title: Stemming Tigrinya Words for Information Retrieval

abstract: The increasing penetration of internet into less developed countries has resulted in the increase in the number of digital documents written in many minor languages. However, many of these languages have limited resources in terms of data, language resources and computational tools. Stemming is the reduction of inflected word forms into common basic form. It is an important analysis process in information retrieval and many natural language processing applications. In highly inflected languages such as Tigrinya, stemming is not always straightforward task. In this paper we present the development of stemmer for Tigrinya words to facilitate the information retrieval. We used a hybrid approach for stemming that combines rule based stemming which removes affixes in successively applied steps and dictionary based stemming which reduces stemming errors by verifying the resulting stem based on word distance measures. The stemmer was evaluated using two sets of Tigrinya words. The results show that it achieved an average accuracy of 89.3%.


title: Consistency Thresholds for the Planted Bisection Model

abstract: The planted bisection model is a random graph model in which the nodes are divided into two equal-sized communities and then edges are added randomly in a way that depends on the community membership. We establish necessary and sufficient conditions for the asymptotic recoverability of the planted bisection in this model. When the bisection is asymptotically recoverable, we give an efficient algorithm that successfully recovers it. We also show that the planted bisection is recoverable asymptotically if and only if with high probability every node belongs to the same community as the majority of its neighbors. Our algorithm for finding the planted bisection runs in time almost linear in the number of edges. It has three stages: spectral clustering to compute an initial guess, a "replica" stage to get almost every vertex correct, and then some simple local moves to finish the job. An independent work by Abbe, Bandeira, and Hall establishes similar (slightly weaker) results but only in the sparse case where pn, qn = Θ(log n /n).


title: Constrained planar cuts - Object partitioning for point clouds

abstract: While humans can easily separate unknown objects into meaningful parts, recent segmentation methods can only achieve similar partitionings by training on human-annotated ground-truth data. Here we introduce a bottom-up method for segmenting 3D point clouds into functional parts which does not require supervision and achieves equally good results. Our method uses local concavities as an indicator for inter-part boundaries. We show that this criterion is efficient to compute and generalizes well across different object classes. The algorithm employs a novel locally constrained geometrical boundary model which proposes greedy cuts through a local concavity graph. Only planar cuts are considered and evaluated using a cost function, which rewards cuts orthogonal to concave edges. Additionally, a local clustering constraint is applied to ensure the partitioning only affects relevant locally concave regions. We evaluate our algorithm on recordings from an RGB-D camera as well as the Princeton Segmentation Benchmark, using a fixed set of parameters across all object classes. This stands in stark contrast to most reported results which require either knowing the number of parts or annotated ground-truth for learning. Our approach outperforms all existing bottom-up methods (reducing the gap to human performance by up to 50 %) and achieves scores similar to top-down data-driven approaches.


title: Novel controllers based on instantaneous p-q power theory for transformerless SSSC and STATCOM

abstract: This paper investigates the operational performances of transformerless static synchronous series compensator (SSSC) and transformerless static synchronous shunt compensator (STATCOM), based on novel control schemes which are founded on instantaneous p-q power theory. In this paper, as reactive power compensators, the transformerless SSSC is used for controlling power flow of a 132 kV transmission line and the transformerless STATCOM is designed to regulate a bus voltage in a 6.6 kV distribution system. The proposed control systems use instantaneous p-q power theory in innovative ways that devote themselves not only to meet the reactive power demand but also to balance the DC link voltages. As a result, it is shown that the DC link voltage balancing control is simpler for the transformerless SSSC compared with the transformerless STATCOM. In addition, the control systems are based on phase shifted carrier modulation strategy, which has no restriction on the cascade number of H-bridge voltage source inverters. Comprehensive simulations are performed based on MATLAB/ Simulink to validate the analysis.


title: Treatment of anxiety in autism spectrum disorders using cognitive behaviour therapy: A systematic review

abstract: Objective: To review studies involving the treatment of anxiety in people with autism spectrum disorders (ASD) using Cognitive Behaviour Therapy (CBT) with the intent to inform practice and to identify areas for future research. Methods: Systematic searches of electronic databases, reference lists and journals identified nine studies. Each identified study that met pre-determined inclusion criteria was analysed and summarized in terms of: (a) participants, (b) intervention procedures, (c) dependent variables, (d) results of intervention and (e) certainty of evidence. To assess the certainty of evidence, each study’s design and related methodological details were critically appraised. Results: Positive outcomes were ubiquitous, suggesting CBT is an effective treatment for anxiety in individuals with Asperger’s. However, data involving other ASD diagnostic sub-types is limited. Conclusions: CBT has been modified for individuals with ASD by adding intervention components typically associated with applied behaviour analysis (e.g. systematic prompting and differential reinforcement). Future research involving a component analysis could potentially elucidate the mechanisms by which CBT reduces anxiety in individuals with ASD, ultimately leading to more efficient or effective interventions.


title: Face-space: A unifying concept in face recognition research

abstract: The concept of a multi-dimensional psychological space, in which faces can be represented according to their perceived properties, is fundamental to the modern theorist in face processing. Yet the idea was not clearly expressed until 1991. The background that led to Valentine’s (1991a) face-space is explained and its continuing influence on theories of face processing is discussed. Research that has explored the properties of the face-space and sought to understand caricature, including facial adaptation paradigms is reviewed. Face-space as a theoretical framework for understanding the effect of ethnicity and the development of face recognition is evaluated. Finally two applications of face-space in the forensic setting are discussed. From initially being presented as a model to explain distinctiveness, inversion and the effect of ethnicity, face-space has become a central pillar in many aspects of face processing. It is currently being developed to help us understand adaptation effects with faces. While being in principle a simple concept, face-space has shaped, and continues to shape, our understanding of face perception.


title: A refined input delay approach to sampled-data control

abstract: This paper considers sampled-data control of linear systems under uncertain sampling with the known upper bound on the sampling intervals. Recently a discontinuous Lyapunov function method was introduced by using impulsive system representation of the sampled-data systems (Naghshtabrizi, Hespanha, & Teel, 2008). The latter method improved the existing results, based on the input delay approach via time-independent Lyapunov functionals. The present paper introduces novel time-dependent Lyapunov functionals in the framework of the input delay approach, which essentially improve the existing results. These Lyapunov functionals do not grow after the sampling times. For the first time, for systems with time-varying delays, the introduced Lyapunov functionals can guarantee the stability under the sampling which may be greater than the analytical upper bound on the constant delay that preserves the stability. We show also that the term of the Lyapunov function, which was introduced in the above mentioned reference for the analysis of systems with constant sampling, is applicable to systems with variable sampling.


title: An Efficient Algorithm for Mining Association Rules in Large Databases

abstract: Mining for a.ssociation rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms. Compared to previous algorithms, our algorithm not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases. We have performed extensive experiments and compared the performance of our algorithm with one of the best existing algorithms. It was found that for large databases, the CPU overhead was reduced by as much as a factor of four and I/O was reduced by almost an order of magnitude. Hence this algorithm is especially suitable for very large size databases.


title: A Closed-Form Solution to Natural Image Matting

abstract: Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper we present a closed form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors, and show that in the resulting expression it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high quality mattes can be obtained on natural images from a surprisingly small amount of user input.


Vitamin D deficiency : a worldwide problem with health consequences 1 – 4


title: How tools in IDEs shape developers' navigation behavior

abstract: Understanding source code is crucial for successful software maintenance, and navigating the call graph is especially helpful to understand source code [12]. We compared maintenance performance across four different development environments: an IDE without any call graph exploration tool, a Call Hierarchy tool as found in Eclipse, and the tools Stacksplorer [7]and Blaze [11]. Using any of the call graph exploration tools more developers could solve certain maintenance tasks correctly. Only Stacksplorer and Blaze, however, were also able to decrease task completion times, although the Call Hierarchy offers access to a larger part of the call graph. To investigate if this result was caused by a change in navigation behavior between the tools, we used a set of predictive models to create formally comparable descriptions of programmer navigation. The results suggest that the decrease in task completion times has been caused by Stacksplorer and Blaze promoting call graph navigation more than the Call Hierarchy tool.


title: Fetal ECG Extraction Using Wavelet Transform

abstract: This paper presents a new algorithm to extract the fetal ECG from an ECG recorded on the mother?s abdomen. The algorithm consists of two steps: first, ECG of fetus is extracted from the original signal using a two-level wavelet transform. We use Daubechie wavelet transform, which is similar in shape to heart beat wave. The resulted signal is then low-pass filtered using the Savitzky-Golay smoothing filter to attenuate the effect of noise. The filtered signal is considered as the fetal ECG. The algorithm was applied on both synthetic and real ECG signals. The results in this paper show that the proposed algorithm has promising performance.


