A hybrid medium access control protocol for underwater wireless networks

Underwater networks allow investigation of many areas of the world not easily accessed by humans, but offer interesting challenges to device and protocol designers due to the unique channel conditions present when using acoustic communications. The high transmit power of acoustic transceivers makes the medium access protocol a primary focus point for reducing energy consumption in energy limited underwater devices. Scheduled protocols use very little power by eliminating collisions, but cannot adapt to changing traffic conditions in the same way as random protocols. We attempt to bridge these two ideas by dividing time into scheduled and unscheduled access periods in order to yield the benefits of each protocol. We show that this technique increases the bits delivered per energy unit in many cases of interest. Additionally, the hybrid technique provides low latency for a wider range of traffic rates than either of the two protocols when considered individually. We also investigate some of the design tradeoffs to consider when using a hybrid protocol.


NativeGuard: protecting android applications from third-party native libraries

Android applications often include third-party libraries written in native code. However, current native components are not well managed by Android's security architecture. We present NativeGuard, a security framework that isolates native libraries from other components in Android applications. Leveraging the process-based protection in Android, NativeGuard isolates native libraries of an Android application into a second application where unnecessary privileges are eliminated. NativeGuard requires neither modifications to Android nor access to the source code of an application. It addresses multiple technical issues to support various interfaces that Android provides to the native world. Experimental results demonstrate that our framework works well with a set of real-world applications, and incurs only modest overhead on benchmark programs.


Working memory deficits in adults with attention-deficit/hyperactivity disorder (ADHD): an examination of central executive and storage/rehearsal processes.

The current study was the first to use a regression approach to examine the unique contributions of central executive (CE) and storage/rehearsal processes to working memory (WM) deficits in adults with ADHD. Thirty-seven adults (ADHD = 21, HC = 16) completed phonological (PH) and visuospatial (VS) working memory tasks. While both groups performed significantly better during the PH task relative to the VS task, adults with ADHD exhibited significant deficits across both working memory modalities. Further, the ADHD group recalled disproportionately fewer PH and VS stimuli as set-size demands increased. Overall, the CE and PH storage/rehearsal processes of adults with ADHD were both significantly impaired relative to those of the healthy control adults; however, the magnitude of the CE effect size was much smaller compared to previous studies of children with the disorder. Collectively, results provide support for a lifelong trajectory of WM deficits in ADHD.


Line-of-sight guidance for formation control of quadrotor

In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. Cooperative control of UAVs are currently being researched for a wide range of applications. An active and important research area of cooperative control of multiple UAVs is the formation control. Formation flying is the disciplined flight of two or more aircraft under the command of a flight leader. The main goal is to coordinate a group of UAVs to achieve a desired formation pattern and avoid inter-vehicle collisions at the same time. Formation flight of a team of UAVs has broad applications such as aerial mapping, traffic monitoring, terrain and utilities inspection, search and rescue operations, perimeter planning, disaster monitoring, reconnaissance mission, surveillance etc. Here we propose a Line Of Sight (LOS) guidance based flight formation control strategy for multiple quad rotor UAVs in leader-follower framework. The guidance laws for 2D formation are implemented with the help of LOS angles, between adjacent quadrotors to keep the current quadrotor's position in formation. The full dynamic model of quadrotor incorporating LOS guidance law is considered for designing PID controller and the simulation analysis was done using MATLAB/ Simulink.


Hand Gesture Recognition With Multiscale Weighted Histogram of Contour Direction Normalization for Wearable Applications

This paper proposes a static hand gesture recognition method with low computation and memory consumption for wearable applications. The hand contour is chosen as the hand gesture feature and support vector machine is used to classify the feature. A multiscale weighted histogram of contour direction-based direction normalization is proposed to ensure good recognition performance. In order to improve efficiency, the proposed histogram only counts the direction of the contour point to focus on the most significant hand feature in the first-person view of wearable devices. Based on the hand’s anatomy, the proposed histogram is weighted by considering each contour point’s position and direction jointly using the direction-angle map, to ensure robustness. Experimental results show that the proposed method can give a recognition accuracy of 97.1% with a frame rate of 30 fps on a PC.


Prototype of automatic accident detection and management in vehicular environment using VANET and IoT

The increase in the population of cities and the number of vehicles leads to increase in congestion on roads and accidents. The lack of quick medical service on road accidents is the major cause of death. In such situation, an automatic accident detection can help to save the loss of life. In this paper, a prototype is designed for an automatic accident detection using Vehicular Adhoc Network (VANET) and Internet of Things (IoT). The application is able to detect accident and severity of the emergency level with the help of mechanical and medical sensors deployed in the vehicle. In case of emergency, the message is sent to a hospital using VANET, where our central server is able to find out the location of the accident and nearest medical center. It sends a message for an ambulance after detecting basic information. In order to clear the path on the way to accident's location, the client application on the ambulance generates alert messages.


Author ' s personal copy Narcissism and recollections of early life experiences

Recent studies have found associations between narcissistic personality features and retrospective accounts of early experiences. The current study sought to extend these previous findings by examining whether adaptive and maladaptive features of narcissism were associated with recollections of early life experiences in a non-clinical sample of undergraduate students (N = 334). Results revealed that the Entitlement/Exploitativeness feature of narcissism was associated with low security, high parental discipline, and high threats of separation. Narcissistic Grandiosity was positively associated with peer affectional support and parental discipline, whereas Narcissistic Vulnerability was not uniquely associated with memories of early life experiences. The results provide partial support for models of narcissism in which parents are recalled as failing to provide a secure base while inducing threats of separation and discipline. 2011 Elsevier Ltd. All rights reserved.


Mixing Qualitative and Quantitative Methods : Triangulation in Action *

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STRATEGIC DECISION PROCESSES IN HIGH VELOCITY ENVIRONMENTS : FOUR CASES IN THE MICROCOMPUTER INDUSTRY *

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Research Commentary: Desperately Seeking the "IT" in IT Research - A Call to Theorizing the IT Artifact

The field of information systems is premised on the centrality of information technology in everyday socio-economic life. Yet, drawing on a review of the full set of articles published in Information Systems Research (ISR) over the past ten years, we argue that the field has not deeply engaged its core subject matter—the information technology (IT) artifact. Instead, we find that IS researchers tend to give central theoretical significance to the context (within which some usually unspecified technology is seen to operate), the discrete processing capabilities of the artifact (as separable from its context or use), or the dependent variable (that which is posited to be affected or changed as technology is developed, implemented, and used). The IT artifact itself tends to disappear from view, be taken for granted, or is presumed to be unproblematic once it is built and installed. After discussing the implications of our findings, we propose a research direction for the IS field that begins to take technology as seriously as its effects, context, and capabilities. In particular, we propose that IS researchers begin to theorize specifically about IT artifacts, and then incorporate these theories explicitly into their studies. We believe that such a research direction is critical if IS research is to make a significant contribution to the understanding of a world increasingly suffused with ubiquitous, interdependent, and emergent information technologies. (Information Systems Research; Information Technology; IT Research; IT Theory; Technological Artifacts; Technology Change)


Generalizing Generalizability in Information Systems Research

Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research. (Research Methodology; Positivist Research; Interpretive Research; Quantitative Research; Qualitative Research; Case Studies; Research Design; Generalizability )


Ranking Through Clustering: An Integrated Approach to Multi-Document Summarization

Multi-document summarization aims to create a condensed summary while retaining the main characteristics of the original set of documents. Under such background, sentence ranking has hitherto been the issue of most concern. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering has been explored in the literature in order to provide more informative summaries. For each topic theme, the rank of terms conditional on this topic theme should be very distinct, and quite different from the rank of terms in other topic themes. Existing cluster-based summarization approaches apply clustering and ranking in isolation, which leads to incomplete, or sometimes rather biased, analytical results. A newly emerged framework uses sentence clustering results to improve or refine the sentence ranking results. Under this framework, we propose a novel approach that directly generates clusters integrated with ranking in this paper. The basic idea of the approach is that ranking distribution of sentences in each cluster should be quite different from each other, which may serve as features of clusters and new clustering measures of sentences can be calculated accordingly. Meanwhile, better clustering results can achieve better ranking results. As a result, ranking and clustering by mutually and simultaneously updating each other so that the performance of both can be improved. The effectiveness of the proposed approach is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004-2007 datasets.


