title: Grandiose and Vulnerable Narcissism: A Nomological Network Analysis

abstract: ABSTRACT Evidence has accrued to suggest that there are 2 distinct dimensions of narcissism, which are often labeled grandiose and vulnerable narcissism. Although individuals high on either of these dimensions interact with others in an antagonistic manner, they differ on other central constructs (e.g., Neuroticism, Extraversion). In the current study, we conducted an exploratory factor analysis of 3 prominent self-report measures of narcissism (N=858) to examine the convergent and discriminant validity of the resultant factors. A 2-factor structure was found, which supported the notion that these scales include content consistent with 2 relatively distinct constructs: grandiose and vulnerable narcissism. We then compared the similarity of the nomological networks of these dimensions in relation to indices of personality, interpersonal behavior, and psychopathology in a sample of undergraduates (n=238). Overall, the nomological networks of vulnerable and grandiose narcissism were unrelated. The current results support the need for a more explicit parsing of the narcissism construct at the level of conceptualization and assessment.


title: BASED ON A SILICON NITRIDE MEMBRANE INCOR- PORATING FABRICATION IMPERFECTIONS AND A LOW-INDEX MATERIAL LAYER

abstract: We detail the optimization of a nanobeam design and show how the fabrication imperfections can afiect the optical performance of the device. Then we propose the design of a novel conflguration of a photonic crystal nanobeam cavity consisting of a membrane structure obtained by sandwiching a layer of Flowable Oxide (FOx) between two layers of Silicon-Nitride (SiN). Finally, we demonstrate that the presence of a low refractive index layer does not impair the performance of the nanobeam cavity that still exhibits a Q factor and mode volume V of the order of 10 5 and 0.02 (‚=n) 3 , respectively.


title: Functional Anatomy of Pursuit Eye Movements in Humans as Revealed by fMRI

abstract: We have investigated the functional anatomy of pursuit eye movements in humans with functional magnetic imaging. The performance of pursuit eye movements induced activations in the cortical eye fields also activated during the execution of visually guided saccadic eye movements, namely in the precentral cortex [frontal eye field (FEF)], the medial superior frontal cortex (supplementary eye field), the intraparietal cortex (parietal eye field), and the precuneus, and at the junction of occipital and temporal cortex (MT/MST) cortex. Pursuit-related areas could be distinguished from saccade-related areas both in terms of spatial extent and location. Pursuit-related areas were smaller than their saccade-related counterparts, three of eight significantly so. The pursuit-related FEF was usually inferior to saccade-related FEF. Other pursuit-related areas were consistently posterior to their saccade-related counterparts. The current findings provide the first functional imaging evidence for a distinction between two parallel cortical systems that subserve pursuit and saccadic eye movements in humans.


Using Topic Models to Investigate Depression on Social Media


title: Predicate Encryption Supporting Disjunctions, Polynomial Equations, and Inner Products

abstract: Predicate encryption is a new paradigm for public-key encryption that generalizes identity-based encryption and more. In predicate encryption, secret keys correspond to predicates and ciphertexts are associated with attributes; the secret key SK f corresponding to a predicate f can be used to decrypt a ciphertext associated with attribute I if and only if f(I)=1. Constructions of such schemes are currently known only for certain classes of predicates. ::: ::: We construct a scheme for predicates corresponding to the evaluation of inner products over ? N (for some large integer N). This, in turn, enables constructions in which predicates correspond to the evaluation of disjunctions, polynomials, CNF/DNF formulas, thresholds, and more. Besides serving as a significant step forward in the theory of predicate encryption, our results lead to a number of applications that are interesting in their own right.


title: No mobile, no life: Self-perception and text-message dependency among Japanese high school students

abstract: A survey was conducted to investigate how self-perception of text-message dependency leads to psychological/behavioral symptoms in relation to personality factors. Japanese high school students completed a self-report questionnaire measuring frequency of text-messages, self-perception of text-message dependency, psychological/behavioral symptoms, extroversion and neuroticism. Self-perception of text-message dependency was composed of three factors: perception of excessive use, emotional reaction, and relationship maintenance. Although message frequency was significantly related to psychological/behavioral symptoms, this effect was qualified by self-perception and personality factors. In particular, self-perception of text-message dependency strongly affected psychological/behavioral symptoms. Importance of distinction between extroverted and neurotic text-message dependency through the process of self-perception of maladaptive behavior is discussed.


title: Accounting benefits and satisfaction in an ERP environment

abstract: Over the past decade, organizations all over the world have adopted enterprise resource planning (ERP) systems. There has been little research at a global scale regarding the accounting benefits of adopting enterprise systems. Moreover, there are virtually no studies that examine in detail the relation between accounting benefits and ERP user satisfaction. Sutton (2006) addressed the need for empirical research on the impact of ERP in terms of accounting advantages and disadvantages. The principal aim of our study was to investigate the accounting benefits that the adoption of an ERP system by companies may entail in relation to ERP user satisfaction. This study explored the impact that the ERP system has had on accounting information and practice. This study also examined whether or not there are differences between accountants and IT professionals concerning how each group assesses ERP accounting benefits and ERP user satisfaction. The participants of this study comprised 175 accountants and 96 IT professionals from 193 companies in Greece. The empirical evidence confirms a number of accounting benefits derived from ERP systems particularly for accounting process. No statistically significant differences were found between the perceptions of accountants and IT professionals concerning ERP accounting benefits. Furthermore, this research identifies factors related to accounting benefits and ERP cost which affect the level of ERP user satisfaction. The implications of these results for practice and research are explored. The findings of this study will be of value to any companies considering the inclusion of their accounting techniques and operations in an ERP system. Furthermore, the results of this study will provide stimulus for consequent research in the field in order to further examine and account for the accounting benefits that can occur from ERP system implementation as well as the effect of those benefits on ERP user satisfaction.


title: A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-Volatile On-Chip Caches

abstract: Recent trends of CMOS scaling and increasing number of on-chip cores have led to a large increase in the size of on-chip caches. Since SRAM has low density and consumes large amount of leakage power, its use in designing on-chip caches has become more challenging. To address this issue, researchers are exploring the use of several emerging memory technologies, such as embedded DRAM, spin transfer torque RAM, resistive RAM, phase change RAM and domain wall memory. In this paper, we survey the architectural approaches proposed for designing memory systems and, specifically, caches with these emerging memorytechnologies. To highlight their similarities and differences, we present a classification of these technologies and architectural approaches based on their key characteristics. We also briefly summarize the challenges in using these technologies for architecting caches. We believe that this survey will help the readers gain insights into the emerging memory device technologies, and their potential use in designing future computing systems.


title: Non-monopolizable caches: Low-complexity mitigation of cache side channel attacks

abstract: We propose a flexibly-partitioned cache design that either drastically weakens or completely eliminates cache-based side channel attacks. The proposed Non-Monopolizable (NoMo) cache dynamically reserves cache lines for active threads and prevents other co-executing threads from evicting reserved lines. Unreserved lines remain available for dynamic sharing among threads. NoMo requires only simple modifications to the cache replacement logic, making it straightforward to adopt. It requires no software support enabling it to automatically protect pre-existing binaries. NoMo results in performance degradation of about 1% on average. We demonstrate that NoMo can provide strong security guarantees for the AES and Blowfish encryption algorithms.


