title: Predictive mutation testing

abstract: Mutation testing is a powerful methodology for evaluating test suite quality. In mutation testing, a large number of mutants are generated and executed against the test suite to check the ratio of killed mutants. Therefore, mutation testing is widely believed to be a computationally expensive technique. To alleviate the efficiency concern of mutation testing, in this paper, we propose predictive mutation testing (PMT), the first approach to predicting mutation testing results without mutant execution. In particular, the proposed approach constructs a classification model based on a series of features related to mutants and tests, and uses the classification model to predict whether a mutant is killed or survived without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (i.e., cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss when predicting mutant execution results, indicating a good tradeoff between efficiency and effectiveness of mutation testing.


Challenges and opportunities for large-scale electrophysiology with Neuropixels probes


title: STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation

abstract: Predicting users' actions based on anonymous sessions is a challenging problem in web-based behavioral modeling research, mainly due to the uncertainty of user behavior and the limited information. Recent advances in recurrent neural networks have led to promising approaches to solving this problem, with long short-term memory model proving effective in capturing users' general interests from previous clicks. However, none of the existing approaches explicitly take the effects of users' current actions on their next moves into account. In this study, we argue that a long-term memory model may be insufficient for modeling long sessions that usually contain user interests drift caused by unintended clicks. A novel short-term attention/memory priority model is proposed as a remedy, which is capable of capturing users' general interests from the long-term memory of a session context, whilst taking into account users' current interests from the short-term memory of the last-clicks. The validity and efficacy of the proposed attention mechanism is extensively evaluated on three benchmark data sets from the RecSys Challenge 2015 and CIKM Cup 2016. The numerical results show that our model achieves state-of-the-art performance in all the tests.


title: ECG Feature Extraction Techniques - A Survey Approach

abstract: ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitudes and intervals value of P-QRS-T segment determines the functioning of heart of every human. Recently, numerous research and techniques have been developed for analyzing the ECG signal. The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal. In addition this paper also provides a comparative study of various methods proposed by researchers in extracting the feature from ECG signal.


title: Improving the accuracy of touch screens: an experimental evaluation of three strategies

abstract: A study comparing the speed, accuracy, and user satisfaction of three different touch screen strategies was performed. The purpose of the experiment was to evaluate the merits of the more intricate touch strategies that are possible on touch screens that return a continuous stream of touch data. The results showed that a touch strategy providing continuous feedback until a selection was confirmed had fewer errors than other touch strategies. The implications of the results for touch screens containing small, densely-packed targets were discussed.


1 Introduction to Reactive Extrusion


title: Molecular Insights and Therapeutic Targets for Diabetic Endothelial Dysfunction

abstract: Diabetes mellitus and its associated complications are major health problems in the developed world. Diabetes mellitus is associated with an increased risk of cardiovascular disease (CVD) even in the presence of intensive glycemic control. Indeed, 75% of diabetic patients will die of CVD. Patients with type 1 and type 2 diabetes mellitus have an increased risk for the 3 major types of macrovascular disease (coronary heart disease, peripheral vascular disease, and stroke).1 A striking feature of diabetic cardiovascular complications is the appearance of accelerated atherosclerosis, which anatomically resembles atherosclerosis in nondiabetic individuals but is more extensive and occurs at an earlier age. Substantial clinical and experimental evidence suggests that endothelial dysfunction is a crucial early step in the development of atherosclerosis. Evidence also suggests that it participates in plaque progression and the clinical emergence of cardiovascular events. Endothelial dysfunction is characterized by impaired endothelium-dependent vasodilation and “endothelial activation,” which is associated with a proinflammatory, proliferative, and procoagulatory milieu that promotes initiation and complications of atherogenesis.2 Endothelial dysfunction associated with insulin resistance appears to precede the development of overt hyperglycemia in patients with type 2 diabetes mellitus.3 Therefore, endothelial dysfunction may be a critical early target for the prevention of atherosclerosis and CVD in patients with diabetes mellitus or insulin resistance.2 A synergistic cross talk exists among the conventional cardiovascular risk factors associated with diabetes mellitus, and such cross talk contributes to disruption of endothelial integrity and acceleration of atherosclerosis. However, the biochemical and cellular links between elevated blood glucose levels and endothelial dysfunction remain incompletely understood. This review will focus on the multifactorial nature of endothelial dysfunction in diabetes mellitus, the relationship between endothelial dysfunction and conventional cardiovascular risk factors, and the translational potential of molecular targets such as the AMP-activated protein kinase (AMPK) for treating …


title: Control of single-stage AC-AC solid state transformer for power exchange between grids

abstract: A power management control method is proposed for the three-phase-to-single-phase matrix converter (MC)-based solid state transformer (SST) linking two grids. The MC-SST presents low weight and high controllability with respect to a conventional line-frequency transformer. Moreover, the single-stage ac-ac power conversion and compact volume are achieved compared to a three-stage SST, owing to the absence of electrolytic capacitors. The current studies focused on the modulation techniques of this type of SST. Whereas, this paper proposes a control method to manage the active power exchange and power flow when such SST applied to distribution grids, which could be different voltage levels and frequency. Simulation studies are carried out to investigate the proposed method.


title: Animated Pedagogical Agents: Face-to-Face Interaction in Interactive Learning Environments

abstract: Recent years have witnessed the birth of a new paradigm for learning environments: animated pedagogical agents. These lifelike autonomous characters cohabit learning environments with students to create rich, face-to-face learning interactions. This opens up exciting new possibilities; for example, agents can demonstrate complex tasks, employ locomotion and gesture to focus students' attention on the most salient aspect of the task at hand, and convey emotional responses to the tutorial situation. Animated pedagogical agents offer great promise for broadening the bandwidth of tutorial communication and increasing learning environments' ability to engage and motivate students. This article sets forth the motivations behind animated pedagogical agents, describes the key capabilities they offer, and discusses the technical issues they raise. The discussion is illustrated with descriptions of a number of animated agents that represent the current state of the art.


title: Cooperation and Conflict in Multiracial School Districts

abstract: This study examines politics in 118 multiracial urban school districts. It starts with an examination of the logic of the "rainbow coalition," suggested as feasible by both academics and politicians. A rival hypothesis based on social distance theory would suggest more intergroup conflict than would the rainbow coalition thesis. Using elections to urban school boards, we find evidence consistent with the notion that Anglos will coalesce with Latinos rather than Blacks. The evidence suggests that in future research the power thesis should be a coequal rival hypothesis with the rainbow coalition thesis.


title: Diffuse Scattering From Rough Surfaces in THz Communication Channels

abstract: Recent years have seen a tremendous increase in the demand for wireless bandwidth. To support this demand by innovative and resourceful use of technology, future communication systems will have to shift towards higher carrier frequencies. Due to the tight regulatory situation, frequencies in the atmospheric attenuation window around 300 GHz appear very attractive to facilitate an indoor, short range, ultra high speed THz communication system. In this paper, we investigate the influence of diffuse scattering at such high frequencies on the characteristics of the communication channel and its implications on the non-line-of-sight propagation path. The Kirchhoff approach is verified by an experimental study of diffuse scattering from randomly rough surfaces commonly encountered in indoor environments using a fiber-coupled terahertz time-domain spectroscopy system to perform angle- and frequency-dependent measurements. Furthermore, we integrate the Kirchhoff approach into a self-developed ray tracing algorithm to model the signal coverage of a typical office scenario.


title: A 90 nm low-power successive approximation register for A/D conversions

abstract: A novel low-power successive approximation register is proposed. The new register is based on gating the clock when there is no data switching activity. It operates with fewer transistors and no redundant transitions which makes it suitable for low power applications. The proposed register consisting of 14 bits has been designed up to the layout level with 1V power supply in 90nm CMOS technology and has been simulated using SPECTRE. Simulation results have shown that the proposed register saves up to 75% of power consumption and 18% of transistor count.


title: Multi-Task Pharmacovigilance Mining from Social Media Posts

abstract: Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported. Aiming to effectively monitor various aspects of Adverse Drug Reactions (ADRs) from diversely expressed social medical posts, we propose a multi-task neural network framework that learns several tasks associated with ADR monitoring with different levels of supervisions collectively. Besides being able to correctly classify ADR posts and accurately extract ADR mentions from online posts, the proposed framework is also able to further understand reasons for which the drug is being taken, known as ‘indications’, from the given social media post. A coverage-based attention mechanism is adopted in our framework to help the model properly identify ‘phrasal’ ADRs and Indications that are attentive to multiple words in a post. Our framework is applicable in situations where limited parallel data for different pharmacovigilance tasks are available. We evaluate the proposed framework on real-world Twitter datasets, where the proposed model outperforms the state-of-the-art alternatives of each individual task consistently.


title: The Web as a Parallel Corpus

abstract: Parallel corpora have become an essential resource for work in multilingual natural language processing. In this article, we report on our work using the STRAND system for mining parallel text on the World Wide Web,first reviewing the original algorithm and results and then presenting a set of significant enhancements. These enhancements include the use of supervised learning based on structural features of documents to improve classification performance, a new content-based measure of translational equivalence, and adaptation of the system to take advantage of the Internet Archive for mining parallel text from the Web on a large scale. Finally, the value of these techniques is demonstrated in the construction of a significant parallel corpus for a low-density language pair.


title: Using machine learning to predict learner emotional state from brainwaves

abstract: Intelligent Tutoring Systems (ITS) learner model has progressively evolved. Initially composed of a cognitive module it was extended with a psychological module and an emotional module. The learner model still remains non-exhaustive. Methods of data collection on the cognitive and emotional state of the learner often lack precision and objectivity. In this paper we introduce an emomental agent. It interacts with an ITS to communicate the emotional state of the learner based upon his mental state. The mental state is obtained from the learner's brainwaves. The agent learns to predict the learner's emotions by using machine learning techniques.