The optimal training load for the development of dynamic athletic performance.

This study was performed to determine which of three theoretically optimal resistance training modalities resulted in the greatest enhancement in the performance of a series of dynamic athletic activities. The three training modalities included 1) traditional weight training, 2) plyometric training, and 3) explosive weight training at the load that maximized mechanical power output. Sixty-four previously trained subjects were randomly allocated to four groups that included the above three training modalities and a control group. The experimental groups trained for 10 wk performing either heavy squat lifts, depth jumps, or weighted squat jumps. All subjects were tested prior to training, after 5 wk of training and at the completion of the training period. The test items included 1) 30-m sprint, 2) vertical jumps performed with and without a countermovement, 3) maximal cycle test, 4) isokinetic leg extension test, and 5) a maximal isometric test. The experimental group which trained with the load that maximized mechanical power achieved the best overall results in enhancing dynamic athletic performance recording statistically significant (P < 0.05) improvements on most test items and producing statistically superior results to the two other training modalities on the jumping and isokinetic tests.


Hate Online : A Content Analysis of Extremist Internet Sites

Extremists, such as hate groups espousing racial supremacy or separation, have established an online presence. A content analysis of 157 extremist web sites selected through purposive sampling was conducted using two raters per site. The sample represented a variety of extremist groups and included both organized groups and sites maintained by apparently unaffiliated individuals. Among the findings were that the majority of sites contained external links to other extremist sites (including international sites), that roughly half the sites included multimedia content, and that half contained racist symbols. A third of the sites disavowed racism or hatred, yet one third contained material from supremacist literature. A small percentage of sites specifically urged violence. These and other findings suggest that the Internet may be an especially powerful tool for extremists as a means of reaching an international audience, recruiting members, linking diverse extremist groups, and allowing maximum image control.


Treatment of oral mucocele-scalpel versus CO2 laser.

OBJECTIVE
To compare the results obtained after oral mucocele resection with the scalpel versus the CO2 laser, based on the complications and recurrences after surgery.

PATIENTS AND METHODS
Of the 68 patients we studied who have mucocele, 38 were resected with a scalpel and the remaining 30 with the CO2 laser (5-7 W). Patient sex and age were documented, along with location of the lesion as well as size, symptoms, duration, etiological factors, type of treatment, complications and recurrences after surgical removal.

RESULTS
The sample comprised 40 males and 28 females, aged between 6-65 years. The histological diagnosis was extravasation mucocele in 95% of the cases. The most frequent location was the lower lip (73.5%). The mean lesion diameter was 9 mm, and in most cases no evident etiological factor was recorded. The mean duration of the lesion was 4 months. Among the cases of conventional surgical removal of mucocele, recurrence was recorded in 8.8% of the cases, and 13.2% of the patients suffered postoperative complications--the most frequent being the presence of fibrous scars. There were no complications or relapses after a minimum follow-up of 12 months in the cases subjected to CO2 laser treatment.

CONCLUSIONS
Oral mucocele ablation with the CO2 laser offers more predictable results and fewer complications and recurrences than conventional resection with the scalpel.


A neuromorphic VLSI design for spike timing and rate based synaptic plasticity

Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock-Cooper-Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.


A Comparative Study on English and Chinese Word Uses with LIWC

This paper compared the linguistic and psychological word uses in English and Chinese languages with LIWC (Linguistic Inquiry and Word Count) programs. A Principal Component Analysis uncovered six linguistic and psychological components, among which five components were significantly correlated. The correlated components were ranked as Negative Valence (r=.92), Embodiment (r=.88), Narrative (r=.68), Achievement (r=.65), and Social Relation (r=.64). However, the results showed the order of the representative features differs in two languages and certain word categories co-occurred with different components in English and Chinese. The differences were interpreted from the perspective of distinctive eastern and western cultures.


Enhancing credit scoring model performance by a hybrid scoring matrix

Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilizes logistic regression to build the application and credit bureau scoring models where the two scoring models are combined for constructing the scoring matrix. The scoring matrix undergoes more accurate risk judgment and segmentation to further identify the parts required enhanced management or control within a personal loan portfolio. The analytical results demonstrate that the predictive ability of the scoring matrix outperforms both the application and credit bureau scoring models. Regarding the K-S value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 18.40 and 5.70%, respectively. Regarding the AUC value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 10.90 and 6.40%, respectively. Furthermore, this study applies the scoring matrix to the credit approval decisions for corresponding risk groups to strengthen bank’s risk management practices.


Tracking in cell and developmental biology.

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.


Measurement-Based Harmonic Modeling of an Electric Vehicle Charging Station Using a Three-Phase Uncontrolled Rectifier

The harmonic pollution of electric vehicle chargers is increasing rapidly as the scale of electric vehicle charging stations enlarges to meet the rising demand for electric vehicles. This paper investigates the operating characteristics of a three-phase uncontrolled rectification electric vehicle charger with passive power factor correction. A method for estimating the equivalent circuit parameters of chargers is proposed based on the measured feature data of voltage and current at the ac side and on the circuit constraint during the conduction charging process. A harmonic analytical model of the charging station is then established by dividing the same charger types into groups. The parameter estimation method and the harmonic model of the charging station are verified through simulation, experiment, and field test. The parameter sensitivities of the equivalent circuit to the charging current harmonic are also studied.


Lemmatization and Morphological Tagging in German and Latin: A Comparison and a Survey of the State-of-the-art

This paper relates to the challenge of morphological tagging and lemmatization in morphologically rich languages by example of German and Latin. We focus on the question what a practitioner can expect when using state-of-the-art solutions out of the box. Moreover, we contrast these with old(er) methods and implementations for POS tagging. We examine to what degree recent efforts in tagger development pay out in improved accuracies — and at what cost, in terms of training and processing time. We also conduct in-domain vs. out-domain evaluation. Out-domain evaluations are particularly insightful because the distribution of the data which is being tagged by a user will typically differ from the distribution on which the tagger has been trained. Furthermore, two lemmatization techniques are evaluated. Finally, we compare pipeline tagging vs. a tagging approach that acknowledges dependencies between inflectional categories.


Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex

Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.