title: Angle of Arrival Localization for Wireless Sensor Networks

abstract: Awareness of the physical location for each node is required by many wireless sensor network applications. The discovery of the position can be realized utilizing range measurements including received signal strength, time of arrival, time difference of arrival and angle of arrival. In this paper, we focus on localization techniques based on angle of arrival information between neighbor nodes. We propose a new localization and orientation scheme that considers beacon information multiple hops away. The scheme is derived under the assumption of noisy angle measurements. We show that the proposed method achieves very good accuracy and precision despite inaccurate angle measurements and a small number of beacons


title: Improvement of Dermatochalasis and Periorbital Rhytides With a High-Energy Pulsed CO2 Laser: A Retrospective Study

abstract: BACKGROUND. Upper eyelid dermatochalasis is typically treated with excisional blepharoplasty. The role of the CO2 laser previously had been confined to that of a vaporizing, incisional, or hemostatic tool. Over the past several years, however, ablative CO2 laser skin resurfacing has been popularized as an adjunctive treatment to blepharoplasty to minimize periorbital rhytides through its vaporizing as well as skin-tightening action. OBJECTIVE. To evaluate the safety and efficacy of a high-energy pulsed CO2 laser as a stand-alone treatment for dermatochalasis and periorbital rhytides. METHODS. Sixty-seven patients (skin phototypes I‐IV) with mild-to-severe upper eyelid dermatochalasis and periorbital rhytides received periocular CO2 laser skin treatment. Global assessment scores of dermatochalasis and rhytides were determined by a side-by-side comparison of periocular photographs preoperatively and 1, 3, and 6 months postoperatively. In addition, caliper measurements of upper eyelids before and 1, 3, and 6 months after treatment were obtained. RESULTS. Both dermatochalasis and periorbital rhytides were significantly improved after periocular CO2 laser skin resurfacing. Patients with more severe dermatochalasis and rhytides showed greater improvement after CO2 laser treatment than did those with mild or moderate involvement. Side effects were limited to erythema and transient hyperpigmentation. No scarring, hypopigmentation, or ectropion were observed. CONCLUSIONS. Periocular skin resurfacing with a CO2 laser can safely and effectively improve upper eyelid dermatochalasis and periorbital rhytides.


title: The contribution of sleep to hippocampus-dependent memory consolidation

abstract: There is now compelling evidence that sleep promotes the long-term consolidation of declarative and procedural memories. Behavioral studies suggest that sleep preferentially consolidates explicit aspects of these memories, which during encoding are possibly associated with activation in prefrontal–hippocampal circuitry. Hippocampus-dependent declarative memory benefits particularly from slow-wave sleep (SWS), whereas rapid-eye-movement (REM) sleep seems to benefit procedural aspects of memory. Consolidation of hippocampus-dependent memories relies on a dialog between the neocortex and hippocampus. Crucial features of this dialog are the neuronal reactivation of new memories in the hippocampus during SWS, which stimulates the redistribution of memory representations to neocortical networks; and the neocortical slow (


title: Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations

abstract: In this paper, we consider the problem of planning a path for a robot to monitor a known set of features of interest in an environment. We represent the environment as a graph with vertex weights and edge lengths. The vertices represent regions of interest, edge lengths give travel times between regions, and the vertex weights give the importance of each region. As the robot repeatedly performs a closed walk on the graph, we define the weighted latency of a vertex to be the maximum time between visits to that vertex, weighted by the importance (vertex weight) of that vertex. Our goal is to find a closed walk that minimizes the maximum weighted latency of any vertex. We show that there does not exist a polynomial time algorithm for the problem. We then provide two approximation algorithms; an $O(\log n)$-approximation algorithm and an $O(\log \rho_G)$-approximation algorithm, where $\rho_G$ is the ratio between the maximum and minimum vertex weights. We provide simulation results which demonstrate that our algorithms can be applied to problems consisting of thousands of vertices, and a case study for patrolling a city for crime.


title: Unified Face Analysis by Iterative Multi-output Random Forests

abstract: In this paper, we present a unified method for joint face image analysis, i.e., simultaneously estimating head pose, facial expression and landmark positions in real-world face images. To achieve this goal, we propose a novel iterative Multi-Output Random Forests (iMORF) algorithm, which explicitly models the relations among multiple tasks and iteratively exploits such relations to boost the performance of all tasks. Specifically, a hierarchical face analysis forest is learned to perform classification of pose and expression at the top level, while performing landmark positions regression at the bottom level. On one hand, the estimated pose and expression provide strong shape prior to constrain the variation of landmark positions. On the other hand, more discriminative shape-related features could be extracted from the estimated landmark positions to further improve the predictions of pose and expression. This relatedness of face analysis tasks is iteratively exploited through several cascaded hierarchical face analysis forests until convergence. Experiments conducted on publicly available real-world face datasets demonstrate that the performance of all individual tasks are significantly improved by the proposed iMORF algorithm. In addition, our method outperforms state-of-the-arts for all three face analysis tasks.


title: Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues

abstract: The Internet of Things (IoT) introduces a vision of a future Internet where users, computing systems, and everyday objects possessing sensing and actuating capabilities cooperate with unprecedented convenience and economical benefits. As with the current Internet architecture, IP-based communication protocols will play a key role in enabling the ubiquitous connectivity of devices in the context of IoT applications. Such communication technologies are being developed in line with the constraints of the sensing platforms likely to be employed by IoT applications, forming a communications stack able to provide the required power-efficiency, reliability, and Internet connectivity. As security will be a fundamental enabling factor of most IoT applications, mechanisms must also be designed to protect communications enabled by such technologies. This survey analyzes existing protocols and mechanisms to secure communications in the IoT, as well as open research issues. We analyze how existing approaches ensure fundamental security requirements and protect communications on the IoT, together with the open challenges and strategies for future research work in the area. This is, as far as our knowledge goes, the first survey with such goals.


title: Why social preferences matter -- the impact of non-selfish motives on competition, cooperation, and incentives

abstract: A substantial number of people exhibit social preferences, which means they are not solely motivated by material self-interest but also care positively or negatively for the material payoffs of relevant reference agents. We show empirically that economists fail to understand fundamental economic questions when they disregard social preferences, in particular, that without taking social preferences into account, it is not possible to understand adequately (i) effects of competition on market outcomes, (ii) laws governing cooperation and collective action, (iii) effects and the determinants of material incentives, (iv) which contracts and property rights arrangements are optimal, and (v) important forces shaping social norms and market failures.


title: Sensorimotor training in virtual reality: A review

abstract: Recent experimental evidence suggests that rapid advancement of virtual reality (VR) technologies has great potential for the development of novel strategies for sensorimotor training in neurorehabilitation. We discuss what the adaptive and engaging virtual environments can provide for massive and intensive sensorimotor stimulation needed to induce brain reorganization. Second, discrepancies between the veridical and virtual feedback can be introduced in VR to facilitate activation of targeted brain networks, which in turn can potentially speed up the recovery process. Here we review the existing experimental evidence regarding the beneficial effects of training in virtual environments on the recovery of function in the areas of gait, upper extremity function and balance, in various patient populations. We also discuss possible mechanisms underlying these effects. We feel that future research in the area of virtual rehabilitation should follow several important paths. Imaging studies to evaluate the effects of sensory manipulation on brain activation patterns and the effect of various training parameters on long term changes in brain function are needed to guide future clinical inquiry. Larger clinical studies are also needed to establish the efficacy of sensorimotor rehabilitation using VR approaches in various clinical populations and most importantly, to identify VR training parameters that are associated with optimal transfer into real-world functional improvements.


title: Control point adjustment for B-spline curve approximation

abstract: Pottmann et al. propose an iterative optimization scheme for approximating a target curve with a B-spline curve based on square distance minimization, or SDM. The main advantage of SDM is that it does not need a parameterization of data points on the target curve. Starting with an initial B-spline curve, this scheme makes an active B-spline curve converge faster towards the target curve and produces a better approximating B-spline curve than existing methods relying on data point parameterization. However, SDM is sensitive to the initial B-spline curve due to its local nature of optimization. To address this, we integrate SDM with procedures for automatically adjusting both the number and locations of the control points of the active spline curve. This leads to a method that is more robust and applicable than SDM used alone. Furthermore, it is observed that the most time consuming part of SDM is the repeated computation of the foot-point on the target curve of a sample point on the active B-spline curve. In our implementation, we speed up the foot-point computation by pre-computing the distance field of the target curve using the Fast Marching Method. Experimental examples are presented to demonstrate the effectiveness of our method. Problems for further research are discussed. q 2003 Elsevier Ltd. All rights reserved.


title: Integration of Parallel Write Ahead Logging and Cicada Concurrency Control Method

abstract: We proposed the idea of combining the Cicada concurrency control mechanism with P-WAL (parallel write-ahead logging) and experimentally evaluated the proposed control mechanism. Our results show that P-WAL can be combined suitably with Cicada without decreasing Cicada's performance. We then experimentally evaluated the most optimal parallel write-ahead logging parameter among (1) conservative lock release; (2) early lock release, the early release of locks on database objects; and (3) group commit, which writes the transaction log to storage all at once. The results show that early lock release and group commit were the most optimal parameters for the combination of Cicada and P-WAL.


