title: Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface

abstract: In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.


title: As time goes by: Hippocampal connectivity changes with remoteness of autobiographical memory retrieval

abstract: The hippocampus is crucial for episodic autobiographical memory retrieval. Functional neuroimaging evidence suggests that it is similarly engaged in recent and remote retrieval when memories are ma ...


Automatic Chemical Design using Variational Autoencoders


title: Liver Ultrasound Image Segmentation Using Region-Difference Filters

abstract: In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05 s compared to the time of 26.44, 24.82, and 28.36 s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.


title: Chrysophycean cysts: indicators of eutrophication in the recent sediments of Frains Lake, Michigan, U.S.A.

abstract: The recent sediments of Frains Lake, Michigan contain a rich and well preserved association of chrysophycean cysts. Forty one forms are revealed by scanning electron microscopy (SEM) and light microscopy (LM). Taxonomic descriptions. and SEM micrographs are provided for the dominant forms. The three dominant taxa throughout the sediments, Cysta minima, C. modica and C. subbavaricum, do not show significant shifts in proportional abundance associated with European settlement and the onset of cultural eutrophication. However, certain subdominant taxa do show clear trends. Density counts indicate a dramatic decline in cyst concentration (by volume and by dry mass) and a sharp increase in absolute accumulation (net annual influx) following settlement.


title: Pop-up mars rover with textile-enhanced rigid-flex PCB body

abstract: This paper presents a novel manufacturing paradigm for constructing origami-inspired pop-up robots for future space exploration missions. The new approach uses a textile-enhanced rigid-flex printed circuit board (PCB) to implement a folding robot chassis using robust, spaceflight-tolerant materials, and integrates the robot electronics directly into the chassis for added compactness. The new approach also decouples the mechanical and electrical functions of the chassis flexures for improved kinematics and lifetime. This manufacturing paradigm was used to build PUFFER (Pop-Up Flat Folding Explorer Robot), a self-actuated pop-up rover being developed to provide a low-payload-cost mobility enhancement for future NASA missions.


title: Violence Detection in Movies

abstract: As violence in movies has harmful influence on children, in this paper, we propose an algorithm to detect violent scene in movies. Under our definition of violence, the task of violent scene detection is decomposed into action scene detection and bloody frame detection. While previous approaches addressed on shot level of video structure only, our approach works on more semantic-complete scene structure of video. The input video (digital movie) is first segmented into several scenes. Based on the filmmaking characteristics of action scene, some features of the scene are extracted to feed into the support vector machine for classification. Finally, the face, blood and motion information are integrated to determine whether the action scene has violent content. Experimental results show that the proposed approach works reasonably well in detecting most of the violent scenes. Compared with related work, our approach is computationally simple yet effective.


title: METTLE: a METamorphic testing approach to assessing and validating unsupervised machine LEarning systems

abstract: Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised machine learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users' requirements and specific application scenarios$\,/\,$contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a $\textbf{MET}$amorphic $\textbf{T}$esting approach to assessing and validating unsupervised machine $\textbf{LE}$arning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users' perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users' generally expected characteristics that should be possessed by machine learning systems. To demonstrate the viability and effectiveness of METTLE we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the machine learning systems from an end-user software engineering's perspective, rather than a designer's or implementor's perspective, who normally adopts a theoretical approach.


A Review of Standard Text Classification Practices for Multi-label Toxicity Identification of Online Content


Conversational User Interfaces for Online Shops? A Categorization of Use Cases


title: Leaky-Wave Antenna With Alternately Loaded Complementary Radiation Elements

abstract: In this letter, we compare the scanning beams of two leaky-wave antennas (LWAs), respectively, loaded with capacitive and inductive radiation elements, which have not been fully discussed in previous publications. It is pointed out that an LWA with only one type of radiation element suffers from a significant gain fluctuation over its beam-scanning band. To remedy this problem, we propose an LWA alternately loaded with inductive and capacitive elements along the host transmission line. The proposed LWA is able to steer its beam continuously from backward to forward with constant gain. A microstrip-based LWA is designed on the basis of the proposed method, and the measurement of its fabricated prototype demonstrates and confirms the desired results. This design method can widely be used to obtain LWAs with constant gain based on a variety of TLs.


title: The contribution of brain sub-cortical loops in the expression and acquisition of action understanding abilities

abstract: Research on action understanding in cognitive neuroscience has led to the identification of a wide "action understanding network" mainly encompassing parietal and premotor cortical areas. Within this cortical network mirror neurons are critically involved implementing a neural mechanism according to which, during action understanding, observed actions are reflected in the motor patterns for the same actions of the observer. We suggest that focusing only on cortical areas and processes could be too restrictive to explain important facets of action understanding regarding, for example, the influence of the observer's motor experience, the multiple levels at which an observed action can be understood, and the acquisition of action understanding ability. In this respect, we propose that aside from the cortical action understanding network, sub-cortical processes pivoting on cerebellar and basal ganglia cortical loops could crucially support both the expression and the acquisition of action understanding abilities. Within the paper we will discuss how this extended view can overcome some limitations of the "pure" cortical perspective, supporting new theoretical predictions on the brain mechanisms underlying action understanding that could be tested by future empirical investigations.


title: A System for Massively Parallel Hyperparameter Tuning

abstract: Modern learning models are characterized by large hyperparameter spaces and long training times. These properties, coupled with the rise of parallel computing and the growing demand to productionize machine learning workloads, motivate the need to develop mature hyperparameter optimization functionality in distributed computing settings. We address this challenge by first introducing a simple and robust hyperparameter optimization algorithm called ASHA, which exploits parallelism and aggressive early-stopping to tackle large-scale hyperparameter optimization problems. Our extensive empirical results show that ASHA outperforms existing state-of-the-art hyperparameter optimization methods; scales linearly with the number of workers in distributed settings; and is suitable for massive parallelism, as demonstrated on a task with 500 workers. We then describe several design decisions we encountered, along with our associated solutions, when integrating ASHA in Determined AI's end-to-end production-quality machine learning system that offers hyperparameter tuning as a service.


title: High Throughput Parallel Implementation of Aho-Corasick Algorithm on a GPU

abstract: Pattern matching is an important operation in various applications such as computer and network security, bioinformatics, image processing, among many others. Aho-Corasick (AC) algorithm is a multiple patterns matching algorithm commonly used for such applications. In order to meet the highly demanding performance requirements imposed on these applications, achieving high performance for AC algorithm is crucial. In this paper, we present a high performance parallel implementation of AC algorithm on a Graphic Processing Unit (GPU) which efficiently utilizes the high degree of on-chip parallelism and the memory hierarchy of the GPU so that the aggregate performance (or throughput) of the GPU can be maximized. For this purpose our approach carefully places and caches the input text data and the reference pattern data used for pattern matching in the on-chip shared memories and the texture caches of the GPU. Furthermore, it efficiently schedules the off-chip global memory loads and the shared memory stores in order to minimize the overheads in loading the input data to the shared memories and also to minimize the shared memory bank conflicts. The proposed approach leads to a significant cut-down of the effective memory access latencies and leads to impressive performance improvements. Experimental results on Nvidia GeForce GTX 285 GPU show that our approach delivers up to 127Gbps throughput performance and up to 222-times speedup compared with a serial version running on 2.2Ghz Core2Duo Intel processor.


title: How Retailers at different Stages of E-Commerce Maturity Evaluate Their Entry to E-Commerce Activities?

abstract: This paper investigates how retailers at different stages of e-commerce maturity evaluate their entry to e-commerce activities. The study was conducted using qualitative approach interviewing 16 retailers in Saudi Arabia. It comes up with 22 factors that are believed the most influencing factors for retailers in Saudi Arabia. Interestingly, there seem to be differences between retailers in companies at different maturity stages in terms of having different attitudes regarding the issues of using e-commerce. The businesses that have reached a high stage of e-commerce maturity provide practical evidence of positive and optimistic attitudes and practices regarding use of e-commerce, whereas the businesses that have not reached higher levels of maturity provide practical evidence of more negative and pessimistic attitudes and practices. The study, therefore, should contribute to efforts leading to greater e-commerce development in Saudi Arabia and other countries with similar context.