Gradient Flow in Recurrent Nets : the Difficulty of Learning Long-Term Dependencies


Defining Creativity: Finding Keywords for Creativity Using Corpus Linguistics Techniques

A computational system that evaluates creativity needs guidance on what creativity actually is. It is by no means straightforward to provide a computer with a formal definition of creativity; no such definition yet exists and viewpoints in creativity literature vary as to what the key components of creativity are considered to be. This work combines several viewpoints for a more general consensus of how we define creativity, using a corpus linguistics approach. 30 academic papers from various academic disciplines were analysed to extract the most frequently used words and their frequencies in the papers. This data was statistically compared with general word usage in written English. The results form a list of words that are significantly more likely to appear when talking about creativity in academic texts. Such words can be considered keywords for creativity, guiding us in uncovering key sub-components of creativity which can be used for computational assessment of creativity.


Weighted Task Regularization for Multitask Learning

Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of tasks is not large, the learning outcome may suffer if there exists outlier tasks that inappropriately bias majority. Rather than identifying or discarding such outlier tasks, we present a weighted regularized multitask learning framework based on regularized multitask learning, which uses statistical metrics, such as Kullback-Leibler divergence, to assign weights prior to regularization process that robustly reduces the impact of outlier tasks and results in better learned models for all tasks. We then show that this formulation can be solved using dual form like optimizing a standard support vector machine with varied kernels. We perform experiments using both synthetic dataset and real-world dataset from petroleum industry which shows that our methodology outperforms existing methods.


A contextual-bandit approach to personalized news article recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc.) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation.
 In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
 The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.


3D hand tracking using Kalman filter in depth space

Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visual information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.


Classification of Monte Carlo tree search variants

Many variations of Monte Carlo tree search have been proposed and tested but relatively little comparison of these variants have occurred. In this study an Agent Case Embedding analysis and agglomorative hierarchical clustering was performed using eight variants of Monte Carlo Tree Search as agents and eight games as cases. This allowed us to compare the variant's abilities on each of the games to determine the type of games each are good at handling as well as which variants are similar to others. This comparison of variants exploits the ability of ACEs to compare different types of objects based on their behavior. By looking at the behavior of MCTS variants on a variety of games we obtain a good notion of the degree to which different MCTS variants exhibit different capabilities. A side effect of comparing MCTS variants with agent-case embeddings is that we also are able to compare the games used to test the MCTS variants.


Group awareness in distributed software development

Open-source software development projects are almost always collaborative and distributed. Despite the difficulties imposed by distance, these projects have managed to produce large, complex, and successful systems. However, there is still little known about how open-source teams manage their collaboration. In this paper we look at one aspect of this issue: how distributed developers maintain group awareness. We interviewed developers, read project communication, and looked at project artifacts from three successful open source projects. We found that distributed developers do need to maintain awareness of one another, and that they maintain both a general awareness of the entire team and more detailed knowledge of people that they plan to work with. Although there are several sources of information, this awareness is maintained primarily through text-based communication (mailing lists and chat systems). These textual channels have several characteristics that help to support the maintenance of awareness, as long as developers are committed to reading the lists and to making their project communication public.


Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information

In this paper we present a method of automatically detecting and segmenting glands in digitized images of prostate histology and to use features derived from gland morphology to distinguish between intermediate Gleason grades. Gleason grading is a method of describing prostate cancer malignancy on a numerical scale from grade 1 (early stage cancer) through grade 5 (highly infiltrative cancer). Studies have shown that gland morphology plays a significant role in discriminating Gleason grades. We present a method of automated detection and segmentation of prostate gland regions. A Bayesian classifier is used to detect candidate gland regions by utilizing low-level image features to find the lumen, epithelial cell cytoplasm, and epithelial nuclei of the tissue. False positive regions identified as glands are eliminated via use of domain-specific knowledge constraints. Following candidate region detection via low-level and empirical domain information, the lumen area is used to initialize a level-set curve, which is evolved to lie at the interior boundary of the nuclei surrounding the gland structure. Features are calculated from the boundaries that characterize the morphology of the lumen and the gland regions, including area overlap ratio, distance ratio, standard deviation and variance of distance, perimeter ratio, compactness, smoothness, and area. The feature space is reduced using a manifold learning scheme (Graph Embedding) that is used to embed objects that are adjacent to each other in the high dimensional feature space into a lower dimensional embedding space. Objects embedded in this low dimensional embedding space are then classified via a support vector machine (SVM) classifier as belonging to Gleason grade 3, grade 4 cancer, or benign epithelium. We evaluate the efficacy of the automated segmentation algorithm by comparing the classification accuracy obtained using the automated segmentation scheme to the accuracy obtained via a user assisted segmentation scheme. Using the automated scheme, the system achieves accuracies of 86.35% when distinguishing Gleason grade 3 from benign epithelium, 92.90% distinguishing grade 4 from benign epithelium, and 95.19% distinguishing between Gleason grades 3 and 4. The manual scheme returns accuracies of 95.14%, 95.35%, and 80.76% for the respective classification tasks, indicating that the automated segmentation algorithm and the manual scheme are comparable in terms of achieving the overall objective of grade classification.


VHDL Implementation of 8-Bit ALU

In this paper VHDL implementation of 8-bit arithmetic logic unit (ALU) is presented. The design was implemented using VHDL Xilinx Synthesis tool ISE 13.1 and targeted for Spartan device. ALU was designed to perform arithmetic operations such as addition and subtraction using 8-bit fast adder, logical operations such as AND, OR, XOR and NOT operations, 1’s and 2’s complement operations and compare. ALU consist of two input registers to hold the data during operation, one output register to hold the result of operation, 8-bit fast adder with 2’s complement circuit to perform subtraction and logic gates to perform logical operation. The maximum propagation delay is 13.588ns and power dissipation is 38mW. The ALU was designed for controller used in network interface card.


String similarity measures and joins with synonyms

A string similarity measure quantifies the similarity between two text strings for approximate string matching or comparison. For example, the strings "Sam" and "Samuel" can be considered similar. Most existing work that computes the similarity of two strings only considers syntactic similarities, e.g., number of common words or q-grams. While these are indeed indicators of similarity, there are many important cases where syntactically different strings can represent the same real-world object. For example, "Bill" is a short form of "William". Given a collection of predefined synonyms, the purpose of the paper is to explore such existing knowledge to evaluate string similarity measures more effectively and efficiently, thereby boosting the quality of string matching.
 In particular, we first present an expansion-based framework to measure string similarities efficiently while considering synonyms. Because using synonyms in similarity measures is, while expressive, computationally expensive (NP-hard), we propose an efficient algorithm, called selective-expansion, which guarantees the optimality in many real scenarios. We then study a novel indexing structure called SI-tree, which combines both signature and length filtering strategies, for efficient string similarity joins with synonyms. We develop an estimator to approximate the size of candidates to enable an online selection of signature filters to further improve the efficiency. This estimator provides strong low-error, high-confidence guarantees while requiring only logarithmic space and time costs, thus making our method attractive both in theory and in practice. Finally, the results from an empirical study of the algorithms verify the effectiveness and efficiency of our approach.


Dynamic 2D/3D registration for the Kinect

Image and geometry registration algorithms are essential components of many computer graphics and computer vision systems. With recent technological advances in RGB-D sensors, robust algorithms that combine 2D image and 3D geometry registration have become an active area of research.
 This course introduces the basics of 2D/3D registration algorithms and provides theoretical explanations and practical tools for designing computer vision and computer graphics systems based on RGB-D devices such as the Microsoft Kinect or Asus Xtion Live. To illustrate the theory and demonstrate practical relevance, the course briefly discusses three applications: rigid scanning, non-rigid modeling, and real-time face tracking.