title: Pose Guided Human Video Generation

abstract: Due to the emergence of Generative Adversarial Networks, video synthesis has witnessed exceptional breakthroughs. However, existing methods lack a proper representation to explicitly control the dynamics in videos. Human pose, on the other hand, can represent motion patterns intrinsically and interpretably, and impose the geometric constraints regardless of appearance. In this paper, we propose a pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation. In the first stage, a Pose Sequence Generative Adversarial Network (PSGAN) learns in an adversarial manner to yield pose sequences conditioned on the class label. In the second stage, a Semantic Consistent Generative Adversarial Network (SCGAN) generates video frames from the poses while preserving coherent appearances in the input image. By enforcing semantic consistency between the generated and ground-truth poses at a high feature level, our SCGAN is robust to noisy or abnormal poses. Extensive experiments on both human action and human face datasets manifest the superiority of the proposed method over other state-of-the-arts.


title: Accelerometer based wireless air mouse using Arduino micro-controller board

abstract: With the day to day advancements in technology, the interaction between the human and the digital world is diminishing. Lot of improvement has taken place in the mobile technology from button keypads to touch screens. However the current PCs still need a pad to operate its mouse and are wired most of the time. The idea is to develop a wireless mouse which works on the hand gestures of the user without the need of a pad making an effortless interaction between the human and the computer. The implementation is done using a sensor named accelerometer to sense the hand gestures. Accelerometer is a motion sensor which sense changes in motion in any of the three axes. In here the accelerometer will be at the users side, attached to the hand to sense the movement and gives the output to a micro-controller to process it. Necessary modifications are done by the microcontroller to these values and are transmitted through a RF module to the PC. At the receiving end a mouse control program which contains functions to control the mouse reads these values and performs the necessary action.


title: Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis

abstract: The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the dependency relation between words in a review, the multiple aspects and its corresponding sentiment can be identified. However, not all types of dependency relation patterns are able to extract candidate aspect and sentiment word pairs. In this paper, a preliminary study was performed on the performance of different type of dependency relation with different POS tag patterns in pre-extracting candidate aspect from customer review. The result contributes to the identification of the specific type dependency relation with it POS tag pattern that lead to high aspect extraction performance. The combination of these dependency relations offers a solution for single aspect single sentiment and multi aspect multi sentiment cases.


title: Audio-Visual Person Recognition in Multimedia Data From the Iarpa Janus Program

abstract: Currently, datasets that support audio-visual recognition of people in videos are scarce and limited. In this paper, we introduce an expansion of video data from the IARPA Janus program to support this research area. We refer to the expanded set, which adds labels for voice to the already-existing face labels, as the Janus Multimedia dataset. We first describe the speaker labeling process, which involved a combination of automatic and manual criteria. We then discuss two evaluation settings for this data. In the core condition, the voice and face of the labeled individual are present in every video. In the full condition, no such guarantee is made. The power of audiovisual fusion is then shown using these publicly-available videos and labels, showing significant improvement over only recognizing voice or face alone. In addition to this work, several other possible paths for future research with this dataset are discussed.


Feature Contraction: New ConvNet Regularization in Image Classification


title: Named Entity Recognition in Tweets: An Experimental Study

abstract: People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented manner. The performance of standard NLP tools is severely degraded on tweets. This paper addresses this issue by re-building the NLP pipeline beginning with part-of-speech tagging, through chunking, to named-entity recognition. Our novel T-ner system doubles F1 score compared with the Stanford NER system. T-ner leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision. LabeledLDA outperforms co-training, increasing F1 by 25% over ten common entity types. ::: ::: Our NLP tools are available at: http://github.com/aritter/twitter_nlp


title: Design of biped robot inspired by cats for fast running

abstract: A novel design of a biped robot inspired by domestic cats for fast running is introduced. The skeletomuscular system of a cat is analysed and applied to determine the link parameters and the linkage structure of the proposed mechanism. The linkage design of the leg mechanism is explained and a kinematic analysis based on vector loop equations is performed. The effectiveness of the proposed mechanism is verified experimentally. The biped robot runs at an average speed of 2 m/s at a step frequency of 4 Hz. This leg mechanism can facilitate the development of fast running robot systems.


title: Using Wiktionary for Computing Semantic Relatedness

abstract: We introduce Wiktionary as an emerging lexical semantic resource that can be used as a substitute for expert-made resources in AI applications. We evaluate Wiktionary on the pervasive task of computing semantic relatedness for English and German by means of correlation with human rankings and solving word choice problems. For the first time, we apply a concept vector based measure to a set of different concept representations like Wiktionary pseudo glosses, the first paragraph of Wikipedia articles, English WordNet glosses, and GermaNet pseudo glosses. We show that: (i) Wiktionary is the best lexical semantic resource in the ranking task and performs comparably to other resources in the word choice task, and (ii) the concept vector based approach yields the best results on all datasets in both evaluations.


title: HSI color model based lane-marking detection

abstract: Lane-marking detection is one of the major concerned topics in the field of driving safety and intelligent vehicle. In this paper, a new method using HSI color model for lane-marking detection, HSILMD, is proposed. In HSILMD, full color images are converted into HSI color representation, within the region of interest (ROI) aiming to detect road surface on host vehicle, the difference of intensity distribution of a row of pixels within ROI is recorded and clustered with fuzzy c-means algorithm. Thresholds of intensity and saturation are selected accordingly. With simple thresholds and operations, lane markings on various road scene images are detected. Results are compared with the same scheme using RGB color model and a different scheme. Robustness of this reduced computation consumption system is observed


title: Tasks and scenario-based evaluation of information visualization techniques

abstract: Usability evaluation of an information visualization technique can only be done by the joint evaluation of both the visual representation and the interaction techniques. This work proposes task models as a key element for carrying out such evaluations in a structured way. We base our work on a taxonomy abstracting from rendering functions supported by information visualization techniques. CTTE is used to model these abstract visual tasks as well as to generate scenarios from this model for evaluation purposes. We conclude that the use of task models allows generating test scenarios which are more effective than informal and unstructured evaluations.


title: QuickXplain: preferred explanations and relaxations for over-constrained problems

abstract: Over-constrained problems can have an exponential number of conflicts, which explain the failure, and an exponential number of relaxations, which restore the consistency. A user of an interactive application, however, desires explanations and relaxations containing the most important constraints. To address this need, we define preferred explanations and relaxations based on user preferences between constraints and we compute them by a generic method which works for arbitrary CP, SAT, or DL solvers. We significantly accelerate the basic method by a divide-and-conquer strategy and thus provide the technological basis for the explanation facility of a principal industrial constraint programming tool, which is, for example, used in numerous configuration applications.


title: MRI assessment of the structural labrum integrity after Bankart repair using knotless bio-anchors

abstract: Purpose ::: Adequate labral restorations after Bankart repair have been demonstrated in cadaver models for knot-tying and knotless anchors and in vivo by magnetic resonance imaging for knot tying. The influence of glenoidal bio-degradation on clinical outcome, and conclusions regarding drill hole enlargement and foreign body reactions remain controversial.


title: Impact of Virtual Reality on Young Adults' Physiological Arousal and Aggressive Thoughts: Interaction versus Observation.

abstract: The purpose of this study was to compare the impact of playing versus observing a violent virtual reality game on young adults' arousal levels, feelings of hostility, and aggressive thoughts. Physiological arousal and aggressive thoughts, but not hostile feelings, were higher for participants than for observers of the virtual reality game. Results suggest that technologies varying from observational to participatory modes of interaction can have differing physiological and cognitive consequences.


title: Circular Polarization and Polarization Reconfigurable Designs for Annular Slot Antennas

abstract: A simple design for circularly-polarized (CP) annular slot antennas is first described. The antenna is fed by a V-shaped coupling strip loaded with a small resistance, and it can generate CP radiation as long as the inclined angle of the V-shaped coupling strip is properly adjusted. Numerical analyses to the effects of varying the angle on CP axial ratio are performed. Several CP prototypes are fabricated. Both simulated and measured results demonstrate that the proposed feeding mechanism can give good CP performances for the case that the slot width is varied from 0.008 to 0.09 λ0. Then, a design for polarization reconfigurable antennas is developed from the feeding mechanism. Only two PIN diodes are involved in the reconfigurable design that can offer the switching among three different polarizations, including one linear polarization and dual orthogonal circular polarizations. Details of the designs and experimental results are shown.


title: QueCC: A Queue-oriented, Control-free Concurrency Architecture

abstract: We investigate a coordination-free approach to transaction processing on emerging multi-sockets, many-core, shared-memory architecture to harness its unprecedented available parallelism. We propose a queue-oriented, control-free concurrency architecture, referred to as QueCC, that exhibits minimal contention among concurrent threads by eliminating the overhead of concurrency control from the critical path of the transaction. QueCC operates on batches of transactions in two deterministic phases of priority-based planning followed by control-free execution. We extensively evaluate our transaction execution architecture and compare its performance against seven state-of-the-art concurrency control protocols designed for in-memory stores. We demonstrate that QueCC can significantly outperform state-of-the-art concurrency control protocols under high-contention by up to 6.3x. Moreover, our results show that QueCC can process nearly 40 million YCSB transactional operations per second while maintaining serializability guarantees with write-intensive workloads. Remarkably, QueCC out-performs H-Store by up to two orders of magnitude.


title: The landscape of long noncoding RNAs in the human transcriptome

abstract: Arul Chinnaiyan and colleagues report the curation of 7,256 RNA sequencing libraries from tumors, normal tissues and cell lines. They find 58,648 lncRNAs, of which 79% are previously unnannotated.


