates well up to about double that frequency). This paper covers circuit operation, improved-accuracy explicit design equations for the published low-order Class E circuit, optimization principles, experimental results, tuning procedures, and gate/base driver circuits. Previously published analytically derived design equations did not include the dependence of output power (P) on load-network loaded Q (Q L ); as a result, the output power was 38% to 10% less than expected, for Q L values in the usual range of 1.8 to 5. This paper includes an accurate new equation for P that includes the effect of Q L .


title: Microbiome-Gut-Brain Axis and Toll-Like Receptors in Parkinson’s Disease

abstract: Parkinson’s disease (PD) is a progressively debilitating neurodegenerative disease characterized by α-synucleinopathy, which involves all districts of the brain-gut axis, including the central, autonomic and enteric nervous systems. The highly bidirectional communication between the brain and the gut is markedly influenced by the microbiome through integrated immunological, neuroendocrine and neurological processes. The gut microbiota and its relevant metabolites interact with the host via a series of biochemical and functional inputs, thereby affecting host homeostasis and health. Indeed, a dysregulated microbiota-gut-brain axis in PD might lie at the basis of gastrointestinal dysfunctions which predominantly emerge many years prior to the diagnosis, corroborating the theory that the pathological process is spread from the gut to the brain. Toll-like receptors (TLRs) play a crucial role in innate immunity by recognizing conserved motifs primarily found in microorganisms and a dysregulation in their signaling may be implicated in α-synucleinopathy, such as PD. An overstimulation of the innate immune system due to gut dysbiosis and/or small intestinal bacterial overgrowth, together with higher intestinal barrier permeability, may provoke local and systemic inflammation as well as enteric neuroglial activation, ultimately triggering the development of alpha-synuclein pathology. In this review, we provide the current knowledge regarding the relationship between the microbiota-gut⁻brain axis and TLRs in PD. A better understanding of the dialogue sustained by the microbiota-gut-brain axis and innate immunity via TLR signaling should bring interesting insights in the pathophysiology of PD and provide novel dietary and/or therapeutic measures aimed at shaping the gut microbiota composition, improving the intestinal epithelial barrier function and balancing the innate immune response in PD patients, in order to influence the early phases of the following neurodegenerative cascade.


title: Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

abstract: Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.


title: DC-DC converter power stage with cascoded transistors and automatic dead time generation

abstract: The paper presents a cascoded power stage with automatic dead time generation. The circuit is using the inter-transistor node voltages of the cascode configuration as feedback control signals to delay turning ON the power transistors. The circuit is designed as the output stage of a fully-integrated buck converter. The steady-state operation is described. The waveforms simulated on 45-nm CMOS process show that in steady-state operation the short-circuit path and body diode conductions are avoided while effective zero-voltage switching (ZVS) are provided both for ground and power supply line; the calculated dead times are in a good agreement with simulation results.


title: Information security culture: A definition and a literature review

abstract: Abstract — Information security culture guides how things are done in organization in regard to information security, with the aim of protecting the information assets and influencing employees’ security behavior. In this paper, we review key literature on information security culture that was published in the period during 2003 - 2013. The objective was to identify the frameworks that were proposed to establish and maintain information security culture inside organizations. Moreover, other issues were investigated, such as the appropriate definition, and methodology used in this field of research. The review identified 62 papers that were published in that period (2003-2013) and were focused on information security culture in organizations as a main topic of that paper. The review draws the attention to the importance of the information security culture and the need for more investigation in the field to provide a comprehensive framework of the establishment of information security culture within organization.


title: Improved Dual-loop Control plus Repetitive Control for PWM Inverters

abstract: This paper presents the analysis and design of a multiple-loop feedback control scheme for single-phase voltage source inverters with a L-C filter. In the proposed control scheme, a PI regulator in the outer voltage loop and a P regulator in the inner current loop are incorporated, and a plug-in repetitive controller is adopted to improve steady-state performance. Load current feed-forward compensation is additional adopted to reach minimum steady state error. Through theoretical analysis, it can be proved that dual-loop controller realizes arbitrary pole placement and thus the design procedure is greatly simplified. The superiority of the control scheme is demonstrated both by simulations and experiments.


title: Stamp Verification for Automated Document Authentication

abstract: Stamps, along with signatures, can be considered as the most widely used extrinsic security feature in paper documents. In contrast to signatures, however, for stamps little work has been done to automatically verify their authenticity. In this paper, an approach for verification of color stamps is presented. We focus on photocopied stamps as non-genuine stamps. Our previously presented stamp detection method is improved and extended to verify that the stamp is genuine and not a copy. Using a variety of features, a classifier is trained that allows successful separation between genuine stamps and copied stamps. Sensitivity and specificity of up to \(95\,\%\) could be obtained on a data set that is publicly available.


title: Background Prior-Based Salient Object Detection via Deep Reconstruction Residual

abstract: Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper.


title: High accuracy approximate multiplier with error correction

abstract: Approximate computing has gained significant attention due to the popularity of multimedia applications. In this paper, we propose a novel inaccurate 4:2 counter that can effectively reduce the partial product stages of the Wallace Multiplier. Compared to the normal Wallace multiplier, our proposed multiplier can reduce 10.74% of power consumption and 9.8% of delay on average, with an error rate from 0.2% to 13.76% The accuracy of amplitude is higher than 99% In addition, we further enhance the design with error-correction units to provide accurate results. The experimental results show that the extra power consumption of correct units is lower than 6% on average. Compared to the normal Wallace multiplier, the average latency of our proposed multiplier with EDC is 6% faster when the bit-width is 32, and the power consumption is still 10% lower than that of the Wallace multiplier.


title: Analysis of Cryptographic Protocols AKI, ARPKI and OPT using ProVerif and AVISPA

abstract: In recent years, the area of formal verification of cryptographic protocols became important because of the active intruders. These intruders can find out the flaws in the protocols and can use them to create attacks. To avoid such possible attacks, the protocols must be verified to check if the protocols contain any flaws. The formal verification tools have helped in verifying and correcting the protocols. Various tools are available these days for verifying the protocols. In this paper, the two verification tools namely ProVerif and AVISPA are used for analysis of protocols - AKI (Accountable Key Infrastructure), ARPKI (Attack Resilient Public Key Infrastructure) and OPT (Origin and Path Trace). A comparative evaluation of the selected tools is presented and revealed security properties of the protocols selected. Index Terms—Formal Verification, Cryptographic Protocols, ProVerif, AVISPA, Comparison of Tools.