Stylometry with R: a suite of tools

Stylometry today uses either stand-alone dedicated programs, custom-made by stylometrists, or applies existing software, often one for each stage of the analysis. Stylometry with R can be placed somewhere in-between, as the powerful open-source statistical programming environment provides, on the one hand, the opportunity of building statistical applications from scratch, and, on the other, allows less advanced researchers to use ready-made scripts and libraries. In our own stylometric adventure with R, one of the aims was to build a tool (or a set of tools) that would combine sophisticated state-of-the-art algorithms of classification and/or clustering with a user-friendly interface. In particular, we wanted to implement a number of multidimensional methods that could be used by scholars without programming skills. And more: it soon became evident that once our R scripts are made, provided with a graphic user interface and more or less documented, they are highly usable in class; experience shows that this is an excellent way to work around R’s normally steep learning curve without losing anything of the environment’s considerable computing power and speed.


A first simulation on optimizing EDS for cabin baggage screening regarding throughput

Airport security screening is vital for secure air transportation. Screening of cabin baggage heavily relies on human operators reviewing X-ray images. Explosive detection systems (EDS) developed for cabin baggage screening can be a very valuable addition security-wise. Depending on the EDS machine and settings, false alarm rates increase, which could reduce throughput. A discrete event simulation was used to investigate how different machine settings of EDS, different groups of X-ray screeners, and different durations of alarm resolution with explosives trace detection (ETD) influence throughput of a specific cabin baggage screening process. For the modelling of screening behavior in the context of EDS and for the estimation of model parameters, data was borrowed from a human-machine interaction experiment and a work analysis. In a second step, certain adaptations were tested for their potential to reduce the impact of EDS on throughput. The results imply that moderate increases in the false alarm rate by EDS can be buffered by employing more experienced and trained X-ray screeners. Larger increases of the false alarm rate require a fast alarm resolution and additional resources for the manual search task.


Misuse of automated decision aids: Complacency, automation bias and the impact of training experience

The present study investigates automation misuse based on complacency and automation bias in interacting with a decision aid in a process control system. The effect of a preventive training intervention which includes exposing participants to rare automation failures is examined. Complacency is reflected in an inappropriate checking and monitoring of automated functions. In interaction with automated decision aids complacency might result in commission errors, i.e., following automatically generated recommendations even though they are false. Yet, empirical evidence proving this kind of relationship is still lacking. A laboratory experiment (N 1⁄4 24) was conducted using a process control simulation. An automated decision aid provided advice for fault diagnosis and management. Complacency was directly measured by the participants’ information sampling behavior, i.e., the amount of information sampled in order to verify the automated recommendations. Possible commission errors were assessed when the aid provided false recommendations. The results provide clear evidence for complacency, reflected in an insufficient verification of the automation, while commission errors were associated with high levels of complacency. Hence, commission errors seem to be a possible, albeit not an inevitable consequence of complacency. Furthermore, exposing operators to automation failures during training significantly decreased complacency and thus represents a suitable means to reduce this risk, even though it might not avoid it completely. Potential applications of this research include the design of training protocols in order to prevent automation misuse in interaction with automated decision aids. r 2008 Elsevier Ltd. All rights reserved.


Airport Security Screener Competency : A Cross-Sectional and Longitudinal Analysis

The performance of 5,717 aviation security screeners in detecting prohibited items in x-ray images of passenger bags was analyzed over a period of 4 years. The measure used in this study was detection performance on the X-Ray Competency Assessment Test (X-Ray CAT) and performance on this measure was obtained on an annual basis. Between tests, screeners performed varying amounts of training in the task of prohibited item detection using an adaptive computer-based training system called X-Ray Tutor (XRT). For both XRT and X-Ray CAT, prohibited items are categorized into guns, knives, improvised explosive devices (IEDs), and other prohibited items. Cross-sectional and longitudinal analyses of the training and test data were performed. Both types of analysis revealed a strong improvement of X-Ray CAT performance as a result of XRT training for all 4 item categories. The results of the study indicate that screener competency in detecting prohibited items in x-ray images of passenger bags can be greatly improved through routine XRT training.


Fleet-sizing for multi-depot and periodic vehicle routing problems using a modular heuristic algorithm

In this paper, we address the problem of determining the optimal fleet size for three vehicle routing problems, i.e., multi-depot VRP, periodic VRP and multidepot periodic VRP. In each of these problems, we consider three kinds of constraints that are often found in reality, i.e., vehicle capacity, route duration and budget constraints. To tackle the problems, we propose a new Modular Heuristic Algorithm (MHA) whose exploration and exploitation strategies enable the algorithm to produce promising results. Extensive computational experiments show that MHA performs impressively well, in terms of solution quality and computational time, for the three problem classes.


Review of Semantic-Free Utterances in Social Human-Robot Interaction

Review of Semantic-Free Utterances in Social Human–Robot Interaction Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst To cite this article: Selma Yilmazyildiz, Robin Read, Tony Belpeame & Werner Verhelst (2016) Review of Semantic-Free Utterances in Social Human–Robot Interaction, International Journal of Human–Computer Interaction, 32:1, 63-85, DOI: 10.1080/10447318.2015.1093856 To link to this article: http://dx.doi.org/10.1080/10447318.2015.1093856


Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach

It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation, interests and background. Our analysis leads to three main findings. First, female applicants demonstrate a wider breadth of experience, whereas male applicants put a greater emphasis on technical depth. Second, more female applicants demonstrate a greater desire to serve society. Third, female applicants are more likely to mention personal influences for studying engineering.


Building Competitive Direct Acoustics-to-Word Models for English Conversational Speech Recognition

Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional subword based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple. Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models. Our work also showed this accuracy gap when using the English Switchboard-Fisher data set. This paper describes a recipe to train an A2W model that closes this gap and is at-par with state-of-the-art sub-word based models. We achieve a word error rate of 8.8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder or language model. We find that model initialization, training data order, and regularization have the most impact on the A2W model performance. Next, we present a joint word-character A2W model that learns to first spell the word and then recognize it. This model provides a rich output to the user instead of simple word hypotheses, making it especially useful in the case of words unseen or rarely-seen during training.


Crime analysis and prediction using data mining

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.


A 27.6 MHz 297 μW 33 ppm/°C CMOS relaxation oscillator with an adjustable temperature compensation scheme

A 27.6 MHz 297 μW relaxation oscillator is presented in this paper by using an 180-nm CMOS technology. The proposed oscillator employs an adjustable temperature compensation feedforward scheme, in which the charging current can be set steady by a four-bit digital trimming signal. We have demonstrated a frequency variation lower than 33.5 ppm/°C which could be close to 0 ppm/°C in theory if the precision is high enough. In practical production, it is effective to calibrate the mismatching and deviation of fabrication because of the novel adjustable temperature compensation scheme.