Brief Overview of Intermodal Transportation


title: Robust frequency and timing synchronization for OFDM

abstract: A rapid synchronization method is presented for an orthogonal frequency-division multiplexing (OFDM) system using either a continuous transmission or a burst operation over a frequency-selective channel. The presence of a signal can be detected upon the receipt of just one training sequence of two symbols. The start of the frame and the beginning of the symbol can be found, and carrier frequency offsets of many subchannels spacings can be corrected. The algorithms operate near the Cramer-Rao lower bound for the variance of the frequency offset estimate, and the inherent averaging over many subcarriers allows acquisition at very low signal-to-noise ratios (SNRs).


title: Two Stream 3D Semantic Scene Completion

abstract: Inferring the 3D geometry and the semantic meaning of surfaces, which are occluded, is a very challenging task. Recently, a first end-to-end learning approach has been proposed that completes a scene from a single depth image. The approach voxelizes the scene and predicts for each voxel if it is occupied and, if it is occupied, the semantic class label. In this work, we propose a two stream approach that leverages depth information and semantic information, which is inferred from the RGB image, for this task. The approach constructs an incomplete 3D semantic tensor, which uses a compact three-channel encoding for the inferred semantic information, and uses a 3D CNN to infer the complete 3D semantic tensor. In our experimental evaluation, we show that the proposed two stream approach substantially outperforms the state-of-the-art for semantic scene completion.


title: The Bias Blind Spot: Perceptions of Bias in Self Versus Others:

abstract: Three studies suggest that individuals see the existence and operation of cognitive and motivational biases much more in others than in themselves. Study 1 provides evidence from three surveys that people rate themselves as less subject to various biases than the “average American,” classmates in a seminar, and fellow airport travelers. Data from the third survey further suggest that such claims arise from the interplay among availability biases and self-enhancement motives. Participants in one follow-up study who showed the better-than-average bias insisted that their self-assessments were accurate and objective even after reading a description of how they could have been affected by the relevant bias. Participants in a final study reported their peer’s self-serving attributions regarding test performance to be biased but their own similarly self-serving attributions to be free of bias. The relevance of these phenomena to naive realism and to conflict, misunderstanding, and dispute resolution is discussed.


title: Hierarchical Grouping to Optimize an Objective Function

abstract: Abstract A procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical. Given n sets, this procedure permits their reduction to n − 1 mutually exclusive sets by considering the union of all possible n(n − 1)/2 pairs and selecting a union having a maximal value for the functional relation, or objective function, that reflects the criterion chosen by the investigator. By repeating this process until only one group remains, the complete hierarchical structure and a quantitative estimate of the loss associated with each stage in the grouping can be obtained. A general flowchart helpful in computer programming and a numerical example are included.


title: TabMCQ: A Dataset of General Knowledge Tables and Multiple-choice Questions

abstract: We describe two new related resources that facilitate modelling of general knowledge reasoning in 4th grade science exams. The first is a collection of curated facts in the form of tables, and the second is a large set of crowd-sourced multiple-choice questions covering the facts in the tables. Through the setup of the crowd-sourced annotation task we obtain implicit alignment information between questions and tables. We envisage that the resources will be useful not only to researchers working on question answering, but also to people investigating a diverse range of other applications such as information extraction, question parsing, answer type identification, and lexical semantic modelling.


title: Logarithmic Time One-Against-Some

abstract: We create a new online reduction of multiclass classification to binary classification for which training and prediction time scale logarithmically with the number of classes. Compared to previous approaches, we obtain substantially better statistical performance for two reasons: First, we prove a tighter and more complete boosting theorem, and second we translate the results more directly into an algorithm. We show that several simple techniques give rise to an algorithm that can compete with one-against-all in both space and predictive power while offering exponential improvements in speed when the number of classes is large.


title: A study on LIWC categories for opinion mining in Spanish reviews

abstract: With the exponential growth of social media, that is, blogs and social networks, organizations and individual persons are increasingly using the number of reviews of these media for decision-making about a product or service. Opinion mining detects whether the emotions of an opinion expressed by a user on Web platforms in natural language are positive or negative. This paper presents extensive experiments to study the effectiveness of the classification of Spanish opinions in five categories: highly positive, highly negative, positive, negative and neutral, using the combination of the psychological and linguistic features of LIWC (Linguistic Inquiry and Word Count). LIWC is a text analysis software that enables the extraction of different psychological and linguistic features from natural language text. For this study, two corpora have been used, one about movies and one about technological products. Furthermore, we conducted a comparative assessment of the performance of various classification techniques, J48, SMO and BayesNet, using precision, recall and F-measure metrics. The findings revealed that the positive and negative categories provide better results than the other categories. Finally, experiments on both corpora indicated that SMO produces better results than BayesNet and J48 algorithms, obtaining an F-measure of 90.4 and 87.2% in each domain.


title: Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph

abstract: In addition to blockchain, a new paradigm is gaining momentum in the filed of distributed ledger technology—directed acyclic graphs. This paper compares the two paradigms focusing on features relevant to distributed systems using the following representative implementations: Bitcoin, Ethereum and Nano. We examine the applied data structures for maintaining the ledger, consensus mechanisms, transaction confirmation confidence, ledger size, and scalability.


title: Practical reinforcement learning in continuous spaces

abstract: Dynamic control tasks are good candidates for the application of reinforcement learning techniques. However, many of these tasks inherently have continuous state or action variables. This can cause problems for traditional reinforcement learning algorithms which assume discrete states and actions. In this paper, we introduce an algorithm that safely approximates the value function for continuous state control tasks, and that learns quickly from a small amount of data. We give experimental results using this algorithm to learn policies for both a simulated task and also for a real robot, operating in an unaltered environment. The algorithm works well in a traditional learning setting, and demonstrates extremely good learning when bootstrapped with a small amount of human-provided data.


title: Peer-to-peer architecture case study: Gnutella network

abstract: Despite recent excitement generated by the P2P paradigm and despite surprisingly fast deployment of some P2P applications, there are few quantitative evaluations of P2P system behavior. Due to its open architecture and achieved scale, Gnutella is an interesting P2P architecture case study. Gnutella, like most other P2P applications, builds at the application level a virtual network with its own routing mechanisms. The topology of this overlay network and the routing mechanisms used have a significant influence on application properties such as performance, reliability, and scalability. We built a 'crawler' to extract the topology of Gnutella's application level network, we analyze the topology graph and evaluate generated network traffic. We find that although Gnutella is not a pure power-law network, its current configuration has the benefits and drawbacks of a power-law structure. These findings lead us to propose changes to the Gnutella protocol and implementations that bring significant performance and scalability improvements.


title: One-class classification: taxonomy of study and review of techniques

abstract: One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/ novelty detection and concept learning. In this paper, we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.


title: Physical activity and risk of venous thromboembolism: systematic review and meta-analysis of prospective cohort studies

abstract: The inverse association between physical activity and arterial thrombotic disease is well established. Evidence on the association between physical activity and venous thromboembolism (VTE) is divergent. We conducted a systematic review and meta-analysis of published observational prospective cohort studies evaluating the associations of physical activity with VTE risk. MEDLINE, Embase, Web of Science, and manual search of relevant bibliographies were systematically searched until 26 February 2019. Extracted relative risks (RRs) with 95% confidence intervals (CIs) for the maximum versus minimal amount of physical activity groups were pooled using random effects meta-analysis. Twelve articles based on 14 unique prospective cohort studies comprising of 1,286,295 participants and 23,753 VTE events were eligible. The pooled fully-adjusted RR (95% CI) of VTE comparing the most physically active versus the least physically active groups was 0.87 (0.79–0.95). In pooled analysis of 10 studies (288,043 participants and 7069 VTE events) that reported risk estimates not adjusted for body mass index (BMI), the RR (95% CI) of VTE was 0.81 (0.70–0.93). The associations did not vary by geographical location, age, sex, BMI, and methodological quality of studies. There was no evidence of publication bias among contributing studies. Pooled observational prospective cohort studies support an association between regular physical activity and low incidence of VTE. The relationship does not appear to be mediated or confounded by BMI.


