title: 1082-989X/11/$12.00 DOI: 10.1037/a0022658 Effect Size Measures for Mediation Models: Quantitative Strategies for Communicating Indirect Effects

abstract: The statistical analysis of mediation effects has become an indispensable tool for helping scientists investigate processes thought to be causal. Yet, in spite of many recent advances in the estimation and testing of mediation effects, little attention has been given to methods for communicating effect size and the practical importance of those effect sizes. Our goals in this article are to (a) outline some general desiderata for effect size measures, (b) describe current methods of expressing effect size and practical importance for mediation, (c) use the desiderata to evaluate these methods, and (d) develop new methods to communicate effect size in the context of mediation analysis. The first new effect size index we describe is a residual-based index that quantifies the amount of variance explained in both the mediator and the outcome. The second new effect size index quantifies the indirect effect as the proportion of the maximum possible indirect effect that could have been obtained, given the scales of the variables involved. We supplement our discussion by offering easy-to-use R tools for the numerical and visual communication of effect size for mediation effects.


title: Frame-Based Editing: Easing the Transition from Blocks to Text-Based Programming

abstract: Block-based programming systems, such as Scratch or Alice, are the most popular environments for introducing young children to programming. However, mastery of text-based programming continues to be the educational goal for students who continue to program into their teenage years and beyond. Transitioning across the significant gap between the two editing styles presents a difficult challenge in school-level teaching of programming. We propose a new style of program manipulation to bridge the gap: frame-based editing. Frame-based editing has the resistance to errors and approachability of block-based programming while retaining the flexibility and more conventional programming semantics of text-based programming languages. In this paper, we analyse the issues involved in the transition from blocks to text and argue that they can be overcome by using frame-based editing as an intermediate step. A design and implementation of a frame-based editor is provided.


title: Isoenzymes of ferredoxin-NADBΔ oxidoreductase from the cyanobacterium Nostoc strain MAC

abstract: Ferredoxin-NADP+ oxidoreductase from the cyanobacterium Nostoc strain MAC was separated into two fractions by ion-exchange chromatography. Both were purified to electrophoretic homogeneity and exhibited diaphorase and ferredoxin-dependent cytochrome c reductase activity. The activities with three different electron carriers in this latter assay were similar for the two fractions, as were the pH optima in both assays. Each fraction, however, could be resolved into several active components by isoelectric focusing, both after initial separation and following apparent purification by gel filtration on Sephadex G-150, further chromatography on DEAE-cellulose, and use of hydroxylapatite columns.


title: Learning Dynamic Generator Model by Alternating Back-Propagation Through Time

abstract: This paper studies the dynamic generator model for spatialtemporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non-linear transformation of a latent state vector, where the non-linear transformation is parametrized by a top-down neural network. The sequence of latent state vectors follows a non-linear auto-regressive model, where the state vector of the next frame is a non-linear transformation of the state vector of the current frame as well as an independent noise vector that provides randomness in the transition. The non-linear transformation of this transition model can be parametrized by a feedforward neural network. We show that this model can be learned by an alternating back-propagation through time algorithm that iteratively samples the noise vectors and updates the parameters in the transition model and the generator model. We show that our training method can learn realistic models for dynamic textures and action patterns.


title: A First Derivative Potts Model for Segmentation and Denoising Using MILP

abstract: Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our approach addresses both problems at the same time. We propose a novel ILP formulation of the first derivative Potts model with the \(\ell _1\) data term, where binary variables are introduced to deal with the \(\ell _0\) norm of the regularization term. The ILP is then solved by a standard off-the-shelf MIP solver. Numerical experiments are compared with the multicut problem.


title: Triggering the Sintering of Silver Nanoparticles at Room Temperature

abstract: A new approach to achieve coalescence and sintering of metallic nanoparticles at room temperature is presented. It was discovered that silver nanoparticles behave as soft particles when they come into contact with oppositely charged polyelectrolytes and undergo a spontaneous coalescence process, even without heating. Utilizing this finding in printing conductive patterns, which are composed of silver nanoparticles, enables achieving high conductivities even at room temperature. Due to the sintering of nanoparticles at room temperature, the formation of conductive patterns on plastic substrates and even on paper is made possible. The resulting high conductivity, 20% of that for bulk silver, enabled fabrication of various devices as demonstrated by inkjet printing of a plastic electroluminescent device.


title: A million spiking-neuron integrated circuit with a scalable communication network and interface

abstract: Inspired by the brain’s structure, we have developed an efficient, scalable, and flexible non–von Neumann architecture that leverages contemporary silicon technology. To demonstrate, we built a 5.4-billion-transistor chip with 4096 neurosynaptic cores interconnected via an intrachip network that integrates 1 million programmable spiking neurons and 256 million configurable synapses. Chips can be tiled in two dimensions via an interchip communication interface, seamlessly scaling the architecture to a cortexlike sheet of arbitrary size. The architecture is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification. With 400-pixel-by-240-pixel video input at 30 frames per second, the chip consumes 63 milliwatts.


Advances on Interactive Machine Learning


title: Towards building a social emotion detection system for online news

abstract: Abstract Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers’ emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction.


title: Event threading within news topics

abstract: With the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner. Previous research focused only on organizing news stories by their topics into a flat hierarchy. We believe viewing a news topic as a flat collection of stories is too restrictive and inefficient for a user to understand the topic quickly. In this work, we attempt to capture the rich structure of events and their dependencies in a news topic through our event models. We call the process of recognizing events and their dependencies event threading. We believe our perspective of modeling the structure of a topic is more effective in capturing its semantics than a flat list of on-topic stories. We formally define the novel problem, suggest evaluation metrics and present a few techniques for solving the problem. Besides the standard word based features, our approaches take into account novel features such as temporal locality of stories for event recognition and time-ordering for capturing dependencies. Our experiments on a manually labeled data sets show that our models effectively identify the events and capture dependencies among them.


Comment on “Acoustic Band Structure of Periodic Elastic Composites”


title: Fast Transient Low-Dropout Voltage Regulator With Hybrid Dynamic Biasing Technique for SoC Application

abstract: This brief presents a low-dropout (LDO) voltage regulator without output capacitors that achieves fast transient responses by hybrid dynamic biasing. The hybrid dynamic biasing in the proposed transient improvement circuit is activated through capacitive coupling. The proposed circuit senses the LDO regulator output change so as to increase the bias current instantly. The proposed circuit was applied to an LDO regulator without output capacitors implemented in standard 0.35- μm CMOS technology. The device consumes only 25 μA of quiescent current with a dropout voltage of 180 mV. The proposed circuit reduces the output voltage spike of the LDO regulator to 80 mV when the output current is changed from 0 to 100 mA. The output voltage spike is reduced to 20 mV when the supply voltage varies between 1.3 and 2.3 V with a load current of 100 mA.


title: Body Satisfaction and Physical Appearance in Gender Dysphoria

abstract: Gender dysphoria (GD) is often accompanied by dissatisfaction with physical appearance and body image problems. The aim of this study was to compare body satisfaction with perceived appearance by others in various GD subgroups. Data collection was part of the European Network for the Investigation of Gender Incongruence. Between 2007 and 2012, 660 adults who fulfilled the criteria of the DSM-IV gender identity disorder diagnosis (1.31:1 male-to-female [MtF]:female-to-male [FtM] ratio) were included into the study. Data were collected before the start of clinical gender-confirming interventions. Sexual orientation was measured via a semi-structured interview whereas onset age was based on clinician report. Body satisfaction was assessed using the Body Image Scale. Congruence of appearance with the experienced gender was measured by means of a clinician rating. Overall, FtMs had a more positive body image than MtFs. Besides genital dissatisfaction, problem areas for MtFs included posture, face, and hair, whereas FtMs were mainly dissatisfied with hip and chest regions. Clinicians evaluated the physical appearance to be more congruent with the experienced gender in FtMs than in MtFs. Within the MtF group, those with early onset GD and an androphilic sexual orientation had appearances more in line with their gender identity. In conclusion, body image problems in GD go beyond sex characteristics only. An incongruent physical appearance may result in more difficult psychological adaptation and in more exposure to discrimination and stigmatization.