A modified antipodal Vivaldi antenna (AVA) with elliptical slotting edge (ESE) for ultra-wideband (UWB) applications

As the demand of UWB system increases nowadays, several types of hybrid technique on antenna design are studied, modified, and proposed for UWB applications. In this work, an enhanced performance of modified antipodal Vivaldi antenna (AVA) for targeting ultra-wideband (UWB) frequency range between 2.17 GHz and 10.6 GHz has been proposed. This proposed UWB antenna is using FR-4 substrate with dielectric constant, εr = 4.4 and the electrical conductivity tangent loss, tanΔ = 0.019. This antenna design is based on the modified from the dual exponential tapered slot antenna (DETSA) with varying of the exponential flares design for obtain the best return loss of the antenna within the frequency bandwidth of the UWB. It has two exponential flares that follow the characteristic of exponential curve. Next, an elliptical slotting edge (ESE) with fixed horizontal radius and varied vertical radius is implemented to the antenna which effectively increases the performance of the antenna at lower frequencies. These modifications are done without changing the antenna size, which is 70 mm × 90 mm in dimension, which is approximately 0.5λ × 0.6λ, where λ is the wavelength of 2 GHz. The effects of the parameters of the antenna characteristic are also studied. In this case, the parametric study on varying distance between elliptical slots, Ks. It shows that, the higher value of Ks will effect to shift to the resonant frequency and increase the return loss of the antenna. At 9.104 GHz of resonant frequencies, it shows of the best return loss of 48.809 dB. Rather than that, the side lobe levels of the radiation pattern of the antenna are reduced in the lower frequencies.


Visual-Quality-Driven Learning for Underwater Vision Enhancement

The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the visual quality of underwater images preserving their edges and also performed well considering the UCIQE metric.


Implementation of power efficient 8-bit reversible linear feedback shift register for BIST

Built-in Self Test (BIST) is more appropriate for testing any VLSI circuits as it provides a larger range for low power applications. Test pattern generator is the vital module in BIST. Linear Feedback Shift Registers (LFSR) is broadly used in BIST for generating test vectors as it produces highly random test pattern. This paper mainly aims at design and implementation of power efficient LFSR using reversible logic for low power applications. Pareek Gate is used to implement reversible D Flip Flop (DFF). 8 bit reversible LFSR is implemented using 8 DFFs, 8 Feynman Gates (FG) and 3 Double Feynman Gates (DFG). It is found from the analysis that, the proposed approach of designing reversible LFSR reduces the power by 10% when compared to conventional design. Thus the proposed design can be used for designing BIST in low power applications.


Graphical Fisheye Views of Graphs

A fisheye lens is a very wide angle lens that shows places nearby in detail while also showing remote regions in successively less detail. This paper describes a system for viewing and browsing planar graphs using a software analog of a fisheye lens. We first show how to implement such a view using solely geometric transformations. We then describe a more general transformation that allows hierarchical, structured information about the graph to modify the views. Our general transformation is a fundamental extension to the previous research in fisheye views.


Gravity compensation in robotics

The actuator power required to resist joint torque caused by the weight of robot links can be a significant problem. Gravity compensation is a well-known technique in robot design to achieve equilibrium throughout the range of motion and as a result to reduce the loads on the actuator. Therefore, it is desirable and commonly implemented in many situations. Various design concepts for gravity compensation are available in the literature. This paper proposes an overview of gravity compensation methods applied in robotics. The examined properties of the gravity compensation are disclosed and illustrated via kinematic schemes. In order to classify the considered balancing schemes three principal groups are distinguished due to the nature of the compensation force: counterweight, spring or active force developed by an auxiliary actuator. Then, each group is reviewed through sub-groups organized via structural features of balancing schemes. The author believes that such an arrangement of gravity compensation methods allows one to carry out a systematized analysis and provides a comprehensive view on the problem.


of the Association of Management RETAINING TALENT : ASSESSING JOB SATISFACTION FACETS MOST SIGNIFICANTLY RELATED TO SOFTWARE DEVELOPER TURNOVER INTENTIONS

Retaining information technology employees has been a problem in many organizations for decades. When key software developers quit, they depart with critical knowledge of business processes and systems that are essential for maintaining a competitive advantage. The primary aim of this study was to assess facets of job satisfaction that are most significantly correlated with software developer turnover intentions. Surveys were collected from a sample of software developers across the United States. Correlations were assessed through multiple linear regression and parametric measures of association. The results indicated a significant predicting relationship between the software developers’ turnover intentions and nine facets of job satisfaction. Also found was a significant negative relationship between satisfaction with the nature of work and turnover intentions when controlling for the effects of the other independent variables. Implications of these findings are discussed along with recommendations for IT professionals and researchers.


Wrist Pulse Rate Monitor Using Self-Injection-Locked Radar Technology

To achieve sensitivity, comfort, and durability in vital sign monitoring, this study explores the use of radar technologies in wearable devices. The study first detected the respiratory rates and heart rates of a subject at a one-meter distance using a self-injection-locked (SIL) radar and a conventional continuous-wave (CW) radar to compare the sensitivity versus power consumption between the two radars. Then, a pulse rate monitor was constructed based on a bistatic SIL radar architecture. This monitor uses an active antenna that is composed of a SIL oscillator (SILO) and a patch antenna. When attached to a band worn on the subject's wrist, the active antenna can monitor the pulse on the subject's wrist by modulating the SILO with the associated Doppler signal. Subsequently, the SILO's output signal is received and demodulated by a remote frequency discriminator to obtain the pulse rate information.


On the Use of Automated Text Summarization Techniques for Summarizing Source Code

During maintenance developers cannot read the entire code of large systems. They need a way to get a quick understanding of source code entities (such as, classes, methods, packages, etc.), so they can efficiently identify and then focus on the ones related to their task at hand. Sometimes reading just a method header or a class name does not tell enough about its purpose and meaning, while reading the entire implementation takes too long. We study a solution which mitigates the two approaches, i.e., short and accurate textual descriptions that illustrate the software entities without having to read the details of the implementation. We create such descriptions using techniques from automatic text summarization. The paper presents a study that investigates the suitability of various such techniques for generating source code summaries. The results indicate that a combination of text summarization techniques is most appropriate for source code summarization and that developers generally agree with the summaries produced.


Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: Implications for improving road safety

This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.


Paraplay: Exploring Playfulness Around Physical Console Gaming

We present the concept of paraplay: playful activities that take place within the context of an interactive game or other play activity, but outside the activity itself. By critically examining work related to gaming and play goals and motivations we argue that the concept of playfulness should have a stronger role in our understanding of gaming sessions, and particularly social gaming sessions. In order to further understand the role of playfulness in social gaming we conducted an empirical field study of physical console gaming. Six families with a total of 32 participants were provided with an Xbox 360 console, Kinect sensor, and three casual physical video games to play together for a period of approximately two weeks. Participants were instructed to record their social gaming sessions. We conducted video analysis on these recordings as well as interviews with many of the participants. We found numerous types and examples of playfulness within the gaming session even from those who were not actively participating in the game. Drawing on the results of this study we present a taxonomy of paraplay and discuss the ways that playfulness can be exhibited in a social play session. We show that participants in a game situation act within a wider context of playfulness, according to a variety of significant roles ranging from active player through to audience member. We explore these roles and their attributes to provide a rich account of paraplay and its importance in understanding playful activities broadly.


Multimodal visualization of the optomechanical response of silicon cantilevers with ultrafast electron microscopy

The manner in which structure at the mesoscale affects emergent collective dynamics has become the focus of much attention owing, in part, to new insights into how morphology on these spatial scales can be exploited for enhancement and optimization of macroscopic properties. Key to advancements in this area is development of multimodal characterization tools, wherein access to a large parameter space (energy, space, and time) is achieved (ideally) with a single instrument. Here, we describe the study of optomechanical responses of single-crystal Si cantilevers with an ultrafast electron microscope. By conducting structural-dynamics studies in both real and reciprocal space, we are able to visualize MHz vibrational responses from atomicto micrometerscale dimensions. With nanosecond selected-area and convergent-beam diffraction, we demonstrate the effects of spatial signal averaging on the isolation and identification of eigenmodes of the cantilever. We find that the reciprocal-space methods reveal eigenmodes mainly below 5 MHz, indicative of the first five vibrational eigenvalues for the cantilever geometry studied here. With nanosecond real-space imaging, however, we are able to visualize local vibrational frequencies exceeding 30 MHz. The heterogeneously-distributed vibrational response is mapped via generation of pixel-by-pixel time-dependent Fourier spectra, which reveal the localized highfrequency modes, whose presence is not detected with parallel-beam diffraction. By correlating the transient response of the three modalities, the oscillation, and dissipation of the optomechanical response can be compared to a linear-elastic model to isolate and identify the spatial threedimensional dynamics.