title: The B 2 C Group-Buying Model on the Internet An Empirical Study Based on Chinese Users ’ Behavior

abstract: Internet-based group-buying has become a new growth point for China's e-commerce. By studying B2C group-buying model, this paper has collected data of 20 major Chinese group-buying websites, studied factors that might influence Internet users' group-buying behavior, proposed a new model for evaluating the value of Internet enterprises, and provided a feasible reference model for the evaluation and performance of Internet enterprises.


title: Intelligent Carpet System, Based on Photonic Guided-Path Tomography, for Gait and Balance Monitoring in Home Environments

abstract: We report on the photonic variant of the previously introduced Guided-Path Tomography (GPT), by demonstrating a system for footstep imaging using Plastic Optical Fiber (POF) sensors. The 1 m × 2 m sensor head is manufactured by attaching 80 POF sensors on a standard commercial carpet underlay. The sensing principle relies on the sensitivity of POF to bending, quantified by measuring light transmission. The Photonic GPT (PGPT) system, comprising the sensor head with processing hardware and software, covered by a mass-production general-purpose carpet top, successfully performs footstep imaging and correctly displays the position and footfall of a person walking on the carpet in real time. We also present the implementation of fast footprint “center of mass” calculations, suitable for recording gait and footfall. A split-screen movie, showing the frame-by-frame camera-captured action next to the reproduced footprints is included in the electronic version of this paper.


title: The value of participation in virtual consumer communities on brand loyalty

abstract: Purpose – To examine the effects of consumers' lurking and posting behaviors in virtual consumer communities on specific brand loyalty.Design/methodology/approach – An online survey of a virtual community of Apple computer users was conducted to test a model of involvement, participation, loyalty, trust, and attitudes toward the brand exist in messages within the community.Findings – The causes and effects of lurking and posting differed. Lurking contributed to brand loyalty more than posting did, and the primary purpose of lurking was to look for information regarding product function/performance, instead of satisfying consumers' affective needs.Research limitations/implications – As a result of the limitations in a computer mediated communication environment, virtual consumer communities should be perceived as an arena for weak‐tied strangers to communicate for word‐of‐mouth (WOM) behavior, rather than as a brand community.Practical implications – Although negative messages in a community can hurt brand...


title: Influence of Kinesio Taping applied over biceps brachii on isokinetic elbow peak torque. A placebo controlled study in a population of young healthy subjects.

abstract: OBJECTIVES ::: To investigate the effect of Kinesio Taping (KT) applied over the biceps brachii on maximal isokinetic elbow torque. ::: ::: ::: DESIGN ::: This study followed a single-blinded, placebo controlled, repeated measures design. ::: ::: ::: METHODS ::: Isokinetic eccentric and concentric elbow peak torques were evaluated without taping (NT), with KT or placebo taping (PT) in 20 healthy participants. Furthermore, all the participants were required to perform a proprioceptive task of elbow joint position sense (JPS) in the same experimental conditions. ::: ::: ::: RESULTS ::: A significant effect of taping condition was found for concentric elbow peak torque (p=0.01). Post hoc analysis revealed a statistically significant concentric elbow peak torque improvement between NT and KT (p<0.05) but not between NT and PT. As regards eccentric elbow peak torque, we found a significant effect of taping condition (p<0.0001). Significant eccentric elbow peak torque differences were observed between NT and PT (p<0.01) and between KT and PT (p<0.001), while the increase observed from NT to KT conditions failed to reach significance at a post hoc analysis. ::: ::: ::: CONCLUSIONS ::: When applied over the biceps brachii, KT increases concentric elbow peak torque in a population of healthy participants, if compared with a PT.


title: Effect of temporal envelope smearing on speech reception

abstract: The effect of smearing the temporal envelope on the speech‐reception threshold (SRT) for sentences in noise and on phoneme identification was investigated for normal‐hearing listeners. For this purpose, the speech signal was split up into a series of frequency bands (width of 1/4, 1/2, or 1 oct) and the amplitude envelope for each band was low‐pass filtered at cutoff frequencies of 0, 1/2, 1, 2, 4, 8, 16, 32, or 64 Hz. Results for 36 subjects show (1) a severe reduction in sentence intelligibility for narrow processing bands at low cutoff frequencies (0–2 Hz); and (2) a marginal contribution of modulation frequencies above 16 Hz to the intelligibility of sentences (provided that lower modulation frequencies are completely present). For cutoff frequencies above 4 Hz, the SRT appears to be independent of the frequency bandwidth upon which envelope filtering takes place. Vowel and consonant identification with nonsense syllables were studied for cutoff frequencies of 0, 2, 4, 8, or 16 Hz in 1/4‐oct bands. Re...


title: Phytochemical, antimicrobial, and antioxidant activities of different citrus juice concentrates

abstract: The search for new antimicrobial compounds is ongoing. Its importance cannot be overemphasized in an era of emerging resistant pathogenic organisms. This study therefore investigated the phytochemical composition and antioxidant and antimicrobial activities of different citrus juice concentrates. Fruit juices of Citrus tangerine (tangerine), Citrus paradisi (grape), Citrus limon (lemon), and Citrus aurantifolia (lime) were evaluated. Antimicrobial activities against five bacterial and three fungal strains were evaluated. The results revealed the presence of alkaloids, flavonoids, steroids, terpenoids, saponins, cardiac glycosides, and reducing sugars in all the juice concentrates. DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging capacities varied with tangerine and grape juices having better scavenging capacities than lemon and lime juices. Grape juice was observed to have a significantly higher (P < 0.05) ferric-reducing antioxidant potential (FRAP) value (364.2 ± 10.25 μmol/L Fe(II)/g of the extract) than the reference antioxidant, ascorbic acid (312.88 ± 5.61 μmol/L). Antimicrobial studies revealed differential antimicrobial activities against different microbial strains. Zones of inhibition ranging from 4 to 26 mm were observed for the antibacterial tests with 0-24 mm for antifungal test. Minimum inhibitory concentrations (MIC) and minimum bacteriostatic concentrations (MBC) for concentrates against bacterial strains ranged from 12.5 to 200 μg/mL. Lemon and lime juice concentrates had lower MIC and MBC values with orange and tangerine having the highest values. Minimum fungicidal concentrations ranged from 50 to 200 μg/mL. The results of this study suggest that these juice concentrates may have beneficial antimicrobial roles that can be exploited in controlling unwanted microbial growth.


title: A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant

abstract: This paper proposes the Top-View Transformation Model for image coordinate transformation, which involves transforming a perspective projection image into its corresponding bird's eye vision. A fitting parameters searching algorithm estimates the parameters that are used to transform the coordinates from the source image. Using this approach, it is not necessary to provide any interior and exterior orientation parameters of the camera. The designed car parking assistant system can be installed at the rear end of the car, providing the driver with a clearer image of the area behind the car. The processing time can be reduced by storing and using the transformation matrix estimated from the first image frame for a sequence of video images. The transformation matrix can be stored as the Matrix Mapping Table, and loaded into the embedded platform to perform the transformation. Experimental results show that the proposed approaches can provide a clearer and more accurate bird's eye view to the vehicle driver.


title: Step Length Estimation Using Handheld Inertial Sensors

abstract: In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user's step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor's signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand's motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.


title: Active Convolution: Learning the Shape of Convolution for Image Classification

abstract: In recent years, deep learning has achieved great success in many computer vision applications. Convolutional neural networks (CNNs) have lately emerged as a major approach to image classification. Most research on CNNs thus far has focused on developing architectures such as the Inception and residual networks. The convolution layer is the core of the CNN, but few studies have addressed the convolution unit itself. In this paper, we introduce a convolution unit called the active convolution unit (ACU). A new convolution has no fixed shape, because of which we can define any form of convolution. Its shape can be learned through backpropagation during training. Our proposed unit has a few advantages. First, the ACU is a generalization of convolution, it can define not only all conventional convolutions, but also convolutions with fractional pixel coordinates. We can freely change the shape of the convolution, which provides greater freedom to form CNN structures. Second, the shape of the convolution is learned while training and there is no need to tune it by hand. Third, the ACU can learn better than a conventional unit, where we obtained the improvement simply by changing the conventional convolution to an ACU. We tested our proposed method on plain and residual networks, and the results showed significant improvement using our method on various datasets and architectures in comparison with the baseline. Code is available at https://github.com/jyh2986/Active-Convolution.


title: Developing a human-centric agricultural model in the IoT environment

abstract: The prevalent state of agriculture, especially in developing countries, is not efficient and organized enough to address the growing demand for food, a direct result of the increasing human population. Internet of things and cloud computing together have provided a promising opportunity to resolve the challenges posed by this increasing demand worldwide. By employing IoT and cloud services, and through precision farming tactics, the efficiency and quality of agricultural production, storage and transportation can be tremendously improved. In this paper, we present the architecture of a multilayered enabling platform for incorporating IoT technologies in the agricultural sector. This work makes important contributions by proposing a feasible human-centric IoT model for agriculture with special emphasis on developing nations.


