title: Program splicing

abstract: We introduce program splicing, a programming methodology that aims to automate the workflow of copying, pasting, and modifying code available online. Here, the programmer starts by writing a "draft" that mixes unfinished code, natural language comments, and correctness requirements. A program synthesizer that interacts with a large, searchable database of program snippets is used to automatically complete the draft into a program that meets the requirements. The synthesis process happens in two stages. First, the synthesizer identifies a small number of programs in the database that are relevant to the synthesis task. Next it uses an enumerative search to systematically fill the draft with expressions and statements from these relevant programs. The resulting program is returned to the programmer, who can modify it and possibly invoke additional rounds of synthesis. We present an implementation of program splicing, called S plicer , for the Java programming language. S plicer uses a corpus of over 3.5 million procedures from an open-source software repository. Our evaluation uses the system in a suite of everyday programming tasks, and includes a comparison with a state-of-the-art competing approach as well as a user study. The results point to the broad scope and scalability of program splicing and indicate that the approach can significantly boost programmer productivity.


title: Applying Feed-Forward Neural Networks to Collaborative Filtering ⋆

abstract: Neural networks have been applied to an entire plethora of learning tasks, such as text recognition, credit rating analysis and predic- tion, and so forth. Surprisingly, there have never been proposals suggest- ing their usage for collaborative filtering (CF) scenarios. The objective of this diploma/master's thesis is to close that gap by virtue of (i) conceiv- ing and applying neural network models to suit CF problems and (ii) evaluating their fitness with respect to benchmark approaches, taking into the eect of diverse tuning parameters.


Novelty Detection in Learning Systems


title: Predicting Information Spreading in Twitter

abstract: We present a new methodology for predicting the spread of information in a social network. We focus on the Twitter network, where information is in the form of 140 character messages called tweets, and information is spread by users forwarding tweets, a practice known as retweeting. Using data of who and what was retweeted, we train a probabilistic collaborative filter model to predict future retweets. We find that the most important features for prediction are the identity of the source of the tweet and retweeter. Our methodology is quite flexible and be used as a basis for other prediction models in social networks.


title: Electromyography(EMG) sensor controlled assistive orthotic robotic arm for forearm movement

abstract: There are nearly 1 in 50 people are affected by paralysis, which restrains the free movements of human body parts. Such people rely on support systems that can aid motion and force enhancement to body parts affect by paralysis. The most commonly used support systems are prosthetic and orthotic devices. Prosthetics are artificial extensions used to replace the missing body parts or supplement the defective body parts. Whereas, orthotic devices are external mechanical structures that support non-functional body parts which are actuated by motors. This study presents an orthotic robotic arm that can help in retraining flexion and extension movements of forearm of person affected by stroke. Orthotic Robotic Arm is a rigid brace having two degrees of freedom. Orthotic Robotic Arm helps to restore function of forearm by enabling individuals with paralysis to self-initiate and control movement of their partially paralyzed forearm using their own muscle signals. Accuracy of device was determined through a series of testing trials on 2 subjects who tested the device for 5 times and device registered 80% accuracy on the total number of trials by the subjects. Preliminary results suggest that the orthotic robotic arm has potential to improve the mobility of hand movements of paralyzed person.


Performance of Maximum ratio combining ( MRC ) MIMO Systems for Rayleigh Fading Channel


title: A service Robot Acceptance Model: User acceptance of humanoid robots during service encounters

abstract: This research examines human-robot acceptance during service encounters. Based on role theory and the technology acceptance model (TAM), we argue that users draw on various categories of expectations, which in turn, leads to a user's acceptance of frontline service robots (FSR). Results of a qualitative study with 63 participants reveal that users form their expectations toward FSR based on three categories: (1) their ideal imagination of a service, (2) their expectations toward a human frontline employee, and (3) their expectations toward a self-service technology. The theoretically developed Robot- Acceptance-Model (RAM) is tested in an experimental services setting with 82 users and service frontline robots.


title: Clique Graphs and Overlapping Communities

abstract: It is shown how to construct a clique graph in which properties of cliques of a fixed order in a given graph are represented by vertices in a weighted graph. Various definitions and motivations for these weights are given. The detection of communities or clusters is used to illustrate how a clique graph may be exploited. In particular a benchmark network is shown where clique graphs find the overlapping communities accurately while vertex partition methods fail.


title: W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis

abstract: With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever. Supervised approaches to Aspect Based Sentiment Analysis obtain good results for the domain/language their are trained on, but having manually labelled data for training supervised systems for all domains and languages are usually very costly and time consuming. In this work we describe W2VLDA, an almost unsupervised system based on topic modelling, that combined with some other unsupervised methods and a minimal configuration, performs aspect/category classifiation, aspect-terms/opinion-words separation and sentiment polarity classification for any given domain and language. We evaluate the performance of the aspect and sentiment classification in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several languages (English, Spanish, French and Dutch) and domains (hotels, restaurants, electronic-devices).


title: Design of Hilbert antenna as partial discharge sensor

abstract: This paper deals with the design of Hilbert antenna as partial discharge sensor. Hilbert antenna was designed to measure partial discharge induced electromagnetic wave in the frequency from 300 MHz to 3 GHz. Dimension of antenna (such as outer dimension, conductor length and conductor width), number of iteration, feed point position, substrate material and thickness affect the frequency response of the antenna. The outer dimension of the antenna is defined 7 cm at the third iteration of Hilbert so that the input impedance of antenna is approximate at 50 Ohm and resonance frequency at 953 MHz. Feed point was selected by trying every angle of the segment starting from its midpoint The selection of the feed point 2 of the Hilbert antenna resulted in that the working frequency of the antenna approaches the desired operating frequency obtained by the calculation (f=953 MHz) and VSWR was close to 1 so that the antenna is expected to work optimally.


title: Human non-synonymous SNPs: server and survey

abstract: Human single nucleotide polymorphisms (SNPs) represent the most frequent type of human population DNA variation. One of the main goals of SNP research is to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. Non-synonymous coding SNPs (nsSNPs) comprise a group of SNPs that, together with SNPs in regulatory regions, are believed to have the highest impact on phenotype. Here we present a World Wide Web server to predict the effect of an nsSNP on protein structure and function. The prediction method enabled analysis of the publicly available SNP database HGVbase, which gave rise to a dataset of nsSNPs with predicted functionality. The dataset was further used to compare the effect of various structural and functional characteristics of amino acid substitutions responsible for phenotypic display of nsSNPs. We also studied the dependence of selective pressure on the structural and functional properties of proteins. We found that in our dataset the selection pressure against deleterious SNPs depends on the molecular function of the protein, although it is insensitive to several other protein features considered. The strongest selective pressure was detected for proteins involved in transcription regulation.


title: ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

abstract: Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated analysis of EEG data offers the potential to improve the quality of patient care by shortening the time to diagnosis and reducing manual error. In this paper, we focus on one of the first steps in interpreting an EEG session - identifying whether the brain activity is abnormal or normal. To address this specific task, we propose a novel recurrent neural network (RNN) architecture termed ChronoNet which is inspired by recent developments from the field of image classification and designed to work efficiently with EEG data. ChronoNet is formed by stacking multiple 1D convolution layers followed by deep gated recurrent unit (GRU) layers where each 1D convolution layer uses multiple filters of exponentially varying lengths and the stacked GRU layers are densely connected in a feed-forward manner. We used the recently released TUH Abnormal EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous studies using this dataset, ChronoNet directly takes time-series EEG as input and learns meaningful representations of brain activity patterns. ChronoNet outperforms previously reported results on this dataset thereby setting a new benchmark.


title: HAL—The Missing Piece of the Puzzle for Hardware Reverse Engineering, Trojan Detection and Insertion

abstract: Hardware manipulations pose a serious threat to numerous systems, ranging from a myriad of smart-X devices to military systems. In many attack scenarios an adversary merely has access to the low-level, potentially obfuscated gate-level netlist. In general, the attacker possesses minimal information and faces the costly and time-consuming task of reverse engineering the design to identify security-critical circuitry, followed by the insertion of a meaningful hardware Trojan. These challenges have been considered only in passing by the research community. The contribution of this work is threefold: First, we present HAL, a comprehensive reverse engineering and manipulation framework for gate-level netlists. HAL allows automating defensive design analysis (e.g., including arbitrary Trojan detection algorithms with minimal effort) as well as offensive reverse engineering and targeted logic insertion. Second, we present a novel static analysis Trojan detection technique ANGEL which considerably reduces the false-positive detection rate of the detection technique FANCI. Furthermore, we demonstrate that ANGEL is capable of automatically detecting Trojans obfuscated with DeTrust. Third, we demonstrate how a malicious party can semi-automatically inject hardware Trojans into third-party designs. We present reverse engineering algorithms to disarm and trick cryptographic self-tests, and subtly leak cryptographic keys without any a priori knowledge of the design's internal workings.