title: Gender-Related Spinal Injury Assessment Considerations in Military Aviation Occupant Protection Modelling

abstract: Abstract In determining the risk of injury in the military aviation environment, a male and female of similar height and weight have been assumed to have the same risk of vertebral injury during an escape or crash scenario. A Quantitative Computed Tomography (QCT) study has been performed to analytically quantify the vertebral strength properties between men and women for C5, T12 and L4. Significant differences were found between bone mineral density (BMD) by gender at C5 and vertebral dimension parameters at all locations by gender. The cross sectional area and BMD product, a measure of compressive strength, was significantly different for only C5 by gender. Predicted strength based on the area-density product was not different by location or gender but when predicted by area, age, gender, location and structure was significantly lower for females at L4. Using multiple and response surface regression, anthropometrical measures predicted BMD for males at T12 and L4; cross sectional area for females at L4 and males at C5; and area-density product at all locations for both genders. Given the lack of significant difference in area-density product at T12 and L4 by gender, males and females that fall within the parameters of this study group would appear to be at the same risk of vertebral compressive injury. Using the same arguments for C5, females demonstrated a 13% decrease in area-density product and would likely be at a greater risk for compressive injury than males. However, whether a relationship exists between the C5 area-density product and C5 compressive strength is not known.


title: Practical Feature Subset Selection for Machine Learning

abstract: Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, or contains extraneous and irrelevant information, machine learning algorithms may produce less accurate and less understandable results, or may fail to discover anything of use at all. Feature subset selectors are algorithms that attempt to identify and remove as much irrelevant and redundant information as possible prior to learning. Feature subset selection can result in enhanced performance, a reduced hypothesis search space, and, in some cases, reduced storage requirement. This paper describes a new feature selection algorithm that uses a correlation based heuristic to determine the “goodness” of feature subsets, and evaluates its effectiveness with three common machine learning algorithms. Experiments using a number of standard machine learning data sets are presented. Feature subset selection gave significant improvement for all three algorithms.


title: Categorical Reparameterization with Gumbel-Softmax

abstract: Categorical variables are a natural choice for representing discrete structure in the world. However, stochastic neural networks rarely use categorical latent variables due to the inability to backpropagate through samples. In this work, we present an efficient gradient estimator that replaces the non-differentiable sample from a categorical distribution with a differentiable sample from a novel Gumbel-Softmax distribution. This distribution has the essential property that it can be smoothly annealed into a categorical distribution. We show that our Gumbel-Softmax estimator outperforms state-of-the-art gradient estimators on structured output prediction and unsupervised generative modeling tasks with categorical latent variables, and enables large speedups on semi-supervised classification.


title: On Ethics and Economics.

abstract: In this elegant critique, Amartya Sen argues that welfare economics can be enriched by paying more explicit attention fo ethics, and that modern ethical studies can also benefit from a closer contact with economics. Predicitive and descriptive economics can be helped by making room for welfare-economic considerations in the explanation of behaviour. In this context, he explores the rationality of behaviour and pays particular attention to social interdependence and internal tensions within consequential reasoning.


Review: Support Vector Machines in Pattern Recognition


title: Why the phased-MIMO radar outperforms the phased-array and MIMO radars

abstract: The phased-multiple-input multiple-output (MIMO) radar has been recently proposed as a new radar technique which combines the advantages of both the phased-array and MIMO radars [1]. Based on the beampattern and signal-to-noise ratio (SNR) analysis of the phased-MIMO radar and comparing it to the beampatterns and SNRs of the phasedarray and MIMO radars, we explain, in this paper, why the phased-MIMO radar outperforms the phased-array and MIMO radars, and why it is the right candidate for future radar systems. Particularly, the phased-MIMO radar beam-pattern decouples into a product of three beampatterns each corresponding to a certain type of processing gain present in the radar system. Only some of such processing gains are present in the phased-array and MIMO radars. Moreover, the phased-MIMO radar improves the SNR gain compared to the MIMO radar by means of using transmit beamforming. Simulation results validate our theoretical developments on the superiority of the phased-MIMO radar.


title: Label-noise-tolerant classification for streaming data

abstract: Label noise-tolerant machine learning techniques address datasets which are affected by mislabelling of the instances. Since labelling quality is a severe issue in particular for large or streaming data sets, this setting becomes more and more relevant in the context of life-long learning, big data and crowd sourcing. In this contribution, we extend a powerful online learning method, soft robust learning vector quantisation, by a probabilistic model for noise tolerance, which is applicable for streaming data, including label-noise drift. The superiority of the technique is demonstrated in several benchmark problems.


title: An integrated ultrasound transducer driver for HIFU applications

abstract: This paper presents for the first time an MRI-compatible integrated CMOS amplifier capable of directly driving a piezoelectric ultrasound transducer for High Intensity Focused Ultrasound (HIFU) applications. The amplifier operates in class DE mode loaded with a piezoelectric ultrasound transducer connected in parallel with a shunt capacitor. There is no need for external inductors. The integrated amplifier is implemented with the Teledyne-DALSA 0.8 μm CMOS process. Modeling of the piezoelectric transducer and amplifier design considerations are presented, followed by simulation results obtained using the Spectre simulator. The proposed design achieves over 80% efficiency with 1 W of output power at 1 MHz and an acceptable third harmonic level. The estimated die area of the amplifier is 2 mm2.


title: A high-voltage driver based on stacked low-voltage transistors with minimized on-resistance for a buck converter in 65 nm CMOS

abstract: This paper presents a high-voltage (HV) driver for switching a buck converter. The circuit is based on 3-stacked CMOS using gate control circuits to drive maximum current which indicates minimized on-resistance of the HV-driver thus achieving faster switching. The circuit is designed and fabricated using 65 nm CMOS TSMC process technology with a nominal voltage of 2.5 V and with a supply voltage of 5.5 V. Since the design is based on stacked CMOS transistors, the circuit is technology-independent. The initial on-resistances of the driver pull-up and the pull-down paths have an improvement of 75% and 36% respectively. Due to a buck converter switched by the designed HV-driver, output voltages in the range of 0.45 V to 2.45 V can be achieved from different high supply voltages in the range of 3.5 V to 5.5 V. The circuit occupies an area of 0.187 mm2.


title: Specular reflection from woven cloth

abstract: The appearance of a particular fabric is produced by variations in both large-scale reflectance and small-scale texture as the viewing and illumination angles change across the surface. This article presents a study of the reflectance and texture of woven cloth that aims to identify and model important optical features of cloth appearance. New measurements are reported for a range of fabrics including natural and synthetic fibers as well as staple and filament yarns. A new scattering model for woven cloth is introduced that describes the reflectance and the texture based on an analysis of specular reflection from the fibers. Unlike data-based models, our procedural model doesn't require image data. It can handle a wide range of fabrics using a small set of physically meaningful parameters that describe the characteristics of the fibers, the geometry of the yarns, and the pattern of the weave. The model is validated against the measurements and evaluated by comparisons to high-resolution video of the real fabrics and to BTF models of two of the fabrics.


title: Jannovar: A Java Library for Exome Annotation

abstract: Transcript-based annotation and pedigree analysis are two basic steps in the computational anal- ysis of whole-exome sequencing experiments in genetic diagnostics and disease-gene discovery projects. Here, we present Jannovar, a stand-alone Java application as well as a Java library designed to be used in larger software frameworks for exome and genome analysis. Jannovar uses an interval tree to identify all transcripts affected by a given variant, and provides Human Genome Varia- tion Society-compliant annotations both for variants af- fecting coding sequences and splice junctions as well as untranslated regions and noncoding RNA transcripts. Jan- novar can also perform family-based pedigree analysis with Variant Call Format (VCF) files with data from mem- bers of a family segregating a Mendelian disorder. Using a desktop computer, Jannovar requires a few seconds to annotate a typical VCF file with exome data. Jannovar is freely available under the BSD2 license. Source code as well as the Java application and library file can be down- loaded from http://compbio.charite.de (with tutorial) and https://github.com/charite/jannovar. Hum Mutat 35:548-555, 2014. C � 2014 Wiley Periodicals, Inc.