title: EEGNET: An Open Source Tool for Analyzing and Visualizing M/EEG Connectome

abstract: The brain is a large-scale complex network often referred to as the “connectome”. Exploring the dynamic behavior of the connectome is a challenging issue as both excellent time and space resolution is required. In this context Magneto/Electroencephalography (M/EEG) are effective neuroimaging techniques allowing for analysis of the dynamics of functional brain networks at scalp level and/or at reconstructed sources. However, a tool that can cover all the processing steps of identifying brain networks from M/EEG data is still missing. In this paper, we report a novel software package, called EEGNET, running under MATLAB (Math works, inc), and allowing for analysis and visualization of functional brain networks from M/EEG recordings. EEGNET is developed to analyze networks either at the level of scalp electrodes or at the level of reconstructed cortical sources. It includes i) Basic steps in preprocessing M/EEG signals, ii) the solution of the inverse problem to localize / reconstruct the cortical sources, iii) the computation of functional connectivity among signals collected at surface electrodes or/and time courses of reconstructed sources and iv) the computation of the network measures based on graph theory analysis. EEGNET is the unique tool that combines the M/EEG functional connectivity analysis and the computation of network measures derived from the graph theory. The first version of EEGNET is easy to use, flexible and user friendly. EEGNET is an open source tool and can be freely downloaded from this webpage: https://sites.google.com/site/eegnetworks/.


title: A Graphene Field-Effect Device

abstract: In this letter, a top-gated field-effect device (FED) manufactured from monolayer graphene is investigated. Except for graphene deposition, a conventional top-down CMOS-compatible process flow is applied. Carrier mobilities in graphene pseudo-MOS structures are compared to those obtained from the top-gated Graphene-FEDs. The extracted values exceed the universal mobility of silicon and silicon-on-insulator MOSFETs


title: Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP

abstract: Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial domains. GNNs employ trainable modules which can be assembled in different configurations that reflect the relational structure of each problem instance. In this paper, we show that GNNs can learn to solve, with very little supervision, the decision variant of the Traveling Salesperson Problem (TSP), a highly relevant NP-Complete problem. Our model is trained to function as an effective message-passing algorithm in which edges (embedded with their weights) communicate with vertices for a number of iterations after which the model is asked to decide whether a route with cost < C exists. We show that such a network can be trained with sets of dual examples: given the optimal tour cost C∗, we produce one decision instance with target cost x% smaller and one with target cost x% larger than C∗. We were able to obtain 80% accuracy training with −2%,+2% deviations, and the same trained model can generalize for more relaxed deviations with increasing performance. We also show that the model is capable of generalizing for larger problem sizes. Finally, we provide a method for predicting the optimal route cost within 2% deviation from the ground truth. In summary, our work shows that Graph Neural Networks are powerful enough to solve NP-Complete problems which combine symbolic and numeric data.


title: e-Democracy Goes Ahead. The Internet As a Tool for Improving Deliberative Policies?

abstract: At first, important features of the current situation of citizens' participation in political procedures such as reasons for political apathy, political reactions with new ways of direct participation and the increase of communication-based means of participation are summarized. In the second part, "information", "consultation" and "active participation" are described as main components of non-organized citizen communication which must be embedded into users' and administrations' environment not only technically, but also economically, legally, organisationally, motivationally, and politically. Two case studies illustrate opportunities given by online-participation and underline the requirements to use online participation as a supplement, not as a replacement of traditional participation and to combine the advantages of various online and offline ways of citizens' involvement in a multi-channel approach.


title: Discrete Choice Methods And Their Applications To Short Term Travel Decisions

abstract: Modeling travel behavior is a key aspect of demand analysis, where aggregate demand is the accumulation of individuals’ decisions. In this chapter, we focus on “short-term” travel decisions. The most important short-term travel decisions include choice of destination for a non-work trip, choice of travel mode, choice of departure time and choice of route. It is important to note that short-term decisions are conditional on long-term travel and mobility decisions such as car ownership and residential and work locations.


title: Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks

abstract: In recent years, there has been a growing interest towards the application of artificial intelligence approaches in software engineering (SE) processes. In the specific area of SE for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and AI. However, just few significant results have been published. This paper briefly studies uncertainty in SASs and surveys techniques that have been developed to engineer SASs in order to tackle uncertainty. In particular, we highlight techniques that use AI concepts. We also report and discuss our own experience using Dynamic Decision Networks (DDNs) to model and support decision-making in SASs while explicitly taking into account uncertainty. We think that Bayesian inference, and specifically DDNs, provide a useful formalism to engineer systems that dynamically adapt themselves at runtime as more information about the environment and the execution context is discovered during execution. We also discuss partial results, challenges and future research avenues.


title: Link prediction in multi-modal social networks

abstract: Online social networks like Facebook recommend new friends to users based on an explicit social network that users build by adding each other as friends. The majority of earlier work in link prediction infers new interactions between users by mainly focusing on a single network type. However, users also form several implicit social networks through their daily interactions like commenting on people's posts or rating similarly the same products. Prior work primarily exploited both explicit and implicit social networks to tackle the group/item recommendation problem that recommends to users groups to join or items to buy. In this paper, we show that auxiliary information from the user-item network fruitfully combines with the friendship network to enhance friend recommendations. We transform the well-known Katz algorithm to utilize a multi-modal network and provide friend recommendations. We experimentally show that the proposed method is more accurate in recommending friends when compared with two single source path-based algorithms using both synthetic and real data sets.


title: R-Mo: A new mobile robotic platform to reduce variations in height and pitch angle on rugged terrain

abstract: This paper presents a new mobile robotic platform (R-Mo) which can reduce unexpected variations in height as well as pitch angle of its main body while traversing rough terrains. As a measure for the smooth movement of mobile platform, the variations in height and pitch angle are chosen in this study. Then, the kinematic analysis on the Rocker-Bogie mechanism is carried out to investigate its variations in height and pitch angle on rough terrains. Based on this result, a new mobile platform is systematically designed by combining the Rocker-Bogie with the inverse four bar linkage. The extensive experiments are carried out by using the Rocker-Bogie mechanism and the proposed R-Mo against rough terrain, which validate that in comparison with the Rocker-Bogie mechanism, the average and maximum variations in height of proposed R-Mo are reduced by 12.72% and 5.96%, respectively, and the average and maximum variations in pitch angle of proposed R-Mo are considerably reduced by 65.87 % and 60.53 %, respectively.


Improved Speech-to-Text Translation with the Fisher and Callhome Spanish–English Speech Translation Corpus


title: Granger Causality Networks for Categorical Time Series

abstract: We present a new framework for learning Granger causality networks for multivariate categorical time series, based on the mixture transition distribution (MTD) model. Traditionally, MTD is plagued by a nonconvex objective, non-identifiability, and presence of many local optima. To circumvent these problems, we recast inference in the MTD as a convex problem. The new formulation facilitates the application of MTD to high-dimensional multivariate time series. As a baseline, we also formulate a multi-output logistic autoregressive model (mLTD), which while a straightforward extension of autoregressive Bernoulli generalized linear models, has not been previously applied to the analysis of multivariate categorial time series. We develop novel identifiability conditions of the MTD model and compare them to those for mLTD. We further devise novel and efficient optimization algorithm for the MTD based on the new convex formulation, and compare the MTD and mLTD in both simulated and real data experiments. Our approach simultaneously provides a comparison of methods for network inference in categorical time series and opens the door to modern, regularized inference with the MTD model.


title: Real-time embedded system for traffic sign recognition based on ZedBoard

abstract: This paper presents a design methodology of a real-time embedded system that processes the detection and recognition of road signs while the vehicle is moving. An efficient algorithm was proposed, which operates in two processing steps: the detection and the recognition. Regions of interest were extracted by using the Maximally Stable Extremal Regions Method. For the recognition phase, Oriented FAST and Rotated BRIEF features were used. A hardware system based on the Xilinx Zynq platform was developed. The designed system can achieve real-time video processing while assuring constraints and a high-level accuracy in terms of detection and recognition rates.


title: A Probabilistic Framework for Location Inference from Social Media

abstract: We study the extent to which we can infer users' geographical locations from social media. Location inference from social media can benefit many applications, such as disaster management, targeted advertising, and news content tailoring. In recent years, a number of algorithms have been proposed for identifying user locations on social media platforms such as Twitter and Facebook from message contents, friend networks, and interactions between users. In this paper, we propose a novel probabilistic model based on factor graphs for location inference that offers several unique advantages for this task. First, the model generalizes previous methods by incorporating content, network, and deep features learned from social context. The model is also flexible enough to support both supervised learning and semi-supervised learning. Second, we explore several learning algorithms for the proposed model, and present a Two-chain Metropolis-Hastings (MH+) algorithm, which improves the inference accuracy. Third, we validate the proposed model on three different genres of data - Twitter, Weibo, and Facebook - and demonstrate that the proposed model can substantially improve the inference accuracy (+3.3-18.5% by F1-score) over that of several state-of-the-art methods.