title: Lexpresso: a Controlled Natural Language

abstract: This paper presents an overview of ‘Lexpresso’, a Controlled Natural Language developed at the Defence Science & Technology Organisation as a bidirectional natural language interface to a high-level information fusion system. The paper describes Lexpresso’s main features including lexical coverage, expressiveness and range of linguistic syntactic and semantic structures. It also touches on its tight integration with a formal semantic formalism and tentatively classifies it against the PENS system.


title: DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING ORIGINAL ARTICLE

abstract: Abstract Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot—even “sexy”—career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic se...


title: Optimization of parallel regenerative braking control strategy

abstract: Regenerative braking system is the own system of electric and hybrid electric vehicle. The system can restore the kinetic energy and potential energy, used during start and accelerating, into battery through electrical machine. The total brake force is composed of friction brake force on front axel, friction brake force on rear axel and regenerative brake force when a vehicle equipped with regenerative braking system brakes. A control strategy, parallel regenerative brake strategy, was proposed to resolve the distribution of the three forces. The parallel regenerative brake strategy was optimized on Saturn SL1 and then simulated under urban 15 drive cycle. As a result, through optimization the parallel brake strategy is not only safe enough but also can restore the largest amount of the brake energy.


title: LD-BSCA: A local-density based spatial clustering algorithm

abstract: Density-based clustering algorithms are very powerful to discover arbitrary-shaped clusters in large spatial databases. However, in many cases, varied local-density clusters exist in different regions of data space. In this paper, a new algorithm LD-BSCA is proposed with introducing the concept of local MinPts (a minimum number of points) and the new cluster expanding condition: ExpandConClId (Expanding Condition of ClId-th Cluster). We minimize the algorithm input down to only one parameter and let the local MinPts diversified as clusters change from one to another simultaneously. Experiments show LD-BSCA algorithm is powerful to discover all clusters in gradient distributing databases. In addition, we introduce an efficient searching method to reduce the runtime of our algorithm. Using several databases, we demonstrate the high quality of the proposed algorithm in clustering the implicit knowledge in asymmetric distribution databases.


title: Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

abstract: Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles -- such as matrix factorization -- perform very poorly in this setting, thus item-to-item recommendations are used most of the time. However the items typically have rich feature representations such as pictures and text descriptions that can be used to model the sessions. Here we investigate how these features can be exploited in Recurrent Neural Network based session models using deep learning. We show that obvious approaches do not leverage these data sources. We thus introduce a number of parallel RNN (p-RNN) architectures to model sessions based on the clicks and the features (images and text) of the clicked items. We also propose alternative training strategies for p-RNNs that suit them better than standard training. We show that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline.


title: A Comparison of Logical and Physical Parallel I/o pAtterns

abstract: Although there are several extant studies of parallel scien tific application request patterns, there is little experimen tal data on the correlation of physical I/O patterns with application I/O stimuli. To understand these correlations, the authors have instrumented the SCSI device drivers of the Intel Paragon OSF/1 operating system to record key physical I/O activities, and have correlated this data with the I/O patterns of scientific applications captured via the Pablo analysis toolkit. This analysis shows that disk hard ware features profoundly affect the distribution of request delays and that current parallel file systems respond to parallel application I/O patterns in nonscalable ways.


title: Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction

abstract: Being able to predict the neural signal in the near future from the current and previous observations has the potential to enable real-time responsive brain stimulation to suppress seizures. We have investigated how to use an auto-encoder model consisting of LSTM cells for such prediction. Recog- nizing that there exist multiple activity pattern clusters, we have further explored to train an ensemble of LSTM mod- els so that each model can specialize in modeling certain neural activities, without explicitly clustering the training data. We train the ensemble using an ensemble-awareness loss, which jointly solves the model assignment problem and the error minimization problem. During training, for each training sequence, only the model that has the lowest recon- struction and prediction error is updated. Intrinsically such a loss function enables each LTSM model to be adapted to a subset of the training sequences that share similar dynamic behavior. We demonstrate this can be trained in an end- to-end manner and achieve significant accuracy in neural activity prediction.


title: A multi-rater framework for studying personality: The trait-reputation-identity model.

abstract: Personality and social psychology have historically been divided between personality researchers who study the impact of traits and social-cognitive researchers who study errors in trait judgments. However, a broader view of personality incorporates not only individual differences in underlying traits but also individual differences in the distinct ways a person's personality is construed by oneself and by others. Such unique insights are likely to appear in the idiosyncratic personality judgments that raters make and are likely to have etiologies and causal force independent of trait perceptions shared across raters. Drawing on the logic of the Johari window (Luft & Ingham, 1955), the Self-Other Knowledge Asymmetry Model (Vazire, 2010), and Socioanalytic Theory (Hogan, 1996; Hogan & Blickle, 2013), we present a new model that separates personality variance into consensus about underlying traits (Trait), unique self-perceptions (Identity), and impressions conveyed to others that are distinct from self-perceptions (Reputation). We provide three demonstrations of how this Trait-Reputation-Identity (TRI) Model can be used to understand (a) consensus and discrepancies across rating sources, (b) personality's links with self-evaluation and self-presentation, and (c) gender differences in traits. We conclude by discussing how researchers can use the TRI Model to achieve a more sophisticated view of personality's impact on life outcomes, developmental trajectories, genetic origins, person-situation interactions, and stereotyped judgments. (PsycINFO Database Record


title: Is anybody out there?: antecedents of trust in global virtual teams

abstract: A global virtual team is an example of a boundaryless network organization form where a temporary team is assembled on an as-needed basis for the duration of a task and staffed by members from different countries. In such teams, coordination is accomplished via trust and shared communication systems. The focus of the reported study was to explore the antecedents of trust in a global virtual-team setting. Seventyfive teams, consisting of four to six members residing in different countries, interacted and worked together for eight weeks. The two-week trust-building exercises did have a significant effect on the team members' perceptions of the other members' ability, integrity, and benevolence. In the early phases of teamwork, team trust was predicted strongest by perceptions of other team members' integrity, and weakest by perceptions of their benevolence. The effect of other members' perceived ability on trust decreased over time. The members' own propensity to trust had a significant, though unchanging, effect on trust. A qualitative analysis of six teams' electronic mail messages explored strategies that were used by the three highest trust teams, but were used infrequently or not at all by the three lowest trust teams. The strategies suggest the presence of "swift" trust. The paper advances a research model for explaining trust in global virtual teams.


title: Microalgae as second generation biofuel. A review

abstract: Microalgae are autotrophic microorganisms having extremely high photosynthetic efficiency and are valued as rich source of lipids, hydrocarbons, and other complex oils for biodiesel besides being an invaluable source of bioethanol, biomethane, and biohydrogen. Biodiesel produced from oilseed crops such as jatropha and soy have lower yields per unit land area and threaten food security. Indeed, microalgae have higher oil yields amounting to about 40 times more oil per unit area of land in comparison to terrestrial oilseed crops such as soy and canola. Further, microalgae production does not require arable land for cultivation. Biofuel is regarded as a proven clean energy source and several entrepreneurs are attempting to commercialize this renewable source. Technology for producing and using biofuel has been known for several years and is frequently modified and upgraded. In view of this, a review is presented on microalgae as second generation biofuel. Microalgal farming for biomass production is the biggest challenge and opportunity for the biofuel industry. These are considered to be more efficient in converting solar energy into chemical energy and are amongst the most efficient photosynthetic plants on earth. Microalgae have simple cellular structure, a lipid-rich composition, and a rapid rate of reproduction. Many microalgal strains can be grown in saltwater and other harsh conditions. Some autotrophic microalgae can also be converted to heterotrophic ones to accumulate high quality oils using organic carbon. However, there are several technical challenges that need to be addressed to make microalgal biofuel profitable. The efficiency of microalgal biomass production is highly influenced by environmental conditions, e.g., light of proper intensity and wavelength, temperature, CO2 concentration, nutrient composition, salinities and mixing conditions, and by the choice of cultivation systems: open versus closed pond systems, photobioreactors. Currently, microalgae for commercial purpose are grown mostly in open circular/elongated “raceway” ponds which generally have low yields and high production costs. However, a hybrid system combining closed photobioreactor and open pond is a better option. The biggest hurdle in commercialization of microalgal biofuel is the high cost and energy requirement for the microalgal biomass production, particularly agitation, harvesting, and drying of biomass. In order to conserve energy and reduce costs, algae are often harvested in a two-step process involving flocculation followed by centrifugation, filtration, or micro-straining to get a solid concentration. However, the major bottlenecks in algal biodiesel production within the cell can be identified and handled by adopting a system approach involving transcriptomics, proteomics, and metabolomics. Research and developments in the field of new materials and advanced designs for cultivation in closed bioreactors, use of waste water for biomass production, screening of efficient strains, high-value coproduct strategy, and cutting-edge metabolic engineering are thought to provide the biggest opportunities to substantially improve the cost effectiveness of such production systems.