Skin Cancer Detection Using Digital Image Processing


title: A formal approach to finding explanations for database queries

abstract: As a consequence of the popularity of big data, many users with a variety of backgrounds seek to extract high level information from datasets collected from various sources and combined using data integration techniques. A major challenge for research in data management is to develop tools to assist users in explaining observed query outputs. In this paper we introduce a principled approach to provide explanations for answers to SQL queries based on intervention: removal of tuples from the database that significantly affect the query answers. We provide a formal definition of intervention in the presence of multiple relations which can interact with each other through foreign keys. First we give a set of recursive rules to compute the intervention for any given explanation in polynomial time (data complexity). Then we give simple and efficient algorithms based on SQL queries that can compute the top-K explanations by using standard database management systems under certain conditions. We evaluate the quality and performance of our approach by experiments on real datasets.


title: Mediated social touch: a review of current research and future directions

abstract: In this paper, we review research and applications in the area of mediated or remote social touch. Whereas current communication media rely predominately on vision and hearing, mediated social touch allows people to touch each other over a distance by means of haptic feedback technology. Overall, the reviewed applications have interesting potential, such as the communication of simple ideas (e.g., through Hapticons), establishing a feeling of connectedness between distant lovers, or the recovery from stress. However, the beneficial effects of mediated social touch are usually only assumed and have not yet been submitted to empirical scrutiny. Based on social psychological literature on touch, communication, and the effects of media, we assess the current research and design efforts and propose future directions for the field of mediated social touch.


title: High Frequency Rogowski Coil Response Characteristics

abstract: The high frequency response characteristics of differentiating and self-integrating Rogowski coils have been calculated for arbitrary values of the coil terminating resistance assuming Ampere's law to be valid. Effects due to a reactive terminating impedance are also discussed. When the displacement current is taken into account in the measurement of the current of a charged particle beam, it is found that an effective rise time is introduced into the self-integrating coil response on the order of a/??, where a is the major radius of the coil, ? is the velocity of the beam, and ? = (1 -?2/c2)-1/2.


title: Developmental and Hormonal Regulation of NR2A mRNA in Forebrain Regions Controlling Avian Vocal Learning

abstract: Developmental changes in the composition of NMDA receptors can alter receptor physiology as well as intracellular signal transduction cascades, potentially shifting thresholds for neural and behavioral plasticity. During song learning in zebra finches, NMDAR currents become faster, and transcripts for the modulatory NR2B subunit of this receptor decrease in lMAN, a region in which NMDAR activation is critical for vocal learning. Using in situ hybridization, we found that NR2A transcripts change reciprocally, increasing significantly in both lMAN (59%) and in another song region, Area X (38%), between posthatch day (PHD) 20 and 40, but not changing further at PHD60 or 80. In adjacent areas not associated with song learning, NR2A mRNA did not change between PHD20-80. Although early song deprivation (which extends the sensitive period for song learning) delays changes in NR2B gene expression and NMDAR physiology within the lMAN, it did not alter NR2A mRNA levels measured at PHD40, 45, or 60. Early testosterone (T) treatment, which disrupts vocal development and accelerates the maturation of both NR2B levels and NMDAR physiology in lMAN, also significantly increased NR2A transcripts measured at PHD35 in lMAN. In Area X, a similar effect of T approached significance. Together with our previous studies, these results show that in a pathway critical for vocal plasticity, the ratio of NR2A:NR2B mRNA rises abruptly early during the sensitive period for song learning. Furthermore, androgen regulation of NMDAR gene expression may alter thresholds for experience-dependent synaptic change.


title: Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding

abstract: This paper presents a technique for content-based image retrieval (CBIR) by exploiting the advantage of low-complexity ordered-dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC compresses an image block into corresponding quantizers and bitmap image. Two image features are proposed to index an image, namely, color co-occurrence feature (CCF) and bit pattern features (BPF), which are generated directly from the ODBTC encoded data streams without performing the decoding process. The CCF and BPF of an image are simply derived from the two ODBTC quantizers and bitmap, respectively, by involving the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC scheme is not only suited for image compression, because of its simplicity, but also offers a simple and effective descriptor to index images in CBIR system.


title: A New Technique for Design of Low-Profile, Second-Order, Bandpass Frequency Selective Surfaces

abstract: In this study, a new method for designing low profile frequency selective surfaces (FSS) with second-order bandpass responses is presented. The FSSs designed using this technique utilize non-resonant subwavelength constituting unit cells with unit cell dimensions and periodicities in the order of 0.15lambda0. It is demonstrated that using the proposed technique, second-order FSSs with an overall thickness of lambda0/30 can be designed. This is considerably smaller than the thickness of second-order FSSs designed using traditional techniques and could be particularly useful at lower frequencies with long wavelengths. To facilitate the design of this structure, an equivalent circuit based synthesis method is also presented in this paper. Two bandpass FSS prototypes operating at X-band are designed, fabricated, and tested. A free space measurement setup is used to thoroughly characterize the frequency responses of these prototypes for both the TE and TM polarizations and various angles of incidence. The frequency responses of these structures are shown to have a relatively low sensitivity to the angle of incidence. Principles of operation, detailed design and synthesis procedure, and measurement results of two fabricated prototypes are presented and discussed in this paper.


title: The Generic Genome Browser : A Building Block for a Model Organism System Database

abstract: The Generic Model Organism System Database Project (GMOD) seeks to develop reusable software components for model organism system databases. In this paper we describe the Generic Genome Browser (GBrowse), a Web-based application for displaying genomic annotations and other features. For the end user, features of the browser include the ability to scroll and zoom through arbitrary regions of a genome, to enter a region of the genome by searching for a landmark or performing a full text search of all features, and the ability to enable and disable tracks and change their relative order and appearance. The user can upload private annotations to view them in the context of the public ones, and publish those annotations to the community. For the data provider, features of the browser software include reliance on readily available open source components, simple installation, flexible configuration, and easy integration with other components of a model organism system Web site. GBrowse is freely available under an open source license. The software, its documentation, and support are available at http://www.gmod.org.


Comment on “Dynamics of Price Elasticity and Brand Life Cycles: An Empirical Study”


title: Historical review: Molecular and cellular mechanisms of opiate and cocaine addiction

abstract: Abstract The National Institute on Drug Abuse was founded in 1974, and since that time there have been significant advances in understanding the processes by which drugs of abuse cause addiction. The initial protein targets for almost all drugs of abuse are now known. Animal models that replicate key features of addiction are available, and these models have made it possible to characterize the brain regions that are important for addiction and other drug effects, such as physical dependence. A large number of drug-induced changes at the molecular and cellular levels have been identified in these brain areas and rapid progress is being made in relating individual changes to specific behavioral abnormalities in animal models of addiction. The current challenges are to translate this increasingly impressive knowledge of the basic neurobiology of addiction to human addicts, and to identify the specific genes that make some individuals either particularly vulnerable or resistant to addiction. In this article, I present a historical review of basic research on opiate and cocaine addiction.


title: Diffeomorphic registration using b-splines

abstract: In this paper we propose a diffeomorphic non-rigid registration algorithm based on free-form deformations (FFDs) which are modelled by B-splines. In contrast to existing non-rigid registration methods based on FFDs the proposed diffeomorphic non-rigid registration algorithm based on free-form deformations (FFDs) which are modelled by B-splines. To construct a diffeomorphic transformation we compose a sequence of free-form deformations while ensuring that individual FFDs are one-to-one transformations. We have evaluated the algorithm on 20 normal brain MR images which have been manually segmented into 67 anatomical structures. Using the agreement between manual segmentation and segmentation propagation as a measure of registration quality we have compared the algorithm to an existing FFD registration algorithm and a modified FFD registration algorithm which penalises non-diffeomorphic transformations. The results show that the proposed algorithm generates diffeomorphic transformations while providing similar levels of performance as the existing FFD registration algorithm in terms of registration accuracy.