title: Hox protein mutation and macroevolution of the insect body plan

abstract: A fascinating question in biology is how molecular changes in developmental pathways lead to macroevolutionary changes in morphology. Mutations in homeotic (Hox) genes have long been suggested as potential causes of morphological evolution1,2, and there is abundant evidence that some changes in Hox expression patterns correlate with transitions in animal axial pattern3. A major morphological transition in metazoans occurred about 400 million years ago, when six-legged insects diverged from crustacean-like arthropod ancestors with multiple limbs4,5,6,7. In Drosophila melanogaster and other insects, the Ultrabithorax (Ubx) and abdominal A (AbdA, also abd-A) Hox proteins are expressed largely in the abdominal segments, where they can suppress thoracic leg development during embryogenesis3. In a branchiopod crustacean, Ubx/AbdA proteins are expressed in both thorax and abdomen, including the limb primordia, but do not repress limbs8,9,10,11. Previous studies led us to propose that gain and loss of transcriptional activation and repression functions in Hox proteins was a plausible mechanism to diversify morphology during animal evolution12. Here we show that naturally selected alteration of the Ubx protein is linked to the evolutionary transition to hexapod limb pattern.


title: A Practical Guide to Wavelet Analysis

abstract: A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El Nino–Southern Oscillation (ENSO). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmoller, cross-wavelet spectra, and coherence are described. The statistical significance tests are used to give a quantitative measure of change...


title: Implications of E-learning systems and self-efficiency on students outcomes: a model approach

abstract: This paper presents a model approach to examine the relationships among e-learning systems, self-efficacy, and students' apparent learning results for university online courses. Independent variables included in this study are e-learning system quality, information quality, computer self-efficacy, system-use, self-regulated learning behavior and user satisfaction as prospective determinants of online learning results. An aggregate of 674 responses of students completing at least one online course from Wawasan Open University (WOU) Malaysia were used to fit the path analysis model. The results indicated that system quality, information quality, and computer self-efficacy all affected system use, user satisfaction, and self-managed learning behavior of students. Proposed path analytical model suggests that hypothesized variables are useful to forecast e-learning results


title: A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data

abstract: Recent compressive sensing results show that it is possible to accurately reconstruct certain compressible signals from relatively few linear measurements via solving nonsmooth convex optimization problems. In this paper, we propose the use of the alternating direction method - a classic approach for optimization problems with separable variables (D. Gabay and B. Mercier, ?A dual algorithm for the solution of nonlinear variational problems via finite-element approximations,? Computer and Mathematics with Applications, vol. 2, pp. 17-40, 1976; R. Glowinski and A. Marrocco, ?Sur lapproximation par elements finis dordre un, et la resolution par penalisation-dualite dune classe de problemes de Dirichlet nonlineaires,? Rev. Francaise dAut. Inf. Rech. Oper., vol. R-2, pp. 41-76, 1975) - for signal reconstruction from partial Fourier (i.e., incomplete frequency) measurements. Signals are reconstructed as minimizers of the sum of three terms corresponding to total variation, ?1-norm of a certain transform, and least squares data fitting. Our algorithm, called RecPF and published online, runs very fast (typically in a few seconds on a laptop) because it requires a small number of iterations, each involving simple shrinkages and two fast Fourier transforms (or alternatively discrete cosine transforms when measurements are in the corresponding domain). RecPF was compared with two state-of-the-art algorithms on recovering magnetic resonance images, and the results show that it is highly efficient, stable, and robust.


title: Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing

abstract: Nonparametric two sample testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. We refer to the most common settings as mean difference alternatives (MDA), for testing differences only in first moments, and general difference alternatives (GDA), which is about testing for any difference in distributions. A large number of test statistics have been proposed for both these settings. This paper connects three classes of statistics - high dimensional variants of Hotelling's t-test, statistics based on Reproducing Kernel Hilbert Spaces, and energy statistics based on pairwise distances. We ask the question: how much statistical power do popular kernel and distance based tests for GDA have when the unknown distributions differ in their means, compared to specialized tests for MDA? ::: We formally characterize the power of popular tests for GDA like the Maximum Mean Discrepancy with the Gaussian kernel (gMMD) and bandwidth-dependent variants of the Energy Distance with the Euclidean norm (eED) in the high-dimensional MDA regime. Some practically important properties include (a) eED and gMMD have asymptotically equal power; furthermore they enjoy a free lunch because, while they are additionally consistent for GDA, they also have the same power as specialized high-dimensional t-test variants for MDA. All these tests are asymptotically optimal (including matching constants) under MDA for spherical covariances, according to simple lower bounds, (b) The power of gMMD is independent of the kernel bandwidth, as long as it is larger than the choice made by the median heuristic, (c) There is a clear and smooth computation-statistics tradeoff for linear-time, subquadratic-time and quadratic-time versions of these tests, with more computation resulting in higher power.


title: A history-based macro by example system

abstract: Many tasks performed using computer interfaces are very repetitive. While programmers can write macros or procedures to automate these repetitive tasks, this requires special skills. Demonstrational systems make macro building accessible to all users, but most provide either no visual representation of the macro or only a textual representation. We have developed a history-based visual representation of commands in a graphical user interface. This representation supports the definition of macros by example in several novel ways. At any time, a user can open a history window, review the commands executed in a session, select operations to encapsulate into a macro, and choose objects and their attributes as arguments. The system has facilities to generalize the macro automatically, save it for future use, and edit it.


中国科技论文在线 Modeling , Analysis and Simulation of a Buck Converter Under Cascade Control


title: PSYCHOTHERAPY AND MEDICATION IN THE TREATMENT OF ADULT AND GERIATRIC DEPRESSION: WHICH MONOTHERAPY OR COMBINED TREATMENT?

abstract: Objective: The authors reviewed the literature with respect to the relative efficacy of medications and psychotherapy alone and in combination in the treatment of depression. Data Sources and Study Selection: Findings from empirical studies comparing medications and psychotherapy alone and in combination were synthesized and prognostic and prescriptive indices identified. We searched both MEDLINE and PsychINFO for items published from January 1980 to October 2004 using the following terms: treatment of depression, psychotherapy and depression, and pharmacotherapy and depression. Studies were selected that randomly assigned depressed patients to combined treatment versus monotherapy. Data Synthesis: Medication typically has a rapid and robust effect and can prevent symptom return so long as it is continued or maintained, but does little to reduce risk once its use is terminated. Both interpersonal psychotherapy (IPT) and cognitive-behavioral therapy (CBT) can be as effective as medications in the acute treatment of depressed outpatients. Interpersonal psychotherapy may improve interpersonal functioning, whereas CBT appears to have an enduring effect that reduces subsequent risk following treatment termination. Ongoing treatment with either IPT or CBT appears to further reduce risk. Treatment with the combination of medication and IPT or CBT retains the specific benefits of each and may enhance the probability of response over either monotherapy, especially in chronic depressions. Conclusion: Both medication and certain targeted psychotherapies appear to be effective in the treatment of depression. Although several prognostic indices have been identified that predict need for longer or more intensive treatment, few prescriptive indices have yet been established to select among the different treatments. Combined treatment can improve response with selected patients and enhance its breadth (IPT) or stability (CBT).


title: Interactive search in image retrieval: a survey

abstract: We are living in an Age of Information where the amount of accessible data from science and culture is almost limitless. However, this also means that finding an item of interest is increasingly difficult, a digital needle in the prover- bial haystack. In this article, we focus on the topic of content- based image retrieval using interactive search techniques, i.e., how does one interactively find any kind of imagery from any source, regardless of whether it is photographic, MRI or X-ray? We highlight trends and ideas from over 170 recent research papers aiming to capture the wide spec- trum of paradigms and methods in interactive search, includ- ing its subarea relevance feedback. Furthermore, we identify promising research directions and several grand challenges for the future.