title: Characterizing, modeling, and generating workload spikes for stateful services

abstract: Evaluating the resiliency of stateful Internet services to significant workload spikes and data hotspots requires realistic workload traces that are usually very difficult to obtain. A popular approach is to create a workload model and generate synthetic workload, however, there exists no characterization and model of stateful spikes. In this paper we analyze five workload and data spikes and find that they vary significantly in many important aspects such as steepness, magnitude, duration, and spatial locality. We propose and validate a model of stateful spikes that allows us to synthesize volume and data spikes and could thus be used by both cloud computing users and providers to stress-test their infrastructure.


title: Could Data from Location-Based Social Networks Be Used to Support Urban Planning?

abstract: A great quantity of information is required to support urban planning. Usually there are many (not integrated) data sources, originating from different government bodies, in distinct formats and variable properties (e.g. reliability, completeness). The effort to handle these data, integrate and analyze them is high, taking to much time for the information to be available to help decision making. We argue that data from location-based social networks (LBSN) could be used to provide useful information in reasonable time, despite several limitations they have. To asses this, as a case study, we used data from different LBSN to calculate the Local Availability Index (IOL) for a Brazilian city. This index is part of a methodology to estimate quality of urban life inside cities and is used to support urban planning. The results suggest that data from LBSN are useful and could be used to provide insights for local governments.


title: Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals

abstract: Driver drowsiness is among the leading causal factors in traffic accidents occurring worldwide. This paper describes a method to monitor driver safety by analyzing information related to fatigue using two distinct methods: eye movement monitoring and bio-signal processing. A monitoring system is designed in Android-based smartphone where it receives sensory data via wireless sensor network and further processes the data to indicate the current driving aptitude of the driver. It is critical that several sensors are integrated and synchronized for a more realistic evaluation of the driver behavior. The sensors applied include a video sensor to capture the driver image and a bio-signal sensor to gather the driver photoplethysmograph signal. A dynamic Bayesian network framework is used for the driver fatigue evaluation. A warning alarm is sounded if driver fatigue is believed to reach a defined threshold. The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection.


title: Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography

abstract: We present a method that uses measured scene radiance and global illumination in order to add new objects to light-based models with correct lighting. The method uses a high dynamic range image-based model of the scene, rather than synthetic light sources, to illuminate the new objects. To compute the illumination, the scene is considered as three components: the distant scene, the local scene, and the synthetic objects. The distant scene is assumed to be photometrically unaffected by the objects, obviating the need for reflectance model information. The local scene is endowed with estimated reflectance model information so that it can catch shadows and receive reflected light from the new objects. Renderings are created with a standard global illumination method by simulating the interaction of light amongst the three components. A differential rendering technique allows for good results to be obtained when only an estimate of the local scene reflectance properties is known. ::: We apply the general method to the problem of rendering synthetic objects into real scenes. The light-based model is constructed from an approximate geometric model of the scene and by using a light probe to measure the incident illumination at the location of the synthetic objects. The global illumination solution is then composited into a photograph of the scene using the differential rendering technique. We conclude by discussing the relevance of the technique to recovering surface reflectance properties in uncontrolled lighting situations. Applications of the method include visual effects, interior design, and architectural visualization.


Blockchain Multiparty Computation Markets at Scale


title: 3D GLOH features for human action recognition

abstract: Human action recognition from videos has wide applicability and receives significant interests. In this work, to better identify spatio-temporal characteristics, we propose a novel 3D extension of Gradient Location and Orientation Histograms, which provides discriminative local features representing not only the gradient orientation, but also their relative locations. We further propose a human action recognition system based on the Bag of Visual Words model, by combining the new 3D GLOH local features with Histograms of Oriented Optical Flow (HOOF) global features. Along with the idea from our recent work to extract features only in salient regions, our overall system outperforms existing feature descriptors for human action recognition for challenging real-world video datasets.


Legal Claim Identification: Information Extraction with Hierarchically Labeled Data


title: Efficient Design of Waveguide Manifold Multiplexers Based on Low-Order EM Distributed Models

abstract: In this paper, a new systematic technique to design manifold-coupled multiplexers in waveguide technology is proposed. The new technique uses generalized low-order electromagnetic (EM) distributed models, which constitute a half-way point between the fast, but imprecise, analytical models, and the more accurate, but costly, full-wave EM models. The method can be applied to contiguous and noncontiguous channel multiplexers, in both $ { E}$ -plane or $ { H}$ -plane configurations. This paper covers the complete design procedure for manifold multiplexers, starting from the required specifications and finishing with the physical dimensions. After explaining the general design technique for multiplexers with channel filters based on transmission-line resonators, the method is specialized for the design of classic multiplexers with circular-waveguide dual-mode filters. As a further application example, the same methodology is also used for the design of a nonconventional tuningless wideband multiplexer with a new type of rectangular waveguide filters. This paper is concluded with a comparison between simulation and measurements for the wideband multiplexer. Excellent agreement between measurements and simulations are demonstrated, thereby validating the complete design procedure.


title: A comparison of questionnaire-based and GUI-based requirements gathering

abstract: Software development includes gathering information about tasks, work practices and design options from users. Traditionally requirements gathering takes two forms. Interviews and participatory design (PD) practices gather rich information about the task and the domain but require face-to-face communication between the software engineers and the users. When such communication is not possible, traditional software engineering frequently relies on questionnaires and other paper-based methods. Unfortunately, questionnaires often fail to capture implicit aspects of user tasks that may be identified through one-on-one interactions. This project investigates a method of gathering requirements whereby users, working independently of software engineers, construct rough interfaces augmented with textual argumentation. Our initial study has compared the use of GRC (Graphical Requirements Collector) with questionnaire-based requirements gathering.


title: Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

abstract: Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.


title: Container Closure Integrity Testing-Practical Aspects and Approaches in the Pharmaceutical Industry.

abstract: The assurance of sterility of a parenteral drug product, prior to any human use, is a regulatory requirement. Hence, all strategies related to container closure integrity (CCI) must demonstrate absence of microbial contamination through leaks as part of the container closure system (CCS) qualification, during manufacturing, for quality control purposes and to ensure microbiological integrity (sterility) during storage and shipment up to the end of product shelf life. Current regulatory guidances, which differ between countries and regions, provide limited detail on how to assess CCI. The new revision of USP <1207> aims to provide extensive and detailed guidance for CCI assessments for sterile products. However, practical questions and considerations are yet to be addressed by the pharmaceutical industry. These may include: (1) choice of method, for example whether a deterministic CCI method (e.g., helium leak) is preferable over the probabilistic CCI method (e.g., microbial ingress), (2) the type of primary packaging (e.g., vial, syringe, device), (3) dosage form (e.g., liquid versus lyophilisate), (4) suitable acceptance criteria, (5) appropriate sample size, (6) the most appropriate way to introduce artificial leaks into the CCS, (7) ensuring suitable assurance of CCI during drug product manufacturing, and (8) evaluating CCI under intended shipment and storage conditions (e.g., in the frozen state).A group of European industry peers have met to discuss these and other related questions in order to provide their viewpoint and best practice on practical approaches to CCI. Their perspective is provided in this white paper. Through these discussions, it became clear that there is currently no gold standard for CCI test methods or for the generation of artificial leaks; therefore flexibility toward CCI approaches is required. Although there should be flexibility, any CCI approach must consider the intended use (e.g., CCS qualification, routine manufacturing, or quality control) and product design (e.g., primary packaging, liquid versus dried product).LAY ABSTRACT: The assurance of sterility of a parenteral drug product prior to any human use is a regulatory requirement. Hence, all strategies related to container closure integrity (CCI) must demonstrate absence of microbial contamination through leaks as part of the container closure system (CCS) qualification, during manufacturing, for quality control purposes and to ensure microbiological integrity (sterility) during storage and shipment up to the end of shelf life. Current regulatory guidances, which differ between countries and regions, provide limited detail on how to assess CCI. The new revision of USP <1207> aims to provide extensive and detailed guidance for CCI assessments for sterile products. However, practical questions and considerations are yet to be addressed by the pharmaceutical industry.A group of European industry peers have met to discuss these and other related questions in order to provide their viewpoint and best practice on practical approaches to CCI. Their perspective is provided in this white paper. Through these discussions, it became clear that there is currently no gold standard for CCI test methods or for the generation of artificial leaks; therefore flexibility toward CCI approaches is required. Although there should be flexibility, any CCI approach must consider the intended use (e.g., CCS qualification, routine manufacturing, or quality control) and product design (e.g., primary packaging, liquid versus dried product).