Hybrid Clustering Algorithm based on Mahalanobis Distance and MST


title: Doubly Fed Induction Generator Wind Turbine Systems Subject to Recurring Symmetrical Grid Faults

abstract: New grid codes demand the wind turbine systems to ride through recurring grid faults. In this paper, the performance of the doubly Ffed induction generator (DFIG) wind turbine system under recurring symmetrical grid faults is analyzed. The mathematical model of the DFIG under recurring symmetrical grid faults is established. The analysis is based on the DFIG wind turbine system with the typical low-voltage ride-through strategy—with rotor-side crowbar. The stator natural flux produced by the voltage recovery after the first grid fault may be superposed on the stator natural flux produced by the second grid fault, so that the transient rotor and stator current and torque fluctuations under the second grid fault may be influenced by the characteristic of the first grid fault, including the voltage dips level and the grid fault angle, as well as the duration between two faults. The mathematical model of the DFIG under recurring grid faults is verified by simulations on a 1.5-MW DFIG wind turbine system model and experiments on a 30-kW reduced scale DFIG test system.


title: Deep convolutional networks for pancreas segmentation in CT imaging

abstract: Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% ± 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.


title: The Capital Asset Pricing Model: An Overview of the Theory

abstract: Although the Capital Asset Pricing Model (CAPM) has been one of the most useful and frequently used theories in determining the required rate of return of a security, the application of this model has been controversial since early 1960s. The CAPM was introduced by Jack Treynor, William Sharpe, John Lintner and Jan Mossin independently, building on the earlier work of Harry Markowitz on diversification and modern portfolio theory. In theory, the capital asset pricing model is employed to set the investor required rate of return on a risky security given the non-diversifiable firm-specific risk, as the systematic risk will be eliminated in a well-diversified portfolio. This research aims to shed the light on this model by discussing the assumptions, the evolution of the Sharpe and Lintner model, and reviewing the literature on the relaxation of model assumptions and the critiques of the CAPM. Finally, the Arbitrage Pricing Model as an extension for the CAPM will be discussed.


title: Complexity of vehicle routing and scheduling problems

abstract: The complexity of a class of vehicle routing and scheduling problems is investigated. We review known NP-hardness results and compile the results on the worst-case performance of approximation algorithms. Some directions for future research are suggested. The presentation is based on two discussion sessions during the Workshop to Investigate Future Directions in Routing and Scheduling of Vehicles and Crews, held at the University of Maryland at College Park, June 4–6, 1979.


title: Exploring Factors Influencing the Adoption of Mobile Commerce

abstract: This study presents an extended technology acceptance model that integrates innovation diffusion theory to investigate what determine user mobile commerce acceptance. This paper models the factors relationships such as perceived usefulness, perceived ease of use, personal innovativeness, subjective norms, behavioral control and intention to adopt mobile commerce. The proposed model was empirically tested using data collected from a survey of mobile commerce consumers. Empirical data from regression analysis reflects users ease of use influence behavioral intention to adopt mobile commerce. The majority of positive relationships between perceived ease of use, subjective norms, behavioral control and intention to adopt are supported by empirical data. Results also reveal that behavioral control and subjective norms influence perceived ease of use which affects then their adoption intention. The paper concludes some important implications for the practitioners.


title: Toward a neurobiology of delusions

abstract: Delusions are the false and often incorrigible beliefs that can cause severe suffering in mental illness. We cannot yet explain them in terms of underlying neurobiological abnormalities. However, by drawing on recent advances in the biological, computational and psychological processes of reinforcement learning, memory, and perception it may be feasible to account for delusions in terms of cognition and brain function. The account focuses on a particular parameter, prediction error - the mismatch between expectation and experience - that provides a computational mechanism common to cortical hierarchies, fronto-striatal circuits and the amygdala as well as parietal cortices. We suggest that delusions result from aberrations in how brain circuits specify hierarchical predictions, and how they compute and respond to prediction errors. Defects in these fundamental brain mechanisms can vitiate perception, memory, bodily agency and social learning such that individuals with delusions experience an internal and external world that healthy individuals would find difficult to comprehend. The present model attempts to provide a framework through which we can build a mechanistic and translational understanding of these puzzling symptoms.


title: Visual Sentiment Prediction with Deep Convolutional Neural Networks

abstract: Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to sentiment analysis of textual data, there has been very limited work that focuses on analyzing sentiment of image data. In this work, we propose a novel visual sentiment prediction framework that performs image understanding with Deep Convolutional Neural Networks (CNN). Specifically, the proposed sentiment prediction framework performs transfer learning from a CNN with millions of parameters, which is pre-trained on large-scale data for object recognition. Experiments conducted on two real-world datasets from Twitter and Tumblr demonstrate the effectiveness of the proposed visual sentiment analysis framework.


title: MegDet: A Large Mini-Batch Object Detector

abstract: The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task.


title: Deformation gradient based kinematic hardening model

abstract: A kinematic hardening model applicable to finite strains is presented. The kinematic hardening concept is based on the residual stresses that evolve due to different obstacles that are present in a polycrystalline material, such as grain boundaries, cross slips, etc. Since these residual stresses are a manifestation of the distortion of the crystal lattice a corresponding deformation gradient is introduced to represent this distortion. The residual stresses are interpreted in terms of the form of a back-stress tensor, i.e. the kinematic hardening model is based on a deformation gradient which determines the back-stress tensor. A set of evolution equations is used to describe the evolution of the deformation gradient. Non-dissipative quantities are allowed in the model and the implications of these are discussed. Von Mises plasticity for which the uniaxial stress–strain relation can be obtained in closed form serves as a model problem. For uniaxial loading, this model yields a kinematic hardening identical to the hardening produced by isotropic exponential hardening. The numerical implementation of the model is discussed. Finite element simulations showing the capabilities of the model are presented.


title: Comparative Evaluation of Direct Torque Control Strategies for Permanent Magnet Synchronous Machines

abstract: This paper presents a detailed comparative evaluation of several direct torque control (DTC) strategies for permanent magnet synchronous machines (PMSMs), namely basic DTC, model predictive DTC (MPDTC) and DTC with duty ratio modulation (DTC-duty). Moreover, field orient control (FOC) is also included in this study. The aforementioned control strategies are reviewed and their performances are analyzed and compared. The comparison is carried out through simulation of a 60 kW PMSM fed by a two-level voltage source inverter (VSI). With the intent to fully reveal advantages and disadvantages of each control strategy, critical evaluation has been conducted on the basis of several criteria: torque and stator flux ripple, inverter switching frequency, machine parameter sensitivity, computational complexity and stator current total harmonic distortion (THD). The choice of control scheme can be determined based on specific requirements of particular application under consideration.


title: Cerebellar learning of accurate predictive control for fast-reaching movements

abstract: Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between efferent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.


title: Are randomly grown graphs really random?

abstract: We analyze a minimal model of a growing network. At each time step, a new vertex is added; then, with probability \delta, two vertices are chosen uniformly at random and joined by an undirected edge. This process is repeated for t time steps. In the limit of large t, the resulting graph displays surprisingly rich characteristics. In particular, a giant component emerges in an infinite-order phase transition at \delta = 1/8. At the transition, the average component size jumps discontinuously but remains finite. In contrast, a static random graph with the same degree distribution exhibits a second-order phase transition at \delta = 1/4, and the average component size diverges there. These dramatic differences between grown and static random graphs stem from a positive correlation between the degrees of connected vertices in the grown graph--older vertices tend to have higher degree, and to link with other high-degree vertices, merely by virtue of their age. We conclude that grown graphs, however randomly they are constructed, are fundamentally different from their static random graph counterparts.