title: A high-performance miniaturized 3 15.5-GHZ 13-DB CMOS distributed amplifier

abstract: In this article, we demonstrate a high-performance 3–15.5-GHz distributed amplifier (DA) for ultra-wideband (UWB) system applications implemented with a standard 0.18-μm CMOS technology. To achieve high gain, wide bandwidth, and small chip-area at the same time, miniaturized inductors with high quality-factor and small parasitic capacitance were adopted, the bias of the field-effect transistors (FETs) was optimized, and cascode FETs were used to replace the traditional common-source FETs. In addition, to improve the stability of the DA, a damping resistor RG was added to the gate of the common-gate FETs. Over the 3–15.5-GHz frequency band, this LNA achieved good forward gain (S21) of 10–13 dB, input return loss (S11) of −11.5 to −31.4 dB, reverse isolation (S12) of −21.6 to −37.1 dB, and noise figure (NF) of 5 to 7.2 dB. Moreover, the DA exhibited good input 1-dB compression point (P1dB) of −1.5 dBm and input third-order inter-modulation point (IIP3) of 12.5 dBm at 6 GHz. The chip area was only 1050.9 μm × 539.8 μm (i.e. 0.567 mm2) excluding the test pads. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2742–2747, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22820


title: HOW TO REVIEW A PAPER

abstract: Most scientists acquire their training in manuscript review not through instruction but by actually doing it. Formal training in manuscript analysis is rarely, if ever, provided. Editors usually choose reviewers because of expertise in a given subject area and availability. If an individual repeatedly submits bad reviews, it is likely that that person will not be asked to review a manuscript again. Being invited to review a manuscript is an honor, not only because you are being recognized for your eminence in a particular area of research but also because of the responsibility and service you provide to the journal and scientific community. The purpose of this article is to define how best to peer review an article. We will stipulate several principles of peer review and discuss some of the main elements of a good manuscript review, the basic responsibilities of a reviewer, and the rewards and responsibilities that accompany this process. Proper reviewer conduct is essential for making the peer review process valuable and the journal trustworthy.


title: Betweenness centrality: algorithms and implementations

abstract: Betweenness centrality is an important metric in the study of social networks, and several algorithms for computing this metric exist in the literature. This paper makes three contributions. First, we show that the problem of computing betweenness centrality can be formulated abstractly in terms of a small set of operators that update the graph. Second, we show that existing parallel algorithms for computing betweenness centrality can be viewed as implementations of different schedules for these operators, permitting all these algorithms to be formulated in a single framework. Third, we derive a new asynchronous parallel algorithm for betweenness centrality that (i) works seamlessly for both weighted and unweighted graphs, (ii) can be applied to large graphs, and (iii) is able to extract large amounts of parallelism. We implemented this algorithm and compared it against a number of publicly available implementations of previous algorithms on two different multicore architectures. Our results show that the new algorithm is the best performing one in most cases, particularly for large graphs and large thread counts, and is always competitive against other algorithms.


title: The automated will: Nonconscious activation and pursuit of behavioral goals

abstract: It is proposed that goals can be activated outside of awareness and then operate nonconsciously to guide self-regulation effectively (J. A. Bargh, 1990). Five experiments are reported in which the goal either to perform well or to cooperate was activated, without the awareness of participants, through a priming manipulation. In Experiment 1 priming of the goal to perform well caused participants to perform comparatively better on an intellectual task. In Experiment 2 priming of the goal to cooperate caused participants to replenish a commonly held resource more readily. Experiment 3 used a dissociation paradigm to rule out perceptual-construal alternative explanations. Experiments 4 and 5 demonstrated that action guided by nonconsciously activated goals manifests two classic content-free features of the pursuit of consciously held goals. Nonconsciously activated goals effectively guide action, enabling adaptation to ongoing situational demands.


title: Decoding by linear programming

abstract: This paper considers the classical error correcting problem which is frequently discussed in coding theory. We wish to recover an input vector $f \in \R^n$ from corrupted measurements $y = A f + e$. Here, $A$ is an $m$ by $n$ (coding) matrix and $e$ is an arbitrary and unknown vector of errors. Is it possible to recover $f$ exactly from the data $y$? We prove that under suitable conditions on the coding matrix $A$, the input $f$ is the unique solution to the $\ell_1$-minimization problem ($\|x\|_{\ell_1} := \sum_i |x_i|$) $$ \min_{g \in \R^n} \| y - Ag \|_{\ell_1} $$ provided that the support of the vector of errors is not too large, $\|e\|_{\ell_0} := |\{i : e_i \neq 0\}| \le \rho \cdot m$ for some $\rho>0$. In short, $f$ can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program). In addition, numerical experiments suggest that this recovery procedure works unreasonably well; $f$ is recovered exactly even in situations where a significant fraction of the output is corrupted.


title: Age-related loss of calbindin from human basal forebrain cholinergic neurons

abstract: Loss of basal forebrain cholinergic neurons (BFCN) occurs in many age-related neurological diseases. Although age is the common risk factor in these disorders, no consistent age-related changes have been reported in the human BFCN. We investigated age-related alterations in choline acetyltransferase (ChAT), low-affinity nerve growth factor receptor (p75 LNGFR ) and calbindin-D 28k (CalBP) immunoreactivity in the human BFCN. No significant age-related changes were observed in ChAT or p75 LNGFR immunoreactivity. By contrast, normal aging was accompanied by a selective, substantial and significant loss of CalBP immunoreactivity from the BFCN. Other CalBP-positive neurons were unchanged. Loss of the calcium buffering capacity conferred by CalBP may leave the BFCN vulnerable to damage in neurodegenerative disorders.


title: SDNHAS: An SDN-Enabled Architecture to Optimize QoE in HTTP Adaptive Streaming

abstract: HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.


title: Good vibrations? Vibrotactile self-stimulation reveals anticipation of body-related action effects in motor control

abstract: Previous research suggests that motor actions are intentionally generated by recollecting their sensory consequences. Whereas this has been shown to apply to visual or auditory consequences in the environment, surprisingly little is known about the contribution of immediate, body-related consequences, such as proprioceptive and tactile reafferences. Here, we report evidence for a contribution of vibrotactile reafferences to action selection by using a response–effect compatibility paradigm. More precisely, anticipating actions to cause spatially incompatible vibrations delayed responding to a small but reliable degree. Whereas this observation suggests functional equivalence of body-related and environment-related reafferences to action control, the future application of the described experimental procedure might reveal functional peculiarities of specific types of sensory consequences in action control.


TRADE: A Transparent, Decentralized Traceability System for the Supply Chain


title: The design of 2 V 2.4 GHz CMOS low-noise low-power bandpass amplifier with parallel spiral inductors

abstract: A 2.4 GHz low-noise low-power single-to-differential bandpass amplifier is implemented in 0.5 /spl mu/m CMOS process. In this bandpass amplifier, the compact tunable positive-feedback circuit is connected to the integrated spiral inductor to enhance its Q value. The simple diode varactor circuit is adopted for center frequency tuning. Two parallel spiral inductors are used to reduce the effective inductance and raise the center frequency. HSPICE simulation has been performed to verify the performance of the designed bandpass amplifier. It has been shown that the amplifier has a gain of 15 dB at Q=45 under 2 V power supply with 39 mW power dissipation. The noise figure is 4.36 dB in the passband. The tunable frequency range is between 2.369 GHz and 2.422 GHz. The gain difference and phase difference between the two differential output nodes are 0.0011905 dB and 179.932/spl deg/ respectively, at 2.4 GHz.


title: POWERING THE FUTURE WITH THE INTEGRATED POWER SYSTEM

abstract: For the past four years, Advanced Surface Machinery Programs (SEA 03R2) has been developing the Integrated Power System (IPS) to reduce ship acquisition and life cycle costs while still meeting all ship performance requirements. IPS provides electrical power to ship service loads and electric propulsion for a wide range of ship applications including surface combatants, aircraft carriers, amphibious ships, auxiliary ships, sealift and high value commercial ships. IPS consists of an architecture and a family of modules from which affordable and high performance configurations can be developed for the full range of ship applications. Two years ago, the initial IPS concepts were presented at ASNE Day 1994. Since then, much has been learned through the Reduced Scale Advanced Development (RSAD) and Full Scale Advanced Development (FSAD) programs. This paper describes the fundamental IPS architecture, details the evolving family of modules and their interface standards, and outlines the Mass Customization based design process for achieving customer performance requirements with an affordable IPS configuration.


title: DDCCast: Meeting Point to Multipoint Transfer Deadlines Across Datacenters using ALAP Scheduling Policy

abstract: Large cloud companies manage dozens of datacenters across the globe connected using dedicated inter-datacenter networks. An important application of these networks is data replication which is done for purposes such as increased resiliency via making backup copies, getting data closer to users for reduced delay and WAN bandwidth usage, and global load balancing. These replications usually lead to network transfers with deadlines that determine the time prior to which all datacenters should have a copy of the data. Inter-datacenter networks have limited capacity and need be utilized efficiently to maximize performance. In this report, we focus on applications that transfer multiple copies of objects from one datacenter to several datacenters given deadline constraints. Existing solutions are either deadline agnostic, or only consider point-to-point transfers. We propose DDCCast, a simple yet effective deadline aware point to multipoint technique based on DCCast and using ALAP traffic allocation. DDCCast performs careful admission control using temporal planning, uses rate-allocation and rate-limiting to avoid congestion and sends traffic over forwarding trees that are carefully selected to reduce bandwidth usage and maximize deadline meet rate. We perform experiments confirming DDCCast's potential to reduce total bandwidth usage by up to $45\%$ while admitting up to $25\%$ more traffic into the network compared to existing solutions that guarantee deadlines.