title: Quantum Inspired Social Evolution (QSE) algorithm for 0-1 knapsack problem

abstract: Abstract Social Evolution (SE) algorithm (Pavithr, 2014 [10] ) is inspired by human interactions and their bias. Generally, human bias influences with whom individuals interact and how they interact. The individual bias may also influence the outcome of interactions to be decisive or indecisive. When the interactions are decisive, the individuals may completely adopt the change. When the interactions are indecisive, individual may consult for a second opinion to further evaluate the indecisive interaction before adopting the change to emerge and evolve (Pavithr, 2014 [10] ). In the last decade, with the integration of emerging quantum computing with the traditional evolutional algorithms, quantum inspired evolutionary algorithm is evolved (Han and Kim, 2000 [2] ). Inspired by Q-bit representation and parallelism and the success of the quantum inspired evolutionary algorithms, in this paper, a quantum inspired Social Evolution algorithm (QSE) is proposed by hybridizing Social evolution algorithm with the emerging quantum inspired evolutionary algorithm. The proposed QSE algorithm is studied on a well known 0-1 knapsack problem and the performance of the algorithm is compared with various evolutionary, swarm and quantum inspired evolutionary algorithm variants. The results indicate that, the performance of QSE algorithm is better than or comparable with the different evolutionary algorithmic variants tested with. An experimental study is also performed to investigate the impact and importance of human bias in selection of individuals for interactions, the rate of individuals seeking for second opinion and the influence of selective learning on the overall performance of Quantum inspired Social Evolution algorithm (QSE).


title: Sensorized Garments and Textrode-Enabled Measurement Instrumentation for Ambulatory Assessment of the Autonomic Nervous System Response in the ATREC Project

abstract: Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers.


title: Overview, Strengths, and Limitations of Systematic Reviews and Meta-Analyses

abstract: While the main focus of this chapter will be meta-analysis, it cannot be completely isolated from several prerequisites assessed in the systematic review. For example, the studies must address a common question. The eligibility criteria of the underlying studies must be well established. Evaluation techniques for endpoints must be reasonably consistent across the studies. In the clinical setting, when making comparisons between a treatment and control, the underlying studies must be properly randomized. Exploratory meta-analyses and meta-regressions may examine associations between interventions, covariates, and secondary events.


title: Kinect depth stream pre-processing for hand gesture recognition

abstract: Over the recent years there has been growing interest to propose a robust and efficient hand gesture recognition (HGR) system, using real-time depth sensors like Microsoft Kinect. The performance of such HGR systems have been affected by the low resolution, noise and quantization error in the depth stream. In this paper, we propose a method to pre-process Kinect depth stream in order to overcome some of these limitations. The design approach utilizes the hand tracker from OpenNI SDK to perform distance invariant segmentation of hand region depth stream. This is followed by the construction of three different projections of hand in XY, ZX and ZY planes. These projections are then further enhanced using a combination of morphological closing and simple averaging based interpolation. The evaluation results show above 80% similarity with ground truth, and 1.45-5.35% increase in accuracy for gestures with recognition accuracy less than 90%.


title: Social Event Extraction: Task, Challenges and Techniques

abstract: Social media (e.g., Facebook and Twitter) serves as a popular platform for online communication and information dissemination, where users can effectively share information such as their recent activities and plans. This kind of information is extremely valuable for building recommendation systems. For example, a user might wish to receive alerts whenever a concert takes place near his current location or when a party will be held in his neighborhood. However, such events may not be widespread across social networks, they have not received sufficient attention. Moreover, traditional event discovery and event extraction techniques trained from formal genres cannot be effectively adapted to this domain. In this paper, we present the first formal definition of social events, discuss the annotation challenges and release a benchmark for the research community. Further more, we propose two novel solutions for extracting elements from social events: (1) an unsupervised content segmentation framework to extract event phrases (2) utilize external knowledge bases to detect fine-grained event locations and unveil their background information. Experimental results convincingly demonstrate that our approach can accurately extract social events from social media.


title: CogMap: A Cognitive Support Approach to Property and Instance Alignment

abstract: The iterative user interaction approach for data integration proposed by Falconer and Noy can be generalized to consider interactions between integration tools generators that generate potential schema mappings and users or analysis tools analyzers that select the best mapping. Each such selection then provides high-confidence guidance for the next iteration of the integration tool. We have implemented this generalized approach in CogMap, a matching system for both property and instance alignments between heterogeneous data. The generator in CogMap uses the instance alignment from the previous iteration to create high-quality property alignments and presents these alignments and their consequences to the analyzer. Our experiments show that multiple iterations as well as the interplay between instance and property alignment serve to improve the final alignments.


title: Teleoperator slave ‐ WAM design methodology

abstract: This paper recommends an improved design methodology for the slave half of teleoperator systems based on the notion of master‐slave symmetry. Traditional slaves consist of a conventional robot retrofitted with force‐torque sensors. The new methodology eliminates dependence on destabilizing force‐torque‐sensor schemes by augmenting existing master design methods with newly invented cable mechanisms. Design goals such as bandwidth, backdrivability, and force fidelity have been applied successfully to optimize design of the trajectory‐and‐force‐controllable Whole‐Arm Manipulation (WAM) robot. Although not yet used as the slave of a teleoperator system, the results from performance tests of the experimental WAM manipulator are promising. Finally, the authors suggest a new concept ‐ Whole‐Arm Haptics ‐ that is only possible with whole‐arm manipulation, where the user steers the kinematic redundancy directly. Whole‐Arm Haptics allow teleoperators to manipulate objects larger than the slave itself.


title: Promethazine overdose: clinical effects, predicting delirium and the effect of charcoal

abstract: Objective: The aim of this study was to describe the clinical effects of promethazine in overdose and explore the relationship between delirium and possible predictor variables. ::: ::: Methods: A case series of promethazine poisonings was identified from a prospective database of poisoning admissions to a regional toxicology service. Data were extracted including demographics, details of ingestion, clinical features including delirium, complications and medical outcomes. In addition to descriptive statistics, a fully Bayesian approach using logistic regression was undertaken to investigate the relationship between predictor variables and delirium. ::: ::: Results: There were 199 patients with 237 presentations, including 57 patients with 78 promethazine alone overdoses. Of these 57 patients who ingested promethazine alone the median age was 22 years [interquartile range (IQR): 17–31] and 42 were female (74%). The median dose ingested was 625 mg (IQR: 350–1250 mg). Median length of stay was 19 h (IQR: 13–27 h), ten were admitted to the intensive care unit (ICU) and four were ventilated. Delirium occurred in 33 patients (42%), tachycardia (HR>100) occurred on 44 occasions (56%) and hypotension only twice. There were no seizures, dysrhythmias or deaths. Multivariate analysis of 215 presentations (in 181 patients) where dose of promethazine ingested was known demonstrated that dose, administration of charcoal within 2 h and co-ingestants predicted whether patients developed delirium. No relationship was shown for sex and age. A plot of probability that a patient will develop delirium vs. dose was constructed which showed the probability of delirium for 250 mg was 31%, 500 mg was 42% and for 1 g was 55% for promethazine alone overdoses. ::: ::: Conclusion: The main feature of promethazine toxicity is delirium, the probability of which can be predicted from the dose ingested. The administration of charcoal and the presence of co-ingestants appears to reduce the probability of delirium in a predictable manner.


title: A NoSQL–SQL Hybrid Organization and Management Approach for Real-Time Geospatial Data: A Case Study of Public Security Video Surveillance

abstract: With the widespread deployment of ground, air and space sensor sources (internet of things or IoT, social networks, sensor networks), the integrated applications of real-time geospatial data from ubiquitous sensors, especially in public security and smart city domains, are becoming challenging issues. The traditional geographic information system (GIS) mostly manages time-discretized geospatial data by means of the Structured Query Language (SQL) database management system (DBMS) and emphasizes query and retrieval of massive historical geospatial data on disk. This limits its capability for on-the-fly access of real-time geospatial data for online analysis in real time. This paper proposes a hybrid database organization and management approach with SQL relational databases (RDB) and not only SQL (NoSQL) databases (including the main memory database, MMDB, and distributed files system, DFS). This hybrid approach makes full use of the advantages of NoSQL and SQL DBMS for the real-time access of input data and structured on-the-fly analysis results which can meet the requirements of increased spatio-temporal big data linking analysis. The MMDB facilitates real-time access of the latest input data such as the sensor web and IoT, and supports the real-time query for online geospatial analysis. The RDB stores change information such as multi-modal features and abnormal events extracted from real-time input data. The DFS on disk manages the massive geospatial data, and the extensible storage architecture and distributed scheduling of a NoSQL database satisfy the performance requirements of incremental storage and multi-user concurrent access. A case study of geographic video (GeoVideo) surveillance of public security is presented to prove the feasibility of this hybrid organization and management approach.