Stress aware layout optimization

Process-induced mechanical stress is used to enhance carrier transport and achieve higher drive currents in current CMOS technologies. In this paper, we study how stress-induced performance enhancements are affected by layout properties and suggest guidelines for improving layouts so that performance gains are maximized. All MOS devices in this work include STI and nitride stress liners as sources of stress. Additionally, the PMOS devices incorporate the stress effects caused by the embedded SiGe S/D layer common in today's processes. First, we study how stress and drive current depend on layout parameters such as active area length and contact placement. We develop an intuition for the drive current dependency on these parameters and propose simple guidelines to improve a layout while considering mechanical stress effects. We then use these guidelines to improve the standard cell layouts in a 65nm industrial library. Experimental results show that we can enhance NMOS and PMOS drive currents by ~5% and ~12%, respectively, while only increasing NMOS leakage current by 1.48X and PMOS leakage current by 3.78X. By applying our guidelines to a 3-input NOR gate and a 3-input NAND gate, we are able to achieve a ~13.5% PMOS drive current improvement in the NOR gate and a ~7% NMOS drive current improvement in the NAND gate, without increasing cell area in either case


Pomegranate juice, total pomegranate ellagitannins, and punicalagin suppress inflammatory cell signaling in colon cancer cells.

Phytochemicals from fruits such as the pomegranate (Punica granatum L) may inhibit cancer cell proliferation and apoptosis through the modulation of cellular transcription factors and signaling proteins. In previous studies, pomegranate juice (PJ) and its ellagitannins inhibited proliferation and induced apoptosis in HT-29 colon cancer cells. The present study examined the effects of PJ on inflammatory cell signaling proteins in the HT-29 human colon cancer cell line. At a concentration of 50 mg/L PJ significantly suppressed TNFalpha-induced COX-2 protein expression by 79% (SE = 0.042), total pomegranate tannin extract (TPT) 55% (SE = 0.049), and punicalagin 48% (SE = 0.022). Additionally, PJ reduced phosphorylation of the p65 subunit and binding to the NFkappaB response element 6.4-fold. TPT suppressed NFkappaB binding 10-fold, punicalagin 3.6-fold, whereas ellagic acid (EA) (another pomegranate polyphenol) was ineffective. PJ also abolished TNFalpha-induced AKT activation, needed for NFkappaB activity. Therefore, the polyphenolic phytochemicals in the pomegranate can play an important role in the modulation of inflammatory cell signaling in colon cancer cells.


Channel Equalization and Interference Analysis for Uplink Narrowband Internet of Things (NB-IoT)

We derive the uplink system model for In-band and Guard-band narrowband Internet of Things (NB-IoT). The results reveal that the actual channel frequency response (CFR) is not a simple Fourier transform of the channel impulse response, due to sampling rate mismatch between the NB-IoT user and long term evolution (LTE) base station. Consequently, a new channel equalization algorithm is proposed based on the derived effective CFR. In addition, the interference is derived analytically to facilitate the co-existence of NB-IoT and LTE signals. This letter provides an example and guidance to support network slicing and service multiplexing in the physical layer.


The Social Impact of Natural Language Processing

Medical sciences have long since established an ethics code for experiments, to minimize the risk of harm to subjects. Natural language processing (NLP) used to involve mostly anonymous corpora, with the goal of enriching linguistic analysis, and was therefore unlikely to raise ethical concerns. As NLP becomes increasingly wide-spread and uses more data from social media, however, the situation has changed: the outcome of NLP experiments and applications can now have a direct effect on individual users’ lives. Until now, the discourse on this topic in the field has not followed the technological development, while public discourse was often focused on exaggerated dangers. This position paper tries to take back the initiative and start a discussion. We identify a number of social implications of NLP and discuss their ethical significance, as well as ways to address them.


Optimizing Multipath Routing With Guaranteed Fault Tolerance in Internet of Things

Internet of Things (IoTs) refers to the rapidly growing network of connected objects and people that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and people, fault tolerance routing has to be significantly considered. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover, and select  $k$ -disjoint paths that tolerates the failure while satisfying the quality of service parameters. Multi-swarm strategy enables determining the optimal directions in selecting the multipath routing while exchanging messages from all positions in the network. The validity of the proposed algorithm is assessed and results demonstrate high-quality solutions compared with the canonical particle swarm optimization (CPSO). Our results indicate the superiority of the multi-swarm and fully PMSO with constriction coefficient, which record an average improvement over CPSO equal to 88.45% in terms of sensors’ count, and 89.15% and 86.51% under the ring and mesh topologies, respectively.


Ancient Kaṭapayādi System Sanskrit Encryption Technique Unified

Computers today, generate enormous amount of data/information with each moment passing by. With the production of such huge amount of information comes its indispensable part of information security. Encryption Algorithms today drastically increase the file size. Hence the secure transmission of data requires extra bandwidth. Here in this paper we propose a system AKS - SETU, which is also the abbreviation to the title of this paper. Using the ancient technique of encryption from Sanskrit, AKS - SETU not only encrypts the information but also attempts on decreasing of the file size. AKS - SETU performs Sanskrit encryption, which we propose to be termed as Sanscryption.


Visualizing and Understanding Convolutional Networks

Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark (Krizhevsky et al., 2012). However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.


Vehicle Logo Recognition Using a SIFT-Based Enhanced Matching Scheme

In this paper, a new algorithm for vehicle logo recognition on the basis of an enhanced scale-invariant feature transform (SIFT)-based feature-matching scheme is proposed. This algorithm is assessed on a set of 1200 logo images that belong to ten distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1200 images to a training set and a testing set, respectively. It is shown that the enhanced matching approach proposed in this paper boosts the recognition accuracy compared with the standard SIFT-based feature-matching method. The reported results indicate a high recognition rate in vehicle logos and a fast processing time, making it suitable for real-time applications.


Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet <xref ref-type="bibr" rid="ref1">[1]</xref> and Fast R-CNN <xref ref-type="bibr" rid="ref2">[2]</xref> have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a <italic>Region Proposal Network</italic> (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features—using the recently popular terminology of neural networks with ’attention’ mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model <xref ref-type="bibr" rid="ref3">[3]</xref> , our detection system has a frame rate of 5 fps (<italic>including all steps</italic>) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.


Scalable triangulation-based logo recognition

We propose a scalable logo recognition approach that extends the common bag-of-words model and incorporates local geometry in the indexing process. Given a query image and a large logo database, the goal is to recognize the logo contained in the query, if any. We locally group features in triples using multi-scale Delaunay triangulation and represent triangles by signatures capturing both visual appearance and local geometry. Each class is represented by the union of such signatures over all instances in the class. We see large scale recognition as a sub-linear search problem where signatures of the query image are looked up in an inverted index structure of the class models. We evaluate our approach on a large-scale logo recognition dataset with more than four thousand classes.