title: FPGA-based Data Partitioning

abstract: Implementing parallel operators in multi-core machines often involves a data partitioning step that divides the data into cache-size blocks and arranges them so to allow concurrent threads to process them in parallel. Data partitioning is expensive, in some cases up to 90% of the cost of, e.g., a parallel hash join. In this paper we explore the use of an FPGA to accelerate data partitioning. We do so in the context of new hybrid architectures where the FPGA is located as a co-processor residing on a socket and with coherent access to the same memory as the CPU residing on the other socket. Such an architecture reduces data transfer overheads between the CPU and the FPGA, enabling hybrid operator execution where the partitioning happens on the FPGA and the build and probe phases of a join happen on the CPU. Our experiments demonstrate that FPGA-based partitioning is significantly faster and more robust than CPU-based partitioning. The results open interesting options as FPGAs are gradually integrated tighter with the CPU.


title: ISO-TimeML Event Extraction in Persian Text

abstract: Recognizing TimeML events and identifying their attributes, are important tasks in natural language processing (NLP). Several NLP applications like question answering, information retrieval, summarization, and temporal information extraction need to have some knowledge about events of the input documents. Existing methods developed for this task are restricted to limited number of languages, and for many other languages including Persian, there has not been any effort yet. In this paper, we introduce two different approaches for automatic event recognition and classification in Persian. For this purpose, a corpus of events has been built based on a specific version of ISO-TimeML for Persian. We present the specification of this corpus together with the results of applying mentioned approaches to the corpus. Considering these methods are the first effort towards Persian event extraction, the results are comparable to that of successful methods in English. TITLE AND ABSTRACT IN PERSIAN


title: Multi-objective UAV mission planning using evolutionary computation

abstract: This investigation develops an innovative algorithm for multiple autonomous unmanned aerial vehicle (UAV) mission routing. The concept of a UAV swarm routing problem (SRP) as a new combinatorics problem, is developed as a variant of the vehicle routing problem with time windows (VRPTW). Solutions of SRP problem model result in route assignments per vehicle that successfully track to all targets, on time, within distance constraints. A complexity analysis and multi-objective formulation of the VRPTW indicates the necessity of a stochastic solution approach leading to a multi-objective evolutionary algorithm. A full problem definition of the SRP as well as a multi-objective formulation parallels that of the VRPTW method. Benchmark problems for the VRPTW are modified in order to create SRP benchmarks. The solutions show the SRP solutions are comparable or better than the same VRPTW solutions, while also representing a more realistic UAV swarm routing solution.


title: Nature-Inspired Techniques in the Context of Fraud Detection

abstract: Electronic fraud is highly lucrative, with estimates suggesting these crimes to be worth millions of dollars annually. Because of its complex nature, electronic fraud detection is typically impractical to solve without automation. However, the creation of automated systems to detect fraud is very difficult as adversaries readily adapt and change their fraudulent activities which are often lost in the magnitude of legitimate transactions. This study reviews the most popular types of electronic fraud and the existing nature-inspired detection methods that are used for them. The common characteristics of electronic fraud are examined in detail along with the difficulties and challenges that these present to computational intelligence systems. Finally, open questions and opportunities for further work, including a discussion of emerging types of electronic fraud, are presented to provide a context for ongoing research.


title: LSTM-Based Memory Profiling for Predicting Data Attacks in Distributed Big Data Systems

abstract: With the increase in sensitivity of data, big data clusters are a primary target for attackers. Most attack prevention and detection methods concentrate on network level analysis. In this paper, we propose a run-time profiling method to predict the memory usage of datanodes that can help in taking necessary actions in advance when there is a data attack in the big data cluster. Our proposed method relies on replication property of big data, models memory mappings as four-dimensional time series data and applies LSTM recurrent neural networks for prediction the memory usage. There are two steps in our proposed method: (1) Local Analysis where an LSTM (Long short-term memory) cell of a datanode predicts its memory usage, and (2) Consensus among replica datanodes regarding any abnormal behavior. We present the results of our proposed method by testing it on Hadoop MapReduce examples and show that our proposed method can successfully predict memory usage of a datanode with an error rate in the range 8-20 for inputs in the range 150-8000.


title: Sample-Efficient Imitation Learning via Generative Adversarial Nets

abstract: GAIL is a recent successful imitation learning architecture that exploits the adversarial training procedure introduced in GANs. Albeit successful at generating behaviours similar to those demonstrated to the agent, GAIL suffers from a high sample complexity in the number of interactions it has to carry out in the environment in order to achieve satisfactory performance. We dramatically shrink the amount of interactions with the environment necessary to learn well-behaved imitation policies, by up to several orders of magnitude. Our framework, operating in the model-free regime, exhibits a significant increase in sample-efficiency over previous methods by simultaneously a) learning a self-tuned adversarially-trained surrogate reward and b) leveraging an off-policy actor-critic architecture. We show that our approach is simple to implement and that the learned agents remain remarkably stable, as shown in our experiments that span a variety of continuous control tasks. Video visualisations available at: \url{https://youtu.be/-nCsqUJnRKU}.


title: Learning to Avoid Errors in GANs by Manipulating Input Spaces

abstract: Despite recent advances, large scale visual artifacts are still a common occurrence in images generated by GANs. Previous work has focused on improving the generator's capability to accurately imitate the data distribution $p_{data}$. In this paper, we instead explore methods that enable GANs to actively avoid errors by manipulating the input space. The core idea is to apply small changes to each noise vector in order to shift them away from areas in the input space that tend to result in errors. We derive three different architectures from that idea. The main one of these consists of a simple residual module that leads to significantly less visual artifacts, while only slightly decreasing diversity. The module is trivial to add to existing GANs and costs almost zero computation and memory.


August 2002, p133-148. Kluwer Academic Publishers. User requirements analysis A review of supporting methods


title: Towards Deep Learning Models Resistant to Adversarial Attacks

abstract: Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models.


title: Management of a mediastinal cyst causing hyperparathyroidism and tracheal obstruction.

abstract: Abstract A 78-year-old man with a history of parathyroidectomy presented with hyperparathyroidism and tracheal obstruction. After excision of the mediastinal parathyroid cyst, his symptoms were completely relieved. The diagnosis, investigations, and management are discussed.


title: On fast chirp modulations and compressed sensing for automotive radar applications

abstract: In the last decade radar based driver assistance systems have increased the safety in all vehicle classes and get the access to mass market applications. Currently slow FMCW waveforms are the state of the art principle for automotive radar sensors at 24 GHz and 77 GHz. This contribution presents an innovative 77 GHz multi channel radar sensor, with the capability for fast FMCW PLL locked chirp modulations down to 10 μs. It gives a brief review on short FMCW chirp modulations and its processing. Furthermore the application of compressed sensing algorithms for r/v estimation is discussed and system simulations and measurements are presented.