title: Fast and Accurate Parametric Curve Length Computation

abstract: This paper describes a simple technique for evaluating the length of a parametric curve. The technique approximates sections of the curve to the arc of a circle using only sample points on the curve; no computation of derivatives or other properties is required. This method is almost as quick to implement and compute as chord-length summation, but it is much more accurate. It also provides recursion criteria for adaptive sampling. An extension of the algorithm to estimate the area of a parametric surface is discussed briefly.


title: Bird Species Classification using Transfer Learning with Multistage Training

abstract: Bird species classification has received more and more attention in the field of computer vision, for its promising applications in biology and environmental studies. Recognizing bird species is difficult due to the challenges of discriminative region localization and fine-grained feature learning. In this paper, we have introduced a Transfer learning based method with multistage training. We have used both Pre-Trained Mask-RCNN and an ensemble model consisting of Inception Nets (InceptionV3&InceptionResNetV2 ) to get localization and species of the bird from the images respectively. Our final model achieves an F1 score of 0.5567 or 55.67 % on the dataset provided in CVIP 2018 Challenge.


title: Does Haze Removal Help CNN-based Image Classification?

abstract: Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance.


title: Management: Research Overview and Prospects

abstract: This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references.


title: Investigations on frequency agile microstrip patch antenna loaded with varactor diode

abstract: A frequency agile dominant mode (TM11) microstrip circular patch antenna by implementing varactor diode (Skyworks: SMV 1234) has been investigated. Frequency agility from 1.95 GHz to 1.55 GHz has been achieved with acceptable radiation performance by varying varactor diode capacitance from 1.32 pF to 9.36 pF. Effect of packaging of the diode compared to the ideal diode is shown. Additional results will be presented during the symposium.


title: Voice activated command and control with speech recognition over WiFi

abstract: This paper presents work conducted to date on the development of a voice activated command and control framework specifically for the control of remote devices in a ubiquitous computing environment. The prototype device is a Java controlled Lego Mindstorm robot. The research considers three different scenario configurations. A recognition grammar for command and control of the robot has been created and implemented in Java, in part in the recognition engine and in part on the robot. The physical topology involves Java at each node endpoint, that is, at the handheld PC (iPaq), the PC workstation, the Linux server and onboard the robot (including its Java based Lejos OS). Network communications is primarily WLAN with an element of IR where the robot is concerned. The speech recognition software used includes Sphinx4, Microsoft SAPI and the Java Speech API. We compare these speech technologies and present their benefits in the context of this research. For each given scenario we present and discuss the implementation challenges encountered and their corresponding solutions, including future plans to create additional grammars to extend the framework’s range of devices.


title: Mean Field Residual Networks: On the Edge of Chaos

abstract: We study randomly initialized residual networks using mean field theory and the theory of difference equations. Classical feedforward neural networks, such as those with tanh activations, exhibit exponential behavior on the average when propagating inputs forward or gradients backward. The exponential forward dynamics causes rapid collapsing of the input space geometry, while the exponential backward dynamics causes drastic vanishing or exploding gradients. We show, in contrast, that by adding skip connections, the network will, depending on the nonlinearity, adopt subexponential forward and backward dynamics, and in many cases in fact polynomial. The exponents of these polynomials are obtained through analytic methods and proved and verified empirically to be correct. In terms of the "edge of chaos" hypothesis, these subexponential and polynomial laws allow residual networks to "hover over the boundary between stability and chaos," thus preserving the geometry of the input space and the gradient information flow. In our experiments, for each activation function we study here, we initialize residual networks with different hyperparameters and train them on MNIST. Remarkably, our initialization time theory can accurately predict test time performance of these networks, by tracking either the expected amount of gradient explosion or the expected squared distance between the images of two input vectors. Importantly, we show, theoretically as well as empirically, that common initializations such as the Xavier or the He schemes are not optimal for residual networks, because the optimal initialization variances depend on the depth. Finally, we have made mathematical contributions by deriving several new identities for the kernels of powers of ReLU functions by relating them to the zeroth Bessel function of the second kind.


title: Spontaneous Rupture of the Imperforate Hymen in an Adolescent Girl with Hematocolpometra

abstract: Background. Imperforate hymen is usually treated with hymenotomy, and the management after its spontaneous rupture is not very well known. Case. In this paper, we present spontaneous rupture of the imperforate hymen in a 13-year-old adolescent girl with hematocolpometra just before a planned hymenotomy operation. The patient was managed conservatively with a satisfactory outcome. Conclusion. Hymenotomy may not be needed in cases with spontaneous rupture of the imperforate hymen if adequate opening for menstrual discharge is warranted.


title: Adversarial TableQA: Attention Supervision for Question Answering on Tables

abstract: The task of answering a question given a text passage has shown great developments on model performance thanks to community efforts in building useful datasets. Recently, there have been doubts whether such rapid progress has been based on truly understanding language. The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table. We show that existing efforts, of using"answers"for both evaluation and supervision for TableQA, show deteriorating performances in adversarial settings of perturbations that do not affect the answer. This insight naturally motivates to develop new models that understand question and table more precisely. For this goal, we propose Neural Operator (NeOp), a multi-layer sequential network with attention supervision to answer the query given a table. NeOp uses multiple Selective Recurrent Units (SelRUs) to further help the interpretability of the answers of the model. Experiments show that the use of operand information to train the model significantly improves the performance and interpretability of TableQA models. NeOp outperforms all the previous models by a big margin.


title: A Rumor Events Detection Method Based on Deep Bidirectional GRU Neural Network

abstract: Traditional rumors detection methods often rely on statistical analysis to manually select features to construct classifiers. Not only is the message feature selection difficult, but the gap, between the representation space where the shallow statistical features of information exist and the representation space where the highly abstract features including semantics and emotion of information exist, is very big. Thus, the result of traditional classifiers based on the shallow or middle features is not so good. Due to this problem, a rumors deteciton method based on Deep Bidirectional Gated Recurrent Unit (D-Bi-GRU) is presented. To capture the evolution of group response information of microblog events over time, we consider the forward and backward sequences of microblog flow of group response information along time line simultaneously. The evolution representations of deep latent space including semantic and emotion learned by stack multi layers Bi-GRUs to rumor detection. Experimental results on a real world data set showed that rumor events detection by considering bidirectional sequence of group response information simultaneously can obtain a better performance, and stack multi-layers Bi-GRUs can better detect rumor events in microblog.


title: An Individual Differentiated Coexisting Mechanism for Multiple Wireless Body Area Networks Based on Game Theory

abstract: Wireless body area network (WBAN) is an emerging technology that has enormous potential to be implemented in medical applications. However, the performance of WBANs can be severely degraded by concomitant inter-WBAN interference in some specific environments, where the multiple WBANs are densely deployed, e.g., hospitals and senior citizen communities. In this paper, a novel coexisting mechanism is proposed to deal with the multi-WBANs coexisting which can provide differentiated communication QoS for different WBANs according to their own priority conditions. The proposed mechanism consists of four parts which are time slot allocation, access control, active part interleaving, and power control. Specifically, the time slot allocation is designed based on the game theory. Access control and Active period scheduling are designed referring to the coexisting methods specified in IEEE 802.15.6 standard. In addition, the power control utilizes mobility prediction to adjust transmitting power for each coexisting WBAN. The simulation results demonstrate that the proposed mechanism is stable and convergent in varying coexisting scenarios and can realize differentiated time slot allocation depending on WBANs’ priority conditions. Furthermore, when compared with the original mechanisms specified in IEEE 802.15.6, transmission performance of coexisting WBANs is improved in terms of transmission outage probability, transmission energy efficiency, and overall throughput.


title: Method of multi-channel calibration for digital array radar

abstract: Amplitude and phase errors among multi-channels in digital array radar will seriously deteriorate the performance of digital beam-forming (DBF) and high resolution direction finding. This paper describes a practical calibration algorithm that calibrates amplitude and phase errors coarsely using interior calibration source and then realizes precise calibration by eigen-decomposition approach. Based on an 8-elements digital array radar test-bed operating at L & S band, experiments are carried out in the anechoic chamber and outdoors. After calibration, the radiation pattern is nearly ideal and the direction of arrival (DOA) estimation of measured data achieves a good performance. The expected results illustrate that interior-auto calibration algorithm is fairly effective and feasible in actual systems.