title: Fehling solution/DIPEA/hydrazine: an alternative catalytic medium for regioselective synthesis of 1,4-disubstituted-1H-1,2,3-triazoles using azide–alkyne cycloaddition reaction

abstract: The development of copper catalyzed azide–alkyne cycloaddition in aqueous solvent system at room temperature is a notable contribution to the field of green chemistry. In this work, 1,4-disubstitued-1H-1,2,3-triazoles are synthesized in the Fehling solution, a widely used system in practical laboratory classes but totally unexplored in research field, with N,N-diisopropylethylamine/hydrazine without using any organic solvent under mild reaction conditions.


title: Recent progress in road and lane detection: a survey

abstract: The problem of road or lane perception is a crucial enabler for advanced driver assistance systems. As such, it has been an active field of research for the past two decades with considerable progress made in the past few years. The problem was confronted under various scenarios, with different task definitions, leading to usage of diverse sensing modalities and approaches. In this paper we survey the approaches and the algorithmic techniques devised for the various modalities over the last 5 years. We present a generic break down of the problem into its functional building blocks and elaborate the wide range of proposed methods within this scheme. For each functional block, we describe the possible implementations suggested and analyze their underlying assumptions. While impressive advancements were demonstrated at limited scenarios, inspection into the needs of next generation systems reveals significant gaps. We identify these gaps and suggest research directions that may bridge them.


title: Content-Based Prediction of Movie Style, Aesthetics, and Affect: Data Set and Baseline Experiments

abstract: The affective content of a movie is often considered to be largely determined by its style and aesthetics. Recently, studies have attempted to estimate affective movie content with computational features, but results have been mixed, one of the main reasons being a lack of data on perceptual stylistic and aesthetic attributes of film, which would provide a ground truth for the features. The distinctions between energetic and tense arousal as well as perceived and felt affect are also often neglected. In this study, we present a data set of ratings by 73 viewers of 83 stylistic, aesthetic, and affective attributes for a selection of movie clips containing complete scenes taken from mainstream movies. The affective attributes include the temporal progression of perceived and felt valence and arousal within the clips. The data set is aimed to be used to train algorithms that predict viewer assessments based on low-level computational features. With this data set, we performed a baseline study modeling the relation between a large selection of low-level computational features (i.e., visual, auditory, and temporal) and perceptual stylistic, aesthetic, and affective attributes of movie clips. Two algorithms were compared in a realistic prediction scenario: linear regression and the neural-network-based Extreme Learning Machine (ELM). Felt and perceived affect as well as stylistic attributes were shown to be equally easy to predict, whereas the prediction of aesthetic attributes failed. The performance of the ELM predictor was overall found to be slightly better than the linear regression. A feature selection experiment illustrated that features from all low-level computational modalities, visual, auditory and temporal, contribute to the prediction of the affect assessments. We have made our assessment data and extracted computational features publicly available.


title: Toward a Detection Framework for Android Botnet

abstract: Android is one of the most popular and widespread operating systems for smartphones. It has several millions of applications that are published at either official or unofficial stores. Botnet applications are kind of malware that can be published using these stores and downloaded by the victims on their smartphones. In this paper, we propose Android botnet detection method based a new set of discriminating features extracted based from the analysis of Android permissions (i.e. Protection levels for all available Android permissions). Then we compared the prediction power of different machine learning models before and after adding these features to the state-of-art requested permissions features in Android. We used four popular ML classifiers (i.e. Random Forest, MultiLayer Perceptron neural networks, Decision trees, and Naive Bayes) for our experiments and we found that the new set of features have a tiny improvement on the performance in the case of decision trees and Random forest classifiers.


title: Wireless multichannel biopotential recording using an integrated FM telemetry circuit

abstract: This paper presents a four-channel telemetric microsystem featuring on-chip alternating current amplification, direct current baseline stabilization, clock generation, time-division multiplexing, and wireless frequency-modulation transmission of microvolt- and millivolt-range input biopotentials in the very high frequency band of 94-98 MHz over a distance of /spl sim/0.5 m. It consists of a 4.84-mm/sup 2/ integrated circuit, fabricated using a 1.5-/spl mu/m double-poly double-metal n-well standard complementary metal-oxide semiconductor process, interfaced with only three off-chip components on a custom-designed printed-circuit board that measures 1.7/spl times/1.2/spl times/0.16 cm/sup 3/, and weighs 1.1 g including two miniature 1.5-V batteries. We characterize the microsystem performance, operating in a truly wireless fashion in single-channel and multichannel operation modes, via extensive benchtop and in vitro tests in saline utilizing two different micromachined neural recording microelectrodes, while dissipating /spl sim/2.2 mW from a 3-V power supply. Moreover, we demonstrate successful wireless in vivo recording of spontaneous neural activity at 96.2 MHz from the auditory cortex of an awake marmoset monkey at several transmission distances ranging from 10 to 50 cm with signal-to-noise ratios in the range of 8.4-9.5 dB.


Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language.


title: A Metamodeling Approach for Reasoning about Requirements

abstract: In requirements engineering, there are several approaches for requirements modeling such as goal-oriented, aspect-driven, and system requirements modeling. In practice, companies often customize a given approach to their specific needs. Thus, we seek a solution that allows customization in a systematic way. In this paper, we propose a metamodel for requirements models (called coremetamodel) and an approach for customizing this metamodel in order to support various requirements modeling approaches. The core metamodel represents the common concepts extracted from some prevalent approaches. We define the semantics of the concepts and the relations in the core metamodel. Based on this formalization, we can perform reasoning on requirements that may detect implicit relations and inconsistencies. Our approach for customization keeps the semantics of the core concepts intact and thus allows reuse of tools and reasoning over the customized metamodel. We illustrate the customization of our core metamodel with SysML concepts. As a case study, we apply the reasoning on requirements of an industrial mobile service application based on this customized core requirements metamodel.


title: Neglected Time: Impaired Temporal Perception of Multisecond Intervals in Unilateral Neglect

abstract: Recent neuroimaging and neuropsychological studies have suggested that the right hemisphere, particularly frontal regions, is important for the perception of the passage of time. We examined the ability to estimate durations of up to 60 sec in a group of eight patients with unilateral neglect. When estimating multisecond intervals, neglect patients grossly underestimated all durations. On average, healthy controls (HC) demonstrated reasonably accurate estimates of all durations tested. Although the right hemisphere lesioned control patients without neglect also tended to underestimate durations, these underestimations were significantly better than the performance of the neglect group. These findings suggest a pivotal role for a right hemisphere fronto-parietal network in the accurate perception of multisecond durations. Furthermore, these findings add to a growing body of literature suggesting that neglect cannot be understood simply in terms of a bias in orienting attention to one side of space. Additional deficits of the kind demonstrated here are likely to be crucial in determining the nature and extent of the loss of conscious awareness for contralesional events.