title: Security and Privacy Preservation Scheme of Face Identification and Resolution Framework Using Fog Computing in Internet of Things

abstract: Face identification and resolution technology is crucial to ensure the identity consistency of humans in physical space and cyber space. In the current Internet of Things (IoT) and big data situation, the increase of applications based on face identification and resolution raises the demands of computation, communication, and storage capabilities. Therefore, we have proposed the fog computing-based face identification and resolution framework to improve processing capacity and save the bandwidth. However, there are some security and privacy issues brought by the properties of fog computing-based framework. In this paper, we propose a security and privacy preservation scheme to solve the above issues. We give an outline of the fog computing-based face identification and resolution framework, and summarize the security and privacy issues. Then the authentication and session key agreement scheme, data encryption scheme, and data integrity checking scheme are proposed to solve the issues of confidentiality, integrity, and availability in the processes of face identification and face resolution. Finally, we implement a prototype system to evaluate the influence of security scheme on system performance. Meanwhile, we also evaluate and analyze the security properties of proposed scheme from the viewpoint of logical formal proof and the confidentiality, integrity, and availability (CIA) properties of information security. The results indicate that the proposed scheme can effectively meet the requirements for security and privacy preservation.


title: A Taxonomy of Cyber Events Affecting Communities

abstract: Communities, whose reliance on critical cyber infrastructures is growing, are threatened by a wide range of cyber events that can adversely affect these systems and networks. The development of computer security taxonomies to classify computer and network vulnerabilities and attacks has led to a greater insight into the causes, effects, mitigation, and remediation of cyber attacks. In developing these taxonomies researchers are better able to understand and address the many different attacks that can occur. No current taxonomy, however, has been developed that takes into account the community aspects of cyber attacks or other cyber events affecting communities. We present a new taxonomy that considers the motivation, methodology, and effects of cyber events that can affect communities. We include a discussion on how our taxonomy is useful to e-government, industry, and security researchers.


title: Extraneous-light resistant multipixel range sensor based on a low-power correlating pixel-circuit

abstract: This work covers a correlation-based, optical time-of-flight matrix range sensor which is able to suppress ambient light up to 150klx full-electronically and autonomously in each single pixel. The CMOS-compatible sensor features 16×16 pixels and permits distance measurements with a standard deviation of 1cm (5cm) up to 1m (3.2m), while the linearity error is within ±2cm for the entire measurement range within a measurement time of 50ms per distance point. A 0.9W LED source is used to illuminate the field of view with near-infrared light, which is modulated at 10MHz. In this design, the current consumption could be reduced from 100µA to 2µA per pixel, while increasing the distance measurement performance at the same time.


title: Heuristic approaches in robot path planning

abstract: Autonomous navigation of a robot is a promising research domain due to its extensive applications. The navigation consists of four essential requirements known as perception, localization, cognition and path planning, and motion control in which path planning is the most important and interesting part. The proposed path planning techniques are classified into two main categories: classical methods and heuristic methods. The classical methods consist of cell decomposition, potential field method, subgoal network and road map. The approaches are simple; however, they commonly consume expensive computation and may possibly fail when the robot confronts with uncertainty. This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature-inspired algorithms and hybrid algorithms. In addition, potential field method is also considered due to the good results. The strengths and drawbacks of each algorithm are discussed and future outline is provided. Autonomous navigation of a robot is a promising research domain due to its extensive applications.This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature inspired algorithms and hybrid algorithms.The strengths and drawbacks of each algorithm are discussed and future outline is provided.


title: Classification of VANET MAC, Routing and Approaches A Detailed Survey

abstract: Human safety considerations linked with rapidly growing auto mobile market has given special attention to the Intelligent Transportation System (ITS). ITS provides a set of stan- dards for inter vehicular communication with emphasis on safety, traffic efficiency and infotain- ment related applications. In ITS, the vehicles acting as mobile nodes, form a specialized ad hoc network, known as Vehicular Ad-hoc Network (VANET). Although, VANET and ITS are under intense research since last decade, technology still lacks large scale deployment. Vehicle to Ve- hicle (V2V) and Vehicle to Infrastructure (V2I) communications are the main research goals of ITS. High relative node velocity and high active node density has presented peculiar challenges to connectivity within VANET. VANET connectivity and routing requirements range from the time critical safety applications, to the time and space hovering, delay tolerant and infotainment applications. This paper reviews connectivity issues in VANET with emphasis on routing, and offers comprehensive literature review on state of the art in VANET routing, with its detailed classifications. It also compares some standard architectures of VANET from MAC, routing and management perspective, i.e., WAVE by IEEE, CALM by ISO, C2CNet by C2C consortium / GeoNet.


title: Viewport-Adaptive Encoding and Streaming of 360-Degree Video for Virtual Reality Applications

abstract: Virtual reality applications use 360-degree videos and head mount displays (HMDs) with stereoscopic capabilities to provide full immersion experience. In these applications it is also common to use 4K resolution or higher per view for 360-degree videos. Consequently, this leads to technical challenges in handling the bandwidth requirements while keeping the system latency to the minimal. When the content is viewed with a HMD, a subset of the entire 360-degree video is displayed at a single point of time. To improve the resolution and picture quality of the displayed content, viewport based coding is desirable. In this regard, we investigated various viewport dependent projection schemes including the existing variants of Pyramidal projection. In this regard we propose the multi-resolution versions of Equirectangular and Cubemap projections. Additionally, we developed a methodology for comparing the rate-distortion performance of these projections. Based on the simulation results, it was observed that multi-resolution projections of Equirectangle and Cubemap outperform other projection schemes, significantly.


title: Differentially private data release for data mining

abstract: Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among the existing privacy models, ∈-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. Most of the existing solutions that ensure ∈-differential privacy are based on an interactive model, where the data miner is only allowed to pose aggregate queries to the database. In this paper, we propose the first anonymization algorithm for the non-interactive setting based on the generalization technique. The proposed solution first probabilistically generalizes the raw data and then adds noise to guarantee ∈-differential privacy. As a sample application, we show that the anonymized data can be used effectively to build a decision tree induction classifier. Experimental results demonstrate that the proposed non-interactive anonymization algorithm is scalable and performs better than the existing solutions for classification analysis.


title: Recall estimation for rare topic retrieval from large corpuses

abstract: The problem of finding documents pertaining to a particular topic finds application in a variety of scenarios. Indeed, the demand for topically pertinent documents has led to myriad companies offering services to find and deliver them (perhaps along with sentiment analysis or clustering) to customers for any topics of interest. The methodologies used to uncover relevant documents range from manually curated keyword filters to trained classification models. Any serious topical analysis requires a sound understanding of key metrics behind the retrieval process, two of the most important being precision and recall. While precision can be easily and inexpensively measured by sampling from classified documents and utilizing (paid) human computation to mark incorrectly classified instances, it is not as straightforward to use the same approach for measuring recall. With most topics occurring relatively sparsely, an unbiased sampling approach becomes prohibitively expensive. In this paper, we introduce a recall measurement procedure requiring only relatively few human judgements. The technique makes use of pairs of sufficiently independent classifiers and the paper provides a detailed discussion of how such classifier pairs can be constructed in practice, with a focus on social media classifiers. We report the performance of the proposed method with simple keyword filters as well as with classifiers of progressive levels of complexity and show that under reasonable conditions, recall can be estimated to within 0.10 absolute error and 15% relative error, and often closer with a reduction of cost by a factor of as much as 1000x as compared with unbiased sampling.


title: Experimental verification of analytical torques enabling a screw drive in-pipe robot to pass through bent pipes

abstract: This paper presents the relationship between spring stiffness and motor torque of a screw drive in-pipe robot that passes through a bent pipe. Their values are derived from a cross-sectional analysis of a bent pipe and a static analysis of the robot that rotates with repeating expansion and contraction of its diameter in that cross-sectional shape. To verify the validity of the analysis results, a simple and light screw drive in-pipe robot capable of moving through small pipes was developed, and experiments were performed to compare the experimentally measured torque with the analytical results.