title: A Rule Based Approach To Discourse Parsing

abstract: In this paper we present an overview of recent developments in discourse theory and parsing under the Linguistic Discourse Model (LDM) framework, a semantic theory of discourse structure. We give a novel approach to the problem of discourse segmentation based on discourse semantics and sketch a limited but robust approach to symbolic discourse parsing based on syntactic, semantic and lexical rules. To demonstrate the utility of the system in a real application, we briefly describe the architecture of the PALSUMM system, a symbolic summarization system being developed at FX Palo Alto Laboratory that uses discourse structures constructed using the theory outlined to summarize written English prose texts. 1


title: An amino acid substitution matrix for protein conformation identification

abstract: Amino acid substitution matrices play an essential role in protein sequence alignment, a fundamental task in bioinformatics. Most widely used matrices, such as PAM matrices derived from homologous sequences and BLOSUM matrices derived from aligned segments of PROSITE, did not integrate conformation information in their construction. There are a few structure-based matrices, which are derived from limited data of structure alignment. Using databases PDB_SELECT and DSSP, we create a database of sequence-conformation blocks which explicitly represent sequence-structure relationship. Members in a block are identical in conformation and are highly similar in sequence. From this block database, we derive a conformation-specific amino acid substitution matrix CBSM60. The matrix shows an improved performance in conformational segment search and homolog detection.


title: Can noise make nonbursting chaotic systems more regular?

abstract: It has been known that noise can enhance the temporal regularity of dynamical systems that exhibit a bursting behavior--the phenomenon of coherence resonance. But can the phenomenon be expected for nonbursting chaotic systems? We present a theoretical argument based on the idea of time-scale matching and provide experimental evidence with a chaotic electronic circuit for coherence resonance in nonbursting chaotic systems.


title: EEG sub-band spectral centroid frequencies extraction based on Hamming and equiripple filters: A comparative study

abstract: The paper discusses on the effects of Hamming and equiripple filters in the extraction of EEG sub-band spectral centroid frequencies. A total of 40 healthy male subjects have participated in the study. EEG signal sub-bands were filtered using Hamming and equiripple filters with similar frequency response characteristics. It has been observed that the mean difference and variance is very small for all major EEG sub-bands. Implementation of Hamming filter however, induced 91% higher computational requirements as compared to that of the equiripple filters. Hence findings reveal that with similar frequency response characteristics, equiripple filters perform as the more efficient alternative for computation of spectral centroid frequencies.


title: The McPAT Framework for Multicore and Manycore Architectures: Simultaneously Modeling Power, Area, and Timing

abstract: This article introduces McPAT, an integrated power, area, and timing modeling framework that supports comprehensive design space exploration for multicore and manycore processor configurations ranging from 90nm to 22nm and beyond. At microarchitectural level, McPAT includes models for the fundamental components of a complete chip multiprocessor, including in-order and out-of-order processor cores, networks-on-chip, shared caches, and integrated system components such as memory controllers and Ethernet controllers. At circuit level, McPAT supports detailed modeling of critical-path timing, area, and power. At technology level, McPAT models timing, area, and power for the device types forecast in the ITRS roadmap. McPAT has a flexible XML interface to facilitate its use with many performance simulators. Combined with a performance simulator, McPAT enables architects to accurately quantify the cost of new ideas and assess trade-offs of different architectures using new metrics such as Energy-Delay-Area2 Product (EDA2P) and Energy-Delay-Area Product (EDAP). This article explores the interconnect options of future manycore processors by varying the degree of clustering over generations of process technologies. Clustering will bring interesting trade-offs between area and performance because the interconnects needed to group cores into clusters incur area overhead, but many applications can make good use of them due to synergies from cache sharing. Combining power, area, and timing results of McPAT with performance simulation of PARSEC benchmarks for manycore designs at the 22nm technology shows that 8-core clustering gives the best energy-delay product, whereas when die area is taken into account, 4-core clustering gives the best EDA2P and EDAP.


title: Probabilistic subgraph matching based on convex relaxation

abstract: We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with a regularization term that measures differences of the relational structure. For the resulting quadratic integer program, a mathematically tight relaxation is derived by exploiting the degrees of freedom of the embedding space of positive semidefinite matrices. We show that the global minimum of the relaxed convex problem can be interpreted as probability distribution over the original space of matching matrices, providing a basis for efficiently sampling all close-to-optimal combinatorial matchings within the original solution space. As a result, the approach can even handle completely ambiguous situations, despite uniqueness of the relaxed convex problem. Exhaustive numerical experiments demonstrate the promising performance of the approach which – up to a single inevitable regularization parameter that weights feature similarity against structural similarity – is free of any further tuning parameters.


title: Divorce and Children?s Adjustment Problems at Home and School: The Role of Depressive/Withdrawn Parenting

abstract: This study examined linkages between divorce, depressive/withdrawn parenting, and child adjustment problems at home and school. Middle class divorced single mother families (n=35) and 2-parent families (n=174) with a child in the fourth grade participated. Mothers and teachers completed yearly questionnaires and children were interviewed when they were in the fourth, fifth, and sixth grades. Structural equation modeling suggested that the association between divorce and child externalizing and internalizing behavior was partially mediated by depressive/withdrawn parenting when the children were in the fourth and fifth grades


title: Towards an observatory for mobile participatory sensing applications

abstract: This paper presents an observatory for registering applications that use participatory sensing to collect data. Cataloging these applications will aid the scientific community to exchange more information, facilitating the comparison between different initiatives. Through an initial research, the applications are categorized in areas usually considered in the literature. We propose a survey to validate the platform and also discuss the taxonomies created as a result of this survey. The main contributions of this paper include the classification of crowdsensing applications in different ontological categories, as well as the proposal of a technology platform that enables the distributed and collaborative cataloging of crowdsensing initiatives.


title: Automated parking space detection using convolutional neural networks

abstract: Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parking lot given the data collected from the University of The Witwatersrand. This paper presents an approach for a real-time parking space classification based on Convolutional Neural Networks (CNN) using Caffe and Nvidia DiGITS framework. The training process has been done using DiGITS and the output is a caffemodel used for predictions to detect vacant and occupied parking spots. The system checks a defined area whether a parking spot (bounding boxes defined at initialization of the system) is containing a car or not (occupied or vacant). Those bounding boxes coordinates are saved from a frame of the video of the parking lot in a JSON format, to be later used by the system for sequential prediction on each parking spot. The system has been trained using the LeNet network with the Nesterov's Accelerated Gradient as solver and the AlexNet network with the Stochastic Gradient Descent as solver. We were able to get an accuracy on the validation set of 99% for both networks. The accuracy on a foreign dataset(PKLot) returned as well 99%. Those are experimental results based on the training set show how robust the system can be when the prediction has to take place in a different parking space.


title: The dark side of social networking sites: Understanding phishing risks

abstract: LinkedIn, with over 1.5 million Groups, has become a popular place for business employees to create private groups to exchange information and communicate. Recent research on social networking sites (SNSs) has widely explored the phenomenon and its positive effects on firms. However, social networking's negative effects on information security were not adequately addressed. Supported by the credibility, persuasion and motivation theories, we conducted 1) a field experiment, demonstrating how sensitive organizational data can be exploited, followed by 2) a qualitative study of employees engaged in SNSs activities; and 3) interviews with Chief Information Security Officers (CISOs). Our research has resulted in four main findings: 1) employees are easily deceived and susceptible to victimization on SNSs where contextual elements provide psychological triggers to attackers; 2) organizations lack mechanisms to control SNS online security threats, 3) companies need to strengthen their information security policies related to SNSs, where stronger employee identification and authentication is needed, and 4) SNSs have become important security holes where, with the use of social engineering techniques, malicious attacks are easily facilitated. Organizations lack the mechanisms to control SNS phishing risks.Phishing effectiveness is facilitated by the influence technique of liking.SNSs are important security hole where the malicious attacks are greatly facilitated.Organizational IS security policies should be adapted to SNS realities.


title: OpenMP task scheduling strategies for multicore NUMA systems

abstract: The recent addition of task parallelism to the OpenMP shared memory API allows programmers to express concurrency at a high level of abstraction and places the burden of scheduling parallel execution on the OpenMP run-time system. Efficient scheduling of tasks on modern multi-socket multicore shared memory systems requires careful consideration of an increasingly complex memory hierarchy, including shared caches and non-uniform memory access (NUMA) characteristics. In order to evaluate scheduling strategies, we extended the open source Qthreads threading library to implement different scheduler designs, accepting OpenMP programs through the ROSE compiler. Our comprehensive performance study of diverse OpenMP task-parallel benchmarks compares seven different task-parallel run-time scheduler implementations on an Intel Nehalem multi-socket multicore system: our proposed hierarchical work-stealing scheduler, a per-core work-stealing scheduler, a centralized scheduler, and LIFO and FIFO versions of the Qthreads round-robin scheduler. In addition, we compare our results against the Intel and GNU OpenMP implementations. ::: ::: Our hierarchical scheduling strategy leverages different scheduling methods at different levels of the hierarchy. By allowing one thread to steal work on behalf of all of the threads within a single chip that share a cache, the scheduler limits the number of costly remote steals. For cores on the same chip, a shared LIFO queue allows exploitation of cache locality between sibling tasks as well as between a parent task and its newly created child tasks. In the performance evaluation, our Qthreads hierarchical scheduler is competitive on all benchmarks tested. On five of the seven benchmarks, it demonstrates speedup and absolute performance superior to both the Intel and GNU OpenMP run-time systems. Our run-time also demonstrates similar performance benefits on AMD Magny Cours and SGI Altix systems, enabling several benchmarks to successfully scale to 192 CPUs of an SGI Altix.