title: Conducting research with the dying: ethical considerations and experience

abstract: This article explores the ethical considerations that arose during a qualitative evaluation study of an Australian domiciliary palliative care agency. The research sought to answer the question: ‘What is the relationship between palliative care philosophy and that received by dying clients and their primary carer?’. Interviews were conducted with clients, carers and agency staff, which exposed particular issues relating to the protection of vulnerable groups such as the dying. The fact that key aspects of research ethics can only be understood when the context of people's lives and experiences are taken into account is explained. Questions raised during the implementation of the study illustrate the complexity of research ethics and form the basis of the discussion.


title: Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

abstract: Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do not fully exploit the local stationarity structures of user/item graphs, and the number of parameters to learn is linear w.r.t. the number of users and items. We propose a novel approach to overcome these limitations by using geometric deep learning on graphs. Our matrix completion architecture combines graph convolutional neural networks and recurrent neural networks to learn meaningful statistical graph-structured patterns and the non-linear diffusion process that generates the known ratings. This neural network system requires a constant number of parameters independent of the matrix size. We apply our method on both synthetic and real datasets, showing that it outperforms state-of-the-art techniques.


title: Effectiveness of error management training: a meta-analysis

abstract: Error management training (EMT) is a training method that involves active exploration as well as explicit encouragement for learners to make errors during training and to learn from them. Past evaluation studies, which compared skill-based training outcomes of EMT with those of proceduralized error- avoidant training or of exploratory training without error encouragement, have yielded considerable variation in effect sizes. The present meta-analysis compiles the results of the existing studies and seeks to explain this variation. Although the mean effect of EMT across all 24 identified studies (N 2,183) was positive and significant (Cohen's d 0.44), there were several moderators. Moderator analyses showed effect sizes to be larger (a) for posttraining transfer (d 0.56) than for within-training performance and (b) for performance tasks that were structurally distinct (adaptive transfer; d 0.80) than for tasks that were similar to training (analogical transfer). In addition, both active exploration and error encouragement were identified as effective elements in EMT. Results suggest that EMT may be better suited than error-avoidant training methods for promotion of transfer to novel tasks. Errors at work are a nuisance. Errors interrupt the work flow; error correction can be time consuming and frustrating, and some workplace errors have severe consequences for individuals and for organizations. It is therefore not surprising that people usually prefer to avoid errors in the first place. Consistent with this approach, many scholars in the area of learning and training have taken a negative view of errors. A famous example is Skinner (1953), who equated errors with punishment that can inhibit be- havior but that does not contribute to learning. Similarly, Bandura (1986) viewed errors as detrimental to learning and promoted a guided and error-free learning environment. In his classical mono- graph on social-cognitive theory, he stated that "without informa- tive guidance, much of one's efforts would be expended on costly errors and needless toil" (Bandura, 1986, p. 47). The present research deals with a training method that, in contrast to these approaches, takes an explicitly positive view of errors during training. This training method, which is called error management training (EMT), is based on the assumption that errors are a natural by-product of active learning: As learners actively explore the environment, errors will inevitably occur. Furthermore, errors can have an informative function for the learner, as they pinpoint where knowledge and skills need further improvement (Ivancic & Hesketh, 1995/1996). Therefore, participants in EMT are explicitly encouraged to make errors during training and to learn from them. In early studies (e.g., Frese et al., 1991), EMT was applied to teach software skills. To determine training effectiveness, these studies compared skill-based training outcomes of EMT with those of alternative training methods. Most of these alternative methods were proceduralized training methods, which mimic conventional tutorials that adopt a negative attitude toward errors: Detailed step-by-step instructions on correct task solutions were provided to prevent participants from making errors. Other studies compared EMT with exploratory training methods that contained no more task information than did the EMT condition and that lacked the explicit encouragement and positive framing of errors during prac- tice or even gave instructions to avoid errors. Early studies con- sistently reported EMT to be effective in terms of posttraining outcomes (i.e., scores on tests given to participants after training). For example, four studies (described in Frese, 1995) reported positive and large effect sizes (Cohen's d of about 1) in favor of


title: Congruences between modular forms and lowering the level mod l^n

abstract: In this article we study the behavior of inertia groups for modular Galois mod l^n representations and in some cases we give a generalization of Ribet's lowering the level result


Inferential SQL Injection Attacks


title: Towards automated classification of fine-art painting style: A comparative study

abstract: This paper presents a comparative study of different classification methodologies for the task of fine-art genre classification. 2-level comparative study is performed for this classification problem. 1st level reviews the performance of discriminative vs. generative models while 2nd level touches the features aspect of the paintings and compares semantic-level features vs low-level and intermediate level features present in the painting.


title: Contactless Air-Filled Substrate Integrated Waveguide

abstract: The contactless version of the air-filled substrate integrated waveguide (AF-SIW) is introduced for the first time. The conventional AF-SIW configuration requires a pure and flawless connection of the covering layers to the intermediate substrate. To operate efficiently at high frequencies, this requires a costly fabrication process. In the proposed configuration, the boundary condition on both sides around the AF guiding medium is modified to obtain artificial magnetic conductor (AMC) boundary conditions. The AMC surfaces on both sides of the waveguide substrate are realized by a single-periodic structure with the new type of unit cells. The PEC–AMC parallel plates prevent the leakage of the AF guiding region. The proposed contactless AF-SIW shows low-loss performance in comparison with the conventional AF-SIW at millimeter-wave frequencies when the layers of both waveguides are connected poorly.


title: A hierarchical control approach for a quadrotor tail-sitter VTOL UAV and experimental verification

abstract: We present a hierarchical control approach that can be used to fulfill autonomous flight, including vertical takeoff, landing, hovering, transition, and level flight, of a quadrotor tail-sitter vertical takeoff and landing unmanned aerial vehicle (VTOL UAV). A unified attitude controller, together with a moment allocation scheme between elevons and motor differential thrust, is developed for all flight modes. A comparison study via real flight tests is performed to verify the effectiveness of using elevons in addition to motor differential thrust. With the well-designed switch scheme proposed in this paper, the aircraft can transit between different flight modes with negligible altitude drop or gain. Intensive flight tests have been performed to verify the effectiveness of the proposed control approach in both manual and fully autonomous flight mode.


title: Mutations in the SHANK2 synaptic scaffolding gene in autism spectrum disorder and mental retardation

abstract: Gudrun Rappold and colleagues report the identification of de novo deletions in SHANK2 in two unrelated individuals. One individual was diagnosed with autism spectrum disorder and the other with mental retardation. The authors also identified a de novo nonsense mutation in an individual diagnosed with autism spectrum disorder.


title: Polyphase P4 code usage for target detection of pulse radar using Matlab and GNU radio

abstract: This paper presents the investigation of polyphase P4 code usage in pulse radar for target detection as part of range resolution. As one of essential parameters in pulse radar, the range resolution is acquired to determine the relative range between two or more close targets in order to be detected as different targets. The generation of phase-coded pulse signal and its processing are performed in Matlabreg; and GNU Radio by utilizing audio waves. From the result, it shows that the phase-coding with polyphase P4 code could provide good compression to the pulse signal. In addition, by making the code longer it generates side lobe level less than −40dB for the code with length of 2000.


title: On the impact of packet spraying in data center networks

abstract: Modern data center networks are commonly organized in multi-rooted tree topologies. They typically rely on equal-cost multipath to split flows across multiple paths, which can lead to significant load imbalance. Splitting individual flows can provide better load balance, but is not preferred because of potential packet reordering that conventional wisdom suggests may negatively interact with TCP congestion control. In this paper, we revisit this “myth” in the context of data center networks which have regular topologies such as multi-rooted trees. We argue that due to symmetry, the multiple equal-cost paths between two hosts are composed of links that exhibit similar queuing properties. As a result, TCP is able to tolerate the induced packet reordering and maintain a single estimate of RTT. We validate the efficacy of random packet spraying (RPS) using a data center testbed comprising real hardware switches. We also reveal the adverse impact on the performance of RPS when the symmetry is disturbed (e.g., during link failures) and suggest solutions to mitigate this effect.