Transforming Models with ATL 1


title: Joint Image Filtering with Deep Convolutional Networks

abstract: Joint image filters leverage the guidance image as a prior and transfer the structural details from the guidance image to the target image for suppressing noise or enhancing spatial resolution. Existing methods either rely on various explicit filter constructions or hand-designed objective functions, thereby making it difficult to understand, improve, and accelerate these filters in a coherent framework. In this paper, we propose a learning-based approach for constructing joint filters based on Convolutional Neural Networks. In contrast to existing methods that consider only the guidance image, the proposed algorithm can selectively transfer salient structures that are consistent with both guidance and target images. We show that the model trained on a certain type of data, e.g., RGB and depth images, generalizes well to other modalities, e.g., flash/non-Flash and RGB/NIR images. We validate the effectiveness of the proposed joint filter through extensive experimental evaluations with state-of-the-art methods.


title: Rheological behaviour of whey protein stabilized emulsions in the presence of gum arabic

abstract: Abstract The rheological behaviour of whey protein isolate-stabilized emulsions was investigated in the presence of gum arabic (0–15 wt%). The flow behaviour of the emulsion was observed to be pseudoplastic at all gum concentrations. The increase in ionic strength reduced the magnitude of the apparent viscosity indicating that electrostatic interactions between the high molecular weight polysaccharide molecules influenced the rheology of the system. The viscosity changed with particle size and the concentration of unbound polysaccharide in the emulsion. The apparent viscosity data was described with the empirical power law equation. Temperature dependency of the viscosity was given with an Arrhenius type relationship. An empirical equation was developed for the prediction of activation energy as a function of shear rate.


title: Protecting Software through Obfuscation: Can It Keep Pace with Progress in Code Analysis?

abstract: Software obfuscation has always been a controversially discussed research area. While theoretical results indicate that provably secure obfuscation in general is impossible, its widespread application in malware and commercial software shows that it is nevertheless popular in practice. Still, it remains largely unexplored to what extent today’s software obfuscations keep up with state-of-the-art code analysis and where we stand in the arms race between software developers and code analysts. The main goal of this survey is to analyze the effectiveness of different classes of software obfuscation against the continuously improving deobfuscation techniques and off-the-shelf code analysis tools. The answer very much depends on the goals of the analyst and the available resources. On the one hand, many forms of lightweight static analysis have difficulties with even basic obfuscation schemes, which explains the unbroken popularity of obfuscation among malware writers. On the other hand, more expensive analysis techniques, in particular when used interactively by a human analyst, can easily defeat many obfuscations. As a result, software obfuscation for the purpose of intellectual property protection remains highly challenging.


title: Extended object and group tracking with Elliptic Random Hypersurface Models

abstract: This paper provides new results and insights for tracking an extended target object modeled with an Elliptic Random Hypersurface Model (RHM). An Elliptic RHM specifies the relative squared Maha-lanobis distance of a measurement source to the center of the target object by means of a one-dimensional random scaling factor. It is shown that uniformly distributed measurement sources on an ellipse lead to a uniformly distributed squared scaling factor. Furthermore, a Bayesian inference mechanisms tailored to elliptic shapes is introduced, which is also suitable for scenarios with high measurement noise. Closed-form expressions for the measurement update in case of Gaussian and uniformly distributed squared scaling factors are derived.


title: Comparison of various spherical antenna array element distributions

abstract: The number of the elements and the determination of how to distribute these elements are two of the most basic parameters that affect the operation and the characteristics of an antenna array. The decision of a suitable array element distribution for a spherical antenna array is not a straight-forward problem. In this paper, various antenna element distributions are presented for a spherical array antenna. Specifically, three different distributions are investigated (the t-design/quasi-uniform distribution, the Leopardi's Algorithm distribution and the spiral distribution). The proposed distributions are also modelled and simulated in MatLab in order to compare their results. The discussion in this paper presents the advantages and the disadvantages of each distribution and places special emphasis on the design and fabrication efficiency.


title: Experimental Study of Cement Alkali-Resistant Glass Fiber (C-ARGF) Grouting Material

abstract: Mixing alkali-resistant glass fiber (ARGF) into grouting slurry can prevent the development of cracks; thus, understanding the properties of ARGF grouting material is important for applications in engineering. Two types of ARGFs (Cem-FIL®60 and Anti-Crak®HD) were selected as mixing materials, and their performance was tested in four areas, namely, compressive strength, tensile strength, flexural strength, and impervious performance, under four different mixing amounts of fiber (0%, 0.25%, 0.5%, and 1.0%). Results demonstrate that the addition of ARGF increased the compressive strength and tensile strength of the grouting slurry, and the best performance was at 0.5%. The effect on the flexural strength and impervious performance was related to the mixing amount, and the fiber may have induced a counter-effect for certain amounts of added ARGF. Mixing ARGF could increase the early strength ratio of grout; however, a high early strength ratio did not necessarily result in high strength, as the flexural strength did not change synchronously with the early strength ratio; a similar pattern was found for the impermeability. Cem-FIL®60 had a better effect on the properties of grouting materials than Anti-Crak®HD. These results were successfully applied in the water-plugging and reinforcement engineering of a karst tunnel.


title: EMG controlled soft robotic glove for assistance during activities of daily living

abstract: This paper presents further developments, characterization and initial evaluation of a recently developed assistive soft robotic glove for individuals with hand pathologies. The glove technology utilizes a combination of elastomeric and inextensible materials to create soft actuators that conform to the user's hand and can generate sufficient hand closing force to assist with activities of daily living. User intent (i.e. desire to close or open hand) is detected by monitoring gross muscle activation signals with surface electromyography electrodes mounted on the user's forearm. In particular, we present an open-loop sEMG logic that distinguishes muscle contractions and feeds the information to a low-level fluidic pressure controller that regulates pressure in pre-selected groups of the glove's actuators. Experiments are conducted to determine the level of assistance provided by the glove by monitoring muscle effort and mapping the pressure distribution during a simple grasping task when the glove is worn. Lastly, quantitative and qualitative results are presented using the sEMG-controlled glove on a healthy participant and on a patient with muscular dystrophy.


title: 3'-minor groove binder-DNA probes increase sequence specificity at PCR extension temperatures.

abstract: DNA probes with conjugated minor groove binder (MGB) groups form extremely stable duplexes with single-stranded DNA targets, allowing shorter probes to be used for hybridization based assays. In this paper, sequence specificity of 3'-MGB probes was explored. In comparison with unmodified DNA, MGB probes had higher melting temperature (T(m)) and increased specificity, especially when a mismatch was in the MGB region of the duplex. To exploit these properties, fluorogenic MGB probes were prepared and investigated in the 5'-nuclease PCR assay (real-time PCR assay, TaqMan assay). A 12mer MGB probe had the same T(m)(65 degrees C) as a no-MGB 27mer probe. The fluorogenic MGB probes were more specific for single base mismatches and fluorescence quenching was more efficient, giving increased sensitivity. A/T rich duplexes were stabilized more than G/C rich duplexes, thereby leveling probe T(m)and simplifying design. In summary, MGB probes were more sequence specific than standard DNA probes, especially for single base mismatches at elevated hybridization temperatures.


title: Diderot in the Painter’s Space

abstract: By responding to Grimm’s requests, Diderot consented to write about art at the Salon of 1759 for the readers of the Correspondence litteraire. An order to deliver? Absolutely; but, finishing the theoretical and practical adventures of a new theater, the circumstances, and the brilliant vintaged spectacle of the squared Salon was convenient to Diderot and it could provide a foil for him. He would get a taste for this; he would return to but not without grumbling about an even greater task which an abundance of ideas made even more difficult to decide. The occasion offered Diderot to speak as he liked to speak: to a specific person, at a specific moment, in front of a series of specific objects, whilst thinking of listeners’ at distances in space and time. All pretexts were primed, and first of all the challenge that the “silent” arts defy speech and the occasion to answer by allowing the painters, sculptors to quarrel on their ways of translating into images poets, dramatists and historians…


title: Implementation of ERP Systems: Accounting and Auditing Implications

abstract: Technology plays a key role in today's business environment. Many companies greatly rely on computers and software to provide accurate information to effectively manage their business. It is becoming increasingly necessary for all businesses to incorporate information technology solutions to operate successfully. One way that many corporations have adopted information technology on a large scale is by installing Enterprise Resource Planning (ERP) systems to accomplish their business transaction and data processing needs. The company named SAP Aktiengesellschaft (commonly known as SAP AG in the business press) is currently the world market and technology leader in providing ERP systems. As such, this paper primarily discusses information technology implications based on the SAP system. The remainder of this introduction provides an overview of ERP software in general, and a discussion of SAP’s ERP software. Section 2 discusses major technological and accounting issues involved in the implementation of SAP and ERP systems. Section 3 discusses data and information integrity and audit issues in ERP systems. The final section provides a conclusion.


title: Symbolic Execution for Deep Neural Networks

abstract: Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. The idea is to translate a DNN into an imperative program, thereby enabling program analysis to assist with DNN validation. A basic translation however creates programs that are very complex to analyze. DeepCheck introduces novel techniques for lightweight symbolic analysis of DNNs and applies them in the context of image classification to address two challenging problems in DNN analysis: 1) identification of important pixels (for attribution and adversarial generation); and 2) creation of 1-pixel and 2-pixel attacks. Experimental results using the MNIST data-set show that DeepCheck's lightweight symbolic analysis provides a valuable tool for DNN validation.