LEARNING PROGRESSIONS: SUPPORTING INSTRUCTION AND FORMATIVE ASSESSMENT MARGARET HERITAGE LEARNING PROGRESSIONS: SUPPORTING INSTRUCTION AND FORMATIVE ASSESSMENT


title: A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems

abstract: The methods introduced so far regarding discriminant non-negative matrix factorization (DNMF) do not guarantee convergence to a stationary limit point. In order to remedy this limitation, a novel DNMF method is presented that uses projected gradients. The proposed algorithm employs some extra modifications that make the method more suitable for classification tasks. The usefulness of the proposed technique to frontal face verification and facial expression recognition problems is demonstrated.


title: Adaptive Speculative Processing of Out-of-Order Event Streams

abstract: Distributed event-based systems are used to detect meaningful events with low latency in high data-rate event streams that occur in surveillance, sports, finances, etc. However, both known approaches to dealing with the predominant out-of-order event arrival at the distributed detectors have their shortcomings: buffering approaches introduce latencies for event ordering, and stream revision approaches may result in system overloads due to unbounded retraction cascades. This article presents an adaptive speculative processing technique for out-of-order event streams that enhances typical buffering approaches. In contrast to other stream revision approaches developed so far, our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal. Our technique outperforms known approaches on both synthetical data and real sensor data from a realtime locating system (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40p on average.


title: Dynamic Programming for Instance Annotation in Multi-Instance Multi-Label Learning

abstract: Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our key contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our methods using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.


title: Visual Roadmaps for Managed Enterprise Architecture Evolution

abstract: Managing the evolution of the Enterprise Architecture (EA) is a key challenge for modern enterprises. Current approaches to address this challenge focus on EA plans, indicating projected future states of the architecture. Nevertheless, these plans neglect the role of the information technology (IT) project, which actually performs the transformation of the current to a planned EA. In this paper, we account for the importance of IT projects as drivers of EA transformation by providing a viewpoint for roadmapping the development of the EA over time. Complementing, we further introduce a conceptual model, which explicates the information demands for such roadmap plans.


title: Popcorn: bridging the programmability gap in heterogeneous-ISA platforms

abstract: The recent possibility of integrating multiple-OS-capable, high-core-count, heterogeneous-ISA processors in the same platform poses a question: given the tight integration between system components, can a shared memory programming model be adopted, enhancing programmability? If this can be done, an enormous amount of existing code written for shared memory architectures would not have to be rewritten to use a new programming paradigm (e.g., code offloading) that is often very expensive and error prone. We propose a new software architecture that is composed of an operating system and a compiler framework to run ordinary shared memory applications, written for homogeneous machines, on OS-capable heterogeneous-ISA machines. Applications run transparently amongst different ISA processors while exploiting the most optimized instruction set for each code block. We have implemented and tested our system, called Popcorn, on a multi-core Intel Xeon machine with a PCIe Intel Xeon Phi to demonstrate the viability of our approach. Application execution on Popcorn demonstrates to be up to 52% faster than the most performant native execution on Linux, on either Xeon or Xeon Phi, while removing the burden of the programmer having to adopt a different programming model than shared memory on a heterogeneous system. When compared to an offloading programming model, Popcorn is shown to be up to 6.2 times faster.


title: Application of data mining methods in diabetes prediction

abstract: Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is help to make predictions on medical data. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The methods strongly based on the data mining techniques can be effectively applied for high blood pressure risk prediction. In this paper, we explore the early prediction of diabetes via five different data mining methods including: GMM, SVM, Logistic regression, ELM, ANN. The experiment result proves that ANN (Artificial Neural Network) provides the highest accuracy than other techniques.


title: Child abuse education: an objective evaluation of resident and attending physician knowledge.

abstract: OBJECTIVE ::: The objective of the study was to evaluate residents' and practicing physicians' medical knowledge of child abuse and maltreatment. ::: ::: ::: METHODS ::: In a freestanding pediatric hospital and community hospital, a 30-question survey was administered to pediatric residents, general pediatricians (GPs), and pediatric emergency medicine (PEM) physicians. The institution's child protection team developed the questions, which were pilot tested for validity. Question content covered major concepts in child abuse and neglect. Information on previous training of child abuse was also collected. ::: ::: ::: RESULTS ::: There were 95 respondents, 64.2% (n = 61) were residents, 19% (n = 18) were GPs, and 16.8% (n = 16) were PEM physicians. Overall, the average score was 63.3% (SD, 13.8%). There was a statistically significant difference in the knowledge of child abuse and neglect across physician categories (P < 0.001). Pediatric emergency medicine physicians scored the highest (76.9 [SD, 9.1]) compared with GPs (66.7 [SD, 12.4]; P = 0.018) and pediatric residents (60.4 [SD, 12.9]; P < 0.001). There was no difference in the level of knowledge across residency training years (P = 0.076). ::: ::: ::: CONCLUSIONS ::: With a mean score in our study of 63.3%, there appears to be an overall lack of knowledge in child abuse. These findings highlight the need for increased education in child maltreatment.


title: Cerebrospinal fluid and plasma biomarkers in Alzheimer disease

abstract: Intense multidisciplinary research has provided detailed knowledge of the molecular pathogenesis of Alzheimer disease (AD). This knowledge has been translated into new therapeutic strategies with putative disease-modifying effects. Several of the most promising approaches, such as amyloid-beta immunotherapy and secretase inhibition, are now being tested in clinical trials. Disease-modifying treatments might be at their most effective when initiated very early in the course of AD, before amyloid plaques and neurodegeneration become too widespread. Thus, biomarkers are needed that can detect AD in the predementia phase or, ideally, in presymptomatic individuals. In this Review, we present the rationales behind and the diagnostic performances of the core cerebrospinal fluid (CSF) biomarkers for AD, namely total tau, phosphorylated tau and the 42 amino acid form of amyloid-beta. These biomarkers reflect AD pathology, and are candidate markers for predicting future cognitive decline in healthy individuals and the progression to dementia in patients who are cognitively impaired. We also discuss emerging plasma and CSF biomarkers, and explore new proteomics-based strategies for identifying additional CSF markers. Furthermore, we outline the roles of CSF biomarkers in drug discovery and clinical trials, and provide perspectives on AD biomarker discovery and the validation of such markers for use in the clinic.