DETECTION OF LEUKEMIA IN BLOOD MICROSCOPIC IMAGES USING FUZZY LOGIC


title: Drug repositioning: identifying and developing new uses for existing drugs

abstract: Biopharmaceutical companies attempting to increase productivity through novel discovery technologies have fallen short of achieving the desired results. Repositioning existing drugs for new indications could deliver the productivity increases that the industry needs while shifting the locus of production to biotechnology companies. More and more companies are scanning the existing pharmacopoeia for repositioning candidates, and the number of repositioning success stories is increasing.


title: An Efficient Partition Based Method for Exact Set Similarity Joins

abstract: We study the exact set similarity join problem, which, given two collections of sets, finds out all the similar set pairs from the collections. Existing methods generally utilize the prefix filter based framework. They generate a prefix for each set and prune all the pairs whose prefixes are disjoint. However the pruning power is limited, because if two dissimilar sets share a common element in their prefixes, they cannot be pruned. To address this problem, we propose a partition-based framework. We design a partition scheme to partition the sets into several subsets and guarantee that two sets are similar only if they share a common subset. To improve the pruning power, we propose a mixture of the subsets and their 1-deletion neighborhoods (the subset of a set by eliminating one element). As there are multiple allocation strategies to generate the mixture, we evaluate different allocations and design a dynamic-programming algorithm to select the optimal one. However the time complexity of generating the optimal one is O(s3) for a set with size s. To speed up the allocation selection, we develop a greedy algorithm with an approximation ratio of 2. To further reduce the complexity, we design an adaptive grouping mechanism, and the two techniques can reduce the complexity to O(s log s). Experimental results on three real-world datasets show our method achieves high performance and outperforms state-of-the-art methods by 2-5 times.


title: Predicting User Replying Behavior on a Large Online Dating Site

abstract: Online dating sites have become popular platforms for people to look for potential romantic partners. Many online dating sites provide recommendations on compatible partners based on their proprietary matching algorithms. It is important that not only the recommended dates match the user’s preference or criteria, but also the recommended users are interested in the user and likely to reciprocate when contacted. The goal of this paper is to predict whether an initial contact message from a user will be replied to by the receiver. The study is based on a large scale real-world dataset obtained from a major dating site in China with more than sixty million registered users. We formulate our reply prediction as a link prediction problem of social networks and approach it using a machine learning framework. The availability of a large amount of user profile information and the bipartite nature of the dating network present unique opportunities and challenges to the reply prediction problem. We extract user-based features from user profiles and graph-based features from the bipartite dating network, apply them in a variety of classification algorithms, and compare the utility of the features and performance of the classifiers. Our results show that the user-based and graph-based features result in similar performance, and can be used to effectively predict the reciprocal links. Only a small performance gain is achieved when both feature sets are used. Among the five classifiers we considered, random forests method outperforms the other four algorithms (naive Bayes, logistic regression, KNN, and SVM). Our methods and results can provide valuable guidelines to the design and performance of recommendation engine for online dating sites.


title: Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue.

abstract: Goal-oriented dialogue has been paid attention for its numerous applications in artificial intelligence. To solve this task, deep learning and reinforcement learning have recently been applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel algorithm for goal-oriented dialogue. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer's intent via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialogue tasks: "MNIST Counting Dialog" and "GuessWhat?!." In our experiments, AQM outperforms comparative algorithms and makes human-like dialogue. We further use AQM as a tool for analyzing the mechanism of deep reinforcement learning approach and discuss the future direction of practical goal-oriented neural dialogue systems.


title: Microservice Ambients: An Architectural Meta-Modelling Approach for Microservice Granularity

abstract: Isolating fine-grained business functionalities byboundaries into entities called microservices is a core activityunderlying microservitization. We define microservitization asthe paradigm shift towards microservices. Determining theoptimal microservice boundaries (i.e. microservice granularity) is among the key microservitization design decisions thatinfluence the Quality of Service (QoS) of the microservice applicationat runtime. In this paper, we provide an architecturecentricapproach to model this decision problem. We build onambients — a modelling approach that can explicitly capturefunctional boundaries and their adaptation. We extend the aspect-oriented architectural meta-modelling approach of ambients—AMBIENT-PRISMA — with microservice ambients. A microservice ambient is a modelling concept that treatsmicroservice boundaries as an adaptable first-class entity. Weuse a hypothetical online movie subscription-based systemto capture a microservitization scenario using our aspectorientedmodelling approach. The results show the ability ofmicroservice ambients to express the functional boundary of amicroservice, the concerns of each boundary, the relationshipsacross boundaries and the adaptations of these boundaries. Additionally, we evaluate the expressiveness and effectivenessof microservice ambients using criteria from ArchitectureDescription Language (ADL) classification frameworkssince microservice ambients essentially support architecturedescription for microservices. The evaluation focuses on thefundamental modelling constructs of microservice ambientsand how they support microservitization properties such asutility-driven design, tool heterogeneity and decentralised governance. The evaluation highlights how microservice ambientssupport analysis, evolution and mobility/location awarenesswhich are significant to quality-driven microservice granularityadaptation. The evaluation is general and irrespective of theparticular application domain and the business competenciesin that domain.


title: New non-alcoholic formulation for hand disinfection

abstract: AbstractHand washing is considered as the single most important strategy to prevent infections. World health organization (WHO) defines hand hygiene as a primary issue of personal care with particular reference to hospital personnel and health facility workers. In this work, we investigated a new combination for hand disinfection as an alternative to alcohol-based and chlorhexidine products. The new combination of 5-pyrrolidone-2-carboxylic acid (PCA) and copper sulphate pentahydrate (CS) was tested upon different bacterial species that normally colonize hands, including Staphylococcus aureus, methicillin resistant S. aureus (MR S. aureus), Staphylococcus epidermidis, multidrug resistant S. epidermidis (MDR S. epidermidis), Streptococcus pyogenes, Streptococcus agalactiae, Escherichia coli, Candida albicans and three clinical isolates: MR S. aureus, MDR S. epidermidis, and an E. coli strain. Minimal inhibitory concentrations (MICs), Minimal bactericidal concentrations (MBCs), fractional inhibitory concent...


title: Solving Occlusion Problem in Pedestrian Detection by Constructing Discriminative Part Layers

abstract: Occlusion handling is one of the most challenging issues for pedestrian detection, and no satisfactory achievement has been found in this issue yet. Using human body parts has been considered as a reasonable way to overcome such an issue. In this paper, we propose a brand new approach based on the fusion of Mid-level body part mining and Convolutional Neural Network (CNN) to solve this problem, named DP-CNN(Discriminative Parts CNN). Two main discussions are included in this paper. First, we take an exhaustive analysis on how to mine useful body parts that contribute to pedestrian detection. Multiple ingredients (e.g. feature representation, pedestrian attributes) are analyzed through a wide range of experiments. Second, we convert the part detectors to the middle layer of CNN and re-train the model to get a better adaption of the dataset. Compare to existing approaches based on fine-tuning CNN models, our method is not only robust to occlusion handling but also has a smaller computational cost.


title: Using Machine Translation for Converting Python 2 to Python 3 Code

abstract: In this paper, we have tried to use Statistical machine translation in order to convert Python 2 code to Python 3 code. We use data from two projects and achieve a high BLEU score. We also investigate the cross-project training and testing to analyze the errors so as to ascertain differences with previous case. We have described a pilot study on modeling programming languages as natural language to build translation models on the lines of natural languages. This can be further worked on to translate between versions of a programming language or crossprogramming-languages code translation.


title: Barycentric coordinates for convex sets

abstract: In this paper we provide an extension of barycentric coordinates from simplices to arbitrary convex sets. Barycentric coordinates over convex 2D polygons have found numerous applications in various fields as they allow smooth interpolation of data located on vertices. However, no explicit formulation valid for arbitrary convex polytopes has been proposed to extend this interpolation in higher dimensions. Moreover, there has been no attempt to extend these functions into the continuous domain, where barycentric coordinates are related to Green’s functions and construct functions that satisfy a boundary value problem. First, we review the properties and construction of barycentric coordinates in the discrete domain for convex polytopes. Next, we show how these concepts extend into the continuous domain to yield barycentric coordinates for continuous functions. We then provide a proof that our functions satisfy all the desirable properties of barycentric coordinates in arbitrary dimensions. Finally, we provide an example of constructing such barycentric functions over regions bounded by parametric curves and show how they can be used to perform freeform deformations.


title: MLC++: A Machine Learning Library in C++

abstract: Data mining algorithms including machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called MLC++, which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. MLC++ not only provides a workbench for such comparisons, but also provides a library of C++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers.


title: AWE: Aviation Weather Data Visualization Environment

abstract: Abstract Weather is one of the major causes of aviation accidents. General aviation (GA) flights account for 92% of all the aviation accidents. Researchers are addressing this problem from various perspectives including improving meteorological forecasting techniques, collecting additional weather data automatically via on-board sensors and “flight” modems, and improving weather data dissemination (often available only in the textual format) and visualization techniques. We approach the problem from the improved dissemination perspective and propose weather visualization methods tailored for general aviation pilots. Although some aviation weather data, such as possible icing (Airman's Meteorological Information (AIRMETs)) or turbulence conditions (Significant Meteorological Conditions (SIGMETs)), or information about precipitation intensity and movement, has already been presented well by existing systems, there is still an urgent need for visualizing several critical weather elements neglected so far. Our system, Aviation Weather Data Visualization Environment (AWE), focuses on graphical displays of these weather elements, namely, meteorological observations, terminal area forecasts, and winds aloft forecasts and maps them onto a cartographic grid specific to the pilot's area of interest. Additional weather graphics such as icing (AIRMETs) or turbulence conditions (SIGMETs) can easily be added to our system to provide a pilot with a more complete visual weather briefing. Decisions regarding the graphical display and design are made based on careful consideration of user needs. Integral visual display of these elements of weather reports is designed for the use of GA pilots as a weather briefing and route selection tool. AWE provides linking of the weather information to the flight's path and schedule. The pilot can interact with the system to obtain aviation-specific weather for the entire area or for his specific route to explore what-if scenarios including the selection of alternates, and make “go/no-go” decisions. AWE, as evaluated by some pilots at National Aeronautics and Space Administration Ames Research Center, was found to be useful.