title: Recommendation System Using Bloom Filter in Mapreduce

abstract: Many clients like to use the Web to discover product details in the form of online reviews. The reviews are provided by other clients and specialists. Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information facilities. Collaborative filtering methods are vital component in recommender systems as they generate high-quality recommendations by influencing the likings of society of similar users. The collaborative filtering method has assumption that people having same tastes choose the same items. The conventional collaborative filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is required to deal with the sparse data problem & produce high quality recommendations in large scale mobile environment. MapReduce is a programming model which is widely used for large-scale data analysis. The described algorithm of recommendation mechanism for mobile commerce is user based collaborative filtering using MapReduce which reduces scalability problem in conventional CF system. One of the essential operations for the data analysis is join operation. But MapReduce is not very competent to execute the join operation as it always uses all records in the datasets where only small fraction of datasets are applicable for the join operation. This problem can be reduced by applying bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will improve the join performance.


title: A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding

abstract: Lossy image and video compression algorithms yield visually annoying artifacts including blocking, blurring, and ringing, especially at low bit-rates. To reduce these artifacts, post-processing techniques have been extensively studied. Recently, inspired by the great success of convolutional neural network (CNN) in computer vision, some researches were performed on adopting CNN in post-processing, mostly for JPEG compressed images. In this paper, we present a CNN-based post-processing algorithm for High Efficiency Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance and to accelerate network training. Experimental results show that using our VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared to HEVC baseline. The VRCNN outperforms previously studied networks in achieving higher bit-rate reduction, lower memory cost, and multiplied computational speedup.


title: Solving the data sparsity problem in destination prediction

abstract: Destination prediction is an essential task for many emerging location-based applications such as recommending sightseeing places and targeted advertising according to destinations. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, almost all the existing techniques use various kinds of extra information such as road network, proprietary travel planner, statistics requested from government, and personal driving habits. Such extra information, in most circumstances, is unavailable or very costly to obtain. Thereby we approach the task of destination prediction by using only historical trajectory dataset. However, this approach encounters the "data sparsity problem", i.e., the available historical trajectories are far from enough to cover all possible query trajectories, which considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) to address the data sparsity problem. SubSyn first decomposes historical trajectories into sub-trajectories comprising two adjacent locations, and then connects the sub-trajectories into "synthesised" trajectories. This process effectively expands the historical trajectory dataset to contain much more trajectories. Experiments based on real datasets show that SubSyn can predict destinations for up to ten times more query trajectories than a baseline prediction algorithm. Furthermore, the running time of the SubSyn-training algorithm is almost negligible for a large set of 1.9 million trajectories, and the SubSyn-prediction algorithm runs over two orders of magnitude faster than the baseline prediction algorithm constantly.


title: Object Detection in Satellite Imagery Using 2-Step Convolutional Neural Networks

abstract: This paper presents an efficient object detection method from satellite imagery. Among a number of machine learning algorithms, we proposed a combination of two convolutional neural networks (CNN) aimed at high precision and high recall, respectively. We validated our models using golf courses as target objects. The proposed deep learning method demonstrated higher accuracy than previous object identification methods.


Resolving the Predicament of Android Custom Permissions


title: User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks

abstract: Scribble colors based line art colorization is a challenging computer vision problem since neither greyscale values nor semantic information is presented in line arts, and the lack of authentic illustration-line art training pairs also increases difficulty of model generalization. Recently, several Generative Adversarial Nets (GANs) based methods have achieved great success. They can generate colorized illustrations conditioned on given line art and color hints. However, these methods fail to capture the authentic illustration distributions and are hence perceptually unsatisfying in the sense that they often lack accurate shading. To address these challenges, we propose a novel deep conditional adversarial architecture for scribble based anime line art colorization. Specifically, we integrate the conditional framework with WGAN-GP criteria as well as the perceptual loss to enable us to robustly train a deep network that makes the synthesized images more natural and real. We also introduce a local features network that is independent of synthetic data. With GANs conditioned on features from such network, we notably increase the generalization capability over "in the wild" line arts. Furthermore, we collect two datasets that provide high-quality colorful illustrations and authentic line arts for training and benchmarking. With the proposed model trained on our illustration dataset, we demonstrate that images synthesized by the presented approach are considerably more realistic and precise than alternative approaches.


title: EXTRACTING TOPOLOGICAL INFORMATION FROM GRID MAPS FOR ROBOT NAVIGATION

abstract: In many robotic navigation-related tasks, abstracting the environment where mobile robots carry out some mission can be of a great benefit. In particular, extracting a simple topological graph-like representation from a more complex and detailed metric map is often required for path-planning and navigation. In this work, an approach to perform such extraction in grid maps is presented. The focus is not only on obtaining a diagram or visual representation of possible paths, but also to propose a new way to obtain graph data information related to the connectivity of the environment that can be passed to robots or to a centralized planner, in order to assist the navigation task. The approach proposed is based on image processing techniques. Simulation results prove its simplicity, accuracy and efficiency.


title: Comprehensive transcriptomic study on horse gram (Macrotyloma uniflorum): De novo assembly, functional characterization and comparative analysis in relation to drought stress

abstract: BackgroundDrought tolerance is an attribute maintained in plants by cross-talk between multiple and cascading metabolic pathways. Without a sequenced genome available for horse gram, it is difficult to comprehend such complex networks and intercalated genes associated with drought tolerance of horse gram (Macrotyloma uniflorum). Therefore, de novo transcriptome discovery and associated analyses was done for this highly drought tolerant yet under exploited legume to decipher its genetic makeup.ResultsEight samples comprising of shoot and root tissues of two horse gram genotypes (drought-sensitive; M-191 and drought-tolerant; M-249) were used for comparison under control and polyethylene glycol-induced drought stress conditions. Using Illumina sequencing technology, a total of 229,297,896 paired end read pairs were generated and utilized for de novo assembly of horse gram. Significant BLAST hits were obtained for 26,045 transcripts while, 3,558 transcripts had no hits but contained important conserved domains. A total of 21,887 unigenes were identified. SSRs containing sequences covered 16.25% of the transcriptome with predominant tri- and mono-nucleotides (43%). The total GC content of the transcriptome was found to be 43.44%. Under Gene Ontology response to stimulus, DNA binding and catalytic activity was highly expressed during drought stress conditions. Serine/threonine protein kinase was found to dominate in Enzyme Classification while pathways belonging to ribosome metabolism followed by plant pathogen interaction and plant hormone signal transduction were predominant in Kyoto Encyclopedia of Genes and Genomes analysis. Independent search on plant metabolic network pathways suggested valine degradation, gluconeogenesis and purine nucleotide degradation to be highly influenced under drought stress in horse gram. Transcription factors belonging to NAC, MYB-related, and WRKY families were found highly represented under drought stress. qRT-PCR validated the expression profile for 9 out of 10 genes analyzed in response to drought stress.ConclusionsDe novo transcriptome discovery and analysis has generated enormous information over horse gram genomics. The genes and pathways identified suggest efficient regulation leading to active adaptation as a basal defense response against drought stress by horse gram. The knowledge generated can be further utilized for exploring other underexploited plants for stress responsive genes and improving plant tolerance.


title: Beamspace channel estimation for 3D lens-based millimeter-wave massive MIMO systems

abstract: Lens-based mmWave massive MIMO can significantly reduce the number of required RF chains without obvious performance loss, where the accurate information of beamspace channel is required. However, existing beamspace channel estimation schemes are based on the 2D beamspace channel model. In this paper, we consider the more general 3D beamspace channel model, and propose an adaptive support detection (ASD)-based channel estimation scheme. The basic idea is to decompose the 3D beamspace channel estimation problem into several sub-problems, and each one only deals with a sparse channel component. For each channel component, we first adaptively detect its support with high accuracy by exploiting the horizontal and vertical sparsity of 3D beamspace channel. Then, we remove the influence of this channel component to detect the support of the next channel component. After the support detections of all channel components, we can estimate the nonzero elements of the beamspace channel with low pilot overhead. Simulation results verify that the proposed scheme enjoys satisfying accuracy, even with low SNR.