title: Design of Compensated Coupled-Stripline 3-dB Directional Couplers, Phase Shifters, and Magic-T's—Part I: Single-Section Coupled-Line Circuits

abstract: The problem of discontinuities in coupled-stripline 3-dB directional couplers, phase shifters, and magic-T's, regarding the connections of coupled and terminating signal lines, has been comprehensively investigated for the first time. The proposed equivalent circuit of these discontinuities, of which parameters can be computed in a process of fitting curves of the circuit and electromagnetic analyses, has been used for accurate modeling of coupled-stripline circuits. It has been shown that parasitic reactances, which result from connections of signal and coupled lines, severely deteriorate the return losses and the isolation of such circuits, and that these discontinuity effects can be substantially reduced by connecting compensating shunt capacitances to both coupled and signal lines. Results of measurements carried out for various designed and manufactured coupled-line circuits are most promising and prove the efficiency of the proposed compensation technique. This paper describes the technique of capacitive compensation of discontinuity effects in single-section coupled-line circuits


title: Comparative biology of chronic and aggressive periodontitis vs. peri-implantitis

abstract: This review was undertaken to address the similarities and dissimilarities between the two disease entities of periodontitis and peri-implantitis. The overall analysis of the literature on the etiology and pathogenesis of periodontitis and peri-implantitis provided an impression that these two diseases have more similarities than differences. First, the initiation of the two diseases is dependent on the presence of a biofilm containing pathogens. While the microbiota associated with periodontitis is rich in gram-negative bacteria, a similar composition has been identified in peri-implant diseases. However, increasing evidence suggests that S. aureus may be an important pathogen in the initiation of some cases of peri-implantitis. Further research into the role of this gram-positive facultative coccus, and other putative pathogens, in the development of peri-implantitis is indicated. While the initial host response to the bacterial challenge in peri-implant mucositis appears to be identical to that encountered in gingivitis, persistent biofilm accumulation may elicit a more pronounced inflammatory response in peri-implant mucosal tissues than in the dentogingival unit. This may be a result of structural differences (such as vascularity and fibroblast-to-collagen ratios). When periodontitis and peri-implantitis were produced experimentally by applying plaque-retaining ligatures, the progression of mucositis to peri-implantitis followed a very similar sequence of events as the development of gingivitis to periodontitis. However, some of the peri-implantitis lesions appeared to have periods of rapid progression, in which the infective lesion reached the alveolar bone marrow. It is therefore reasonable to assume that peri-implantitis in humans may also display periods of accelerated destruction that are more pronounced than that observed in cases of chronic periodontitis. From a clinical point of view the identified and confirmed risk factors for periodontitis may be considered as identical to those for peri-implantitis. In addition, patients susceptible to periodontitis appear to be more susceptible to peri-implantitis than patients without a history of periodontitis. As both periodontitis and peri-implantitis are opportunistic infections, their therapy must be antiinfective in nature. The same clinical principles apply to debridement of the lesions and the maintenance of an infection-free oral cavity. However, in daily practice, such principles may occasionally be difficult to apply in peri-implantitis treatment. Owing to implant surface characteristics and limited access to the microbial habitats, surgical access may be required more frequently, and at an earlier stage, in periimplantitis treatment than in periodontal therapy. In conclusion, it is evident that periodontitis and peri-implantitis are not fundamentally different from the perspectives of etiology, pathogenesis, risk assessment, diagnosis and therapy. Nevertheless, some difference in the host response to these two infections may explain the occasional rapid progression of peri-implantitis lesions. Consequently, a diagnosed peri-implantitis should be treated without delay.


title: Planar inductive sensor for small displacement

abstract: This paper presents a meander type displacement inductive sensor, developed in printed circuit board (PCB) technology. It also describes design, realization and the input inductance measurement of sensor. The displacement in two directions (less than 0.5 mm) can be detected by using two sensor's elements (i.e. two pairs of meander coils). In each pair, one of the coils is fixed, and between its terminals the input inductance was measured, while other coil is short-circuited. If one coil is moved above the other in directions of x- and z-axes, coupling between coils will change, as well as input inductance, which serves as a measure of displacement. In order to achieve better linearity of the sensor, gap is inserted in the middle of each conductive segment of fixed coils. Four sets of inductive sensors were realized: without gap and with three different gaps: 0.25 mm, 0.51 mm and 0.76 mm. In addition, a simple model of the inductive sensor is proposed. Using this model, the input inductance of displacement sensor was calculated and compared with measured values. A good agreement was found.


title: SoMaJo: State-of-the-art tokenization for German web and social media texts

abstract: In this paper we describe SoMaJo, a rulebased tokenizer for German web and social media texts that was the best-performing system in the EmpiriST 2015 shared task with an average F1-score of 99.57. We give an overview of the system and the phenomena its rules cover, as well as a detailed error analysis. The tokenizer is available as free software.


title: How, What and Why to test an ontology

abstract: Ontology development relates to software development in that they both involve the production of formal computational knowledge. It is possible, therefore, that some of the techniques used in software engineering could also be used for ontologies; for example, in software engineering testing is a well-established process, and part of many different methodologies. ::: The application of testing to ontologies, therefore, seems attractive. The Karyotype Ontology is developed using the novel Tawny-OWL library. This provides a fully programmatic environment for ontology development, which includes a complete test harness. ::: In this paper, we describe how we have used this harness to build an extensive series of tests as well as used a commodity continuous integration system to link testing deeply into our development process; this environment, is applicable to any OWL ontology whether written using Tawny-OWL or not. Moreover, we present a novel analysis of our tests, introducing a new classification of what our different tests are. For each class of test, we describe why we use these tests, also by comparison to software tests. We believe that this systematic comparison between ontology and software development will help us move to a more agile form of ontology development.


title: Starting over: Gm/Id-based MOSFET modeling as a basis for modernized analog design methodologies

abstract: 1 Gilgamesh Associates, Fletcher, Vermont, USA; dfoty@sover.net. 2 University of North Carolina/Charlotte, Charlotte, North Carolina, USA; dmbinkle@uncc.edu. 3 National Technological University of Athens, Zagraphou, Athens, Greece; mbucher@elab.ntua.gr. Abstract – A method of interpreting MOSFET behavior is described which is more coherent for modern analog CMOS circuit design. This method supercedes the use of simple but antiquated equations in design, and replaces them with an approach based on the inversion coefficient of the individual transistors in the design. Measurements and modeling confirm that this method can be used directly to arbitrate among the various countervailing requirements of demanding analog designs.


title: An equalization technique for orthogonal frequency-division multiplexing systems in time-variant multipath channels

abstract: A loss of subchannel orthogonality due to time-variant multipath channels in orthogonal frequency division multiplexing (OFDM) systems leads to interchannel interference (ICI) which increases the error floor in proportion to the Doppler frequency. A simple frequency-domain equalization technique which can compensate for the effect of ICI in a multipath fading channel is proposed. In this technique, the equalization of the received OFDM signal is achieved by using the assumption that the channel impulse response (CIR) varies in a linear fashion during a block period and by compensating for the ICI terms that significantly affect the bit-error rate (BER) performance.


Best practices in business process redesign : an overview and qualitative evaluation of successful redesign heuristics


FINDING THE OPTIMAL PATH TO THE LOYAL CLUSTER BASED ON RATE OF PROFIT 1


title: Blackboard vs. moodle: Comparing user experience of learning management systems

abstract: Learning management systems are becoming ubiquitous technology adopted at institutions of higher learning. Before these systems can be considered effective the user experience must be studied and analyzed to provide the optimum solution to meet pedagogical needs of both faculty and students. This study compared the user experience between the leading proprietary solution, Blackboard, and the leading open source solution, Moodle. We established a control group that only used the proprietary solution and two study groups, a faculty group and a student group that used the open source solution, but had previous experience with the proprietary solution. We used online surveys to compare the user experience of the basic functionality of each system such as communication tools, student-student interaction tools, student-instructor interaction tools. The study was conducted during the Fall 2006 semester at California State University Monterey Bay and included five upper division courses with the learning management systems used as an adjunct to a traditional face-to-face delivery modality.