title: The relationship between cell phone use, academic performance, anxiety, and Satisfaction with Life in college students

abstract: While functional differences between today's cell phones and traditional computers are becoming less clear, one difference remains plain - cell phones are almost always on-hand and allow users to connect with an array of services and networks at almost any time and any place. The Pew Center's Internet and American Life Project suggests that college students are the most rapid adopters of cell phone technology and research is emerging which suggests high frequency cell phone use may be influencing their health and behavior. Thus, we investigated the relationships between total cell phone use (N=496) and texting (N=490) on Satisfaction with Life (SWL) in a large sample of college students. It was hypothesized that the relationship would be mediated by Academic Performance (GPA) and anxiety. Two separate path models indicated that the cell phone use and texting models had good overall fit. Cell phone use/texting was negatively related to GPA and positively related to anxiety; in turn, GPA was positively related to SWL while anxiety was negatively related to SWL. These findings add to the debate about student cell phone use, and how increased use may negatively impact academic performance, mental health, and subjective well-being or happiness.


title: Intuitive experimentation in the physical world

abstract: Abstract Many aspects of our physical environment are hidden. For example, it is hard to estimate how heavy an object is from visual observation alone. In this paper we examine how people actively “experiment” within the physical world to discover such latent properties. In the first part of the paper, we develop a novel framework for the quantitative analysis of the information produced by physical interactions. We then describe two experiments that present participants with moving objects in “microworlds” that operate according to continuous spatiotemporal dynamics similar to everyday physics (i.e., forces of gravity, friction, etc.). Participants were asked to interact with objects in the microworlds in order to identify their masses, or the forces of attraction/repulsion that governed their movement. Using our modeling framework, we find that learners who freely interacted with the physical system selectively produced evidence that revealed the physical property consistent with their inquiry goal. As a result, their inferences were more accurate than for passive observers and, in some contexts, for yoked participants who watched video replays of an active learner’s interactions. We characterize active learners’ actions into a range of micro-experiment strategies and discuss how these might be learned or generalized from past experience. The technical contribution of this work is the development of a novel analytic framework and methodology for the study of interactively learning about the physical world. Its empirical contribution is the demonstration of sophisticated goal directed human active learning in a naturalistic context.


title: NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks

abstract: Internet of Things (IoT) makes physical objects and devices interact with each other through wireless technologies. IoT is expected to deliver a significant role in our lives in near future. However, at the current stage, IoT is vulnerable to various kinds of security threats just like other wired and wireless networks. Our work mainly focuses on protecting an IoT infrastructure from distributed denial-of-service attacks generated by the intruders. We present a new approach of using Naïve Bayes classification algorithm applied in intrusion detection systems (IDSs). IDSs are deployed in the form of multi-agents throughout the network to sense the misbehaving or irregular traffic and actions of nodes. In the paper, we also discuss the fundamental concepts related to our work and recent research done in similar area.


title: A New Post-Quantum Blind Signature From Lattice Assumptions

abstract: At present, several post-quantum cryptosystems have been proposed, and lattice-based cryptography is the main candidate. Especially in the direction of digital signatures, there are now many practical lattice-based signature schemes. However, there exist few lattice-based signatures with special property such as blind signature. Blind signature was introduced by Chaum for creating untraceable payment system. Then, it is widely used in e-cash and voting, especially in the revolutionary digital cash system based on blockchain. In our paper, we present a method to construct a post-quantum blind signature based on lattice assumptions, and we proved that any existential forger against the security of the resulting scheme can solve the ${\text {SIS}}_{q, n, m, \beta }$ problem for $\beta = \widetilde {O}(dn)$ . Our main technique is the rejection sampling theory. The expected number of times needed to output a blind signature is at most $e^{2}$ under aborting, and our new scheme has much smaller signature size than those of all the previously proposed blind signature schemes over lattices.


title: A survey on transition from GMPLS control plane for optical multilayer networks to SDN control plane

abstract: Software-defined network (SDN)/OpenFlow research and development have attracted the attention of many researchers and companies. The key idea of an SDN is characterized by decoupling the control plane from the data plane. This paper surveys the state-of-the-art of SDN and latest developments in this active research area of SDN. This work also compiles a comprehensive survey of programmable networks from early ideas to recent developments. SDN architecture and the OpenFlow standard in particular are initially presented followed by current alternatives for implementation and SDN protocols and services. It is also discussed why OpenFlow/SDN can succeed where Generalized Multi-Protocol Label Switching (GMPLS) failed in terms of optimal strategy transition from GMPLS control plane for optical multilayer networks to SDN control plane. At the end of the work are presented SDN applications and further research topics with some suggested open research challenges.


SMS Spam Detection using Machine Learning Approach


Strategic Ambidexterity and Sales Growth : A Longitudinal Test in the Software Sector


title: Prolapse and sexual function 8 years after neovagina according to Shears: a study of 43 cases with Mayer–von Rokitansky–Küster–Hauser syndrome

abstract: INTRODUCTION AND HYPOTHESIS ::: To investigate sexual and anatomical outcome after Shears neovagina in patients with Mayer-von Rokitansky-Kuster-Hauser syndrome (MRKH). ::: ::: ::: METHODS ::: Forty-three consecutive patients with MRKH syndrome underwent surgery creating a neovagina according to Shears. Follow-up was 8 years. Sexual function was assessed using the Female Sexual Function Index (FSFI) as patient reported outcome; anatomical results were assessed measuring anterior, posterior and apical prolapse using the ICS Pelvic Organ Prolapse Score (ICS-POP Score). Pelvic floor contraction was measured applying the Oxford grading system. ::: ::: ::: RESULTS ::: Thirty-seven of the patients were regularly sexually active and filled in the FSFI; mean total FSFI was 27.2 (range 24-28). No significant prolapse of the neovagina was noted, 11 patients had a grade I cystocele, rectocele or apical descent that was asymptomatic. Oxford grading of the pelvic floor contraction was 4 in median (range 2-5). ::: ::: ::: CONCLUSION ::: The neovagina according to Shears is a valuable surgical option with good sexual and anatomical outcome 8 years after therapy.


title: Double Encoder Conditional GAN for Facial Expression Synthesis

abstract: Photorealistic facial expression synthesis from single face image is already a highly challenging research work, in part due to a paucity of labeled and paired facial expression samples. Most existing facial expression synthesis works attempt to learn the transformation between expression domains and thus would require the paired samples as well as the labeled query image. In this paper, we propose the Double Encoder Conditional GAN (DECGAN) for facial expression synthesis. Generative Adversarial Networks (GANs) have demonstrated to successfully approximate complex data distributions. And cGANs, which contain external information, can determine the specific relationship between images. This work inspires us to modify the structure of GAN, and use the target facial expression feature as a condition. In this work, we propose two encoders to encode the original expression and the target expression, respectively, to extract the latent vectors and conditional labels features of the real image. In the meantime, associative learning is used to associate unpaired original emoticons with target emoticons in the database and to share identities.


title: Global Optimality of Local Search for Low Rank Matrix Recovery

abstract: We show that there are no spurious local minima in the non-convex factorized parametrization of low-rank matrix recovery from incoherent linear measurements. With noisy measurements we show all local minima are very close to a global optimum. Together with a curvature bound at saddle points, this yields a polynomial time global convergence guarantee for stochastic gradient descent {\em from random initialization}.


title: A low-tech sensing system for particulate pollution

abstract: We present an ultra low-cost sensing system, which enables participants to see and reflect on the particulates in their air. Drawing on prior work in paper computing, we introduce small sensors for particulate pollution that can be easily assembled from common paper materials for less than $1 USD, and mailed by regular postal service to residents of entire neighborhoods, cities, or geographic regions. Recipients collect particulate samples using these sensors and mail them back to a central location, where the particles are viewed and analyzed via a microscope. The data, which includes rich images of actual air pollution particles, can then be broadcast to larger audiences. This paper details the design of our system and its deployment with a local air quality activist community. We conclude by highlighting the tradeoffs between high-tech and low-tech sensing, and suggest opportunities for tangible interaction to support rich, new ways of seeing our environment.


title: Headon: real-time reenactment of human portrait videos

abstract: HeadOn is the first real-time source-to-target reenactment approach for human portrait videos that enables the transfer of torso and head motion, facial expression, and eye gaze.