title: Commensal cuckoo: secure group partitioning for large-scale services

abstract: We present commensal cuckoo,* a secure group partitioning scheme for large-scale systems that maintains the correctness of many small groups, despite a Byzantine adversary that controls a constant (global) fraction of all nodes. In particular, the adversary is allowed to repeatedly rejoin faulty nodes to the system in an arbitrary adaptive manner, e.g., to collocate them in the same group. Commensal cuckoo addresses serious practical limitations of the state-ofthe- art scheme, the cuckoo rule of Awerbuch and Scheideler, tolerating 32x--41x more faulty nodes with groups as small as 64 nodes (as compared to the hundreds required by the cuckoo rule). Secure group partitioning is a key component of highly-scalable, reliable systems such as Byzantine faulttolerant distributed hash tables (DHTs).


title: Preliminary investigations into a simple and effective rectenna for RF energy harvesting

abstract: Design, realisation and measurements of an effective rectenna system for dedicated wireless energy harvesting at 2.45 GHz ISM band are presented. The rectenna consists of a microstrip patch antenna array and a 2-stage Cockcroft Walton voltage multiplier circuit. The harvested power from a dedicated RF source is successfully used to operate a selection of typical electronic devices such as a 2.1v green LED, digital clock timer and digital humidity sensor, up to the range of 3.5 meters.


title: A comparison of the performance of children with and without autism on the Sensory Profile.

abstract: OBJECTIVE ::: The purpose of this study was to determine whether the Sensory Profile discriminates between children with and without autism and which items on the profile best discriminate between these groups. ::: ::: ::: METHOD ::: Parents of 32 children with autism aged 3 to 13 years and of 64 children without autism aged 3 to 10 years completed the Sensory Profile. A descriptive analysis of the data set of children with autism identified the distribution of responses on each item. A multivariate analysis of covariance (MANCOVA) of each category of the Sensory Profile identified possible differences among subjects without autism, with mild or moderate autism, and with severe autism. Follow-up univariate analyses were conducted for any category that yielded a significant result on the MANCOVA: ::: ::: ::: RESULTS ::: Eight-four of 99 items (85%) on the Sensory Profile differentiated the sensory processing skills of subjects with autism from those without autism. There were no group differences between subjects with mild or moderate autism and subjects with severe autism. ::: ::: ::: CONCLUSION ::: The Sensory Profile can provide information about the sensory processing skills of children with autism to assist occupational therapists in assessing and planning intervention for these children.


title: Development of automatic chicken feeder using Arduino Uno

abstract: Nowadays, chicken poultry industry is an important industry for sustainable food supply in our country. Several innovation and invention have been developed to improve the healthiness of chicken in coop that lead to possibly high production of chicken meat and eggs. Consequently, the development of an automatic chicken feeding machine can be very useful to the growth of the poultry industry. This machine can replace the worker for feeding the chicken thus overcome the labour problems in the industry and introduce a semi-automatic process in the poultry industry. The feeding machine for chicken is using an Arduino Uno board as its main controller. In this machine, it has two main parts, first, an Arduino to control servomotor from storage to food container, and the second part is an Arduino to control the temperature sensor for the freshness of the chicken food. This helps to improve the environment in the coop while reducing labor cost, saving food and chicken feeding on time, control the temperature sensor for the freshness of the chicken food and avoid contaminated food from chicken faeces and insects.


title: Effective Quantization Approaches for Recurrent Neural Networks

abstract: Deep learning, Recurrent Neural Networks (RNN) in particular have shown superior accuracy in a large variety oftasks including machine translation, language understanding, and movie frames generation. However, these deep learning approaches are very expensive in terms of computation. In most cases, Graphic Processing Units (GPUs) are in used for large scale implementations. Meanwhile, energy efficient RNN approaches are proposed for deploying solutions on special purpose hardware including Field Programming Gate Arrays (FPGAs) and mobile platforms. In this paper, we propose an effective quantization approach for Recurrent Neural Networks (RNN) techniques including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolutional Long Short Term Memory (ConvLSTM). We have implemented different quantization methods including Binary Connect $\{-1, 1\}$, Ternary Connect $\{-1, 0$, 1}, and Quaternary Connect $\{-1, -0.5, 0.5, 1\}$. These proposed approaches are evaluated on different datasets for sentiment analysis on IMDB and video frame predictions on the moving MNIST dataset. The experimental results are compared against the full precision versions of the LSTM, GRU, and ConvLSTM. They show promising results for both sentiment analysis and video frame prediction.


title: Multi-GPU Graph Analytics

abstract: We present a single-node, multi-GPU programmable graph processing library that allows programmers to easily extend single-GPU graph algorithms to achieve scalable performance on large graphs with billions of edges. Directly using the single-GPU implementations, our design only requires programmers to specify a few algorithm-dependent concerns, hiding most multi-GPU related implementation details. We analyze the theoretical and practical limits to scalability in the context of varying graph primitives and datasets. We describe several optimizations, such as direction optimizing traversal, and a just-enough memory allocation scheme, for better performance and smaller memory consumption. Compared to previous work, we achieve best-of-class performance across operations and datasets, including excellent strong and weak scalability on most primitives as we increase the number of GPUs in the system


title: Rumors in a Network: Who's the Culprit?

abstract: We provide a systematic study of the problem of finding the source of a rumor in a network. We model rumor spreading in a network with the popular susceptible-infected (SI) model and then construct an estimator for the rumor source. This estimator is based upon a novel topological quantity which we term rumor centrality. We establish that this is a maximum likelihood (ML) estimator for a class of graphs. We find the following surprising threshold phenomenon: on trees which grow faster than a line, the estimator always has nontrivial detection probability, whereas on trees that grow like a line, the detection probability will go to 0 as the network grows. Simulations performed on synthetic networks such as the popular small-world and scale-free networks, and on real networks such as an internet AS network and the U.S. electric power grid network, show that the estimator either finds the source exactly or within a few hops of the true source across different network topologies. We compare rumor centrality to another common network centrality notion known as distance centrality. We prove that on trees, the rumor center and distance center are equivalent, but on general networks, they may differ. Indeed, simulations show that rumor centrality outperforms distance centrality in finding rumor sources in networks which are not tree-like.


title: Coloring-Based Inter-WBAN Scheduling for Mobile Wireless Body Area Networks

abstract: In this study, random incomplete coloring (RIC) with low time-complexity and high spatial reuse is proposed to overcome in-between wireless-body-area-networks (WBAN) interference, which can cause serious throughput degradation and energy waste. Interference-avoidance scheduling of wireless networks can be modeled as a problem of graph coloring. For instance, high spatial-reuse scheduling for a dense sensor network is mapped to high spatial-reuse coloring; fast convergence scheduling for a mobile ad hoc network (MANET) is mapped to low time-complexity coloring. However, for a dense and mobile WBAN, inter-WBAN scheduling (IWS) should simultaneously satisfy both of the following requirements: 1) high spatial-reuse and 2) fast convergence, which are tradeoffs in conventional coloring. By relaxing the coloring rule, the proposed distributed coloring algorithm RIC avoids this tradeoff and satisfies both requirements. Simulation results verify that the proposed coloring algorithm effectively overcomes inter-WBAN interference and invariably supports higher system throughput in various mobile WBAN scenarios compared to conventional colorings.


title: An Arene-Stabilized Cobalt(I) Aryl: Reactions with CO and NO

abstract: The half-sandwich cobalt(I) complex (η6-C7H8)CoAr*-3,5-iPr2 (Ar*-3,5-iPr2 = −C6H-2,6-(C6H2-2,4,6-iPr3)2-3,5-iPr2) was synthesized by reduction of [3,5-iPr2Ar*Co(μ-Cl)]2 in toluene. It reacts with CO or NO to afford the unusual complexes [3,5-iPr2Ar*C(O)Co(CO)] or [3,5-iPr2Ar*N(NO)OCo(NO)2].