title: Movie Summarization Based on Alignment of Plot and Shots

abstract: This paper proposes a method of assisting movie summarization using plotinformation. A plot of a movie available at Wikipedia contains a majorstory of the movie. From such a plot of a movie, we extract severalimportant sentences as the content of summary. For summarizing movie, the key work is finding the best alignment between sentences of plot andshots which are segmented from a movie. There are two cues used tomeasure the similarity between a sentence and a shot. One is based oncharacter appearing in both sentence and shot, another is based on wordsmatching. Then an alignment method based on dynamic programming is applyto optimize the alignment. Finally an experiment on movie "RomanHoliday" and "Alice in the wonderland" show the effectiveness of this method.


title: THE IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON CONSUMER LOYALTY

abstract: More firms today are beginning to realise the importance of corporate social responsibility (CSR), and its impact on societal well being, apart from the overall governance of the supply chain processes of every organisation. Ratings are being developed to measure performances of most firms with regard to their CSR efforts besides their financial performance. This rating would then be used to measure each firm's progress in the business world. Many studies have been conducted linking CSR to consumer loyalty. It is found that the higher the CSR rating the stronger the bond would be between these two. Given this scenario, the main objective of this study is to find relevant evidence to link consumer loyalty and CSR initiatives within a company. A case study approach was used to empirically test our research model derived from the literature. Quantitative analysis based on a total of 100 responses from a leading electronics manufacturer in Malaysia was used to address the research objectives. The findings of this study suggest that the higher the practice of CSR the stronger is the consumer loyalty towards a particular firm. The study also shows that by investing in CSR, a firm could achieve a positive outcome in terms of its financial performance. CSR can provide the additional competitive edge to a particular firm. However, the linearity of this relationship warrants more testing and empirical research.


title: A Biological Imitation Game

abstract: The digital reconstruction of a slice of rat somatosensory cortex from the Blue Brain Project provides the most complete simulation of a piece of excitable brain matter to date. To place these efforts in context and highlight their strengths and limitations, we introduce a Biological Imitation Game, based on Alan Turing's Imitation Game, that operationalizes the difference between real and simulated brains.


title: Employee engagement and personal performance in hotel service industry of China

abstract: To investigate the status of employee engagement and its effects on service performance in China hotel industry, the total of 402 samples are collected from full-time employees in 8 hotels in China by Multiple Employee Engagement Inventory (MEEI). The results show that the hotel employee engagement is significantly different with professional characteristics in age, education level, work position, length of service and salary. Generally speaking, the employee engagement increases with age, education level, work position, length of service and salary. However, the senior managers with higher education and the staff whose length of service is 11 to 15 years appear unusual decline in engagement. The performances of the groups with different level of employee engagement are significantly different, i.e., the higher the employee engagement, the better the performance. In conclusion, employee engagement with industrial characteristics is a significant predictor of staff performance in hotel, and besides, the demographic variables, and some other comprehensive factors impacting employee engagement, such as the organization change and career planning, should be further considered for future research.


Experimental Investigation of Surface Roughness by Considering Different Parameters on CNC Turning for EN-8


title: Work-efficient parallel non-maximum suppression for embedded GPU architectures

abstract: With the emergence of GPU computing, deep neural networks have become a widely used technique for advancing research in the field of image and speech processing. In the context of object and event detection, sliding-window classifiers require to choose the best among all positively discriminated candidate windows. In this paper, we introduce the first GPU-based non-maximum suppression (NMS) algorithm for embedded GPU architectures. The obtained results show that the proposed parallel algorithm reduces the NMS latency by a wide margin when compared to CPUs, even clocking the GPU at 50% of its maximum frequency on an NVIDIA Tegra K1. In this paper, we show results for object detection in images. The proposed technique is directly applicable to speech segmentation tasks such as speaker diarization.


title: Taxonomic and thematic semantic systems.

abstract: Object concepts are critical for nearly all aspects of human cognition, from perception tasks like object recognition, to understanding and producing language, to making meaningful actions. Concepts can have 2 very different kinds of relations: similarity relations based on shared features (e.g., dog-bear), which are called "taxonomic" relations, and contiguity relations based on co-occurrence in events or scenarios (e.g., dog-leash), which are called "thematic" relations. Here, we report a systematic review of experimental psychology and cognitive neuroscience evidence of this distinction in the structure of semantic memory. We propose 2 principles that may drive the development of distinct taxonomic and thematic semantic systems: differences between which features determine taxonomic versus thematic relations, and differences in the processing required to extract taxonomic versus thematic relations. This review brings together distinct threads of behavioral, computational, and neuroscience research on semantic memory in support of a functional and neural dissociation, and defines a framework for future studies of semantic memory. (PsycINFO Database Record


title: Influence of magnet size and rotor topology on the air-gap flux density of a radial flux PMSM

abstract: When designing the rotor of a radial flux permanent magnet synchronous machine (PMSM), one key part is the sizing of the permanent magnets (PM) in the rotor to produce the required air-gap flux density. This paper focuses on the effect that different coefficients have on the air-gap flux density of four radial flux PMSM rotor topologies. A direct connection is shown between magnet volume and flux producing magnet area with the aid of static finite element model simulations of the four rotor topologies. With this knowledge, the calculation of the flux producing magnet area can be done with ease once the minimum magnet volume has been determined. This technique can also be applied in the design of line-start PMSM rotors where the rotor area is limited.


title: Adhesion ofPseudomonas aeruginosa to collagen biomaterials: Effect of amikacin and ciprofloxacin on the colonization and survival of the adherent organisms

abstract: The adherence of P. aeruginosa to collagen membrane, sponge, and to a new anti-infective COLL dressing and the susceptibility of the organisms attached to the biomaterials to amikacin were investigated in vitro. After 17 h of attachment, the bacteria demonstrated an increased resistance to amikacin compared with their free-floating counterparts. Amikacin, even at a concentration exceeding 150 times the minimal bactericidal concentration (MBC) for the strain tested, did not eradicate the attached bacteria from the surface of collagen membrane. However, when the drug at a high concentration (over 16 times the minimal inhibitory concentration, MIC) was present in the incubation medium before it had been inoculated with P. aeruginosa, a reduction of 2 log10 units in the organisms adherent to the surface of collagen membrane was observed. We conclude that slow release of the antibiotic from the COLL dressing could control the bacterial colonization on the surface. In fact, the released amikacin at the final concentration of 32 times the MBC reduced the number of adherent bacteria by 6 log10 units. In contrast, ciprofloxacin at the same final bactericidal concentration completely eradicated the bacteria from the surface of COLL dressing. However, as ciprofloxacin is not recommended for use as a topical antimicrobial agent, a further search is needed to find an agent with a similar anticolonization activity.


title: Enhancing Cognitive Engagement of Pre-clinical Undergraduate Medical Students via Video Cases and Interactive Quizzes in Problem-based Learning

abstract: Background Problem-based learning (PBL) is one of the main pedagogical approaches utilized in the undergraduate medical education (UGME) program at a private medical college in Karachi, Pakistan. Video-enhanced cases and formative assessments were introduced at the end of PBL sessions to evaluate their effectiveness in enhancing student engagement. Methods A mixed methods study was conducted with Year 2 medical students (n=102; divided into 11 groups) and faculty (n=11) facilitating the PBL process. Of the 10 PBL cases, five were converted to video-enhanced cases and five were kept as paper-based, "traditional" cases. "Micro" videos were used to introduce clinical scenarios, augmented by a set of guided questions related to the scenario. In addition, a formative quiz was conducted to assess concepts at the end of video-enhanced PBL sessions. At the end of a module, students and facilitators completed an online survey regarding this modified learning experience, and this was followed by a focus group discussion with the PBL facilitators. Results More than two-thirds (71%) of the students and all facilitators preferred video-enhanced over paper-based cases. Seventy-nine percent of the students agreed that this method increased peer-peer and peer-facilitator engagement, while 66% (n=68) of the students and 81% (n=9) of the faculty agreed that the end of PBL formative assessment activity would support the "Universal Design for Learning" framework. Conclusion Video-enhanced PBL used during the introduction of the case and formative assessment activities at the end of the PBL sessions improved student engagement and contributed positively to the discussions and their understanding.


title: On event-based sampling for LQG-optimal control

abstract: We consider the problem of finding an event-based sampling scheme that optimizes the trade-off between average sampling rate and control performance in a linear-quadratic-Gaussian (LQG) control problem setting with output feedback. Our analysis is based on a recently presented sampled-data controller structure, which remains LQG-optimal for any choice of sampling scheme. We show that optimization of the sampling scheme is related to an elliptic convection-diffusion type partial differential equation over a domain with free boundary, a so called Stefan problem. A numerical method is presented to solve this problem for second order systems, and thus obtain an optimal sampling scheme. The method also directly generalizes to higher order systems, although with a higher computational cost. For the special case of multidimensional integrator systems, we present the optimal sampling scheme on closed form, and prove that it will always outperform its periodic counterpart. Tight bounds on the improvement are presented. The improved performance is also demonstrated in numerical examples, both for an integrator system and a more general case.