title: Robot Motion Planning in Learned Latent Spaces

abstract: This letter presents latent sampling-based motion planning (L-SBMP), a methodology toward computing motion plans for complex robotic systems by learning a plannable latent representation. Recent works in control of robotic systems have effectively leveraged local, low-dimensional embeddings of high-dimensional dynamics. In this letter, we combine these recent advances with techniques from sampling-based motion planning (SBMP) in order to design a methodology capable of planning for high-dimensional robotic systems beyond the reach of traditional approaches (e.g., humanoids, or even systems where planning occurs in the visual space). Specifically, the learned latent space is constructed through an autoencoding network, a dynamics network, and a collision checking network, which mirror the three main algorithmic primitives of SBMP, namely state sampling, local steering, and collision checking. Notably, these networks can be trained through only raw data of the system's states and actions along with a supervising collision checker. Building upon these networks, an RRT-based algorithm is used to plan motions directly in the latent space —we refer to this exploration algorithm as learned latent RRT. This algorithm globally explores the latent space and is capable of generalizing to new environments. The overall methodology is demonstrated on two planning problems, namely a visual planning problem, whereby planning happens in the visual (pixel) space, and a humanoid robot planning problem.


title: Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features

abstract: Registration of multi-temporal remote sensing images has been widely applied in military and civilian fields, such as ground target identification, urban development assessment, and geographic change assessment. Ground surface change challenges feature point detection in amount and quality, which is a common dilemma faced by feature-based registration algorithms. Under severe appearance variation, detected feature points may contain a large proportion of outliers, whereas inliers may be inadequate and unevenly distributed. This paper presents a convolutional neural network (CNN) feature-based multi-temporal remote sensing image registration method with two key contributions: (i) we use a CNN to generate robust multi-scale feature descriptors and (ii) we design a gradually increasing selection of inliers to improve the robustness of feature point registration. Extensive experiments on feature matching and image registration are performed over a multi-temporal satellite image data set and a multi-temporal unmanned aerial vehicle image dataset. Our method outperforms four state-of-the-art methods in most scenarios.


title: WinHeap Explorer: Efficient and Transparent Heap-Based Bug Detection in Machine Code

abstract: Despite all the efforts of the research community, buffer overflows remain one of the most dangerous bugs for modern IT systems. The problem is compounded by the fact that there are many developers who do not follow the basic rules of a secure software development lifecycle, supplying proprietary vulnerable products. To address this problem, the industry has proposed a number of techniques that perform analysis at the binary level. While most of them focus on problems that lead a program to crash or exception, there is a large class of more complex bugs that it is not possible to detect using only this criterion.In this paper we propose WinHeap Explorer, a highperformance solution for heap based bug detection in machine code using an original approach called light-weight dynamic binary instrumentation. The light-weight instrumentation is based on preliminary static analysis of code paths to highlight potentially erroneous parts, due to which we are able to decrease the overhead. Moreover, WinHeap Explorer does not change any memory allocation mechanisms, which preserves transparency of the tool towards the operating system. Our experiments have proven the ability of WinHeap Explorer to detect heapbased bugs as well as decrease runtime overhead for widely-used complex applications ranging between 24.2%-71.1% along with the same level of memory overhead in comparison with existing solutions. WinHeap Explorer is distributed under BSD license and available at [1].


Religion and politics in islam : tha case of muhammadiyah in Indonesia's new order


title: Analysis of LoRaWAN technology in an Outdoor and an Indoor Scenario in Duisburg-Germany

abstract: Low Power Wide Area Network (LPWAN) technology is a new technology that is nowadays drawing a lot of attention in the Internet of Things (IoT) segment. In this study, we analyze the behavior of LoRaWAN technology in urban settings in both an indoor and an outdoor scenario, with respect to parameters such as Received Signal Strength Indicator, Signal to Noise Ratio, Packet Delivery Rate, Coverage, and Deep Indoor Penetration. The results are presented with different Spreading Factors (SF7 to SF12) in an indoor analysis. We built a private LoRaWAN gateway with the help of an IC880a concentrator board. The experiments took place in urban settings with frequency noises and obstacles, such as tall buildings, trees, cars, and people. We compared our study results in Duisburg-Germany downtown with related studies.


title: A New Image Classification Model Based on Brain Parallel Interaction Mechanism

abstract: Abstract The brain vision mechanism provides a source for the structural design of convolution neural networks (CNNs). Inspired by the multi-stage interaction between the parallel ventral and dorsal stream of the human brain in the process of image recognition, we introduce a new image classification model called parallel interaction model (PIM). The feature extractor of PIM consists of two parallel CNNs, one of them is the main of the feature extractor connected to the classifier, and the other as a secondary of the feature extractor which can be used as a multi-stage interaction with the main one to help extract more effective features. Using the proposed PIM, we improve on two different CNNs, and validate model effects on Cifar-10, Aircrafts100 and Flower-17 data sets. We found that PIM produced significant performance improvements over the base networks. At last, before and after the interaction, the feature maps in one of the improved networks are visualized and analyzed.


title: Context-Dependent Diffusion Network for Visual Relationship Detection

abstract: Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate-object lie on an extreme diversity space, such asperson-behind-person andcar-behind-building, while suffering from the problem of combinatorial explosion. In this paper, we propose a context-dependent diffusion network (CDDN) framework to deal with visual relationship detection. To capture the interactions of different object instances, two types of graphs, word semantic graph and visual scene graph, are constructed to encode global context interdependency. The semantic graph is built through language priors to model semantic correlations across objects, whilst the visual scene graph defines the connections of scene objects so as to utilize the surrounding scene information. For the graph-structured data, we design a diffusion network to adaptively aggregate information from contexts, which can effectively learn latent representations of visual relationships and well cater to visual relationship detection in view of its isomorphic invariance to graphs. Experiments on two widely-used datasets demonstrate that our proposed method is more effective and achieves the state-of-the-art performance.


title: Almost Isometric Mesh Parameterization through Abstract Domains

abstract: In this paper, we propose a robust, automatic technique to build a global hi-quality parameterization of a two-manifold triangular mesh. An adaptively chosen 2D domain of the parameterization is built as part of the process. The produced parameterization exhibits very low isometric distortion, because it is globally optimized to preserve both areas and angles. The domain is a collection of equilateral triangular 2D regions enriched with explicit adjacency relationships (it is abstract in the sense that no 3D embedding is necessary). It is tailored to minimize isometric distortion, resulting in excellent parameterization qualities, even when meshes with complex shape and topology are mapped into domains composed of a small number of large continuous regions. Moreover, this domain is, in turn, remapped into a collection of 2D square regions, unlocking many advantages found in quad-based domains (e.g., ease of packing). The technique is tested on a variety of cases, including challenging ones, and compares very favorably with known approaches. An open-source implementation is made available.


title: On the Design of Circular Fractal Antenna with U-Shape Slot in CPW-Feed

abstract: A new ultawideband circular fractal antenna with notched-band characteristics is presented. A notched-band characteristic is achieved by employing a U-shape slot in 50 Ω feed lines. The ultra-wideband impedance matching and compact size have been obtained by using CPW-feed technique and the fractal concept. The measured result of proposed fractal antenna exhibits the ultra wideband characteristics from 3.0 to 18.0 GHz at VSWR 2:1 except notched-band frequency. The proposed antenna has been analyzed theoretically and experimentally with respect to design parameters. The measured radiation pattern of fractal antenna is nearly omnidirectional in azimuth plane throughout the operating frequency. This antenna is useful for UBW communication system.


title: Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs

abstract: Recently, Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based interactive mobile applications, and implement their backend computation parts on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.


title: Modeling Newswire Events using Neural Networks for Anomaly Detection

abstract: Automatically identifying anomalous newswire events is a hard problem. We discuss the complexity of the problem and introduce a novel technique to model events based on recursive neural networks to represent events as composition of their semantic arguments. Our model learns to differentiate between normal and anomalous events. We model anomaly detection as a binary classification problem and show that the model learns useful features to classify anomaly. We use headlines from the weird news category publicly available on newswire websites to extract anomalous training examples and those from Gigaword as normal examples. We evaluate the classifier on human annotated data and obtain an accuracy of 65.44%. We also show that our model is at least as competent as the least competent human annotator in anomaly detection.


title: Predicting taxi demand at high spatial resolution: Approaching the limit of predictability

abstract: In big cities, taxi service is imbalanced. In some areas, passengers wait too long for a taxi, while in others, many taxis roam without passengers. Knowledge of where a taxi will become available can help us solve the taxi demand imbalance problem. In this paper, we employ a holistic approach to predict taxi demand at high spatial resolution. We showcase our techniques using two real-world data sets, yellow cabs and Uber trips in New York City, and perform an evaluation over 9,940 building blocks in Manhattan. Our approach consists of two key steps. First, we use entropy and the temporal correlation of human mobility to measure the demand uncertainty at the building block level. Second, to identify which predictive algorithm can approach the theoretical maximum predictability, we implement and compare three predictors: the Markov predictor (a probability-based predictive algorithm), the Lempel-Ziv-Welch predictor (a sequence-based predictive algorithm), and the Neural Network predictor (a predictive algorithm that uses machine learning). The results show that predictability varies by building block and, on average, the theoretical maximum predictability can be as high as 83%. The performance of the predictors also vary: the Neural Network predictor provides better accuracy for blocks with low predictability, and the Markov predictor provides better accuracy for blocks with high predictability. In blocks with high maximum predictability, the Markov predictor is able to predict the taxi demand with an 89% accuracy, 11% better than the Neural Network predictor, while requiring only 0.03% computation time. These findings indicate that the maximum predictability can be a good metric for selecting prediction algorithms.