The Impact of Clinical Empathy on Patients and Clinicians: Understanding Empathy's Side Effects


title: Enhancing Video Summarization via Vision-Language Embedding

abstract: This paper addresses video summarization, or the problem of distilling a raw video into a shorter form while still capturing the original story. We show that visual representations supervised by freeform language make a good fit for this application by extending a recent submodular summarization approach [9] with representativeness and interestingness objectives computed on features from a joint vision-language embedding space. We perform an evaluation on two diverse datasets, UT Egocentric [18] and TV Episodes [45], and show that our new objectives give improved summarization ability compared to standard visual features alone. Our experiments also show that the vision-language embedding need not be trained on domainspecific data, but can be learned from standard still image vision-language datasets and transferred to video. A further benefit of our model is the ability to guide a summary using freeform text input at test time, allowing user customization.


title: Ten-fold Improvement in Visual Odometry Using Landmark Matching

abstract: Our goal is to create a visual odometry system for robots and wearable systems such that localization accuracies of centimeters can be obtained for hundreds of meters of distance traveled. Existing systems have achieved approximately a 1% to 5% localization error rate whereas our proposed system achieves close to 0.1% error rate, a ten-fold reduction. Traditional visual odometry systems drift over time as the frame-to-frame errors accumulate. In this paper, we propose to improve visual odometry using visual landmarks in the scene. First, a dynamic local landmark tracking technique is proposed to track a set of local landmarks across image frames and select an optimal set of tracked local landmarks for pose computation. As a result, the error associated with each pose computation is minimized to reduce the drift significantly. Second, a global landmark based drift correction technique is proposed to recognize previously visited locations and use them to correct drift accumulated during motion. At each visited location along the route, a set of distinctive visual landmarks is automatically extracted and inserted into a landmark database dynamically. We integrate the landmark based approach into a navigation system with 2 stereo pairs and a low-cost inertial measurement unit (IMU) for increased robustness. We demonstrate that a real-time visual odometry system using local and global landmarks can precisely locate a user within 1 meter over 1000 meters in unknown indoor/outdoor environments with challenging situations such as climbing stairs, opening doors, moving foreground objects etc..


A Novel Correlation for Predicting the Density of Tetrazole–N-oxide Salts as Green Energetic Materials through Their Molecular Structure


title: A Review on Internet of Things (IoT)

abstract: Internet, a revolutionary invention, is always transforming into some new kind of hardware and software making it unavoidable for anyone. The form of communication that we see now is either human-human or human-device, but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-machine (M2M). This paper aims to provide a comprehensive overview of the IoT scenario and reviews its enabling technologies and the sensor networks. Also, it describes a six-layered architecture of IoT and points out the related key challenges.


title: Deep neural network ensemble architecture for eye movements classification

abstract: Up to now, eye tracking technologies have been used for different purposes in various industries, from medical to gaming. Eye tracking methods could include predicting fixations, gaze mapping or movement classification. Recent advances in deep learning techniques provide possibilities for solving many computer vision tasks with high accuracy. Authors of this paper propose a novel deep learning based architecture for eye movement classification task. Proposed architecture is an ensemble approach which employs deep convolutional neural networks that run in parallel, for both eyes separately, for visual feature extractions along with recurrent layers for temporal information gathering. Dataset images for training and validation were gathered from standard web camera and pre-processed automatically using dedicated tools. Overall accuracy of developed classifier on the validation set was 92%. Proposed architecture uses relatively small networks which brings the possibility of real time usage (successfully tested on 15–20fps) on regular CPU. Classifier achieved overall accuracy of 88% on the real-time test, using standard laptop and web camera.


BiNChE: A web tool and library for chemical enrichment analysis based on the ChEBI ontology


title: 3D-fractal engineering based on oxide-only corner lithography

abstract: This paper reports a new highly simplified machining process for three dimensional (3D)-fractal nanofabrication based on oxide-only corner lithography. It consists of a repeated sequence of wet etching (silicon), thermal oxidation and wet etching (silicon oxide). The previously reported 3D-fractal fabrication process needed additional low pressure chemical vapor deposition (LPCVD) steps of silicon nitride, as well as local oxidation of silicon (LOCOS). Employing this new procedure, a three generation folded silicon oxide fractal sheet with approx. a 10 µm footprint has been fabricated.


title: In Vivo Hyperoxic Preconditioning Prevents Myocardial Infarction by Expressing Bcl-2

abstract: Preconditioning with oxidative stress has been demonstrated in vitro to stimulate the cellular adaptation to subsequent severe oxidative stress. However, it is uncertain whether this preconditioning works in vivo. In the present study, we examined in vivo the beneficial effect of oxidative preconditioning. After rats were pretreated with whole-body hyperoxygenation (100% O 2 at 3 atmosphere for 20 mins, four cycles with 20-min intermission), isolated hearts were subjected to 45-min ischemia followed by 90-min reperfusion. This hyperoxic preconditioning significantly reduced infarct size, cytochrome-c release, DNA fragmentation, and terminal deoxynucleotidyl transferase-mediated dUTD nick-end labeling-positive cell frequency in the left ventricle, biphasically with an early (30-min) and a delayed (48-hr) effect after the hyperoxygenation. Mechanistically, the NF-κB activity and Bcl-2 expression were enhanced in the hearts, and a NF-xB inhibitor, pyrrolidine dithiocarbamate, abolished the Bcl-2 induction as well as the infarct-limiting effect. An antioxidant, N-acetylcysteine, and protein kinase C (PKC) inhibitors chelerythrine and Go 6983 also blocked the preconditioning effects. These results indicate that hyperoxia induces myocardial tolerance against ischemia-reperfusion injury in association with Bcl-2 induction by NF-KB activation through reactive oxygen species and PKC-dependent signaling pathway.


title: Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task

abstract: Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M$^2$ on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.


title: Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks

abstract: Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences. In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification. Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences. With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences. Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.


title: Radio Frequency Identification Sensors

abstract: Radio Frequency Identification (RFID) Systems are used when non-line-of-sight operation is required to identify an object and store its information. Some of the applications of this system are toll collection and inventory control. The following paper presents the ongoing research for the Auburn University Detection and Food Safety (AUDFS) project to use RFID technology combined with sensors for detection of pathogens in food. AUDFS aims to integrate the breakthroughs in the detection of food borne illnesses with advances in wireless and biosensor technologies. It explains the basic building blocks and operation of an RFID system. It also illustrates the data transfer process taking place in the system.


title: A 25-to-28 Gb/s High-Sensitivity ( $-$9.7 dBm) 65 nm CMOS Optical Receiver for Board-to-Board Interconnects

abstract: To address the two main challenges concerning the design of a transimpedance amplifier (TIA), namely, improving the sensitivity of the TIA without sacrificing bandwidth and suppressing inter-symbol interference (ISI) due to insertion loss, a 25 Gb/s optical receiver (RX) based on 65 nm CMOS technology, including a TIA and a PD operating at 1.3 μm wavelength, was developed. The key components of the TIA are a two-stage offset-cancelation scheme and a low-voltage output driver with peaking gain of 7.7 dB at 12.5 GHz. This driver performance is achieved by separating the equalizer function and output buffer of the driver. The TIA attains a sensitivity of -9.7 dBm (86- μApp) OMA and an eye opening of 65% at data rate of 25 Gb/s. The optical RX operates at 28 Gb/s with sensitivity of -8.2 dBm (121- μApp) OMA. Utilizing the CMOS TIA, an implemented VCSEL-based optical link operating at 850 nm wavelength achieves sensitivity of -7.3 dBm (98- μApp) OMA. Power consumption and power efficiency of the optical RX at data rate of 28 Gb/s are respectively 137.5 mW and 4.9 mW/Gb/s.