Algorithm 777: HOMPACK90: A Suite of Fortran 90 Codes for Globally Convergent Homotopy Algorithms

HOMPACK90 is a Fortran 90 version of the Fortran 77 package HOMPACK (Algorithm 652), a collection of codes for finding zeros or fixed points of nonlinear systems using globally convergent probability-one homotopy algorithms. Three qualitatively different algorithms— ordinary differential equation based, normal flow, quasi-Newton augmented Jacobian matrix—are provided for tracking homotopy zero curves, as well as separate routines for dense and sparse Jacobian matrices. A high level driver for the special case of polynomial systems is also provided. Changes to HOMPACK include numerous minor improvements, simpler and more elegant interfaces, use of modules, new end games, support for several sparse matrix data structures, and new iterative algorithms for large sparse Jacobian matrices.


Evaluation of the Spiritual Well-Being Scale in a Sample of Korean Adults

This study explored the psychometric qualities and construct validity of the Spiritual Well-Being Scale (SWBS; Ellison in J Psychol Theol 11:330–340, 1983) using a sample of 470 Korean adults. Two factor analyses, exploratory factor analysis and confirmatory factor analysis, were conducted in order to test the validity of the SWBS. The results of the factor analyses supported the original two-dimensional structure of the SWBS—religious well-being (RWB) and existential well-being (EWB) with method effects associated with negatively worded items. By controlling for method effects, the evaluation of the two-factor structure of SWBS is confirmed with clarity. Further, the differential pattern and magnitude of correlations between the SWB subscales and the religious and psychological variables suggested that two factors of the SWBS were valid for Protestant, Catholic, and religiously unaffiliated groups except Buddhists. The Protestant group scored higher in RWB compared to the Buddhist, Catholic, and unaffiliated groups. The Protestant group scored higher in EWB compared to the unaffiliated groups. Future studies may need to include more Buddhist samples to gain solid evidence for validity of the SWBS on a non-Western religious tradition.


Fuzzy Set Theory ― and Its Applications

Spend your time even for only few minutes to read a book. Reading a book will never reduce and waste your time to be useless. Reading, for some people become a need that is to do every day such as spending time for eating. Now, what about you? Do you like to read a book? Now, we will show you a new book enPDFd fuzzy set theory and its applications that can be a new way to explore the knowledge. When reading this book, you can get one thing to always remember in every reading time, even step by step.


Towards Text Generation with Adversarially Learned Neural Outlines

Recent progress in deep generative models has been fueled by two paradigms – autoregressive and adversarial models. We propose a combination of both approaches with the goal of learning generative models of text. Our method first produces a high-level sentence outline and then generates words sequentially, conditioning on both the outline and the previous outputs. We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders. This provides strong, informative conditioning for the autoregressive stage. Our quantitative evaluations suggests that conditioning information from generated outlines is able to guide the autoregressive model to produce realistic samples, comparable to maximum-likelihood trained language models, even at high temperatures with multinomial sampling. Qualitative results also demonstrate that this generative procedure yields natural-looking sentences and interpolations.


Service-oriented Communities: Models and Concepts Towards Fractal Social Organizations

It is described an abstract model for the definition and the dynamic evolution of "communities of actants" originating from a given reference society of roles. Multiple representations are provided, showing how communities evolve with respect to their reference societies. In particular we show how such representations are self-similar and factorisable into "prime" constituents. An operating model is then introduced that describes the life-cycle of the communities of actants. After this a software component is presented -- the service-oriented community -- and its features are described in terms of the above mentioned models. Finally it is shown how such component can constitute the building block of a novel architecture for the design of fractal social organizations.


Distressed Stocks in Distressed Times

Financially distressed stocks do not underperform healthy stocks when the entire economy is in distress. The asset beta and financial leverage of distressed stocks rise significantly after major market downturns, resulting in a dramatic increase in equity beta. Hence, a long/short healthyminus-distressed trading strategy leads to significant losses when the market rebounds. Managing this risk mitigates the severe losses of financial distress strategies, and significantly improves their Sharpe ratios.


Real-time three-dimensional smooth path planning for unmanned aerial vehicles in completely unknown cluttered environments

This paper presents a real-time three-dimensional path planning algorithm for improving autonomous navigation of Unmanned Aerial Vehicles (UAVs) operating in completely unknown cluttered environments. The algorithm generates smooth paths consisting of continuous piecewise Bézier curves in real time. Specifically, a RRT-based waypoint generation algorithm is firstly proposed for the exploration of collision-free waypoints successively during flight. Besides, a novel real-time path smoothing technique is developed to generate continuous collision-free paths that satisfy the motion constraints of UAVs. This is achieved by fitting Bézier curves between consecutive waypoints based on the particle swarm optimization (PSO) algorithm. Lastly, a path selection strategy is also introduced to seek for an optimum path when multiple solutions are available. The simulation results demonstrate the superiority of the proposed real-time three-dimensional smooth path planning algorithm.


Application of data mining methods for link prediction in social networks

Using social networking services is becoming more popular day by day. Social network analysis views relationships in terms of nodes (people) and edges (links or connections—the relationship between the people). The websites of the social networks such as Facebook currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines such as Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. It is hard to find one method that can identify relation between people in the social network. The purpose of this paper is to forecast the friendship relation between individuals among a social network, especially the likelihood of a relation between an existing member with a new member. For this purpose, we used a few hypotheses to make the graph of relationships between members of social network, and we used the method of logistic regression to complete the graph. Test data from Flickr website are used to evaluate the proposed method. The results show that the method has achieved 99 % accuracy in prediction of friendship relationships.


Energy Management of Fuel Cell/Battery/Supercapacitor Hybrid Power Sources Using Model Predictive Control

Well known as an efficient and eco-friendly power source, fuel cell, unfortunately, offers slow dynamics. When attached as primary energy source in a vehicle, fuel cell would not be able to respond to abrupt load variations. Supplementing battery and/or supercapacitor to the system will provide a solution to this shortcoming. On the other hand, a current regulation that is vital for lengthening time span of the energy storage system is needed. This can be accomplished by keeping fuel cell's and batteries' current slope in reference to certain values, as well as attaining a stable dc output voltage. For that purpose, a feedback control system for regulating the hybrid of fuel cell, batteries, and supercapacitor was constructed for this study. Output voltage of the studied hybrid power sources (HPS) was administered by assembling three dc-dc converters comprising two bidirectional converters and one boost converter. Current/voltage output of fuel cell was regulated by boost converter, whereas the bidirectional converters regulated battery and supercapacitor. Reference current for each converter was produced using Model Predictive Control (MPC) and subsequently tracked using hysteresis control. These functions were done on a controller board of a dSPACE DS1104. Subsequently, on a test bench made up from 6 V, 4.5 Ah battery and 7.5 V, 120 F supercapacitor together with a fuel cell of 50 W, 10 A, experiment was conducted. Results show that constructing a control system to restrict fuel cell's and batteries' current slope and maintaining dc bus voltage in accordance with the reference values using MPC was feasible and effectively done.


Interventions shown to aid executive function development in children 4 to 12 years old.