title: Distributional vectors encode referential attributes

abstract: Distributional methods have proven to excel at capturing fuzzy, graded aspects of meaning (Italy is more similar to Spain than to Germany). In contrast, it is difficult to extract the values of more specific attributes of word referents from distributional representations, attributes of the kind typically found in structured knowledge bases (Italy has 60 million inhabitants). In this paper, we pursue the hypothesis that distributional vectors also implicitly encode referential attributes. We show that a standard supervised regression model is in fact sufficient to retrieve such attributes to a reasonable degree of accuracy: When evaluated on the prediction of both categorical and numeric attributes of countries and cities, the model consistently reduces baseline error by 30%, and is not far from the upper bound. Further analysis suggests that our model is able to “objectify” distributional representations for entities, anchoring them more firmly in the external world in measurable ways.


title: Fast and robust three-dimensional best path phase unwrapping algorithm

abstract: What we believe to be a novel three-dimensional (3D) phase unwrapping algorithm is proposed to unwrap 3D wrapped-phase volumes. It depends on a quality map to unwrap the most reliable voxels first and the least reliable voxels last. The technique follows a discrete unwrapping path to perform the unwrapping process. The performance of this technique was tested on both simulated and real wrapped-phase maps. And it is found to be robust and fast compared with other 3D phase unwrapping algorithms.


title: Complexity and performance in parallel programming languages

abstract: Several parallel programming languages, libraries and environments have been developed to ease the task of writing programs for multiprocessors. Proponents of each approach often point out various language features that are designed to provide the programmer with a simple programming interface. However, virtually no data exists that quantitatively evaluates the relative ease of use of different parallel programming languages. The paper borrows techniques from the software engineering field to quantify the complexity of three predominate programming models: shared memory, message passing and High-Performance Fortran. It is concluded that traditional software complexity metrics are effective indicators of the relative complexity of parallel programming languages. The impact of complexity on run-time performance is also discussed in the context of message passing versus HPF on an IBM SP2.


1 Website Forgery : Understanding Phishing Attacks &


title: Digital Health Devices for Everyone!

abstract: Smart health devices monitor certain health parameters, are connected to an Internet service, and target primarily a lay consumer seeking a healthy lifestyle rather than the medical expert or the chronically ill person. These devices offer tremendous opportunities for wellbeing and self-management of health. This department reviews smart health devices from a pervasive computing perspective, discussing various devices and their functionality, limitations, and potential.


title: Digital PM demodulator for brazilian data collecting system

abstract: This paper presents the project and implementation results of a digital PM demodulator system for processing LEO satellite signals from Brazilian Data Collecting System. The demodulator was implemented on the Altera Cyclone II DSP Development Kit equipped with FPGA EP2C70. Demodulation is done with a second order Digital Phase Locked Loop (DPLL) with -π to π linear phase detector realized by a CORDIC algorithm operating on vectoring mode. The parameters of the DPLL were calculated using control system theory.


title: Wearable Sensor-Based Hand Gesture and Daily Activity Recognition for Robot-Assisted Living

abstract: In this paper, we address natural human-robot interaction (HRI) in a smart assisted living (SAIL) system for the elderly and the disabled. Two common HRI problems are studied: hand gesture recognition and daily activity recognition. For hand gesture recognition, we implemented a neural network for gesture spotting and a hierarchical hidden Markov model for context-based recognition. For daily activity recognition, a multisensor fusion scheme is developed to process motion data collected from the foot and the waist of a human subject. Experiments using a prototype wearable sensor system show the effectiveness and accuracy of our algorithms.


title: A vacuum-tube guitar amplifier model using a recurrent neural network

abstract: Rock and blues guitar players prefer the use of vacuum-tube amplifiers due to the harmonic structures developed when the amplifiers are overdriven. The disadvantages of vacuum tubes compared against solid-state implementations, such as power consumption, reliability, cost, etc., are far outweighed by the desirable sound characteristics of the overdriven vacuum-tube amplifier. There are many approaches to modeling vacuum-tube amplifier behaviors in solid-state implementations. These include a variety of both analog and digital techniques, some of which are judged to be good approximations to the tube sound. In this paper we present early results of experiments in using a neural network to model the distortion produced by an overdriven vacuum-tube amplifier. Our approach is to use artificial neural networks of the recurrent variety, specifically a Nonlinear AutoRegressive eXogenous (NARX) network, to capture the nonlinear, dynamic characteristics of vacuum-tube amplifiers. NARX networks of various sizes have been trained on data sets consisting of samples of both sinusoidal and raw electric guitar signals and the amplified output of those signals applied to a tube-based amplifier driven at various levels of saturation. Models are evaluated using both quantitative (e.g., RMS error) and qualitative (listening tests) assessment methods on data sets that were not used in the network training. Listening tests- considered by us to be the most important evaluation method-at this point in the work, are indicative of the potential for success in the modeling of a vacuum-tube amplifier using a recurrent neural network.


title: Accessible Bar Charts for Visually Impaired Users

abstract: This paper presents a novel approach to enabling visually impaired users to gain access to bar charts on the Web. Our approach differs from previous work by providing the user with the message and knowledge that one would gain from viewing the graphic rather than providing alternative access to the appearance of the graphic. The user interface to the system is implemented as a browser extension. The output of the system is a textual summary, the core content of which is the hypothesized intended message of the graphic designer, as inferred by our Bayesian network. The summary is conveyed to the user by screen reading software. User evaluations have shown the system to be both useful and effective.


LX-SSD : Enhancing the Lifespan of NAND Flash-based Memory via Recycling Invalid Pages


title: Prototyping nfv-based multi-access edge computing in 5G ready networks with open baton

abstract: With the increasing acceptance of Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies, a radical transformation is currently occurring inside network providers infrastructures. The trend of Software-based networks foreseen with the 5th Generation of Mobile Network (5G) is drastically changing requirements in terms of how networks are deployed and managed. One of the major changes requires the transaction towards a distributed infrastructure, in which nodes are built with standard commodity hardware. This rapid deployment of datacenters is paving the way towards a different type of environment in which the computational resources are deployed up to the edge of the network, referred to as Multi-access Edge Computing (MEC) nodes. However, MEC nodes do not usually provide enough resources for executing standard virtualization technologies typically used in large datacenters. For this reason, software containerization represents a lightweight and viable virtualization alternative for such scenarios. This paper presents an architecture based on the Open Baton Management and Orchestration (MANO) framework combining different infrastructural technologies supporting the deployment of container-based network services even at the edge of the network.


title: Combining computation and communication optimizations in system synthesis for streaming applications

abstract: Data streaming is a widely-used technique to exploit task-level parallelism in many application domains such as video processing, signal processing and wireless communication. In this paper we propose an efficient system-level synthesis flow to map streaming applications onto FPGAs with consideration of simultaneous computation and communication optimizations. The throughput of a streaming system is significantly impacted by not only the performance and number of replicas of the computation kernels, but also the buffer size allocated for the communications between kernels. In general, module selection/replication and buffer size optimization were addressed separately in previous work. Our approach combines these optimizations together in system scheduling which minimizes the area cost for both logic and memory under the required throughput constraint. We first propose an integer linear program (ILP) based solution to the combined problem which has the optimal quality of results. Then we propose an iterative algorithm which can achieve the near-optimal quality of results but has a significant improvement on the algorithm scalability for large and complex designs. The key contribution is that we have a polynomial-time algorithm for an exact schedulability checking problem and a polynomial-time algorithm to improve the system performance with better module implementation and buffer size optimization. Experimental results show that compared to the separate scheme of module select/replication and buffer size optimization, the combined optimization scheme can gain 62% area saving on average under the same performance requirements. Moreover, our heuristic can achieve 2 to 3 orders of magnitude of speed-up in runtime, with less than 10% area overhead compared to the optimal solution by ILP.


Sentiment Analysis of App Store Reviews


title: Microstrip Grid and Comb Array Antennas

abstract: The design of a high-gain microstrip grid array antenna (MGAA) for 24-GHz automotive radar sensor applications is first presented. An amplitude tapering technique utilizing variable line width on the individual radiating element is then applied to lower sidelobe level. Next, the MGAA is simplified to a microstrip comb array antenna (MCAA). The MCAA shows broader impedance bandwidth and lower cross-polarization radiation as compared with those of the MGAA. The MCAA is designed not as a travelling-wave but a standing-wave antenna. As a result, the match load and the reflection-cancelling structure can be avoided, which is important, especially in the millimeter-wave frequencies. Finally, an emphasis is given to 45° linearly-polarized MCAA because the radiation with the orthogonal polarization from cars coming from the opposite direction does not affect the radar operation.