Is Seeing Believing? How Recommender Interfaces Affect Users' Opinions


title: Depth-Based Human Fall Detection via Shape Features and Improved Extreme Learning Machine

abstract: Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras. Language: en


title: Context-Aware Single Image Rain Removal

abstract: Rain removal from a single image is one of the challenging image denoising problems. In this paper, we present a learning-based framework for single image rain removal, which focuses on the learning of context information from an input image, and thus the rain patterns present in it can be automatically identified and removed. We approach the single image rain removal problem as the integration of image decomposition and self-learning processes. More precisely, our method first performs context-constrained image segmentation on the input image, and we learn dictionaries for the high-frequency components in different context categories via sparse coding for reconstruction purposes. For image regions with rain streaks, dictionaries of distinct context categories will share common atoms which correspond to the rain patterns. By utilizing PCA and SVM classifiers on the learned dictionaries, our framework aims at automatically identifying the common rain patterns present in them, and thus we can remove rain streaks as particular high-frequency components from the input image. Different from prior works on rain removal from images/videos which require image priors or training image data from multiple frames, our proposed self-learning approach only requires the input image itself, which would save much pre-training effort. Experimental results demonstrate the subjective and objective visual quality improvement with our proposed method.


title: Information-centric networking for the internet of things: challenges and opportunities

abstract: In view of evolving the Internet infrastructure, ICN is promoting a communication model that is fundamentally different from the traditional IP address-centric model. The ICN approach consists of the retrieval of content by (unique) names, regardless of origin server location (i.e., IP address), application, and distribution channel, thus enabling in-network caching/replication and content-based security. The expected benefits in terms of improved data dissemination efficiency and robustness in challenging communication scenarios indicate the high potential of ICN as an innovative networking paradigm in the IoT domain. IoT is a challenging environment, mainly due to the high number of heterogeneous and potentially constrained networked devices, and unique and heavy traffic patterns. The application of ICN principles in such a context opens new opportunities, while requiring careful design choices. This article critically discusses potential ways toward this goal by surveying the current literature after presenting several possible motivations for the introduction of ICN in the context of IoT. Major challenges and opportunities are also highlighted, serving as guidelines for progress beyond the state of the art in this timely and increasingly relevant topic.


Analysis and Comparison of Algorithms for Lossless Data Compression


title: Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media.

abstract: Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create “cultural bridges,” or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.


title: Toward Everyday Gaze Input: Accuracy and Precision of Eye Tracking and Implications for Design

abstract: For eye tracking to become a ubiquitous part of our everyday interaction with computers, we first need to understand its limitations outside rigorously controlled labs, and develop robust applications that can be used by a broad range of users and in various environments. Toward this end, we collected eye tracking data from 80 people in a calibration-style task, using two different trackers in two lighting conditions. We found that accuracy and precision can vary between users and targets more than six-fold, and report on differences between lighting, trackers, and screen regions. We show how such data can be used to determine appropriate target sizes and to optimize the parameters of commonly used filters. We conclude with design recommendations and examples how our findings and methodology can inform the design of error-aware adaptive applications.


title: Encouraging LSTMs to Anticipate Actions Very Early

abstract: In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos. As such, it is therefore key to the success of computer vision applications requiring to react as early as possible, such as autonomous navigation. In this paper, we propose a new action anticipation method that achieves high prediction accuracy even in the presence of a very small percentage of a video sequence. To this end, we develop a multi-stage LSTM architecture that leverages context-aware and action-aware features, and introduce a novel loss function that encourages the model to predict the correct class as early as possible. Our experiments on standard benchmark datasets evidence the benefits of our approach; We outperform the state-of-the-art action anticipation methods for early prediction by a relative increase in accuracy of 22.0% on JHMDB-21, 14.0% on UT-Interaction and 49.9% on UCF-101.


title: Average Torque Improvement of Interior Permanent-Magnet Machine Using Third Harmonic in Rotor Shape

abstract: A rotor shaping technique with optimal third harmonic is presented to enhance the average torque of interior permanent-magnet (IPM) machines in this paper. The optimal value of third harmonic injected into the rotor outer surface shape has been derived and further confirmed by both finite-element analyses and experiments. The impact of the optimal third harmonic to the rotor shape on the electromagnetic performance, including harmonics in the back electromotive forces, cogging torque, average torque, and torque ripple, is investigated. It is demonstrated that without any modification of the costly rare earth permanent magnet employed for the inverse-cosine-shaped rotor, the average torque of the machine of an inverse cosine injected with an optimal third-harmonic-shaped rotor can be improved by 6%. Simultaneously, the torque ripple remains almost unchanged, and the saliency ratio is also improved, further boosting the average torque. Finally, the machines with both conventional and optimal third harmonic rotors are prototyped and tested to validate the analysis.


title: Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism

abstract: This paper presents a proposal of a method to extract important byte sequences in malware samples to reduce the workload of human analysts who investigate the functionalities of the samples. This method, by applying convolutional neural network (CNN) with a technique called attention mechanism to an image converted from binary data, enables calculation of an "attention map," which shows regions having higher importance for classification in the image. This distinction of regions enables extraction of characteristic byte sequences peculiar to the malware family from the binary data and can provide useful information for the human analysts without a priori knowledge. Furthermore, the proposed method calculates the attention map for all binary data including the data section. Thus, it can process packed malware that might contain obfuscated code in the data section. Results of our evaluation experiment using malware datasets show that the proposed method provides higher classification accuracy than conventional methods. Furthermore, analysis of malware samples based on the calculated attention maps confirmed that the extracted sequences provide useful information for manual analysis, even when samples are packed.


title: Iconicity: literary texts

abstract: This article defines the nature of iconicity from a semiotic and linguistic point of view and discusses the three types of icons that form part of iconicity: images, diagrams and metaphors. The discussion concentrates on images and diagrams (metaphors are discussed in a separate entry) that play a role in literary texts and includes a short history of the critical discussion of iconicity since the 1940s. Images depend on aural/oral and visual phenomena and are therefore discussed on the phonetic and the typographic level only. Diagrams are of a more abstract nature and occur also on the morphosyntactic and discourse level. The discussion of the various types on each linguistic level is illustrated with many examples, mainly from English literature, to show the effect and function of iconicity, and how it adds meaning to the text.