Training Deeper Models by GPU Memory Optimization on TensorFlow


title: Introducing a large dataset of Persian license plate characters

abstract: A large dataset of Persian license plate characters is introduced. These extracted characters are provided by Bani Nick Pardazesh Company, which is a pioneer in the intelligent transportation systems field, and its license plate recognition system has been used for many applications in Iran. Natural scene vehicle images delivered from this company were in various conditions. Most of them were taken with visible light during day and a few of them by infrared light with 850 nm wavelength during night. The vehicle images were achieved by color, black and white, or infrared cameras from front view and back view of automobiles in ∼20 different indoor and outdoor locations, such as streets, roads, and parking lots, for different purposes, such as traffic control, issuing fines, etc. The images are different in size and angle and were taken in light and dark backgrounds, where the direction and intensity of the light varied. Also, some of the license plates were muddy and had parts that were shadowed. Out of all the available images, ∼20,145 Persian characters were extracted by an intelligent system and verified by human observers. The extracted images are different in size, and some of them suffer from elimination, distortion, rotation, and noise.


title: A compiler framework for extracting superword level parallelism

abstract: SIMD (single-instruction multiple-data) instruction set extensions are quite common today in both high performance and embedded microprocessors, and enable the exploitation of a specific type of data parallelism called SLP (Superword Level Parallelism). While prior research shows that significant performance savings are possible when SLP is exploited, placing SIMD instructions in an application code manually can be very difficult and error prone. In this paper, we propose a novel automated compiler framework for improving superword level parallelism exploitation. The key part of our framework consists of two stages: superword statement generation and data layout optimization. The first stage is our main contribution and has two phases, statement grouping and statement scheduling, of which the primary goals are to increase SIMD parallelism and, more importantly, capture more superword reuses among the superword statements through global data access and reuse pattern analysis. Further, as a complementary optimization, our data layout optimization organizes data in memory space such that the price of memory operations for SLP is minimized. The results from our compiler implementation and tests on two systems indicate performance improvements as high as 15.2% over a state-of-the-art SLP optimization algorithm.


title: Multiobjective Optimization Model for Service Node Selection Based on a Tradeoff Between Quality of Service and Resource Consumption in Mobile Crowd Sensing

abstract: As mobile crowd sensing (MCS) cannot provide reliable services, the quality of service (QoS) is a major research interest. Since the service node makes a decisive impact on two vital factors of QoS, service node selection is becoming a novel research direction in MCS. In this paper, we analyze the factors that need attention when selecting proper service nodes in MCS and define the service node selection problem (SNSP) as follows: finding the optimal set of service nodes, provided that optimizes multiple metrics of QoS simultaneously and satisfies the network resource constraint. Accordingly, we formulate a multiobjective optimization model (MOOM), which converts SNSP to a multiobjective optimization problem (MOOP). Since the MOOM considers the comprehensive effect of all service nodes on one metric as one objective of MOOP, it can handle the diversity of metrics and conflicts between them; in particular, it can flexibly change the metric system of QoS depending on different demands. To demonstrate the value and effectiveness of the proposed MOOM, we propose a paradigm of it and design a corresponding multiobjective optimization selection mechanism. This paradigm focuses on the influence of node spatiotemporal mobility on both data collection and data transmission. Extensive experiments and comparison on a real-world data show that MOOM is an effective model for selecting service nodes with both good coverage and transmission performances.


title: Interactions Between Frontal Cortex and Basal Ganglia in Working Memory: A Computational Model

abstract: The frontal cortex and the basal ganglia interact via a relatively well understood and elaborate system of interconnections. In the context of motor function, these interconnections can be understood as disinhibiting, or “releasing the brakes,” on frontal motor action plans: The basal ganglia detect appropriate contexts for performing motor actions and enable the frontal cortex to execute such actions at the appropriate time. We build on this idea in the domain of working memory through the use of computational neural network models of this circuit. In our model, the frontal cortex exhibits robust active maintenance, whereas the basal ganglia contribute a selective, dynamic gating function that enables frontal memory representations to be rapidly updated in a task-relevant manner. We apply the model to a novel version of the continuous performance task that requires subroutine-like selective working memory updating and compare and contrast our model with other existing models and theories of frontal-cortex-basal-ganglia interactions.


title: A Step Beyond Local Observations with a Dialog Aware Bidirectional GRU Network for Spoken Language Understanding

abstract: Architectures of Recurrent Neural Networks (RNN) recently become a very popular choice for Spoken Language Understanding (SLU) problems; however, they represent a big family of different architectures that can furthermore be combined to form more complex neural networks. In this work, we compare different recurrent networks, such as simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Gated Memory Units (GRU) and their bidirectional versions, on the popular ATIS dataset and on MEDIA, a more complex French dataset. Additionally, we propose a novel method where information about the presence of relevant word classes in the dialog history is combined with a bidirectional GRU, and we show that combining relevant word classes from the dialog history improves the performance over recurrent networks that work by solely analyzing the current sentence.


title: POSTECH's U-Health Smart Home for elderly monitoring and support

abstract: With the increase of the aging society worldwide, hospitals, medical practitioners and health insurers are now increasingly seeking ways to reduce the cost of healthcare while maitaining its quality. One solution subject to attention from gouvernement and healthcare providers is the U-Health Smart Home that aims to provide non-intrusive and non-invasive monitoring and assistance to the elderly directly in their own home. At POSTECH, the U-Health Smart Home project is focused on building a smart home along with an autonomic system to monitor the home as well as the inhabitants to provide intelligent support and assistance in any situation at anytime. This paper presents our initial results from this project. The first contribution is a general framework for the U-Health smart home and the second one is an initial semantic model that can be used in the autonomic system to provide autonomic support to the elderly.


title: Multi-Objective Signal Processing Optimization: The Way to Balance Conflicting Metrics in 5G Systems

abstract: The evolution of cellular networks is driven by the dream of ubiquitous wireless connectivity: any data service is instantly accessible everywhere. With each generation of cellular networks, we have moved closer to this wireless dream; first by delivering wireless access to voice communications, then by providing wireless data services, and recently by delivering a Wi-Fi-like experience with wide-area coverage and user mobility management. The support for high data rates has been the main objective in recent years [1], as seen from the academic focus on sum-rate optimization and the efforts from standardization bodies to meet the peak rate requirements specified in IMT-Advanced. In contrast, a variety of metrics/objectives are put forward in the technological preparations for fifth-generation (5G) networks: higher peak rates, improved coverage with uniform user experience, higher reliability and lower latency, better energy efficiency (EE), lower-cost user devices and services, better scalability with number of devices, etc. These multiple objectives are coupled, often in a conflicting manner such that improvements in one objective lead to degradation in the other objectives. Hence, the design of future networks calls for new optimization tools that properly handle the existence of multiple objectives and tradeoffs between them.


title: Technical market indicators optimization using evolutionary algorithms

abstract: Real world stock markets predictions such as stock prices, unpredictability, and stock selection for portfolios, are challenging problems. Technical indicators are applied to interpret stock market trending and investing decision. The main difficulty of an indicator usage is deciding its appropriate parameter values, as number of days of the periods or quantity and kind of indicators. Each stock index, price or volatility series is different among the rest. In this work, Evolutionary Algorithms are proposed to discover correct indicator parameters in trading. In order to check this proposal the Moving Average Convergence-Divergence (MACD) technical indicator has been selected. Preliminary results show that this technique could work well on stock index trending. Indexes are smoother and easier to predict than stock prices. Required future works should include several indicators and additional parameters.


title: SLACID - sparse linear algebra in a column-oriented in-memory database system

abstract: Scientific computations and analytical business applications are often based on linear algebra operations on large, sparse matrices. With the hardware shift of the primary storage from disc into memory it is now feasible to execute linear algebra queries directly in the database engine. This paper presents and compares different approaches of storing sparse matrices in an in-memory column-oriented database system. We show that a system layout derived from the compressed sparse row representation integrates well with a columnar database design and that the resulting architecture is moreover amenable to a wide range of non-numerical use cases when dictionary encoding is used. Dynamic matrix manipulation operations, like online insertion or deletion of elements, are not covered by most linear algebra frameworks. Therefore, we present a hybrid architecture that consists of a read-optimized main and a write-optimized delta structure and evaluate the performance for dynamic sparse matrix workloads by applying workflows of nuclear science and network graphs.