title: Glutamate and GABA Imbalance Following Traumatic Brain Injury

abstract: Traumatic brain injury (TBI) leads to multiple short- and long-term changes in neuronal circuits that ultimately conclude with an imbalance of cortical excitation and inhibition. Changes in neurotransmitter concentrations, receptor populations, and specific cell survival are important contributing factors. Many of these changes occur gradually, which may explain the vulnerability of the brain to multiple mild impacts, alterations in neuroplasticity, and delays in the presentation of posttraumatic epilepsy. In this review, we provide an overview of normal glutamate and GABA homeostasis and describe acute, subacute, and chronic changes that follow injury. We conclude by highlighting opportunities for therapeutic interventions in this paradigm.


title: Computational Creativity: The Final Frontier?

abstract: Notions relating to computational systems exhibiting creative behaviours have been explored since the very early days of computer science, and the field of Computational Creativity research has formed in the last dozen years to scientifically explore the potential of such systems. We describe this field via a working definition; a brief history of seminal work; an exploration of the main issues, technologies and ideas; and a look towards future directions. As a society, we are jealous of our creativity: creative people and their contributions to cultural progression are highly valued. Moreover, creative behaviour in people draws on a full set of intelligent abilities, so simulating such behaviour represents a serious technical challenge for Artificial Intelligence research. As such, we believe it is fair to characterise Computational Creativity as a frontier for AI research beyond all others—maybe, even, the final frontier.


title: Indication of participation of caspase-2 and caspase-5 in mechanisms of human cervical malignancy.

abstract: INTRODUCTION ::: When apoptosis is disrupted, the transformed cells can survive, proliferate, and evolve into a malignancy. The strictly conserved caspase genes and the reliable experimental data clearly show that some caspases play a crucial role in apoptosis even if some of them have no apoptotic activity and others exhibit both apoptotic and nonapoptotic properties. Although caspase-2 belongs to initiator caspases, its normal role remains unclear. Experimental studies have shown that it is primarily necessary for the execution of apoptosis in mutagenic cells. Human caspase-5 is classified as an inflammatory caspase, although its substrate has not been identified yet. In this research, the activities of caspase-2 and caspase-5 have been estimated during the progression of human cervical malignancy. ::: ::: ::: METHODS ::: The experimental material includes human cervical tissue samples (normal and pathological) and blood serum samples of the corresponding tissue donors, where enzyme activities have been measured colorimetrically. ::: ::: ::: RESULTS ::: Both caspases' activities showed the highest increase, statistically significant (P < 0.01, by t test) compared with the controls, in the low-grade squamous intraepithelial lesion tissues. Caspase-2 of all pathological tissues was proved more active than the controls. Serum caspases' activities were significantly lower than those of the tissues. Serum caspase-2's activity in patients with low-grade squamous intraepithelial lesion stage showed no statistically significant increase compared with the controls. Serum caspase-5's activity of all patients with malignancy stages was presented elevated, whereas that of the serum of patients with cervical cancer had the highest activity (P < 0.01, by t test). ::: ::: ::: CONCLUSIONS ::: The changes of caspase-2 and caspase-5 activities could be indicative of their involvement in the cervical malignancy mechanisms.


8 Statistical Methodology for a SMART Design in the Development of Adaptive Treatment Strategies


title: Quantification of Dexterity as the Dynamical Regulation of Instabilities: Comparisons Across Gender, Age, and Disease

abstract: Dexterous manipulation depends on using the fingertips to stabilize unstable objects. The Strength-Dexterity paradigm consists of asking subjects to compress a slender and compliant spring prone to buckling. The maximal level of compression (requiring low fingertip forces We then measured finger dexterity in 42 hands of older patients following treatment for osteoarthritis of the base of the thumb (CMC OA, 33F, 65.8±9.7yrs), and 31 hands from patients being treated for Parkinson’s disease (PD, 6F, 10M, 67.68±8.5yrs). Importantly, we found no differences in finger compression force among patients or controls. But we did find significantly stronger age-related declines in performance in the patients with PD (slope -2.7 gf/yr, p=0.002) than in those with CMC OA (slope -1.4 gf/yr, p=0.015), than in controls (slope -0.86 gf/yr). In addition, the temporal variability of forces during spring compression shows clearly different dynamics in the clinical populations compared to the controls (pLastly, we compared dexterity across extremities. We found stronger age (p=0.005) and gender (p=0.002) effects of leg compression force in 188 healthy subjects who compressed a larger spring with the foot of an isolated leg (73F, 115M, 14-92yrs). In 81 subjects who performed tests with all four limbs, we found finger and leg compression force to be significantly correlated (females ρ=0.529 p=0.004, males ρ=0.403 p=0.003; 28F, 53M, 20-85yrs), but surprisingly found no differences between dominant and non-dominant limbs. These results have important clinical implications, and suggest the existence—and compel the investigation—of systemic vs. limb-specific mechanisms for dexterity.


title: Enabling warping on stereoscopic images

abstract: Warping is one of the basic image processing techniques. Directly applying existing monocular image warping techniques to stereoscopic images is problematic as it often introduces vertical disparities and damages the original disparity distribution. In this paper, we show that these problems can be solved by appropriately warping both the disparity map and the two images of a stereoscopic image. We accordingly develop a technique for extending existing image warping algorithms to stereoscopic images. This technique divides stereoscopic image warping into three steps. Our method first applies the user-specified warping to one of the two images. Our method then computes the target disparity map according to the user specified warping. The target disparity map is optimized to preserve the perceived 3D shape of image content after image warping. Our method finally warps the other image using a spatially-varying warping method guided by the target disparity map. Our experiments show that our technique enables existing warping methods to be effectively applied to stereoscopic images, ranging from parametric global warping to non-parametric spatially-varying warping.


title: Sentiment analysis and subjectivity

abstract: An intraocular lens for implantation in an eye comprising an optic configured so that the optic can be deformed to permit the intraocular lens to be passed through an incision into the eye. A peripheral zone circumscribes the optical zone of the optic and one or more fixation members coupled to the peripheral zone and extending outwardly from the peripheral zone to retain the optic in the eye are provided. In one embodiment the fixation member or members are located so that the optical zone is free of such member or members. The peripheral zone preferably has a maximum axial thickness which is larger than the maximum axial thickness of the periphery of the optical zone.


title: Fingerprint Synthesis: Evaluating Fingerprint Search at Scale

abstract: A database of a large number of fingerprint images is highly desired for designing and evaluating large scale fingerprint search algorithms. Compared to collecting a large number of real fingerprints, which is very costly in terms of time, effort and expense, and also involves stringent privacy issues, synthetic fingerprints can be generated at low cost and does not have any privacy issues to deal with. However, it is essential to show that the characteristics and appearance of real and synthetic fingerprint images are sufficiently similar. We propose a Generative Adversarial Network (GAN) to generate 512X512 rolled fingerprint images. Our generative model for rolled fingerprints is highly efficient (12ms/image) with characteristics of synthetic rolled prints close to real rolled images. Experimental results show that our model captures the properties of real rolled fingerprints in terms of (i) fingerprint image quality, (ii) distinctiveness and (iii) minutiae configuration. Our synthetic fingerprint images are more realistic than other approaches.


title: Causes of the 2011–14 California Drought*

abstract: AbstractThe causes of the California drought during November–April winters of 2011/12–2013/14 are analyzed using observations and ensemble simulations with seven atmosphere models forced by observed SSTs. Historically, dry California winters are most commonly associated with a ridge off the west coast but no obvious SST forcing. Wet winters are most commonly associated with a trough off the west coast and an El Nino event. These attributes of dry and wet winters are captured by many of the seven models. According to the models, SST forcing can explain up to a third of California winter precipitation variance. SST forcing was key to sustaining a high pressure ridge over the west coast and suppressing precipitation during the three winters. In 2011/12 this was a response to a La Nina event, whereas in 2012/13 and 2013/14 it appears related to a warm west–cool east tropical Pacific SST pattern. All models contain a mode of variability linking such tropical Pacific SST anomalies to a wave train with a ridge o...