title: Are Mobile Payment and Banking the Killer Apps for Mobile Commerce?

abstract: Mobile commerce had been expected to become a major force of e-commerce in the 21st century. However, the rhetoric has far exceeded the reality so far. While academics and practitioners have presented many views about the lack of rapid growth of mobile commerce, we submit that the anticipated mobile commerce take-off hinges on the emergence of a few killer apps. After reviewing the recent history of technologies that have dramatically changed our way of life and work, we propose a set of criteria for identifying and evaluating killer apps. From this vantage point, we argue that mobile payment and banking are the most likely candidates for the killer apps that could bring the expectation of a world of ubiquitous mobile commerce to fruition. Challenges and opportunities associated with this argument are discussed.


title: TRACCS: Trajectory-Aware Coordinated Urban Crowd-Sourcing

abstract: We investigate the problem of large-scale mobile crowdtasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker’s current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon, as opposed to just instantaneous location. We formulate such task assignment as an optimization problem, that seeks to maximize the total payoff from all assigned tasks, subject to a maximum bound on the detour (from the expected path) that a worker will experience to complete her assigned tasks. We develop credible computationally-efficient heuristics to address this optimization problem (whose exact solution requires solving a complex integer linear program), and show, via simulations with realistic topologies and commuting patterns, that a specific heuristic (called Greedy-ILS) increases the fraction of assigned tasks by more than 20%, and reduces the average detour overhead by more than 60%, compared to the current decentralized approach.


title: Electrically Large Zero-Phase-Shift Line Grid-Array UHF Near-Field RFID Reader Antenna

abstract: A grid-array antenna using a zero-phase-shift (ZPS) line is proposed to enlarge the interrogation zone of a reader antenna for near-field ultra-high-frequency (UHF) radio frequency identification (RFID) system. The proposed grid-array antenna is composed of a number of grid cells and a double-sided parallel-strip line feeding network. Each grid cell, namely segmented loop constructed by the ZPS line, has uniform and single-direction flowing current along the line. By configuration of the cells sharing a common side with its adjacent grid cells which carry reverse-direction flowing current, a grid-array antenna is formed to generate a strong and uniform magnetic-field distribution over a large interrogation zone even when the perimeter of the interrogation zone reaches up to 3λ (where λ is the operating wavelength in free space) or larger. As an example, a grid-array antenna with 1 × 2 segmented ZPS line loop cells implemented onto a piece of FR4 printed board (PCB) is designed and prototyped. The results show that the grid-array antenna achieves the impedance matching over the frequency range from 790 to 1040 MHz and produces strong and uniform magnetic-field distribution over an interrogation zone of 308 mm × 150 mm.


title: Effective missing data prediction for collaborative filtering

abstract: Memory-based collaborative filtering algorithms have been widely adopted in many popular recommender systems, although these approaches all suffer from data sparsity and poor prediction quality problems. Usually, the user-item matrix is quite sparse, which directly leads to inaccurate recommendations. This paper focuses the memory-based collaborative filtering problems on two crucial factors: (1) similarity computation between users or items and (2) missing data prediction algorithms. First, we use the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items. Second, we propose an effective missing data prediction algorithm, in which information of both users and items is taken into account. In this algorithm, we set the similarity threshold for users and items respectively, and the prediction algorithm will determine whether predicting the missing data or not. We also address how to predict the missing data by employing a combination of user and item information. Finally, empirical studies on dataset MovieLens have shown that our newly proposed method outperforms other state-of-the-art collaborative filtering algorithms and it is more robust against data sparsity.


title: Adaptive line tracking with multiple hypotheses for augmented reality

abstract: We present a real-time model-based line tracking approach with adaptive learning of image edge features that can handle partial occlusion and illumination changes. A CAD (VRML) model of the object to track is needed. First, the visible edges of the model with respect to the camera pose estimate are sorted out by a visibility test performed on standard graphics hardware. For every sample point of every projected visible 3D model line a search for gradient maxima in the image is then carried out in a direction perpendicular to that line. Multiple hypotheses of these maxima are considered as putative matches. The camera pose is updated by minimizing the distances between the projection of all sample points of the visible 3D model lines and the most likely matches found in the image. The state of every edge's visual properties is updated after each successful camera pose estimation. We evaluated the algorithm and showed the improvements compared to other tracking approaches.


title: Optimal choice for number of strands in a litz-wire transformer winding

abstract: The number and diameter of strands to minimize loss in a litz-wire transformer winding is determined. With fine stranding, the AC resistance factor can be decreased, but DC resistance increases as a result of the space occupied by insulation. A power law to model insulation thickness is combined with standard analysis of proximity-effect losses to find the optimal stranding. Suboptimal choices under other constraints are also determined.


title: BIOMedical Search Engine Framework : Lightweight and customized implementation of domain-specific biomedical search engines

abstract: The wealth of knowledge laying in biomedical publications is invaluable to make new scientific discoveries.Domain-specific search engines aim to improve retrieval performance and to enhance user search experience.The BIOMedical Search Engine Framework aims to speed up the construction of biomedical domain-specific search engines.The BIOMedical Search Engine Framework enables the retrieval and annotation of biomedical documents and the customised visualisation of indexed concepts and documents.The BIOMedical Search Engine Framework supported the construction of the Smart Drug Search (SDS) in assistance of antimicrobial research. Background and objectivesText mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. MethodsThe BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. ResultsThe construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. ConclusionsThe BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef.


title: HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

abstract: In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories. In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HD-CNN training, component-wise pretraining is followed by global finetuning with a multinomial logistic loss regularized by a coarse category consistency term. In addition, conditional executions of fine category classifiers and layer parameter compression make HD-CNNs scalable for large-scale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different HD-CNNs and they lower the top-1 error of the standard CNNs by 2.65%, 3.1% and 1.1%, respectively.


title: Domain Adaptation Network for Cross-Scene Classification

abstract: In this paper, we present a domain adaptation network to deal with classification scenarios subjected to the data shift problem (i.e., labeled and unlabeled images acquired with different sensors and over completely different geographical areas). We rely on the power of pretrained convolutional neural networks (CNNs) to generate an initial feature representation of the labeled and unlabeled images under analysis, referred as source and target domains, respectively. Then we feed the resulting features to an extra network placed on the top of the pretrained CNN for further learning. During the fine-tuning phase, we learn the weights of this network by jointly minimizing three regularization terms, which are: 1) the cross-entropy error on the labeled source data; 2) the maximum mean discrepancy between the source and target data distributions; and 3) the geometrical structure of the target data. Furthermore, to obtain robust hidden representations we propose a mini-batch gradient-based optimization method with a dynamic sample size for the local alignment of the source and target distributions. To validate the method, in the experiments we use the University of California Merced data set and a new multisensor data set acquired over several regions of the Kingdom of Saudi Arabia. The experiments show that: 1) pretrained CNNs offer an interesting solution for image classification compared to state-of-the-art methods; 2) their performances can be degraded when dealing with data sets subjected to the data shift problem; and 3) how the proposed approach represents a promising solution for effectively handling this issue.


title: FPGA implementation of the Lucy-Richardson algorithm for fast space-variant image deconvolution

abstract: The Lucy-Richardson algorithm is a very well-known method for non-blind image deconvolution. It can also deal with space-variant problems, but it is seldom used in these cases because of its iterative nature and complexity of realization. In this paper we show that exploiting the sparse structure of the deconvolution matrix, and utilizing a specifically devised architecture, the restoration can be performed almost in real-time on VGA-size images.