title: Squeaky Wheel Optimization

abstract: We describe a general approach to optimization which we term "Squeaky Wheel" Optimization (swo). In swo, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. That analysis is used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found.SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This "coupled search" has some interesting properties, which we discuss.We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that swo is a general technique for optimization.


title: UAV with two passive rotating hemispherical shells for physical interaction and power tethering in a complex environment

abstract: For the past few years, unmanned aerial vehicles (UAVs) have been successfully employed in several investigations and exploration tasks such as aerial inspection and manipulations. However, most of these UAVs are limited to open spaces distant from any obstacles because of the high risk of falling as a result of an exposed propeller or not enough protection. On the other hand, a UAV with a passive rotating spherical shell can fly over a complex environment but cannot engage in physical interaction and perform power tethering because of the passive rotation of the spherical shell. In this study, we propose a new mechanism that allows physical interaction and power tethering while the UAV is well-protected and has a good flight stability, which enables exploration in a complex environment such as disaster sites. We address the current problem by dividing the whole shell into two separate hemispherical shells that provide a gap unaffected by passive rotation. In this paper, we mainly discuss the concept, general applications, and design of the proposed system. The capabilities of the proposed system for physical interaction and power tethering in a complex space were initially verified through laboratory-based test flights of our experimental prototype.


title: A tree branch instead of a ligature: an unusual accidental hanging

abstract: A unique case of accidental hanging due to compression of the neck of an adult by the branches of a coffee tree is reported. The decedent was a 42-year-old male who was found dead in a semi prone position on a slope. His neck was lodged in a wedge formed by two branches of a coffee tree, with his legs angled downwards on the slope. Autopsy revealed two friction abrasions located horizontally on either side of the front of the neck, just above the larynx. The findings were compatible with compression of the neck by the branches of the tree, with the body weight of the decedent contributing to compression. Subsequent complete autopsy examination confirmed the cause of death as hanging. Following an inquest the death was ruled to be accidental.


title: Towards a ROS-based autonomous cloud robotics platform for data center monitoring

abstract: Data center monitoring has been a critical subject of research in recent years. In this paper we present a robotic system, based on the Robot Operating System (ROS), in which a mobile robot equipped with a laser range sensor and an Inertial Motion Unit (IMU) is able to autonomously navigate in a data center room for accurate monitoring of critical measurements, such as servers' external temperature, humidity and other physical quantities. The robot is able to autonomously create a map of a previously unknown room, localize therein and execute a list of measurements at different locations, which are provided by the user via a web Graphical User Interface (GUI). The application is based on a cloud robotics infrastructure which encloses the ROS nodes and exposes REST APIs to the user. We discuss our implementation choices with regards to the particular requirements of the scenario, both in terms of robot navigation and software infrastructure, and present some preliminary results in a real scenario.


title: Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods

abstract: In this paper we propose a novel gradient algorithm to learn a policy from an expert's observed behavior assuming that the expert behaves optimally with respect to some unknown reward function of a Markovian Decision Problem. The algorithm's aim is to find a reward function such that the resulting optimal policy matches well the expert's observed behavior. The main difficulty is that the mapping from the parameters to policies is both nonsmooth and highly redundant. Resorting to sub differentials solves the first difficulty, while the second one is overcome by computing natural gradients. We tested the proposed method in two artificial domains and found it to be more reliable and efficient than some previous methods.


title: Data-Intensive Text Processing with MapReduce

abstract: This half-day tutorial introduces participants to data-intensive text processing with the MapReduce programming model [1], using the open-source Hadoop implementation. The focus will be on scalability and the tradeoffs associated with distributed processing of large datasets. Content will include general discussions about algorithm design, presentation of illustrative algorithms, case studies in HLT applications, as well as practical advice in writing Hadoop programs and running Hadoop clusters.


title: Certified Defenses against Adversarial Examples

abstract: While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no attack that perturbs each pixel by at most \epsilon = 0.1 can cause more than 35% test error.


title: DeeperBind: Enhancing prediction of sequence specificities of DNA binding proteins

abstract: Transcription factors (TFs) are macromolecules that bind to cis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several in vivo and in vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.


title: Random-Key Cuckoo Search for the Travelling Salesman Problem

abstract: Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random-key cuckoo search (RKCS) algorithm for solving the famous travelling salesman problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in both spaces using Levy flights. The performance of the proposed RKCS is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that RKCS is superior to some other metaheuristic algorithms.


title: Cognitive technology model to enhanced academic support services with chatbots

abstract: In this paper, we propose a cognitive technology model to enhance academic support services with chatbots. The difference between the conventional models and the model proposed in this paper lies in adding Customer Experience patterns to the chatbot. This could substantially improve the interaction between the chatbot and the student in the institution. Our proposed model also emphasizes the use of best practices for real-time data processing. The model consists of five phases: 1. Capture; 2. Understanding Natural Language; 3. Dialog management; 4. Generated responses; 5. Consumption. We evaluate our cognitive technology model with undergraduate students at Universidad Peruana de Ciencias Aplicadas. The preliminary results showed a reduction of more than 99.9% in the average response time expected per query and an acceptance level of 80% within the target population.


title: Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

abstract: In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradients in parallel, aggregates all gradients in a single server to obtain the average, and updates each worker’s local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead.


title: Smart Refrigerator Based on Internet of Things (IoT): An Approach to Efficient Food Management

abstract: This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.


title: On the applicability of Young–Laplace equation for nanoscale liquid drops

abstract: Debates continue on the applicability of the Young-Laplace equation for droplets, vapor bubbles and gas bubbles in nanoscale. It is more meaningful to find the error range of the Young-Laplace equation in nanoscale instead of making the judgement of its applicability. To do this, for seven liquid argon drops (containing 800, 1000, 1200, 1400, 1600, 1800, or 2000 particles, respectively) at T = 78 K we determined the radius of surface of tension R (s) and the corresponding surface tension gamma (s) by molecular dynamics simulation based on the expressions of R (s) and gamma (s) in terms of the pressure distribution for droplets. Compared with the two-phase pressure difference directly obtained by MD simulation, the results show that the absolute values of relative error of two-phase pressure difference given by the Young-Laplace equation are between 0.0008 and 0.027, and the surface tension of the argon droplet increases with increasing radius of surface of tension, which supports that the Tolman length of Lennard-Jones droplets is positive and that Lennard-Jones vapor bubbles is negative. Besides, the logic error in the deduction of the expressions of the radius and the surface tension of surface of tension, and in terms of the pressure distribution for liquid drops in a certain literature is corrected.


title: Analyzing road accident data using machine learning paradigms

abstract: To determine the main factors associated with road traffic accidents is one of main objectives of accident data analysis. Due heterogeneity nature of road accident data makes analysis tricky. To overcome heterogeneity of data partitioning is used. The proposed method uses k-means clustering method as the main task of segmentation of road accident data. Further, association rule mining is applied to discover the situations related with the occurrence of the whole data set and the occurrence of clusters recognized by the k-means clustering algorithm. The combined result of k-means clustering and association rule produces major information.


title: Free viewpoint video (FVV) survey and future research direction

abstract: Free viewpoint video (FVV) is one of the new trends in the development of advanced visual media type that aims to provide a new immersive user experience and interactivity that goes beyond higher image quality (HD/4K TV) and higher realism (3D TV). Potential applications include interactive personal visualization and free viewpoint navigation. The goal of this paper is to provide an overview of the FVV system and some target application scenarios. Associated standardization activities and technological barriers to overcome are also described. This paper is organized as follows: a general description of the FVV system and functionalities is given in Section I. Since an FVV system is composed of a chain of processing modules, an in-depth functional description of each module is provided in Section II. Examples of emerging FVV applications and use cases are given in Section III. A summary of technical challenges to overcome for wider usage and market penetration of FVV is given in Section IV.