title: Prevention and Treatment of Motion Sickness

abstract: Motion sickness is a common syndrome that occurs upon exposure to certain types of motion. It is thought to be caused by conflict between the vestibular, visual, and other proprioceptive systems. Although nausea is the hallmark symptom, it is often preceded by stomach awareness, malaise, drowsiness, and irritability. Early self-diagnosis should be emphasized, and patients should be counseled about behavioral and pharmacologic strategies to prevent motion sickness before traveling. Patients should learn to identify situations that will lead to motion sickness and minimize the amount of unpleasant motion they are exposed to by avoiding difficult conditions while traveling or by positioning themselves in the most stable part of the vehicle. Slow, intermittent exposure to the motion can reduce symptoms. Other behavioral strategies include watching the true visual horizon, steering the vehicle, tilting their head into turns, or lying down with their eyes closed. Patients should also attempt to reduce other sources of physical, mental, and emotional discomfort. Scopolamine is a first-line medication for prevention of motion sickness and should be administered transdermally several hours before the anticipated motion exposure. First-generation antihistamines, although sedating, are also effective. Nonsedating antihistamines, ondansetron, and ginger root are not effective in the prevention and treatment of motion sickness.


title: The Myth of the "Biphasic" Hyaluronic Acid Filler.

abstract: BACKGROUND ::: The terms "biphasic" and "monophasic" have been used frequently as a means of differentiating hyaluronic acid (HA) fillers. This type of categorization is based on misinterpretations of the term "phase" and provides no help to the practitioner when selecting the most appropriate product for each indication, patient, and injection technique. ::: ::: ::: OBJECTIVE ::: The purpose of this study was to analyze the properties of 2 HA filler families; Juvederm (JUV) (Allergan), often stated to be monophasic and Restylane (RES) (Galderma), often stated to be biphasic, and discuss what properties may have led to the use of the terms monophasic and biphasic. ::: ::: ::: MATERIALS AND METHODS ::: Three different methods were used for JUV and RES: determination of extractable HA; determination of water uptake; and microscopy. ::: ::: ::: RESULTS ::: The analyzed products were shown to contain both observable gel particles and extractable HA and have the ability to absorb added water. ::: ::: ::: CONCLUSION ::: The categorization of HA fillers as biphasic or monophasic was shown to be scientifically incorrect and should therefore be avoided. Further analytical measurement of the properties leading to this misinterpretation can provide information to discriminate and categorize HA fillers on a sounder scientific basis.


title: A Model of Coherence Based on Distributed Sentence Representation

abstract: Coherence is what makes a multi-sentence text meaningful, both logically and syntactically. To solve the challenge of ordering a set of sentences into coherent order, existing approaches focus mostly on defining and using sophisticated features to capture the cross-sentence argumentation logic and syntactic relationships. But both argumentation semantics and crosssentence syntax (such as coreference and tense rules) are very hard to formalize. In this paper, we introduce a neural network model for the coherence task based on distributed sentence representation. The proposed approach learns a syntacticosemantic representation for sentences automatically, using either recurrent or recursive neural networks. The architecture obviated the need for feature engineering, and learns sentence representations, which are to some extent able to capture the ‘rules’ governing coherent sentence structure. The proposed approach outperforms existing baselines and generates the stateof-art performance in standard coherence evaluation tasks 1 .


title: A 60GHz on-chip RF-Interconnect with λ/4 coupler for 5Gbps bi-directional communication and multi-drop arbitration

abstract: A 5Gbps bi-directional RF-Interconnect (RF-I) with multi-drop and arbitration capabilities is designed and realized in 65nm CMOS. The baseband data are modulated in RF-I by using a 60GHz carrier in ASK format. An on-chip differential transmission line (TL) is used as the communication channel, which minimizes the latency (9ps/mm) only under the speed-of-light limitation. We insert λ/4 directional couplers for implementing multi-drops without signal reflection. We also use MOS switches along the signal path to reconfigure/arbitrate communication priority for multi-drops. This design consists of four TX/RX drops along a 5.5mm TL ring, supports destructive reading with fixed priority, and can reconfigure any drop as the transmitter. The tested data rate of the RF-I is 5Gbps with lower than 10−12 BER. The average power consumptions for the link are 1.33pJ/b and 0.24pJ/b/mm.


title: Revealing the Traces of JPEG Compression Anti-Forensics

abstract: Due to the lossy nature of transform coding, JPEG introduces characteristic traces in the compressed images. A forensic analyst might reveal these traces by analyzing the histogram of discrete cosine transform (DCT) coefficients and exploit them to identify local tampering, copy-move forgery, etc. At the same time, it has been recently shown that a knowledgeable adversary can possibly conceal the traces of JPEG compression, by adding a dithering noise signal in the DCT domain, in order to restore the histogram of the original image. In this paper, we study the processing chain that arises in the case of JPEG compression anti-forensics. We take the perspective of the forensic analyst, and we show how it is possible to counter the aforementioned anti-forensic method revealing the traces of JPEG compression, regardless of the quantization matrix being used. Tests on a large image dataset demonstrated that the proposed detector was able to achieve an average accuracy equal to 93%, rising above 99% when excluding the case of nearly lossless JPEG compression.


title: Towards better CPU power management on multicore smartphones

abstract: Although multicore smartphones have become increasingly mainstream, it is unclear whether and how smartphone applications can utilize multicore CPUs to improve performance. In this paper we study the performance of mobile applications using multicore CPUs, in terms of power and computation cost. Using Web browsing as an example, our preliminary measurement results show that even large applications like Web browsers with multi-threading acceleration cannot fully utilize the multicore CPUs. Furthermore, we find that the existing CPU power models on smartphones are ill-suited for modern multicore CPUs. We develop a new CPU power model with a high accuracy, 95.6% on average. Our work helps to better understand the performance of multicore smartphones and paves the way towards better CPU power management on multicore smartphones.


title: Preparation and characterization of solid dispersions of carvedilol with PVP K30

abstract: Solid dispersions in water-soluble carriers have attracted considerable interest as a means of improving the dissolution rate, and hence possibly bioavailability, of a range of hydrophobic drugs. The poor solubility of carvedilol leads to poor dissolution and hence variation in bioavailability. The purpose of the present investigation was to increase the solubility and dissolution rate of carvedilol for enhancement of oral bioavailability. In the present investigation solid dispersions with PVP K30 were prepared by solvent evaporation method. The physical mixture and solid dispersion (s) were characterized for drug-carrier interaction, drug content, solubility and dissolution rate. The solubility of drug increased with increasing polymer concentration. The dissolution rate was substantially improved for carvedilol from its solid dispersion compared with pure drug and physical mixture. As indicated from X-ray diffraction pattern and DSC thermograms carvedilol was in the amorphous form, which confirmed the better dissolution rate of solid dispersions. The solid dispersion was stable under accelerated storage conditions. The solid dispersion technique with PVP K30 as a carrier provides a promising way to enhance the solubility and dissolution rate of carvedilol.


title: A model for estimating agile project process and schedule acceleration

abstract: Accelerating development schedules is increasingly important in a competitive world. Reduced time-to-market is a key response to competitive threats in the commercial sphere, and rapid response in deploying military systems may save lives in a geopolitical environment characterized by rapidly emerging and ever-changing physical threats. Agile/lean development methodologies show promise in providing the desired schedule acceleration, but it can be difficult for planners to determine the effects of these factors on schedule duration, and to make appropriate choices to optimize project performance. The Constructive Rapid Application Development Model (CORADMO) attempts to quantify the effects of key schedule drivers, and thus enable planners to estimate the relative schedule that will result from varying these parameters.


title: Information retrieval, fusion, completion, and clustering for employee expertise estimation

abstract: Estimating the skills, talents, and expertise of employees is essential for human capital management in knowledge-based organizations across industries and sectors. In this paper, we describe an approach to infer the expertise of employees from their enterprise data and digital footprints. Using a novel big data workflow with components of information retrieval and search, data fusion, matrix completion, and ordinal regression clustering, we are able to automatically find evidence of expertise and determine appropriate evidence weights for different queries and data sources that we merge and present in a manner consumable by businesspeople. We illustrate the system on sample data from the IBM Corporation where it has been deployed.


title: A Modified Level-Shifted PWM Strategy for Fault-Tolerant Cascaded Multilevel Inverters With Improved Power Distribution

abstract: To increase the fault tolerance for cascaded H-bridge multilevel inverters (CHMIs) based on level-shifted pulse width modulation (LS-PWM), a modified LS-PWM method is proposed for the inverters operating under faulty conditions. To cover both open- and short-device fault cases, two types of switching scheme are included in this PWM method. With this PWM method, the neutral shift can be applied as fault-tolerant control for CHMIs based on LS-PWM. Finally, three-phase-balanced line-to-line voltages and currents with minimum harmonic distortion are produced for the inverter with disabled power cells. In addition, this LS-PWM method has the capability to improve power distribution among the H-bridge power cells under the faulty conditions. Simulation and experimental results show that the proposed PWM method performs satisfactorily to achieve fault tolerance.


title: Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs

abstract: The crowdsourced digital footprints from Location Based Social Networks (LBSNs) contain not only rich information about locations, but also individual's feeling about locations and associated entities. This new data source provides us with an unprecedented opportunity to massively and cheaply collect location related information, and to subtly characterize individual's fine-grained preference about those places and associated entities. In this paper, we propose SEALs - a fine-grained preference-aware location search framework leveraging the crowdsourced traces in LBSNs. We first collect user check-ins and tips from Foursquare and use them as direct user feedback on locations. Second, we extract users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference. Third, we incorporate such fine-grained user preference into personalized location ranking using tensor factorization techniques. Experimental results show that SEALs can achieve better location ranking comparing to the state-of-the-art solutions.