Hippocampal sharp-wave ripples in awake mice are entrained by respiration


title: A survey on NB-IoT downlink scheduling: Issues and potential solutions

abstract: The NarrowBand Internet of Things (NB-IoT) is one of the most promising technologies that fits the requirements of the low-power wide area networks (LPWAN). NB-IoT targets on supporting communications for small amount of data during a relatively long period of time (i.e., delay tolerant), which is one of the key features of IoT applications. Several studies have been conducted by some standard working groups to define the standards for NB-IoT. For example, the working group 3GPP aims to provide communication protocols with low energy consumption, good coverage penetration, and so on. Compared with the existing wireless communication protocols, less spectrum is allocated for NB-IoT. Thus, how to more efficiently use the resource/spectrum (i.e., resource allocation and scheduling) is one of the key issues. In this paper, we first describe the design of the physical layer for the downlink communication, especially the scheduling process. Secondly, we discuss the issues for resource allocation in NB-IoT while the delay requirement in the physical layer is satisfied. Thirdly, we discuss the open questions and possible solutions.


title: Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples.

abstract: Advances in deep learning have led to the broad adoption of Deep Neural Networks (DNNs) to a range of important machine learning problems, e.g., guiding autonomous vehicles, speech recognition, malware detection. Yet, machine learning models, including DNNs, were shown to be vulnerable to adversarial samples-subtly (and often humanly indistinguishably) modified malicious inputs crafted to compromise the integrity of their outputs. Adversarial examples thus enable adversaries to manipulate system behaviors. Potential attacks include attempts to control the behavior of vehicles, have spam content identified as legitimate content, or have malware identified as legitimate software. Adversarial examples are known to transfer from one model to another, even if the second model has a different architecture or was trained on a different set. We introduce the first practical demonstration that this cross-model transfer phenomenon enables attackers to control a remotely hosted DNN with no access to the model, its parameters, or its training data. In our demonstration, we only assume that the adversary can observe outputs from the target DNN given inputs chosen by the adversary. We introduce the attack strategy of fitting a substitute model to the input-output pairs in this manner, then crafting adversarial examples based on this auxiliary model. We evaluate the approach on existing DNN datasets and real-world settings. In one experiment, we force a DNN supported by MetaMind (one of the online APIs for DNN classifiers) to mis-classify inputs at a rate of 84.24%. We conclude with experiments exploring why adversarial samples transfer between DNNs, and a discussion on the applicability of our attack when targeting machine learning algorithms distinct from DNNs.


title: A Crowdsourced Alternative to Eye-tracking for Visualization Understanding

abstract: In this study we investigate the utility of using mouse clicks as an alternative for eye fixations in the context of understanding data visualizations. We developed a crowdsourced study online in which participants were presented with a series of images containing graphs and diagrams and asked to describe them. Each image was blurred so that the participant needed to click to reveal bubbles - small, circular areas of the image at normal resolution. This is similar to having a confined area of focus like the human eye fovea. We compared the bubble click data with the fixation data from a complementary eye-tracking experiment by calculating the similarity between the resulting heatmaps. A high similarity score suggests that our methodology may be a viable crowdsourced alternative to eye-tracking experiments, especially when little to no eye-tracking data is available. This methodology can also be used to complement eye-tracking studies with an additional behavioral measurement, since it is specifically designed to measure which information people consciously choose to examine for understanding visualizations. \


title: An integrated CMOS micromechanical resonator high- oscillator

abstract: A completely monolithic high-Q oscillator, fabricated via a combined CMOS plus surface micromachining technology, is described, for which the oscillation frequency is controlled by a polysilicon micromechanical resonator with the intent of achieving high stability. The operation and performance of micromechanical resonators are modeled, with emphasis on circuit and noise modeling of multiport resonators. A series resonant oscillator design is discussed that utilizes a unique, gain-controllable transresistance sustaining amplifier. We show that in the absence of an automatic level control loop, the closed-loop, steady-state oscillation amplitude of this oscillator depends strongly upon the dc-bias voltage applied to the capacitively driven and sensed /spl mu/resonator. Although the high-Q of the micromechanical resonator does contribute to improved oscillator stability, its limited power-handling ability outweighs the Q benefits and prevents this oscillator from achieving the high short-term stability normally expected of high-Q oscillators.


title: Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation

abstract: There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.


title: State Representation Learning in Robotics: Using Prior Knowledge about Physical Interaction.

abstract: State representations critically affect the effectiveness of learning in robots. In this paper, we propose a roboticsspecific approach to learning such state representations. Robots accomplish tasks by interacting with the physical world. Physics in turn imposes structure on both the changes in the world and on the way robots can effect these changes. Using prior knowledge about interacting with the physical world, robots can learn state representations that are consistent with physics. We identify five robotic priors and explain how they can be used for representation learning. We demonstrate the effectiveness of this approach in a simulated slot car racing task and a simulated navigation task with distracting moving objects. We show that our method extracts task-relevant state representations from highdimensional observations, even in the presence of task-irrelevant distractions. We also show that the state representations learned by our method greatly improve generalization in reinforcement learning.


title: Texting, techspeak, and tweens: The relationship between text messaging and English grammar skills:

abstract: The perpetual use of mobile devices by adolescents has fueled a culture of text messaging, with abbreviations and grammatical shortcuts, thus raising the following question in the minds of parents and teachers: Does increased use of text messaging engender greater reliance on such ‘textual adaptations’ to the point of altering one’s sense of written grammar? A survey (N = 228) was conducted to test the association between text message usage of sixth, seventh and eighth grade students and their scores on an offline, age-appropriate grammar assessment test. Results show broad support for a general negative relationship between the use of techspeak in text messages and scores on a grammar assessment, with implications for Social Cognitive Theory and Low-Road/High-Road Theory of Transfer of Learning. These results indicate that adolescents may learn through observation in communication technologies, and that these learned adaptations may be transferred to standard English through Low-Road transfer of learning...