title: Torque ripple alleviation of a radial magnetic gearbox using step skewing approach

abstract: Recently, magnetic gearboxes (MGBs) are serious contenders to their conventional mechanical counterparts in terms of reduced maintenance requirements, improved reliability, tolerance to mechanical inaccuracies, and inherent overload protection. MGBs are preferably employed in high speed applications and compact harsh environments. One of the challenging design aspects of MGBs is the proper selection of the combination of the number of pole-pairs of both inner and outer rotors, yielding specific gear ratios, to minimize cogging torque magnitude. For any general desired gear ratio, the torque ripple magnitude may be unacceptable. Since MGBs generally belongs to permanent magnet (PM) machines family, the same techniques for torque ripple mitigation can be employed. This paper introduces a new technique for torque ripple reduction in MGB based on step skew technique. In the proposed technique, the MGB is divided into two equal parts along its stack length and the optimum skew angle between the two parts is determined for minimum torque ripple. A conventional 14/4 MGB, which is normally results in a significant cogging torque magnitude, is designed and simulated using 3D finite-element analysis (FEA) for different skew angles. Based on the simulation results, the required optimum skew angle that optimizes torque transmission while minimizes the corresponding cogging torque is recognized.


title: A review of supervised machine learning algorithms

abstract: Supervised machine learning is the construction of algorithms that are able to produce general patterns and hypotheses by using externally supplied instances to predict the fate of future instances. Supervised machine learning classification algorithms aim at categorizing data from prior information. Classification is carried out very frequently in data science problems. Various successful techniques have been proposed to solve such problems viz. Rule-based techniques, Logic-based techniques, Instance-based techniques, stochastic techniques. This paper discusses the efficacy of supervised machine learning algorithms in terms of the accuracy, speed of learning, complexity and risk of over fitting measures. The main objective of this paper is to provide a general comparison with state of art machine learning algorithms.


title: A Monolithic Data Acquisition Channel

abstract: A monolithic data acquisition channel has been fabricated using a double level polysilicon NMOS process. The channel consists of a switched-capacitor filter, a continuous-time active RC filter, and a successive approximation analog-to digital converter. The IC is in the form of a parts kit for a data acquisition channel so that each functional component can be characterized independently. Stitch bonds are used to connect the elements together to form the complete monolithic channel. Each element is implemented with a symmetric, fully differential structure. The measured dynamic range of the channel is 86.1 dB when using +- 5 V supplies.


title: Randomized controlled pilot study of an occupational time-use intervention for people with serious mental illness.

abstract: OBJECTIVE ::: We assessed the efficacy and clinical utility of a new occupational time-use intervention, Action Over Inertia, designed to improve occupational balance and engagement among community-dwelling people with serious mental illness. ::: ::: ::: METHOD ::: Using a randomized controlled design, we assigned 24 participants to an intervention group or standard care group. Participants were community-dwelling people with serious mental illness receiving assertive community treatment services. Data on time use, occupational balance, and engagement were collected and compared at baseline and 12-wk posttest. ::: ::: ::: RESULTS ::: Eighteen participants completed the pilot study. Treatment group participants increased their occupational balance by spending an average of 47 min more per day in activity than the control group (p = .05). Differences in occupational engagement were not shown, but evidence of clinical utility was found. ::: ::: ::: CONCLUSION ::: This pilot study of Action Over Inertia has shown evidence of efficacy and clinical utility.


SFEDL’04 Preliminary Version Semantics and Analysis of Instruction List Programs Abstract


Maternal Undernutrition and Fetal Programming: Role of the Placenta


A Survey of the Functional Splits Proposed for 5 G Mobile Crosshaul Networks Line


title: Matroids and the greedy algorithm

abstract: Linear-algebra rank is the solution to an especially tractable optimization problem. This tractability is viewed abstractly, and extended to certain more general optimization problems which are linear programs relative to certain derived polyhedra.


title: Enriched expression of genes associated with autism spectrum disorders in human inhibitory neurons

abstract: Autism spectrum disorder (ASD) is highly heritable but genetically heterogeneous. The affected neural circuits and cell types remain unclear and may vary at different developmental stages. By analyzing multiple sets of human single cell transcriptome profiles, we found that ASD candidates showed relatively enriched gene expression in neurons, especially in inhibitory neurons. ASD candidates were also more likely to be the hubs of the co-expression gene module that is highly expressed in inhibitory neurons, a feature not detected for excitatory neurons. In addition, we found that upregulated genes in multiple ASD cortex samples were enriched with genes highly expressed in inhibitory neurons, suggesting a potential increase of inhibitory neurons and an imbalance in the ratio between excitatory and inhibitory neurons in ASD brains. Furthermore, the downstream targets of several ASD candidates, such as CHD8, EHMT1 and SATB2, also displayed enriched expression in inhibitory neurons. Taken together, our analyses of single cell transcriptomic data suggest that inhibitory neurons may be a major neuron subtype affected by the disruption of ASD gene networks, providing single cell functional evidence to support the excitatory/inhibitory (E/I) imbalance hypothesis.


title: A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

abstract: Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is required only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.


Molecular Mechanism for the Function of Bacteriorhodopsin and Visual Pigments: Studies With Model Compounds and Artificial Pigments


title: World Lines

abstract: In this paper we present World Lines as a novel interactive visualization that provides complete control over multiple heterogeneous simulation runs. In many application areas, decisions can only be made by exploring alternative scenarios. The goal of the suggested approach is to support users in this decision making process. In this setting, the data domain is extended to a set of alternative worlds where only one outcome will actually happen. World Lines integrate simulation, visualization and computational steering into a single unified system that is capable of dealing with the extended solution space. World Lines represent simulation runs as causally connected tracks that share a common time axis. This setup enables users to interfere and add new information quickly. A World Line is introduced as a visual combination of user events and their effects in order to present a possible future. To quickly find the most attractive outcome, we suggest World Lines as the governing component in a system of multiple linked views and a simulation component. World Lines employ linking and brushing to enable comparative visual analysis of multiple simulations in linked views. Analysis results can be mapped to various visual variables that World Lines provide in order to highlight the most compelling solutions. To demonstrate this technique we present a flooding scenario and show the usefulness of the integrated approach to support informed decision making.


title: Big Data, Simulations and HPC Convergence

abstract: Two major trends in computing systems are the growth in high performance computing (HPC) with in particular an international exascale initiative, and big data with an accompanying cloud infrastructure of dramatic and increasing size and sophistication. In this paper, we study an approach to convergence for software and applications/algorithms and show what hardware architectures it suggests. We start by dividing applications into data plus model components and classifying each component (whether from Big Data or Big Compute) in the same way. This leads to 64 properties divided into 4 views, which are Problem Architecture (Macro pattern); Execution Features (Micro patterns); Data Source and Style; and finally the Processing (runtime) View. We discuss convergence software built around HPC-ABDS (High Performance Computing enhanced Apache Big Data Stack) and show how one can merge Big Data and HPC (Big Simulation) concepts into a single stack and discuss appropriate hardware.


title: Adaptive usage of cellular and WiFi bandwidth: an optimal scheduling formulation

abstract: Worldwide, mobile data connectivity is now widespread, but not yet ubiquitous due to coverage limits and cost concerns. Mobile data offloading to WiFi---where available---could greatly decrease the usage of cellular data networks. In delay-tolerant applications, one could delay network communication in order to exploit free WiFi connections expected to appear soon. However, WiFi connectivity is limited, and even delay-tolerant applications must meet quality-of-service deadlines. To explore such bandwidth scheduling issues, we develop an optimal MILP-based scheduling framework. Our framework schedules multiple application data streams with varying size and delay tolerance, onto networks with varying coverage and bandwidth, in order to minimize cellular data usage. The ability to subdivide data streams into scheduling units is important, because it allows applications to exploit brief windows of WiFi coverage and it allows tradeoffs between solution quality and solver runtime.


title: Design of image encryption algorithm based on different chaotic mapping

abstract: Converting information (data) from its original form to another form is known as encryption as in order to access rising the information protection. The encryption created on chaos theory includes some dynamic methods to improve the protection of image encryption. In this work, encryption image is achieved by using a different chaotic mapping. The original image is split into blocks and then different chaotic maps are employed for five stages of proposed encryption algorithm. First, the cubic map is employed to permute the pixels which contained within the blocks. Second, Henon map has been used to diffuse the permuted pixels. Third, a quadratic map has been worked to permute the blocks. Fourth, a logistic map has been used to permute all the pixels entire an image. Finally, XOR Henon map has been worked to diffuse the permuted image. High-efficiency for encryption quality algorithm has been shown by the performance analysis of proposed algorithm.


title: A MATLAB toolbox for Granger causal connectivity analysis.

abstract: Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox - 'Granger causal connectivity analysis' (GCCA) - which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including 'causal density' and 'causal flow'. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language