Advances in industrial microbiome based on microbial consortium for biorefinery


title: Evolution and field performance of a rescue robot

abstract: Robots are slowly finding their way into the hands of search and rescue groups. One of the robots contributing to this effort is the Inuktun VGTV-Xtreme series by American Standard Robotics. This capable robot is one of the only robots engineered specifically for the search and rescue domain. This paper describes the adaptation of the VGTV platform from an industrial inspection robot into a capable and versatile search and rescue robot. These adaptations were based on growing requirements established by rescue groups, academic research, and extensive field trials. A narrative description of a successful search of a damaged building during the aftermath of Hurricane Katrina is included to support these claims. Finally, lessons learned from these deployments and guidelines for future robot development is discussed. © 2008 Wiley Periodicals, Inc.


title: N- Dimensional Tensor Voting and Application to Epipolar Geometry Estimation

abstract: We address the problem of epipolar geometry estimation by formulating it as one of hyperplane inference from a sparse and noisy point set in an 8D space. Given a set of noisy point correspondences in two images of a static scene without correspondences, even in the presence of moving objects, our method extracts good matches and rejects outliers. The methodology is novel and unconventional, since, unlike most other methods optimizing certain scalar, objective functions, our approach does not involve initialization or any iterative search in the parameter space. Therefore, it is free of the problem of local optima or poor convergence. Further, since no search is involved, it is unnecessary to impose simplifying assumption to the scene being analyzed for reducing the search complexity. Subject to the general epipolar constraint only, we detect wrong matches by a computation scheme, 8D tensor voting, which is an instance of the more general N-dimensional tensor voting framework. In essence, the input set of matches is first transformed into a sparse 8D point set. Dense, 8D tensor kernels are then used to vote for the most salient hyperplane that captures all inliers inherent in the input. With this filtered set of matches, the normalized eight-point algorithm can be used to estimate the fundamental matrix accurately. By making use of efficient data structure and locality, our method is both time and space efficient despite the higher dimensionality. We demonstrate the general usefulness of our method using example image pairs for aerial image analysis, with widely different views, and from nonstatic 3D scenes. Each example contains a considerable number of wrong matches.


title: A Study of Probabilistic Password Models

abstract: A probabilistic password model assigns a probability value to each string. Such models are useful for research into understanding what makes users choose more (or less) secure passwords, and for constructing password strength meters and password cracking utilities. Guess number graphs generated from password models are a widely used method in password research. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. We conduct a systematic evaluation of a large number of probabilistic password models, including Markov models using different normalization and smoothing methods, and found that, among other things, Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.


title: Functional Gastrointestinal Disorders: History, Pathophysiology, Clinical Features, and Rome IV

abstract: Functional gastrointestinal disorders (FGIDs), the most common diagnoses in gastroenterology, are recognized by morphologic and physiological abnormalities that often occur in combination including motility disturbance, visceral hypersensitivity, altered mucosal and immune function, altered gut microbiota, and altered central nervous system processing. Research on these gut–brain interaction disorders is based on using specific diagnostic criteria. The Rome Foundation has played a pivotal role in creating diagnostic criteria, thus operationalizing the dissemination of new knowledge in the field of FGIDs. Rome IV is a compendium of the knowledge accumulated since Rome III was published 10 years ago. It improves upon Rome III by: (1) updating the basic and clinical literature; (2) offering new information on gut microenvironment, gut–brain interactions, pharmacogenomics, biopsychosocial, gender and cross-cultural understandings of FGIDs; (3) reduces the use of imprecise and occasionally stigmatizing terms when possible; (4) uses updated diagnostic algorithms; and (5) incorporates information on the patient illness experience, and physiological subgroups or biomarkers that might lead to more targeted treatment. This introductory article sets the stage for the remaining 17 articles that follow and offers a historical overview of the FGID field, differentiates FGIDs from motility and structural disorders, discusses the changes from Rome III, reviews the Rome committee process, provides a biopsychosocial pathophysiological conceptualization of FGIDs, and offers an approach to patient care.


title: “Was It Good? It Was Provocative.” Learning the Meaning of Scalar Adjectives

abstract: Texts and dialogues often express information indirectly. For instance, speakers' answers to yes/no questions do not always straightforwardly convey a 'yes' or 'no' answer. The intended reply is clear in some cases (Was it good? It was great!) but uncertain in others (Was it acceptable? It was unprecedented.). In this paper, we present methods for interpreting the answers to questions like these which involve scalar modifiers. We show how to ground scalar modifier meaning based on data collected from the Web. We learn scales between modifiers and infer the extent to which a given answer conveys 'yes' or 'no'. To evaluate the methods, we collected examples of question-answer pairs involving scalar modifiers from CNN transcripts and the Dialog Act corpus and use response distributions from Mechanical Turk workers to assess the degree to which each answer conveys 'yes' or 'no'. Our experimental results closely match the Turkers' response data, demonstrating that meanings can be learned from Web data and that such meanings can drive pragmatic inference.


title: Digital pulse width modulator architectures

abstract: This paper presents a survey and classification of architectures for integrated circuit implementation of digital pulse-width modulators (DPWM) targeting digital control of high-frequency switching DC-DC power converters. Previously presented designs are identified as particular cases of the proposed classification. In order to optimize circuit resources in terms of occupied area and power consumption, a general architecture based on tapped delay lines is proposed, which includes segmentation of the input digital code to drive binary weighted delay cells and thermometer-decoded unary delay cells. Integrated circuit design of a particular example of the segmented DPWM is described. The segmented DPWM prototype chip operates at 1 MHz switching frequency and has low power consumption and very small silicon area (0.07 mm/sup 2/ in a standard 0.5 micron CMOS process). Experimental results validate the functionality of the proposed segmented DPWM.


title: Stochastic Optimization with Bandit Sampling

abstract: Many stochastic optimization algorithms work by estimating the gradient of the cost function on the fly by sampling datapoints uniformly at random from a training set. However, the estimator might have a large variance, which inadvertently slows down the convergence rate of the algorithms. One way to reduce this variance is to sample the datapoints from a carefully selected non-uniform distribution. In this work, we propose a novel non-uniform sampling approach that uses the multi-armed bandit framework. Theoretically, we show that our algorithm asymptotically approximates the optimal variance within a factor of 3. Empirically, we show that using this datapoint-selection technique results in a significant reduction in the convergence time and variance of several stochastic optimization algorithms such as SGD, SVRG and SAGA. This approach for sampling datapoints is general, and can be used in conjunction with any algorithm that uses an unbiased gradient estimation -- we expect it to have broad applicability beyond the specific examples explored in this work.


title: A GPS based traffic light pre-emption control system for emergency vehicles

abstract: Emergency vehicles such as ambulances, police force vehicles and fire fighting vehicles are required to reach their destination as quickly as possible. Any unnecessary delays experienced on them along their way could eventually cause dangerous events; leading to damages in lives or properties. One of the most important delays is the time that is consumed on travelling between the starting point and the destination point of these emergency vehicles; and in intersections with traffic lights, especially when these intersections are congested. In this paper we discuss the design and implementation of an automatic preemption traffic control system, based on the global positioning system satellites. It ensures the arrival of emergency vehicles to their destinations on the minimum time possible. The results indicate that the proposed system has an optimum solution for the delay time experienced on the emergency vehicles along their path.


title: Evaluation of a self-management plan for chronic obstructive pulmonary disease.