title: Compress and Control

abstract: This paper describes a new information-theoretic policy evaluation technique for reinforcement learning. This technique converts any compression or density model into a corresponding estimate of value. Under appropriate stationarity and ergodicity conditions, we show that the use of a sufficiently powerful model gives rise to a consistent value function estimator. We also study the behavior of this technique when applied to various Atari 2600 video games, where the use of suboptimal modeling techniques is unavoidable. We consider three fundamentally different models, all too limited to perfectly model the dynamics of the system. Remarkably, we find that our technique provides sufficiently accurate value estimates for effective on-policy control. We conclude with a suggestive study highlighting the potential of our technique to scale to large problems.


title: Semantic Labeling of Aerial and Satellite Imagery

abstract: Inspired by the recent success of deep convolutional neural networks (CNNs) and feature aggregation in the field of computer vision and machine learning, we propose an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features. Both CNN and hand-crafted features are applied to dense image patches to produce per-pixel class probabilities. Conditional random fields (CRFs) are applied as a postprocessing step. The CRF infers a labeling that smooths regions while respecting the edges present in the imagery. The combination of these factors leads to a semantic labeling framework which outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing (ISPRS) two-dimensional Semantic Labeling Challenge dataset. We advance state-of-the-art results by improving the overall accuracy to $88\%$ on the ISPRS Semantic Labeling Contest. In this paper, we also explore the possibility of applying the proposed framework to other types of data. Our experimental results demonstrate the generalization capability of our approach and its ability to produce accurate results.


title: Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching

abstract: Fog computing is a promising architecture to provide economical and low latency data services for future Internet of Things (IoT)-based network systems. Fog computing relies on a set of low-power fog nodes (FNs) that are located close to the end users to offload the services originally targeting at cloud data centers. In this paper, we consider a specific fog computing network consisting of a set of data service operators (DSOs) each of which controls a set of FNs to provide the required data service to a set of data service subscribers (DSSs). How to allocate the limited computing resources of FNs to all the DSSs to achieve an optimal and stable performance is an important problem. Therefore, we propose a joint optimization framework for all FNs, DSOs, and DSSs to achieve the optimal resource allocation schemes in a distributed fashion. In the framework, we first formulate a Stackelberg game to analyze the pricing problem for the DSOs as well as the resource allocation problem for the DSSs. Under the scenarios that the DSOs can know the expected amount of resource purchased by the DSSs, a many-to-many matching game is applied to investigate the pairing problem between DSOs and FNs. Finally, within the same DSO, we apply another layer of many-to-many matching between each of the paired FNs and serving DSSs to solve the FN-DSS pairing problem. Simulation results show that our proposed framework can significantly improve the performance of the IoT-based network systems.


title: Benchmarking Super-Resolution Algorithms on Real Data

abstract: Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. ::: To facilitate comprehensive studies, this paper introduces the publicly available Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variations.


title: Biology and the Future of Psychoanalysis: A New Intellectual Framework for Psychiatry Revisited

abstract: The American Journal of Psychiatry has received a number of letters in response to my earlier “Framework” article (1). Some of these are reprinted elsewhere in this issue, and I have answered them briefly there. However, one issue raised by some letters deserves a more detailed answer, and that relates to whether biology is at all relevant to psychoanalysis. To my mind, this issue is so central to the future of psychoanalysis that it cannot be addressed with a brief comment. I therefore have written this article in an attempt to outline the importance of biology for the future of psychoanalysis.


title: Biometric Recognition Based on Free-Text Keystroke Dynamics

abstract: Accurate recognition of free text keystroke dynamics is challenging due to the unstructured and sparse nature of the data and its underlying variability. As a result, most of the approaches published in the literature on free text recognition, except for one recent one, have reported extremely high error rates. In this paper, we present a new approach for the free text analysis of keystrokes that combines monograph and digraph analysis, and uses a neural network to predict missing digraphs based on the relation between the monitored keystrokes. Our proposed approach achieves an accuracy level comparable to the best results obtained through related techniques in the literature, while achieving a far lower processing time. Experimental evaluation involving 53 users in a heterogeneous environment yields a false acceptance ratio (FAR) of 0.0152% and a false rejection ratio (FRR) of 4.82%, at an equal error rate (EER) of 2.46%. Our follow-up experiment, in a homogeneous environment with 17 users, yields FAR=0% and FRR=5.01%, at EER=2.13%.


title: RSSI-Based Indoor Localization With the Internet of Things

abstract: In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a cost-efficient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, global positioning system (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and efficiently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-efficient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network. These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The received signal strength indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI data set is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application requirements.


title: Compact and Small Planar Monopole Antenna With Symmetrical L- and U-Shaped Slots for WLAN/WiMAX Applications

abstract: A small and compact triple-band microstrip-fed printed monopole antenna for Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) is presented. The proposed antenna consists of a rectangular radiating patch with L- and U-shaped slots and ground plane. A parametric study on the lengths of the U- and L-shaped slots of the proposed antenna is provided to obtain the required operational frequency bands-namely, WLAN (2.4/5.2/5.8 GHz) and WiMAX (2.5/3.5/5.5 GHz). The proposed antenna is small (15 × 15 × 1.6 mm 3) when compared to previously well-known double- and triple-band monopole antennas. The simulation and measurement results show that the designed antenna is capable of operating over the 2.25-2.85, 3.4-4.15, and 4.45-8 GHz frequency bands while rejecting frequency ranges between these three bands. Omnidirectional radiation pattern and acceptable antenna gain are achieved over the operating bands.


A Review of Various Agent Based Resolution Modelling Methods


title: The Friendship Questionnaire: an investigation of adults with Asperger syndrome or high-functioning autism, and normal sex differences.

abstract: Friendship is an important part of normal social functioning, yet there are precious few instruments for measuring individual differences in this domain. In this article, we report a new selfreport questionnaire, the Friendship Questionnaire (FQ), for use with adults of normal intelligence. A high score on the FQ is achieved by the respondent reporting that they enjoy close, empathic, supportive, caring friendships that are important to them; that they like and are interested in people; and that they enjoy interacting with others for its own sake. The FQ has a maximum score of 135 and a minimum of zero. In Study 1, we carried out a study of n = 76 (27 males and 49 females) adults from a general population, to test for previously reported sex differences in friendships. This confirmed that women scored significantly higher than men. In Study 2, we employed the FQ with n = 68 adults (51 males, 17 females) with Asperger Syndrome or high-functioning autism to test the theory that autism is an extreme form of the male brain. The adults with Asperger Syndrome or high-functioning autism scored significantly lower on the FQ than both the male and female controls from Study 1. The FQ thus reveals both a sex difference in the style of friendship in the general population, and provides support for the extreme male brain theory of autism.