title: A Meta-Top-Down Method for Large-Scale Hierarchical Classification

abstract: Recent large-scale hierarchical classification tasks typically have tens of thousands of classes on which the most widely used approach to multiclass classification--one-versus-rest--becomes intractable due to computational complexity. The top-down methods are usually adopted instead, but they are less accurate because of the so-called error-propagation problem in their classifying phase. To address this problem, this paper proposes a meta-top-down method that employs metaclassification to enhance the normal top-down classifying procedure. The proposed method is first analyzed theoretically on complexity and accuracy, and then applied to five real-world large-scale data sets. The experimental results indicate that the classification accuracy is largely improved, while the increased time costs are smaller than most of the existing approaches.


title: Hand Gesture Recognition Systems: A Survey

abstract: Gesture was the first mode of communication for the primitive cave men. Later on human civilization has developed the verbal communication very well. But still nonverbal communication has not lost its weightage. Such non – verbal communication are being used not only for the physically challenged people, but also for different applications in diversified areas, such as aviation, surveying, music direction etc. It is the best method to interact with the computer without using other peripheral devices, such as keyboard, mouse. Researchers around the world are actively engaged in development of robust and efficient gesture recognition system, more specially, hand gesture recognition system for various applications. The major steps associated with the hand gesture recognition system are; data acquisition, gesture modeling, feature extraction and hand gesture recognition. There are several sub-steps and methodologies associated with the above steps. Different researchers have followed different algorithm or sometimes have devised their own algorithm. The current research work reviews the work carried out in last twenty years and a brief comparison has been performed to analyze the difficulties encountered by these systems, as well as the limitation. Finally the desired characteristics of a robust and efficient hand gesture recognition system have been described. General Terms Hand gesture recognition, comparison


title: Deep learning for neuroimaging: a validation study

abstract: Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.


Large Scaled Relation Extraction with Reinforcement Learning


title: Paleoseismic events and the 1596 Keicho-Fushimi large earthquake produced by a slip on the Gosukebashi fault at the eastern Rokko Mountains, Japan

abstract: Field surveys and trench excavation investigations revealed that there were at least four large seismic events produced by slips on the Gosukebashi fault in the Holocene in the southeastern Rokko Mountains of Japan. The characteristics of deformed topographies and three-dimensionally excavated exposures show that this fault is a right-lateral strike–slip fault having an average slip rate of 1.0 mm/year, with a reverse displacement component. The principle indicators of past faulting events are: (i) termination of secondary faults; (ii) sedimentary deposits related to faulting; and (iii) injection veins of fault gouge related to seismic faulting in the fractured zone. Radiocarbon dates indicate that the events occurred pre-1660 BC, 1660 BC–220 AD, from ∼ 30–220 to 600 AD and 15th century AD. The youngest event is probably associated with the large 1596 AD Keicho-Fushimi earthquake which occurred in the area around Kyoto and Kobe Cities. The second younger event is probably correlated with the 416 AD Yamato earthquake, which is the oldest historic earthquake in Japanese historic records. The results of trench surveys show that the horizontal displacement produced by an individual event is ∼ 1.5 m, and the recurrence of seismic event intervals is ∼ 1200 years in the Gosukebashi fault.


Pavlovian conditioning: It’s not what you think it is


title: CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES

abstract: In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.


title: Activity Recognition Using Wrist-Worn Sensors for Human Performance Evaluation

abstract: Recent development of wearable technology has opened up great opportunities for human performance evaluation applications in various domains. In order to measure the physical activities of an individual, wrist-worn sensors embedded in smartwatches, fitness bands, and clip-on devices can be used to collect various types of data, as the subject performs regular daily activities. In this paper, we propose using the acceleration data generated by wrist-worn sensors to recognize ambulation activities for performance evaluation purposes. Twelve features are extracted from the raw accelerometer data, then feed into individual classifiers as well as their combinations for training and validation. The classifiers we consider in this paper are Naive Bayes, Support Vector Machines, Decision Tree, k-Nearest Neighbors, Multilayer Perceptron, and Random Forest, as they have been reported effective by a few previous works. We evaluate the set of selected classifiers with real-world data sets generated by three subjects, including data from both left and right wrists. The best accuracy performance is the combination of classifiers which is above 90%. The result shows that data generated by wrist-worn accelerometer sensor is sufficient for ambulation activity recognition and can be used for human performance evaluation applications.


title: Towards unified depth and semantic prediction from a single image

abstract: Depth estimation and semantic segmentation are two fundamental problems in image understanding. While the two tasks are strongly correlated and mutually beneficial, they are usually solved separately or sequentially. Motivated by the complementary properties of the two tasks, we propose a unified framework for joint depth and semantic prediction. Given an image, we first use a trained Convolutional Neural Network (CNN) to jointly predict a global layout composed of pixel-wise depth values and semantic labels. By allowing for interactions between the depth and semantic information, the joint network provides more accurate depth prediction than a state-of-the-art CNN trained solely for depth prediction [6]. To further obtain fine-level details, the image is decomposed into local segments for region-level depth and semantic prediction under the guidance of global layout. Utilizing the pixel-wise global prediction and region-wise local prediction, we formulate the inference problem in a two-layer Hierarchical Conditional Random Field (HCRF) to produce the final depth and semantic map. As demonstrated in the experiments, our approach effectively leverages the advantages of both tasks and provides the state-of-the-art results.


title: Bayesian Unsupervised Topic Segmentation

abstract: This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of well-formed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian framework.


title: Automatic cardiac T2* relaxation time estimation from magnetic resonance images using region growing method with automatically initialized seed points

abstract: Novel automatic cardiac T2* relaxation time estimation algorithm from MR images.Novel automatic ROI segmentation algorithm in cardiac MR images.Segmentation results from our technique are very close to the experts' opinions.Cardiac T2* values from our technique are very close to that calculated by experts.Our method does not need manual processes in ROI segmentation and T2* calculation. Background and objectiveHeart failure due to iron-overload cardiomyopathy is one of the main causes of mortality. The cardiomyopathy is reversible if intensive iron chelation treatment is done in time, but the diagnosis is often delayed because the cardiac iron deposition is unpredictable and the symptoms are lately detected. There are many ways to assess iron-overload. However, the widely used and approved method is by using MRI which is performed by calculating the T2* (T2-star). In order to compute the T2* value, the region of interest (ROI) is manually selected by an expert which may require considerable time and skills. The aim of this work is hence to develop the cardiac T2* measurement by using region growing algorithm for automatically segmenting the ROI in cardiac MR images. Mathematical morphologies are also used to reduce some errors. MethodsThirty MR images with free-breathing and respiratory-trigger technique were used in this work. The segmentation algorithm yields good results when compared with the manual segmentation performed by two experts. ResultsThe averages of positive predictive value, the sensitivity, the Hausdorff distance, and the Dice similarity coefficient are 0.76, 0.84, 7.78 pixels, and 0.80 when compared with the two experts' opinions. The T2* values were carried out based on the automatically segmented ROI's. The mean difference of T2* values between the proposed technique and the experts' opinion is about 1.40ms. ConclusionsThe results demonstrate the accuracy of the proposed method in T2* value estimation. Some previous methods were implemented for comparisons. The results show that the proposed method yields better segmentation and T2* value estimation performances.


title: GEMSEC: Graph Embedding with Self Clustering

abstract: Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC - a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters.


title: DOM tree based approach for Web content extraction

abstract: The World Wide Web plays an important role while searching for information in the data network. Users are constantly exposed to an ever-growing flood of information. Our approach will help in searching for the exact user relevant content from multiple search engines thus, making the search more efficient and reliable. Our framework will extract the relevant result records based on two approaches i.e. Stored URL list and Run time Generated URL list. Finally, the unique set of records is displayed in a common framework's search result page. The extraction is performed using the concepts of Document Object Model (DOM) tree. The paper comprises of a concept of threshold and data filters to detect and remove irrelevant & redundant data from the web page. The data filters will also be used to further improve the similarity check of data records. Our system will be able to extract 75%–80% user relevant content by eliminating noisy content from the different structured web pages like blogs, forums, articles etc. in the dynamic environment. Our approach shows significant advantages in both precision and recall.