abstract: We hypothesized that the use of an Action Plan might assist self-management for patients with chronic obstructive pulmonary disease (COPD). A pilot process and randomized, controlled study were undertaken to evaluate an Action Plan that provided advice on management of usual care and exacerbations, together with a booklet on self-management. Fifty six subjects with COPD recruited through general practitioners (GPs) completed the 6 month study, 27 in the control group and 29 in the intervention group. The control group received usual care from their GP, and the intervention group received a booklet and Action Plan from their practice nurse plus a supply of prednisone and antibiotic from their GP. The two groups were demographically similar with a mean age of 68 yrs. The resources were well received by GPs, practice nurses and intervention group subjects. After 6 months, there were no differences in quality of life scores or pulmonary function. There were significant changes in self-management behaviour in the intervention group compared to controls. In response to deteriorating symptoms, 34 versus 7% (p=0.014) initiated prednisone treatment and 44 versus 7% (p=0.002) initiated antibiotics. Subjects in the intervention group readily adopted self-management skills but did not show any difference in quality of life or lung function parameters. A larger, prospective, controlled, clinical trial of this approach is warranted.


title: Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine

abstract: Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.e., Winner-Takes-All SVM, Max-Wins-Voting SVM, and Directed Acyclic Graph SVM. Meanwhile, three kinds of kernels were chosen, i.e., linear kernel, Homogeneous Polynomial kernel, and Gaussian Radial Basis kernel; finally, the SVMs were trained using 5-fold stratified cross validation with the reduced feature vectors as input. The experimental results demonstrated that the Max-Wins-Voting SVM with Gaussian Radial Basis kernel achieves the best classification accuracy of 88.2%. For computation time, the Directed Acyclic Graph SVMs performs swiftest.


title: Secure CoAP Using Enhanced DTLS forInternet of Things

abstract: The Internet of Things (IoT) is next generation technology that is intended to improve and optimize daily life by operating intelligent sensors and smart objects together. At application layer, communication of resourceconstrained devices is expected to use constrained application protocol (CoAP).Communication security is an important aspect of IoT environment. However closed source security solutions do not help in formulating security in IoT so that devices can communicate securely with each other. To protect the transmission of confidential information secure CoAP uses datagram transport layer security (DTLS) as the security protocol for communication and authentication of communicating devices. DTLS was initially designed for powerful devices that are connected through reliable and high bandwidth link. This paper proposes a collaboration of DTLS and CoAP for IoT. Additionally proposed DTLS header compression scheme that helps to reduce packet size, energy consumption and avoids fragmentation by complying the 6LoWPAN standards. Also proposed DTLS header compression scheme does not compromises the point-to-point security provided by DTLS. Since DTLS has chosen as security protocol underneath the CoAP, enhancement to the existing DTLS also provided by introducing the use of raw public key in DTLS.


title: Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking

abstract: While there is little doubt that risk-taking is generally more prevalent during adolescence than before or after, the underlying causes of this pattern of age differences have long been investigated and debated. One longstanding popular notion is the belief that risky and reckless behavior in adolescence is tied to the hormonal changes of puberty. However, the interactions between pubertal maturation and adolescent decision making remain largely understudied. In the current review, we discuss changes in decision making during adolescence, focusing on the asynchronous development of the affective, reward-focused processing system and the deliberative, reasoned processing system. As discussed, differential maturation in the structure and function of brain systems associated with these systems leaves adolescents particularly vulnerable to socio-emotional influences and risk-taking behaviors. We argue that this asynchrony may be partially linked to pubertal influences on development and specifically on the maturation of the affective, reward-focused processing system.


title: Anatomic Study of the Retaining Ligaments of the Face and Applications for Facial Rejuvenation

abstract: BACKGROUND ::: The retaining ligaments of the face support the facial soft tissue in a normal anatomic position, thereby resisting gravitational change. In this study, a technique utilizing surgical plication of the superficial musculoaponeurotic system (SMAS) to the retaining ligaments of the face and finger-assisted malar elevation (FAME) dissection of the midface is presented. ::: ::: ::: METHODS ::: The anatomy of the facial retaining ligaments was studied in 20 half-faces of ten fresh cadavers, and the localization of the ligaments was examined macroscopically. Surgical correction of facial aging with plication of the SMAS to the retaining ligaments and FAME dissection of the midface has been performed in 74 face-lift patients since 2006. Outcomes were determined by case notes, clinical review, and a patient questionnaire. ::: ::: ::: RESULTS ::: The studied ligaments (zygomatic and masseteric) were present in all cadaver dissections. The zygomatic ligament was located 4.3-5.5 cm from the tragus and originates near the inferior border of the anterior zygomatic arch. The masseteric ligament was located 3.7-5.2 cm from the tragus below the junction of the zygomatic arch and masseter muscle. All the patients answered a satisfaction questionnaire and reported high levels of satisfaction at least 1 year after treatment. ::: ::: ::: CONCLUSIONS ::: We have identified the facial retaining ligaments in all cadaver dissections and their relationship with other structures of the face are described here. This study demonstrates that our face-lift technique is safe and produces highly predictable and natural results. ::: ::: ::: LEVEL OF EVIDENCE IV ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: Denoising with kernel prediction and asymmetric loss functions

abstract: We present a modular convolutional architecture for denoising rendered images. We expand on the capabilities of kernel-predicting networks by combining them with a number of task-specific modules, and optimizing the assembly using an asymmetric loss. The source-aware encoder---the first module in the assembly---extracts low-level features and embeds them into a common feature space, enabling quick adaptation of a trained network to novel data. The spatial and temporal modules extract abstract, high-level features for kernel-based reconstruction, which is performed at three different spatial scales to reduce low-frequency artifacts. The complete network is trained using a class of asymmetric loss functions that are designed to preserve details and provide the user with a direct control over the variance-bias trade-off during inference. We also propose an error-predicting module for inferring reconstruction error maps that can be used to drive adaptive sampling. Finally, we present a theoretical analysis of convergence rates of kernel-predicting architectures, shedding light on why kernel prediction performs better than synthesizing the colors directly, complementing the empirical evidence presented in this and previous works. We demonstrate that our networks attain results that compare favorably to state-of-the-art methods in terms of detail preservation, low-frequency noise removal, and temporal stability on a variety of production and academic datasets.


title: Opening the black box of deep learning

abstract: The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brain mechanism. At present, most of the theoretical research on deep learning is based on mathematics. This dissertation proposes that the neural network of deep learning is a physical system, examines deep learning from three different perspectives: microscopic, macroscopic, and physical world views, answers multiple theoretical puzzles in deep learning by using physics principles. For example, from the perspective of quantum mechanics and statistical physics, this dissertation presents the calculation methods for convolution calculation, pooling, normalization, and Restricted Boltzmann Machine, as well as the selection of cost functions, explains why deep learning must be deep, what characteristics are learned in deep learning, why Convolutional Neural Networks do not have to be trained layer by layer, and the limitations of deep learning, etc., and proposes the theoretical direction and basis for the further development of deep learning now and in the future. The brilliance of physics flashes in deep learning, we try to establish the deep learning technology based on the scientific theory of physics.


title: The most dangerous code in the browser

abstract: Browser extensions are ubiquitous. Yet, in today's browsers, extensions are the most dangerous code to user privacy. Extensions are third-party code, like web applications, but run with elevated privileges. Even worse, existing browser extension systems give users a false sense of security by considering extensions to be more trustworthy than web applications. This is because the user typically has to explicitly grant the extension a series of permissions it requests, e.g., to access the current tab or a particular website. Unfortunately, extensions developers do not request minimum privileges and users have become desensitized to install-time warnings. Furthermore, permissions offered by popular browsers are very broad and vague. For example, over 71% of the top-500 Chrome extensions can trivially leak the user's data from any site. In this paper, we argue for new extension system design, based on mandatory access control, that protects the user's privacy from malicious extensions. A system employing this design can enable a range of common extensions to be considered safe, i.e., they do not require user permissions and can be ensured to not leak information, while allowing the user to share information when desired. Importantly, such a design can make permission requests a rarity and thus more meaningful.


title: Large-Scale Price Optimization via Network Flow

abstract: This paper deals with price optimization, which is to find the best pricing strategy that maximizes revenue or profit, on the basis of demand forecasting models. Though recent advances in regression technologies have made it possible to reveal price-demand relationship of a number of multiple products, most existing price optimization methods, such as mixed integer programming formulation, cannot handle tens or hundreds of products because of their high computational costs. To cope with this problem, this paper proposes a novel approach based on network flow algorithms. We reveal a connection between supermodularity of the revenue and cross elasticity of demand. On the basis of this connection, we propose an efficient algorithm that employs network flow algorithms. The proposed algorithm can handle hundreds or thousands of products, and returns an exact optimal solution under an assumption regarding cross elasticity of demand. Even in case in which the assumption does not hold, the proposed algorithm can efficiently find approximate solutions as good as can other state-of-the-art methods, as empirical results show.