title: Why have child maltreatment and child victimization declined

abstract: Various forms of child maltreatment and child victimization declined as much as 40‐70% from 1993 until 2004, including sexual abuse, physical abuse, sexual assault, homicide, aggravated assault, robbery, and larceny. Other child welfare indicators also improved during the same period, including teen pregnancy, teen suicide, and children living in poverty. This article reviews a wide variety of possible explanations for these changes: demography, fertility and abortion legalization, economic prosperity, increased incarceration of offenders, increased agents of social intervention, changing social norms and practices, the dissipation of the social changes from the 1960s, and psychiatric pharmacology. Multiple factors probably contributed. In particular, economic prosperity, increasing agents of social intervention, and psychiatric pharmacology have advantages over some of the other explanations in accounting for the breadth and timing of the improvements. The worrisome stories about crimes against children that regularly fill the media have unfortunately obscured some more positive news from the statistical reports on these same offenses. Child victimization of various types has been declining since the early 1990s, in some cases declining dramatically. Facts about the Decline


title: CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

abstract: Since the resurgence of CNNs the robotic vision community has developed a range of algorithms that perform classification, semantic segmentation and structure prediction (depths, normals, surface curvature) using neural networks. While some of these models achieve state-of-the art results and super human level performance, deploying these models in a time critical robotic environment remains an ongoing challenge. Real-time frameworks are of paramount importance to build a robotic society where humans and robots integrate seamlessly. To this end, we present a novel real-time structure prediction framework that predicts depth at 30 frames per second on an NVIDIA-TX2. At the time of writing, this is the first piece of work to showcase such a capability on a mobile platform. We also demonstrate with extensive experiments that neural networks with very large model capacities can be leveraged in order to train accurate condensed model architectures in a “from teacher to student” style knowledge transfer.


title: Noncontact Wearable Wireless ECG Systems for Long-Term Monitoring

abstract: Electrocardiography (ECG) is the most common and extensively used vital sign monitoring method in modern healthcare systems. Different designs of ambulatory ECG systems were developed as alternatives to the commonly used 12-lead clinical ECG systems. These designs primarily focus on portability and user convenience, while maintaining signal integrity and lowering power consumption. Here, a wireless ECG monitoring system is developed using flexible and dry capacitive electrodes for long-term monitoring of cardiovascular health. Our capacitive-coupled dry electrodes can measure ECG signals over a textile-based interface material between the skin and electrodes. The electrodes are connected to a data acquisition system that receives the raw ECG signals from the electrodes and transmits the data using Bluetooth to a computer. A software application was developed to process, store, and display the ECG signal in real time. ECG measurements were obtained over different types of textile materials and in the presence of body movements. Our experimental results show that the performance of our ECG system is comparable to other reported ECG monitoring systems. In addition, to put this research into perspective, recent ambulatory ECG monitoring systems, ECG systems-on-chip, commercial ECG monitoring systems, and different state-of-the-art ECG systems are reviewed, compared, and critically discussed.


MRI characteristics of systemic mastocytosis of the lumbosacral spine


title: Current Developments of RF Energy Harvesting System for Wireless Sensor Networks

abstract: Energy harvesting or energy scavenging is basically a conversion process of the ambient energy into the electrical energy. The ambient energy exists around us in many different forms including thermal, chemical, electrical and radio frequency (RF). This technique significantly reduces the costs of replacing batteries periodically. Hence, energy harvesting offers various environmental friendly alternative energy sources, which include the vibration, electromagnetic wave, wind energy and solar power. This study will focus on RF energy harvesting that involves the generating of a small amount of the electrical power to drive circuits in wireless communication electronics devices. Recently, wireless ::: sensor network (WSN) has been a crucial part of our daily life. The importance of WSN can be described by the use of sensors in many devices for home security including light sensors, room thermostat and alarm systems. This paper presents an overview and the progress achieved in RF ::: energy harvesting, which involves the integration of antenna with rectifying circuit. Different combinations of antenna and rectifier topologies yield diverse results. Therefore, this study is expected to give an indication on the appropriate techniques to develop an efficient RF energy harvesting system.


title: Centroidal Voronoi tessellations: Applications and algorithms

abstract: A centroidal Voronoi tessellation is a Voronoi tessellation whose generating points are the centroids (centers of mass) of the corresponding Voronoi regions. We give some applications of such tessellations to problems in image compression, quadrature, finite difference methods, distribution of resources, cellular biology, statistics, and the territorial behavior of animals. We discuss methods for computing these tessellations, provide some analyses concerning both the tessellations and the methods for their determination, and, finally, present the results of some numerical experiments.


title: A Practical Guide to Randomized Matrix Computations with MATLAB Implementations

abstract: Matrix operations such as matrix inversion, eigenvalue decomposition, singular value decomposition are ubiquitous in real-world applications. Unfortunately, many of these matrix operations so time and memory expensive that they are prohibitive when the scale of data is large. In real-world applications, since the data themselves are noisy, machine-precision matrix operations are not necessary at all, and one can sacrifice a reasonable amount of accuracy for computational efficiency. In recent years, a bunch of randomized algorithms have been devised to make matrix computations more scalable. Mahoney (2011) and Woodruff (2014) have written excellent but very technical reviews of the randomized algorithms. Differently, the focus of this manuscript is on intuition, algorithm derivation, and implementation. This manuscript should be accessible to people with knowledge in elementary matrix algebra but unfamiliar with randomized matrix computations. The algorithms introduced in this manuscript are all summarized in a user-friendly way, and they can be implemented in lines of MATLAB code. The readers can easily follow the implementations even if they do not understand the maths and algorithms.


title: Correlation between the human fecal microbiota and depression.

abstract: BACKGROUND ::: Depression is a chronic syndrome with a pathogenesis linked to various genetic, biological, and environmental factors. Several links between gut microbiota and depression have been established in animal models. In humans, however, few correlations have yet been demonstrated. The aim of our work was therefore to identify potential correlations between human fecal microbiota (as a proxy for gut microbiota) and depression. ::: ::: ::: METHODS ::: We analyzed fecal samples from 55 people, 37 patients, and 18 non-depressed controls. Our analyses were based on data generated by Illumina deep sequencing of 16S rRNA gene amplicons. ::: ::: ::: KEY RESULTS ::: We found several correlations between depression and fecal microbiota. The correlations, however, showed opposite directions even for closely related Operational Taxonomic Units (OTU's), but were still associated with certain higher order phylogroups. The order Bacteroidales showed an overrepresentation (p = 0.05), while the family Lachnospiraceae showed an underrepresentation (p = 0.02) of OTU's associated with depression. At low taxonomic levels, there was one clade consisting of five OTU's within the genus Oscillibacter, and one clade within Alistipes (consisting of four OTU's) that showed a significant association with depression (p = 0.03 and 0.01, respectively). ::: ::: ::: CONCLUSIONS & INFERENCES ::: The Oscillibacter type strain has valeric acid as its main metabolic end product, a homolog of neurotransmitter GABA, while Alistipes has previously been shown to be associated with induced stress in mice. In conclusion, the taxonomic correlations detected here may therefore correspond to mechanistic models.


title: Evolving large-scale neural networks for vision-based TORCS

abstract: The TORCS racing simulator has become a standard testbed used in many recent reinforcement learning competitions, where an agent must learn to drive a car around a track using a small set of task-specific features. In this paper, large, recurrent neural networks (with over 1 million weights) are evolved to solve a much more challenging version of the task that instead uses only a stream of images from the driver’s perspective as input. Evolving such large nets is made possible by representing them in the frequency domain as a set of coefficients that are transformed into weight matrices via an inverse Fourier-type transform. To our knowledge this is the first attempt to tackle TORCS using vision, and successfully evolve a neural network controllers of this size.


title: Assessing the Validity of Online Drug Forums as a Source for Estimating Demographic and Temporal Trends in Drug Use

abstract: OBJECTIVES ::: Addiction researchers have begun monitoring online forums to uncover self-reported details about use and effects of emerging drugs. The use of such online data sources has not been validated against data from large epidemiological surveys. This study aimed to characterize and compare the demographic and temporal trends associated with drug use as reported in online forums and in a large epidemiological survey. ::: ::: ::: METHODS ::: Data were collected from the Web site, drugs-forum.com, from January 2007 through August 2012 (143,416 messages posted by 8087 members) and from the US National Survey on Drug Use and Health (NSDUH) from 2007 to 2012. Measures of forum participation levels were compared with and validated against 2 measures from the NSDUH survey data: percentage of people using the drug in past 30 days and percentage using the drug more than 100 times in the past year. ::: ::: ::: RESULTS ::: For established drugs (eg, cannabis), significant correlations were found across demographic groups between drugs-forum.com and the NSDUH survey data, whereas weaker, nonsignificant correlations were found with temporal trends. Emerging drugs (eg, Salvia divinorum) were strongly associated with male users in the forum, in agreement with survey-derived data, and had temporal patterns that increased in synchrony with poison control reports. ::: ::: ::: CONCLUSIONS ::: These results offer the first assessment of online drug forums as a valid source for estimating demographic and temporal trends in drug use. The analyses suggest that online forums are a reliable source for estimation of demographic associations and early identification of emerging drugs, but a less reliable source for measurement of long-term temporal trends.