title: Gathering Information About Word Similarity from Neighbor Sentences

abstract: In this paper we present the first results of detecting word semantic similarity on the Russian translations of Miller-Charles and Rubenstein-Goodenough sets prepared for the first Russian word semantic evaluation Russe-2015. The experiments were carried out on three text collections: Russian Wikipedia, a news collection, and their united collection. We found that the best results in detection of lexical paradigmatic relations are achieved using the combination of word2vec with the new type of features based on word co-occurrences in neighbor sentences.


title: Forensic data acquisition from cloud-of-things devices: windows Smartphones as a case study

abstract: Summary ::: The continued amalgamation of cloud technologies into all aspects of our daily lives and the technologies we use (i.e. cloud-of-things) creates business opportunities, security and privacy risks, and investigative challenges (in the event of a cybersecurity incident). This study examines the extent to which data acquisition from Windows phone, a common cloud-of-thing device, is supported by three popular mobile forensics tools. The effect of device settings modification (i.e. enabling screen lock and device reset operations) and alternative acquisition processes (i.e. individual and combined acquisition) on the extraction results are also examined. Our results show that current mobile forensic tool support for Windows Phone 8 remains limited. The results also showed that logical acquisition support was more complete in comparison to physical acquisition support. In one example, the tool was able to complete a physical acquisition of a Nokia Lumia 625, but its deleted contacts and SMSs could not be recovered/extracted. In addition we found that separate acquisition is needed for device removable media to maximize acquisition results, particularly when trying to recover deleted data. Furthermore, enabling flight-mode and disabling location services are highly recommended to eliminate the potential for data alteration during the acquisition process. These results should provide practitioners with an overview of the current capability of mobile forensic tools and the challenges in successfully extracting evidence from the Windows phone platform. Copyright © 2016 John Wiley & Sons, Ltd.


title: A social-cognitive information-processing model for school-based aggression reduction and prevention programs: Issues for research and practice

abstract: Student aggression in schools continues to be a problem. School-based programs are a critical part of the solution. In this article we review research on the development of aggressive behavior within a social-cognitive informationprocessing (SCIP) framework. Huesmann (1998) presented a "unified" SCIP model in an attempt to integrate extant models. This model focuses on individuals' (a) attention to and interpretation of situational cues; (b) search for and retrieval of scripts for behavior; (c) script evaluation based on beliefs about aggression, outcome expectancies, and selfefficacy for aggressing or inhibiting aggression; and (d) interpretation of environmental responses to their behavior. We highlight components of best practice school programs that address these steps. Limitations of the SCIP framework are discussed as directions for future research. Applied recommendations based on a unified SCIP model are offered.


title: A research framework for the adoption of Business Intelligence by Small and Medium-sized enterprises

abstract: Due to the complexities of making effective and timely business decisions in highly competitive markets, Data-driven decision-making using Business Intelligence (BI) applications has attracted many organisations worldwide. However, despite these applications being suited for use in most organisations regardless of size, only the larger enterprises have reached a stage of maturity in BI use while small and medium-size enterprises (SMEs) still lag behind. Whilst many academic researchers have conducted BI research focused on large organisations, literature relating to BI adoption within SMEs has remained limited. To fill this research gap and support the adoption rate of BI in SMEs, this paper proposes a research framework for identifying the current state of BI adoption by SMEs and the enabling factors that impact BI adoption in SMEs. Future research directions for using this framework are also discussed.


title: Restoration of motor control and proprioceptive and cutaneous sensation in humans with prior upper-limb amputation via multiple Utah Slanted Electrode Arrays (USEAs) implanted in residual peripheral arm nerves

abstract: Background ::: Despite advances in sophisticated robotic hands, intuitive control of and sensory feedback from these prostheses has been limited to only 3-degrees-of-freedom (DOF) with 2 sensory percepts in closed-loop control. A Utah Slanted Electrode Array (USEA) has been used in the past to provide up to 81 sensory percepts for human amputees. Here, we report on the advanced capabilities of multiple USEAs implanted in the residual peripheral arm nerves of human amputees for restoring control of 5 DOF and sensation of up to 131 proprioceptive and cutaneous hand sensory percepts. We also demonstrate that USEA-restored sensory percepts provide a useful source of feedback during closed-loop virtual prosthetic hand control.


title: Detecting semantic events in soccer games: towards a complete solution

abstract: This paper presents a new method for detecting semantic events which may happen in a soccer game. Our method requires the position information of the players and ball as input, which can be provided by a tracking system from processing either microwave signals or video sequences. Based on the tracking data, the method detects a wide range of player actions and game events using a set of heuristic rules which are derived from a hierarchical entity-relationship model representing the prior knowledge of soccer events. We have applied the proposed method as a key enabling module in a realworld sports entertainment application and the experimental results are given in the paper.


title: Multidimensional Recommender Systems: A Data Warehousing Approach

abstract: In this paper, we present a new data-warehousing-based approach to recommender systems. In particular, we propose to extend traditional two-dimensional user/item recommender systems to support multiple dimensions, as well as comprehensive profiling and hierarchical aggregation (OLAP) capabilities. We also introduce a new recommendation query language RQL that can express complex recommendations taking into account the proposed extensions. We describe how these extensions are integrated into a framework that facilitates more flexible and comprehensive user interactions with recommender systems.


title: Pharmacological inhibition of endocytic pathways: is it specific enough to be useful?

abstract: Eukaryotic cells constantly form and internalize plasma membrane vesicles in a process known as endocytosis. Endocytosis serves a variety of housekeeping and specialized cellular functions, and it can be mediated by distinct molecular pathways. Among them, internalization via clathrin-coated pits, lipid raft/caveolae-mediated endocytosis and macropinocytosis/phagocytosis are the most extensively characterized. The major endocytic pathways are usually distinguished on the basis of their differential sensitivity to pharmacological/chemical inhibitors, although the possibility of nonspecific effects of such inhibitors is frequently overlooked. This review provides a critical evaluation of the selectivity of the most widely used pharmacological inhibitors of clathrin-mediated, lipid raft/caveolae-mediated endocytosis and macropinocytosis/phagocytosis. The mechanisms of actions of these agents are described with special emphasis on their reported side effects on the alternative internalization modes and the actin cytoskeleton. The most and the least-selective inhibitors of each major endocytic pathway are highlighted.


Deep learning methods and applications - Classification of traffic signs and detection of Alzheimer's disease from images


title: Agency Problems and Dividend Policies around the World

abstract: This paper addresses the question of why firms pay dividends, the so-called outline two agency models of dividends. On what we call outcome minority shareholders to force corporate outsiders to disgorge cash. Under this model, stronger minority shareholder rights should be associated with higher dividends. On what we call substitute a reputation for decent treatment of minority shareholders so that firms can raise equity finance in the future. Under this model, stronger minority shareholder rights reduce the need for establishing a reputation, and so should be associated with lower dividends. We compare these models on a cross-section of 4,000 companies from around the world, which operate in 33 countries with different levels of shareholder protection, and therefore different strength of minority shareholder rights. The findings on payout levels and other results support the outcome agency model of dividends.


title: Design of low power logic gates by using 32nm and 16nm FinFET technology

abstract: In today's world different technologies are present in case of electronics market. The electronics market grown very rapidly because of most of the devices are compact, innovative, giving more efficiency and consumes less power with very small supply voltage. Because of the advancement in the semiconductor technology, integration of whole electronics system on a single chip is practicable. We have seen the change in the semiconductor technology from personal computer to the laptops and then to the cell phones and thus the mobile and computing markets are continue to innovate at dramatic rate. For all this, the most viable successor is a CMOS technology. But the 22nm node of CMOS fails to perform these operations because the shrinking of this CMOS leads to the short channel effect. Multigate FET technology is the most feasible successor to planar CMOS technology at the 22-nm node and beyond. These multi-gate transistors are called fin field-effect transistors (FinFET). Design of NOR and NAND circuits is present in this paper, to improve the speed and power of these gates. Also in this paper, the comparative study of different performance parameters for CMOS and FinFET technology for basic logic gates is present. Dynamic power, current, static power are some parameters which are evaluated with the help of tanner EDA tool.