title: Scalable Person Re-identification: A Benchmark

abstract: This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.


title: Statistical Parametric Speech Synthesis Using Bottleneck Representation From Sequence Auto-encoder

abstract: In this paper, we describe a statistical parametric speech synthesis approach with unit-level acoustic representation. In conventional deep neural network based speech synthesis, the input text features are repeated for the entire duration of phoneme for mapping text and speech parameters. This mapping is learnt at the frame-level which is the de-facto acoustic representation. However much of this computational requirement can be drastically reduced if every unit can be represented with a fixed-dimensional representation. Using recurrent neural network based auto-encoder, we show that it is indeed possible to map units of varying duration to a single vector. We then use this acoustic representation at unit-level to synthesize speech using deep neural network based statistical parametric speech synthesis technique. Results show that the proposed approach is able to synthesize at the same quality as the conventional frame based approach at a highly reduced computational cost.


title: The urgency for effective user privacy-education to counter social engineering attacks on secure computer systems

abstract: Trusted people can fail to be trustworthy when it comes to protecting their aperture of access to secure computer systems due to inadequate education, negligence, and various social pressures. People are often the weakest link in an otherwise secure computer system and, consequently, are targeted for social engineering attacks. Social Engineering is a technique used by hackers or other attackers to gain access to information technology systems by getting the needed information (for example, a username and password) from a person rather than breaking into the system through electronic or algorithmic hacking methods. Such attacks can occur on both a physical and psychological level. The physical setting for these attacks occurs where a victim feels secure: often the workplace, the phone, the trash, and even on-line. Psychology is often used to create a rushed or officious ambiance that helps the social engineer to cajole information about accessing the system from an employee. Data privacy legislation in the United States and international countries that imposes privacy standards and fines for negligent or willful non-compliance increases the urgency to measure the trustworthiness of people and systems. One metric for determining compliance is to simulate, by audit, a social engineering attack upon an organization required to follow data privacy standards. Such an organization commits to protect the confidentiality of personal data with which it is entrusted. This paper presents the results of an approved social engineering audit made without notice within an organization where data security is a concern. Areas emphasized include experiences between the Social Engineer and the audited users, techniques used by the Social Engineer, and other findings from the audit. Possible steps to mitigate exposure to the dangers of Social Engineering through improved user education are reviewed.


title: Approach to Adaptive Resource Management Based on System Environments for Multi-beam Satellite Networks

abstract: Multi-spot beam satellite network can achieve the power and bandwidth gain of parallel multi-beam with the appropriate resource management. The effective resource management is important not only to prevent the unnecessary resource waste but also to increase in the resource utilization efficiency in the satellite systems with limited resource environment. In this paper, we refer two resource management schemes adjusting power and bandwidth of parallel multi-beam, in order to achieve the substantial performance gain and reasonable proportional fairness. These schemes dealt with the tradeoff problem between different aims of system objectives. In this paper, we compare the performance of these schemes, and investigate the gaps of those that are shown according to amount of traffic overload and dominant resource. The performance is evaluated by taking into account the impact to the system environment, and we propose a possible approach to the adaptive resource management method in the multi-beam satellite networks.


title: Out-of-Band Millimeter Wave Beamforming and Communications to Achieve Low Latency and High Energy Efficiency in 5G Systems

abstract: Communications in the millimeter wave (mmWave) band faces significant challenges due to variable channels, intermittent connectivity, and high energy usage. Moreover, speeds for electronic processing of data is of the same order as typical rates for mmWave interfaces, making the use of complex algorithms for tracking channel variations and adjusting resources impractical. In order to mitigate some of these challenges, we propose an architecture that integrates the sub-6 GHz and mmWave technologies. Our system exploits the spatial correlations between the sub-6 GHz and mmWave interfaces for beamforming and data transfer . Based on extensive experimentation in indoor and outdoor settings, we demonstrate that analog beamforming can be used in mmWave without incurring large overhead, thanks to the spatial correlations with sub-6 GHz. In addition, we incorporate the sub-6 GHz interface as a fallback (secondary) data transfer mechanism such that: 1) the negative effects of highly intermittent mmWave connectivity are mitigated and 2) the abundant mmWave capacity is fully exploited. To achieve these goals, we consider the problem of scheduling the arrival traffic over the mmWave or sub-6 GHz in order to maximize the mmWave throughput while delay (due to mmWave outages) is guaranteed to be bounded. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. Numerical results demonstrate that our scheduler provides a bounded mmWave delay performance, while it achieves a similar throughput performance as the throughput-optimal policies (e.g., MaxWeight).


title: Automatic docking system for recharging home surveillance robots

abstract: This paper presents the development and characterization of a surveillance robot with automatic docking and recharging capabilities for home security. The proposed system is composed of a surveillance robot and a docking station. The palm-sized surveillance robot has a triangular shape with three wheels. It communicates with the general wireless home router through WiFi. It communicates with the docking station through ZigBee and serves as a mobile wireless sensor network gateway. The docking station has a trapezoidal structure with an arc-shaped docking interface. A docking method based on the self-localization of the robot and the infrared detectors of the docking station is proposed. The robot can return to the docking station for recharging operations when the on-board battery is too low. The experimental results show that the prototype robot achieved a success rate of 90% after 60 different docking attempts.


title: A Bayesian Network Model for Predicting Insider Threats

abstract: This paper introduces a Bayesian network model for the motivation and psychology of the malicious insider. First, an initial model was developed based on results in the research literature, highlighting critical variables for the prediction of degree of interest in a potentially malicious insider. Second, a survey was conducted to measure these predictive variables in a common sample of normal participants. Third, a structural equation model was constructed based on the original model, updated based on a split-half sample of the empirical survey data and validated against the other half of the dataset. Fourth, the Bayesian network was adjusted in light of the results of the empirical analysis. Fifth, the updated model was used to develop an upper bound on the quality of model predictions of its own simulated data. When empirical data regarding psychological predictors were input to the model, predictions of counterproductive behavior approached the upper bound of model predictiveness.


title: Multi-View Clustering via Joint Nonnegative Matrix Factorization

abstract: Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In this paper, we propose a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views. The key idea is to formulate a joint matrix factorization process with the constraint that pushes clustering solution of each view towards a common consensus instead of fixing it directly. The main challenge is how to keep clustering solutions across different views meaningful and comparable. To tackle this challenge, we design a novel and effective normalization strategy inspired by the connection between NMF and PLSA. Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.


title: Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

abstract: Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs). We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions from the predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a dense 3D CRF that accounts for both spatial coherence and appearance. CFCN models were trained in a 2-fold cross-validation on the abdominal CT dataset 3DIRCAD comprising 15 hepatic tumor volumes. Our results show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over \(94\,\%\) for liver with computation times below 100 s per volume. We experimentally demonstrate the robustness of the proposed method as a decision support system with a high accuracy and speed for usage in daily clinical routine.


title: Combined Manual Therapy Techniques for the Treatment of Women With Infertility: A Case Series

abstract: Current treatment options for infertility, including hormone therapy, intrauterine insemination, and in vitro fertilization, tend to be expensive, are not necessarily covered by insurance, and carry different levels of short-term and long-term health risks. Many of the issues that contribute to infertility can be traced to scar tissue, fascial restriction, and lymphatic congestion in the pelvic region. Manual therapy techniques exist to release fascial restrictions, to mobilize tight ligaments, and to drain congested lymphatics, all of which can be applied to the reproductive system. In this case series, 10 infertile women were treated with 1 to 6 sessions of manual therapy applied to the pelvic region. Techniques included muscle energy, lymphatic drainage, and visceral manipulation. Six of the 10 women conceived within 3 months of the last treatment session, and all 6 of those women delivered at full term.


title: Detecting Danger: The Dendritic Cell Algorithm

abstract: The “Dendritic Cell Algorithm” (DCA) is inspired by the function of the dendritic cells of the human immune system. In nature, dendritic cells are the intrusion detection agents of the human body, policing the tissue and organs for potential invaders in the form of pathogens. In this research, an abstract model of dendritic cell (DC) behavior is developed and subsequently used to form an algorithm—the DCA. The abstraction process was facilitated through close collaboration with laboratory-based immunologists, who performed bespoke experiments, the results of which are used as an integral part of this algorithm. The DCA is a population-based algorithm, with each agent in the system represented as an “artificial DC”. Each DC has the ability to combine multiple data streams and can add context to data suspected as anomalous. In this chapter, the abstraction process and details of the resultant algorithm are given. The algorithm is applied to numerous intrusion detection problems in computer security including the detection of port scans and botnets, where it has produced impressive results with relatively low rates of false positives.