title: Importance filtering for image retargeting

abstract: Content-aware image retargeting has attracted a lot of interests recently. The key and most challenging issue for this task is how to balance the tradeoff between preserving the important contents and minimizing the visual distortions on the consistency of the image structure. In this paper we present a novel filtering-based technique to tackle this issue, called ”importance filtering”. Specifically, we first filter the image saliency guided by the image itself to achieve a structure-consistent importance map. We then use the pixel importance as the key constraint to compute the gradient map of pixel shifts from the original resolution to the target. Finally, we integrate the shift gradient across the image using a weighted filter to construct a smooth shift map and render the target image. The weight is again controlled by the pixel importance. The two filtering processes enforce to maintain the structural consistency and yet preserve the important contents in the target image. Furthermore, the simple nature of filter operations allows highly efficient implementation for real-time applications and easy extension to video retargeting, as the structural constraints from the original image naturally convey the temporal coherence between frames. The effectiveness and efficiency of our importance filtering algorithm are confirmed in extensive experiments.


title: Designing Anthropomorphic Robot Hand With Active Dual-Mode Twisted String Actuation Mechanism and Tiny Tension Sensors

abstract: In this letter, using the active dual-mode twisted string actuation (TSA) mechanism and tiny tension sensors on the tendon strings, an anthropomorphic robot hand is newly designed in a compact manner. Thanks to the active dual-mode TSA mechanism, which is a miniaturized transmission, the proposed robot hand dose has a wide range of operation in terms of the grasping force and speed. It experimentally produces maximally the fingertip force 31.3 N and minimally the closing time of 0.5 s in average. Also, tiny tension sensor with the dimension of 4.7 (width) × 4.0 (height) × 10.75 (length) mm is newly presented and embedded at the fingertips in order to measure the tension on the tendon strings, which would allow the grasping force control. The kinetic and kinematic analyses are performed and the performance is verified by experiments.


title: Effect of extracorporeal shock wave therapy on scar pain in burn patients: A prospective, randomized, single-blind, placebo-controlled study

abstract: BACKGROUND ::: Extracorporeal shock wave therapy (ESWT) has been used to reduce pain in patients with various musculoskeletal diseases and wounds. We investigated the effect of ESWT on scar pain after complete wound epithelialization in burn patients. ::: ::: ::: METHODS ::: A prospective, single-blind, placebo-controlled study was conducted from February 2014 to 2015. Forty patients with burn scar pain despite standard therapy (medication, physical therapy, and burn rehabilitation massage therapy) were randomized into ESWT or control (sham ESWT) groups. ESWT was administered at 100 impulses/cm (0.05-0.15 mJ/mm) once per week for 3 weeks. The treatment effects were assessed using the numerical rating scale (NRS), pain threshold, Nirschl pain phase system, and Roles and Maudsley scores. ::: ::: ::: RESULTS ::: The characteristics of patients between the 2 study groups were balanced (P >0.05) for age, sex, and total burn surface area (%). In both groups, the NRS, pain threshold (Ib/cm), and Nirschl pain phase system values significantly improved (P <0.05) after 3 sessions of ESWT or sham therapy, and there were significant differences between the 2 groups in terms of these 3 variables (P <0.001, P <0.001, P = 0.013, respectively). The Roles and Maudsley scores significantly improved; among 20 patients, 17 reported a score of poor (85%) and 3 reported fair (15%) before ESWT, whereas 3 reported poor (15%), 8 reported fair (40%), 5 reported good (25%), and 4 reported excellent (20%) after ESWT (P = 0.004). The scores did not improve in the control group (P = 0.128). ::: ::: ::: CONCLUSION ::: ESWT significantly reduced scar pain in burn patients after wound recovery.


title: Remembering the past and imagining the future: a neural model of spatial memory and imagery

abstract: The neural mechanisms underlying spatial cognition are modelled, integrating neuronal, systems and behavioural data, and addressing the relationships between long-term memory, short-term memory and imagery, and between egocentric and allocentric and visual and idiothetic representations. Long-term spatial memory is modeled as attractor dynamics within medial-temporal allocentric representations, and short-term memory as egocentric parietal representations driven by perception, retrieval and imagery, and modulated by directed attention. Both encoding and retrieval/ imagery require translation between egocentric and allocentric representations, mediated by posterior parietal and retrosplenial areas and utilizing head direction representations in Papez’s circuit. Thus hippocampus effectively indexes information by real or imagined location, while Papez’s circuit translates to imagery or from perception according to the direction of view. Modulation of this translation by motor efference allows “spatial updating” of representations, while prefrontal simulated motor efference allows mental exploration. The alternating temporo-parietal flows of information are organized by the theta rhythm. Simulations demonstrate the retrieval and updating of familiar spatial scenes, hemispatial neglect in memory, and the effects on hippocampal place cell firing of lesioned head direction representations and of conflicting visual and ideothetic inputs.


title: Continuous emotion detection using EEG signals and facial expressions

abstract: Emotions play an important role in how we select and consume multimedia. Recent advances on affect detection are focused on detecting emotions continuously. In this paper, for the first time, we continuously detect valence from electroencephalogram (EEG) signals and facial expressions in response to videos. Multiple annotators provided valence levels continuously by watching the frontal facial videos of participants who watched short emotional videos. Power spectral features from EEG signals as well as facial fiducial points are used as features to detect valence levels for each frame continuously. We study the correlation between features from EEG and facial expressions with continuous valence. We have also verified our model's performance for the emotional highlight detection using emotion recognition from EEG signals. Finally the results of multimodal fusion between facial expression and EEG signals are presented. Having such models we will be able to detect spontaneous and subtle affective responses over time and use them for video highlight detection.


title: Is playfulness a benefit to work? Empirical evidence of professionals in Taiwan

abstract: This paper discusses the constructs of playfulness and its relationship to work. About 1493 Taiwan professionals in four fields art and media, education, hitech and agriculture and manufacturing participated and responded to a self-reported questionnaire survey in this study. Results indicate that playfulness is positively related to both affective (Job Satisfaction (JS))and behavioural (Innovative Behaviour (IB) and Job Performance (JP)) work outcomes. Moreover, playfulness trait and playfulness state have differential relationships with outcome variables. Implications of these findings for personnel selection and motivation are discussed.


title: The Feeling of What Happens: Body and Emotion in the Making of Consciousness

abstract: One of the world's leading experts on the neurophysiology of emotions, Professor Damasio shows how our consciousness, our sense of being, arose out of development of emotion. At its core, human consciousness is consciousness of the feeling, experiencing self, the 'very thought of' oneself. Brilliantly wide-ranging in his scope, Damasio illustrates his thesis with fascinating and illuminating neurological case studies that are both stimulating and provocative.


title: A Comprehensive Survey on Pose-Invariant Face Recognition

abstract: The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, pose-invariant face recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this paper, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, i.e., pose-robust feature extraction approaches, multi-view subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.


title: Hierarchical Hyperdimensional Computing for Energy Efficient Classification

abstract: Brain-inspired Hyperdimensional (HD) computing emulates cognition tasks by computing with hypervectors rather than traditional numerical values. In HD, an encoder maps inputs to high dimensional vectors (hypervectors) and combines them to generate a model for each existing class. During inference, HD performs the task of reasoning by looking for similarities of the input hypervector and each pre-stored class hypervector However, there is not a unique encoding in HD which can perfectly map inputs to hypervectors. This results in low HD classification accuracy over complex tasks such as speech recognition. In this paper we propose MHD, a multi-encoder hierarchical classifier, which enables HD to take full advantages of multiple encoders without increasing the cost of classification. MHD consists of two HD stages: a main stage and a decider stage. The main stage makes use of multiple classifiers with different encoders to classify a wide range of input data. Each classifier in the main stage can trade between efficiency and accuracy by dynamically varying the hypervectors' dimensions. The decider stage, located before the main stage, learns the difficulty of the input data and selects an encoder within the main stage that will provide the maximum accuracy, while also maximizing the efficiency of the classification task. We test the accuracy/efficiency of the proposed MHD on speech recognition application. Our evaluation shows that MHD can provide a 6.6× improvement in energy efficiency and a 6.3× speedup, as compared to baseline single level HD.


title: Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation

abstract: Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose a new variant of LSTM, named STLSTM, which implements time gates and distance gates into LSTM to capture the spatio-temporal relation between successive check-ins. Specifically, one-time gate and one distance gate are designed to control short-term interest update, and another time gate and distance gate are designed to control long-term interest update. Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. Our experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.