To be successful takes creativity, flexibility, self-control, and discipline. Central to all those are executive functions, including mentally playing with ideas, giving a considered rather than an impulsive response, and staying focused. Diverse activities have been shown to improve children's executive functions: computerized training, noncomputerized games, aerobics, martial arts, yoga, mindfulness, and school curricula. All successful programs involve repeated practice and progressively increase the challenge to executive functions. Children with worse executive functions benefit most from these activities; thus, early executive-function training may avert widening achievement gaps later. To improve executive functions, focusing narrowly on them may not be as effective as also addressing emotional and social development (as do curricula that improve executive functions) and physical development (shown by positive effects of aerobics, martial arts, and yoga).


Jointly Embedding Entities and Text with Distant Supervision

Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications. However, recent entity embedding methods have relied on structured resources that are expensive to create for new domains and corpora. We present a distantly-supervised method for jointly learning embeddings of entities and text from an unnanotated corpus, using only a list of mappings between entities and surface forms. We learn embeddings from open-domain and biomedical corpora, and compare against prior methods that rely on human-annotated text or large knowledge graph structure. Our embeddings capture entity similarity and relatedness better than prior work, both in existing biomedical datasets and a new Wikipedia-based dataset that we release to the community. Results on analogy completion and entity sense disambiguation indicate that entities and words capture complementary information that can be effectively combined for downstream use.


Universal Language Model Fine-tuning for Text Classification

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 1824% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data. We opensource our pretrained models and code1.


Normative Social Influence in Persuasive Technology: Intensity versus Effectiveness

Persuasion is a form of social influence that implies intentional but voluntary change of a person’s behaviours, feelings or thoughts about an issue, object or action. Successful persuasion by means of persuasive technology relies on a number of factors and motivators. Recently, the so called social acceptance or rejection motivator has formed the backbone of a new type of persuasion, called Mass Interpersonal Persuasion, or MIP for short. This type of persuasion uses the social influence that is generated on online social networks to persuade people. Though it has been established that normative social influence can be used effectively in persuasive technology, it is unknown if the application of more social pressure also makes it more effective. In order to test the hypothesis that the effectiveness of persuasion increases when the persuasion becomes more intense, a quantitative experiment was conducted on the online social network of Facebook. Although evidence to support the hypothesis was found, it cannot be concluded from this experiment that when utilizing normative social influence in persuasive technology more intense persuasion is also more effective.


Hadoop Oriented Smart Cities Architecture

A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.


Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning

Customer churn refers to when a customer ceases their relationship with a company. A churn rate, used to estimate growth, is now considered as important a metric as financial profit. With growing competition in the market, companies are desperate to keep the churn rate as low as possible. Thus, churn prediction has gained critical importance, not just for existing customers, but also for predicting trends of future customers. This paper demonstrates prediction of churn on a Telco dataset using a Deep Learning Approach. A multilayered Neural Network was designed to build a non-linear classification model. The churn prediction model works on customer features, support features, usage features and contextual features. The possibility of churn as well as the determining factors are predicted. The trained model then applies the final weights on these features and predict the possibility of churn for that customer. An accuracy of 80.03% was achieved. Since the model also provides the churn factors, it can be used by companies to analyze the reasons for these factors and take steps to eliminate them.


' s personal copy Comments and Controversies Ten ironic rules for non-statistical reviewers

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o Keywords: Statistical testing Sample-size Effect size Power Classical inference As an expert reviewer, it is sometimes necessary to ensure a paper is rejected. This can sometimes be achieved by highlighting improper statistical practice. This technical note provides guidance on how to critique the statistical analysis of neuroimaging studies to maximise the chance that the paper will be declined. We will review a series of critiques that can be applied universally to any neuroimaging paper and consider responses to potential rebuttals that reviewers might encounter from authors or editors. Introduction This technical note is written for reviewers who may not have sufficient statistical expertise to provide an informed critique during the peer-reviewed process, but would like to recommend rejection on the basis of inappropriate or invalid statistical analysis. This guidance follows the 10 simple rules format and hopes to provide useful tips and criticisms for reviewers who find themselves in this difficult position. These rules are presented for reviewers in an ironic way 1 that makes it easier (and hopefully more entertaining) to discuss the issues from the point of view of both the reviewer and author — and to caricature both sides of the arguments. Some key issues are presented more formally in (non-ironic) appendices. There is a perceived need to reject peer-reviewed papers with the advent of open access publishing and the large number of journals available to authors. Clearly, there may be idiosyncratic reasons to block a paper – to ensure your precedence in the literature, personal rivalry etc. – however, we will assume that there is an imperative to reject papers for the good of the community: handling editors are often happy to receive recommendations to decline a paper. This is because they are placed under pressure to maintain a high rejection rate. This pressure is usually exerted by the editorial board (and publishers) and enforced by circulating quantitative information about their rejection rates (i.e., naming and shaming lenient editors). All journals want to maximise rejection rates, because this increases the quality of submissions, increases their impact factor …


From Visual Data Exploration to Visual Data Mining: A Survey

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.


LaserStroke: Mid-air Tactile Experiences on Contours Using Indirect Laser Radiation

This demonstration presents a novel form of mid-air tactile display, LaserStroke, that makes use of a laser irradiated on the elastic medium attached to the skin. LaserStroke extends a laser device with an orientation control platform and a magnetic tracker so that it can elicit tapping and stroking sensations to a user's palm from a distance. LaserStroke offers unique tactile experiences while a user freely moves his/her hand in midair.


Cannabis - from cultivar to chemovar.

The medicinal use of Cannabis is increasing as countries worldwide are setting up official programs to provide patients with access to safe sources of medicinal-grade Cannabis. An important question that remains to be answered is which of the many varieties of Cannabis should be made available for medicinal use. Drug varieties of Cannabis are commonly distinguished through the use of popular names, with a major distinction being made between Indica and Sativa types. Although more than 700 different cultivars have already been described, it is unclear whether such classification reflects any relevant differences in chemical composition. Some attempts have been made to classify Cannabis varieties based on chemical composition, but they have mainly been useful for forensic applications, distinguishing drug varieties, with high THC content, from the non-drug hemp varieties. The biologically active terpenoids have not been included in these approaches. For a clearer understanding of the medicinal properties of the Cannabis plant, a better classification system, based on a range of potentially active constituents, is needed. The cannabinoids and terpenoids, present in high concentrations in Cannabis flowers, are the main candidates. In this study, we compared cultivars obtained from multiple sources. Based on the analysis of 28 major compounds present in these samples, followed by principal component analysis (PCA) of the quantitative data, we were able to identify the Cannabis constituents that defined the samples into distinct chemovar groups. The study indicates the usefulness of a PCA approach for chemotaxonomic classification of Cannabis varieties.


Shunt active filter for harmonic and reactive power compensation using p-q theory

Now-a-days, decadence of power quality is foremost issue of electrical society. In practice, utility of switching device is increased in industrial as well as in domestic applications. Non-linearity causes adverse effects on system efficiency, utility of the power supply, power factor, etc. As reduction in power factor increases reactive power which does not have any contribution in energy transfer so its compensation is needed. So efforts are made to upgrade power quality, concept of Filter is in demand. In contrast to passive filter, active filters are popular due to its smaller size and weight. In this paper, work is done on shunt active power filter (SAPF) using P-Q theory for current harmonic mitigation and reactive power compensation. Here the simulation as well as its parameters, with or without active filter is well presented. We commence the paper with power quality issues then better understanding of PQ theory, simulation circuits, results, comparison and finally ended with conclusion. An effort is made to achieve THD value of source current below 5% to meet the required IEEE standards.


Generating timeline summaries with social media attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines. We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.