title: Isolated and Continuous Bangla Speech Recognition : Implementation , Performance and application perspective

abstract: Research on automatic speech recognition has been approach progressively since 1930 and the major advances are since 1980 with the introduction of the statistical modeling of speech with the key technology Hidden Markov Model (HMM) and the stochastic language model (B. H. Juang, 2005). However, the existing reported research works on Bangla speech recognition didn’t yet incorporate the HMM technique and language model. This paper presents two different type of Bangla speech recognition from the implementation, performance and application perspective. We used HMM technique for pattern classification and also incorporate stochastic language model with the system. At the signal preprocessing level we perform adaptive noise elimination and end point detection. Spectral feature vectors such as Mel Frequency Cepstral Coefficients (MFCC) with the addition of first and second order coefficients are extracted from each speech wave signal. HMM is used for pattern classification. The system is implemented using the Cambridge Hidden Markov Modeling Toolkit (HTK) (S. Young, 2001-2005).


title: Semi-Supervised Support Vector Machines

abstract: We introduce a semi-supervised support vector machine (S3VM) method. Given a training set of labeled data and a working set of unlabeled data, S3VM constructs a support vector machine using both the training and working sets. We use S3VM to solve the transduction problem using overall risk minimization (ORM) posed by Vapnik. The transduction problem is to estimate the value of a classification function at the given points in the working set. This contrasts with the standard inductive learning problem of estimating the classification function at all possible values and then using the fixed function to deduce the classes of the working set data. We propose a general S3VM model that minimizes both the misclassification error and the function capacity based on all the available data. We show how the S3VM model for 1-norm linear support vector machines can be converted to a mixed-integer program and then solved exactly using integer programming. Results of S3VM and the standard 1-norm support vector machine approach are compared on ten data sets. Our computational results support the statistical learning theory results showing that incorporating working data improves generalization when insufficient training information is available. In every case, S3VM either improved or showed no significant difference in generalization compared to the traditional approach.


title: Vertebrate Burrow Complexes from the Early Triassic Cynognathus Zone (Driekoppen Formation, Beaufort Group) of the Karoo Basin, South Africa

abstract: Abstract A scratch-marked burrow complex with multiple branching tunnels and terminal chambers was excavated from the Lower Triassic Driekoppen Formation, northeastern Free State, South Africa. The burrow complex is attributed to the therapsid Trirachodon, based on disarticulated but fairly complete skulls and skeletons of at least 20 individuals recovered from a nearby, less well-preserved system. The entrance shaft slopes gently downward and is characterized by a bilobate floor and vaulted roof. The bilobate floor has a central flat-topped scratch-marked ridge flanked by two smooth grooves, each approximately the width of the occupant. At deeper levels, tunnels display tighter lateral curvatures, progressively decreasing burrow diameters, variable orientations, and some right angle branches. Burrow floors at these levels are vaguely bilobate. Distally, burrows flatten dorso-ventrally, becoming wedge-shaped before they terminate. These complexes are interpreted as colonial dwelling structures. Numerous branching tunnels and terminal chambers, as well as an enlarged entrance, constitute an unrealistically high expenditure of energy for a single occupant. Furthermore, the bilobate floor is atypical of a single occupant system. Absence of scratch marks in the depressions reflects the regular locomotory activity of Trirachodon. In single-occupant burrows, the center of the structure is worn. The bilobate floor with preferential wear along the flanks suggests routine travel on one side of the tunnel or the other, as multiple occupants moved past one another during daily activity. The presence of numerous fossilized Trirachodon individuals in terminal chambers at the second locality supports multiple cohabitation. There, the uniform sedimentary fill and vertebrate taphonomy suggest that the occupants were drowned in a flash flood. Vertebrate remains are absent at the first locality, where sediment influx was incremental, permitting the escape of burrow occupants. This constitutes the earliest record of multiple cohabitation of a burrow complex by tetrapods. The Trirachodon (Cynodontia) of the Early Triassic Cynognathus Zone probably displayed complex social behaviors previously regarded to be restricted to the mammals of the Cenozoic. This fossorial behavior may have been for thermoregulation, protection from predators, sites of reproduction, and the rearing of young.


title: Trajectory control for an autonomous bicycle with balancer

abstract: In this paper, we propose a new trajectory tracking and balancing control for an unmanned bicycle with a balancer using a simplified model. The bicycle with the balancer dynamics is derived from Lagrangian and nonholonomic constraints with respect to translation and rotation relative to the ground plane. The trajectory tracking control is derived by an input-output linearization approach and an output-zeroing control is applied to the balancer for balancing the bicycle. The proposed control algorithm is guaranteed to maintain bicycle stability even when the linear velocity is zero without requiring a secondary controller. Numerical simulation show the effectiveness of the proposed control system.


title: An Audit Model for ISO 9001 Traceability Requirements in Agile-XP Environments

abstract: Software organizations that develop their software products using the agile software processes such as Extreme Programming (XP) face a number of challenges in their effort to demonstrate that their process activities conform to ISO 9001 requirements, a major one being product traceability: software organizations must provide evidence of ISO 9001 conformity, and they need to develop their own procedures, tools, and methodologies to do so. This paper proposes an auditing model for ISO 9001 traceability requirements that is applicable in agile (XP) environments. The design of our model is based on evaluation theory, and includes the use of several auditing “yardsticks” derived from the principles of engineering design, the SWEBOK Guide, and the CMMI-DEV guidelines for requirement management and traceability for each yardstick. Finally, five approaches for agile-XP traceability approaches are audited based on the proposed audit model. Index Terms— Agile Software Certification, Extreme Programming, Software Process Improvement, ISO 9001


title: Tensors for Data Mining and Data Fusion: Models, Applications, and Scalable Algorithms

abstract: Tensors and tensor decompositions are very powerful and versatile tools that can model a wide variety of heterogeneous, multiaspect data. As a result, tensor decompositions, which extract useful latent information out of multiaspect data tensors, have witnessed increasing popularity and adoption by the data mining community. In this survey, we present some of the most widely used tensor decompositions, providing the key insights behind them, and summarizing them from a practitioner’s point of view. We then provide an overview of a very broad spectrum of applications where tensors have been instrumental in achieving state-of-the-art performance, ranging from social network analysis to brain data analysis, and from web mining to healthcare. Subsequently, we present recent algorithmic advances in scaling tensor decompositions up to today’s big data, outlining the existing systems and summarizing the key ideas behind them. Finally, we conclude with a list of challenges and open problems that outline exciting future research directions.


title: Simulator for inter-vehicle communication based on traffic modeling

abstract: In order to increase security in road traffic, driver assistance systems became an important topic in research. For these systems inter-vehicle communication is very promising as it can serve the systems with additional information. Therefore, communication protocols and routing strategies are needed to distribute information between vehicles. These have to be tested in large networks provided by a simulation tool that models the movement of vehicles on a road network and can be used as a platform for testing different communication protocols. The movement of vehicles is described in various traffic models. These models normally aim at the examination of traffic dynamics, and so a proper model for the purpose of inter-vehicle communication has to be chosen. It has to give the possibility to add a communication stack for the single nodes and to concern different effects of wireless communication and mobile ad-hoc networks. In this paper, a concept for such a simulation tool is presented, which is based on a traffic simulation that models the movement of vehicles. Besides this, it becomes possible to implement various communication layers to simulate different effects of communication protocols.


title: Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

abstract: Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning. Recently, Nguyen et al. [37] showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network. In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227 × 227) than previous generative models, and does so for all 1000 ImageNet categories. In addition, we provide a unified probabilistic interpretation of related activation maximization methods and call the general class of models Plug and Play Generative Networks. PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable condition network C that tells the generator what to draw. We demonstrate the generation of images conditioned on a class (when C is an ImageNet or MIT Places classification network) and also conditioned on a caption (when C is an image captioning network). Our method also improves the state of the art of Multifaceted Feature Visualization [40], which generates the set of synthetic inputs that activate a neuron in order to better understand how deep neural networks operate. Finally, we show that our model performs reasonably well at the task of image inpainting. While image models are used in this paper, the approach is modality-agnostic and can be applied to many types of data.