Salsify: Low-Latency Network Video through Tighter Integration between a Video Codec and a Transport Protocol


title: Efficient & Effective Selective Query Rewriting with Efficiency Predictions

abstract: To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine.


title: Calibrated alar base excision: A 20-year experience

abstract: Conflicting guidelines for excisions about the alar base led us to develop calibrated alar base excision, a modification of Weir's approach. In approximately 20% of 1500 rhinoplasties this technique was utilized as a final step. Of these patients, 95% had lateral wall excess ("tall nostrils"), 2% had nostril floor excess ("wide nostrils"), 2% had a combination of these ("tall-wide nostrils"), and 1% had thick nostril rims. Lateral wall excess length is corrected by a truncated crescent excision of the lateral wall above the alar crease. Nasal floor excess is improved by an excision of the nasal sill. Combination noses (e.g., tall-wide) are approached with a combination alar base excision. Finally, noses with thick rims are improved with diamond excision. Closure of the excision is accomplished with fine simple external sutures. Electrocautery is unnecessary and deep sutures are utilized only in wide noses. Few complications were noted. Benefits of this approach include straightforward surgical guidelines, a natural-appearing correction, avoidance of notching or obvious scarring, and it is quick and simple.


title: Common mode DC bus filter for Active Front-End converter

abstract: Active Front-End (AFE) converter operation produces electrically noisy DC bus on common mode basis. This results in higher ground current as compared to three phase diode bridge rectifier. Filter topologies for DC bus have to deal problems with switching frequency and harmonic currents. The proposed filter approach reduces common mode voltage and circulates third harmonic current within the system, resulting in minimal ground current injection. The filtering technique, its constrains and design to attenuate common mode voltage and eliminate lower order harmonics injection to ground is discussed. The experimental results for operation of the converter with both SPWM and CSVPWM are presented.


Convergent and Divergent Operations in Cognitive Search


title: Mobile information access: A study of emerging search behavior on the mobile Internet

abstract: It is likely that mobile phones will soon come to rival more traditional devices as the primary platform for information access. Consequently, it is important to understand the emerging information access behavior of mobile Internet (MI) users especially in relation to their use of mobile handsets for information browsing and query-based search. In this article, we describe the results of a recent analysis of the MI habits of more than 600,000 European MI users, with a particular emphasis on the emerging interest in mobile search. We consider a range of factors including whether there are key differences between browsing and search behavior on the MI compared to the Web. We highlight how browsing continues to dominate mobile information access, but go on to show how search is becoming an increasingly popular information access alternative especially in relation to certain types of mobile handsets and information needs. Moreover, we show that sessions involving search tend to be longer and more data-rich than those that do not involve search. We also look at the type of queries used during mobile search and the way that these queries tend to be modified during the course of a mobile search session. Finally we examine the overlap among mobile search queries and the different topics mobile users are interested in.


A Type-Theoretic Interpretation of Standard ML


title: Impact of waiting time on tourists satisfaction in a theme park: An empirical investigation

abstract: As service organizations have realized that service is fundamental to establish their competitive advantage, waiting time management has been a subject of much service research. This study aims to explore how waiting time impact on tourist satisfaction in theme parks. The findings from a survey of 102 tourists from the theme park in Shenzhen City in China confirm that perceived waiting time, waiting information and waiting environment are significant determinants of tourist satisfaction. Providing waiting information and improving waiting environment are effective ways to enhance tourists' service satisfaction.


Where Is the Mind? Knowing and Knowledge in Cobb's Constructivist and Sociocultural Perspectives


title: User Interface Menu Design Performance and User Preferences: A Review and Ways Forward

abstract: This review paper is about menus on web pages and applications and their positioning on the user screen. The paper aims to provide the reader with a succinct summary of the major research in this area along with an easy to read tabulation of the most important studies. Furthermore, the paper concludes with some suggestions for future research regarding how menus and their positioning on the screen could be improved. The two principal suggestions concern trying to use more qualitative methods for investigating the issues and to develop in the future more universally designed menus.


title: Achieving and Managing Availability SLAs with ITIL Driven Processes, DevOps, and Workflow Tools

abstract: System and application availability continues to be a fundamental characteristic of IT services. In recent years the IT Operations team at Wolters Kluwer CT Corporation has placed special focus on this area. Using a combination of goals, metrics, processes, organizational models, communication methods, corrective maintenance, root cause analysis, preventative engineering, automated alerting, and workflow automation significant progress has been made in meeting availability SLAs or Service Level Agreements. This paper presents the background of this work, approach, details of its implementation, and results. A special focus is provided on the use of a classical ITIL view as operationalized in an Agile and DevOps environment. ::: Keywords: System Availability, Software Reliability, ITIL, Workflow Automation, Process Engineering, Production Support, Customer Support, Product Support, Change Management, Release Management, Incident Management, Problem Management, Organizational Design, Scrum, Agile, DevOps, Service Level Agreements, Software Measurement, Microsoft SharePoint.


title: Models and languages for parallel computation

abstract: We survey parallel programming models and languages using six criteria to assess their suitability for realistic portable parallel programming. We argue that an ideal model should by easy to program, should have a software development methodology, should be architecture-independent, should be easy to understand, should guarantee performance, and should provide accurate information about the cost of programs. These criteria reflect our belief that developments in parallelism must be driven by a parallel software industry based on portability and efficiency. We consider programming models in six categories, depending on the level of abstraction they provide. Those that are very abstract conceal even the presence of parallelism at the software level. Such models make software easy to build and port, but efficient and predictable performance is usually hard to achieve. At the other end of the spectrum, low-level models make all of the messy issues of parallel programming explicit (how many threads, how to place them, how to express communication, and how to schedule communication), so that software is hard to build and not very portable, but is usually efficient. Most recent models are near the center of this spectrum, exploring the best tradeoffs between expressiveness and performance. A few models have achieved both abstractness and efficiency. Both kinds of models raise the possibility of parallelism as part of the mainstream of computing.


title: TWIN FETUS IN FETU WITH IMMATURE TERATOMA: A CASE REPORT AND REVIEW OF THE LITERATURE

abstract: Fetus in fetu is an extremely rare condition in which a fetus or fetus-like structure with a vertebral axis is seen in the body of its twin. This paper presents a case of fetus in fetu in a twoday-old female newborn who was referred for an abdominal mass, biliary vomiting, and feeding intolerance. After plain abdominal X-ray and ultrasonography, the patient underwent abdominal surgery with the primary diagnosis of teratoma or fetus in fetu. We found a retroperitoneal mass that consisted of double fetus in fetu and a separate undetermined mass. The pathologic examination confirmed double fetus in fetu and revealed a separate immature teratoma. She was discharged from the hospital after seven days in a healthy and normal condition.The level of serum alpha-fetoprotein was normal after three months of follow-up.


title: Counterfunctional things: exploring possibilities in designing digital limitations

abstract: This paper presents a set of design studies and discussions investigating new possibilities in designing digital limitations. Focusing on digital photography as a medium, we present design prototypes and experiments including Ultra-Low Resolution Displays, Inaccessible Cameras, and a set of point-and-shoot digital camera variants. Our design work is based on the concept of a counterfunctional thing-a thing that figuratively counters some of its own functionality. We present the concept of counterfunctionality as a way of approaching the design of interactive technology. In conclusion we connect our work with critical discourses surrounding technology and the value of designing limitations.


title: Improving Product Classification Using Images

abstract: Product classification in Commerce search (\eg{} Google Product Search, Bing Shopping) involves associating categories to offers of products from a large number of merchants. The categorized offers are used in many tasks including product taxonomy browsing and matching merchant offers to products in the catalog. Hence, learning a product classifier with high precision and recall is of fundamental importance in order to provide high quality shopping experience. A product offer typically consists of a short textual description and an image depicting the product. Traditional approaches to this classification task is to learn a classifier using only the textual descriptions of the products. In this paper, we show that the use of images, a weaker signal in our setting, in conjunction with the textual descriptions, a more discriminative signal, can considerably improve the precision of the classification task, irrespective of the type of classifier being used. We present a novel classification approach, \Cross Adapt{} (\CrossAdaptAcro{}), that is cognizant of the disparity in the discriminative power of different types of signals and hence makes use of the confusion matrix of dominant signal (text in our setting) to prudently leverage the weaker signal (image), for an improved performance. Our evaluation performed on data from a major Commerce search engine's catalog shows a 12\% (absolute) improvement in precision at 100\% coverage, and a 16\% (absolute) improvement in recall at 90\% precision compared to classifiers that only use textual description of products. In addition, \CrossAdaptAcro{} also provides a more accurate classifier based only on the dominant signal (text) that can be used in situations in which only the dominant signal is available during application time.


title: Narcissistic personality disorder. Descriptive features and differential diagnosis.

abstract: This article brings the scattered literature on narcissistic personality disorder together and synthesizes its various descriptive aspects into a multifaceted profile of the condition. The disorder is viewed as consisting of characteristic “overt” and “covert” findings in six areas of psychosocial functioning: (1) self-concept; (2) interpersonal relations; (3) social adaptation; (4) ethics, standards and ideals; (5) love and sexuality; and (6) cognitive style. Overtly, an individual with narcissistic personality disorder is grandiose, exploitative, moralistic, often successful, seductive, charming, and impressively knowledgeable and articulate. Covertly, however, he is doubt ridden, deeply envious of others, chronically bored, corruptible, unable to love, forgetful of details, and inattentive to objective aspects of events. Such conceptualization is superior to ordinary checklist methods of diagnosis, as it combines descriptive and psychodynamic observations and correlates the behavioral and psychostructural (hence developmental) aspects of the narcissistic character pathology. This profile also helps in more meaningful differential diagnosis of narcissistic from related personality disorders.