title: Commitment and employee behavior: comparison of affective commitment and continuance commitment with perceived organizational support

abstract: The social exchange view of commitment (R. Eisenberger, R. Huntington, S. Hutchison, & D. Sowa, 1986) suggests that employees' perceptions of the organization's commitment to them (perceived organizational support, or POS) creates feelings of obligation to the employer, which enhances employees' work behavior. The authors addressed the question of whether POS or the more traditional commitment concepts of affective commitment (AC) and continuance commitment (CC) were better predictors of employee behavior (organizational citizenship and impression management). Participants were 383 employees and their managers. Although results showed that both AC and POS were positively related to organizational citizenship and that CC was negatively related to organizationa l citizenship, POS was the best predictor. These findings support the social exchange view that POS creates feelings of obligation that contribute to citizenship behaviors. In addition, CC was unrelated, whereas AC and POS were positively correlated, with some impression management behaviors.


title: Node connectivity and arc connectivity of a fuzzy graph

abstract: The fuzzy graph approach is more powerful in cluster analysis than the usual graph - theoretic approach due to its ability to handle the strengths of arcs effectively. The concept of node-strength sequence is introduced and is studied in a complete fuzzy graph. Two new connectivity parameters in fuzzy graphs namely, fuzzy node connectivity (@k) and fuzzy arc connectivity (@k^') are introduced and obtained the fuzzy analogue of Whitney's theorem. Fuzzy node cut, fuzzy arc cut and fuzzy bond are defined. Fuzzy bond is a special type of a fuzzy bridge. It is proved that at least one of the end nodes of a fuzzy bond is a fuzzy cutnode. It is shown that @k=@k^' for a fuzzy tree and it is the minimum of the strengths of its strong arcs. The relationships of the new parameters with already existing vertex and edge connectivity parameters are studied and is shown that the value of all these parameters are equal in a compete fuzzy graph. Also a new clustering technique based on fuzzy arc connectivity is introduced.


title: Ontology based CBR with jCOLIBRI

abstract: jCOLIBRI1 is a Java framework that helps designing Case Based Reasoning systems. This paper presents the incorporation of Description Logics reasoning capabilities to the new release of the framework. With this extension jCOLIBRI facilitates the development of Knowledge Intensive CBR applications. Ontologies are useful regarding different aspects: as the vocabulary to describe cases and/or queries, as a knowledge structure where the cases are located, and as the knowledge source to achieve semantic reasoning methods for similarity assessment and case adaptation that are reusable across different domains.


title: Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback

abstract: Providing effective feedback on resource consumption in the home is a key challenge of environmental conservation efforts. One promising approach for providing feedback about residential energy consumption is the use of ambient and artistic visualizations. Pervasive computing technologies enable the integration of such feedback into the home in the form of distributed point-of-consumption feedback devices to support decision-making in everyday activities. However, introducing these devices into the home requires sensitivity to the domestic context. In this paper we describe three abstract visualizations and suggest four design requirements that this type of device must meet to be effective: pragmatic, aesthetic, ambient, and ecological. We report on the findings from a mixed methods user study that explores the viability of using ambient and artistic feedback in the home based on these requirements. Our findings suggest that this approach is a viable way to provide resource use feedback and that both the aesthetics of the representation and the context of use are important elements that must be considered in this design space.


title: Quaternion-Based Image Hashing for Adaptive Tampering Localization

abstract: Image-hashing-based tampering detection methods have been widely studied with continuous advancements. However, most of existing models are designed for a specific tampering. In this paper, we propose a novel quaternion-based image hashing to detect almost all types of tampering, including color changing, copy move, splicing, and so on. First, the quaternion Fourier–Mellin transform is used to calculate the geometric hash to eliminate the influence of geometric distortions. Then, a new quaternion image construction method, which combines advantages of both color and structural features, is proposed to implement the quaternion Fourier transform to calculate the image feature hash to locate the tampered regions. The objective is to provide a reasonably short image hashing with good performance, i.e., being perceptually robust against various content-preserving attacks while capable of detecting and locating almost all types of tampering. Furthermore, an adaptive tampering localization algorithm is proposed based on clustering analysis to improve the detection accuracy. The experimental results show that the proposed tampering detection model outperforms the existing state-of-the-art models and is very robust against various content-preserving attacks.


title: Synaptic Basis of Cortical Persistent Activity: the Importance of NMDA Receptors to Working Memory

abstract: Delay-period activity of prefrontal cortical cells, the neural hallmark of working memory, is generally assumed to be sustained by reverberating synaptic excitation in the prefrontal cortical circuit. Previous model studies of working memory emphasized the high efficacy of recurrent synapses, but did not investigate the role of temporal synaptic dynamics. In this theoretical work, I show that biophysical properties of cortical synaptic transmission are important to the generation and stabilization of a network persistent state. This is especially the case when negative feedback mechanisms (such as spike-frequency adaptation, feedback shunting inhibition, and short-term depression of recurrent excitatory synapses) are included so that the neural firing rates are controlled within a physiological range (10-50 Hz), in spite of the exuberant recurrent excitation. Moreover, it is found that, to achieve a stable persistent state, recurrent excitatory synapses must be dominated by a slow component. If neuronal firings are asynchronous, the synaptic decay time constant needs to be comparable to that of the negative feedback; whereas in the case of partially synchronous dynamics, it needs to be comparable to a typical interspike interval (or oscillation period). Slow synaptic current kinetics also leads to the saturation of synaptic drive at high firing frequencies that contributes to rate control in a persistent state. For these reasons the slow NMDA receptor-mediated synaptic transmission is likely required for sustaining persistent network activity at low firing rates. This result suggests a critical role of the NMDA receptor channels in normal working memory function of the prefrontal cortex.


title: Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease

abstract: Objective Patients with IBD display substantial heterogeneity in clinical characteristics. We hypothesise that individual differences in the complex interaction of the host genome and the gut microbiota can explain the onset and the heterogeneous presentation of IBD. Therefore, we performed a case–control analysis of the gut microbiota, the host genome and the clinical phenotypes of IBD. Design Stool samples, peripheral blood and extensive phenotype data were collected from 313 patients with IBD and 582 truly healthy controls, selected from a population cohort. The gut microbiota composition was assessed by tag-sequencing the 16S rRNA gene. All participants were genotyped. We composed genetic risk scores from 11 functional genetic variants proven to be associated with IBD in genes that are directly involved in the bacterial handling in the gut: NOD2, CARD9, ATG16L1, IRGM and FUT2. Results Strikingly, we observed significant alterations of the gut microbiota of healthy individuals with a high genetic risk for IBD: the IBD genetic risk score was significantly associated with a decrease in the genus Roseburia in healthy controls (false discovery rate 0.017). Moreover, disease location was a major determinant of the gut microbiota: the gut microbiota of patients with colonic Crohn's disease (CD) is different from that of patients with ileal CD, with a decrease in alpha diversity associated to ileal disease (p=3.28×10−13). Conclusions We show for the first time that genetic risk variants associated with IBD influence the gut microbiota in healthy individuals. Roseburia spp are acetate-to-butyrate converters, and a decrease has already been observed in patients with IBD.


title: YouMove: enhancing movement training with an augmented reality mirror

abstract: YouMove is a novel system that allows users to record and learn physical movement sequences. The recording system is designed to be simple, allowing anyone to create and share training content. The training system uses recorded data to train the user using a large-scale augmented reality mirror. The system trains the user through a series of stages that gradually reduce the user's reliance on guidance and feedback. This paper discusses the design and implementation of YouMove and its interactive mirror. We also present a user study in which YouMove was shown to improve learning and short-term retention by a factor of 2 compared to a traditional video demonstration.


title: High Performance Lithography Hotspot Detection With Successively Refined Pattern Identifications and Machine Learning

abstract: Under the real and evolving manufacturing conditions, lithography hotspot detection faces many challenges. First, real hotspots become hard to identify at early design stages and hard to fix at post-layout stages. Second, false alarms must be kept low to avoid excessive and expensive post-processing hotspot removal. Third, full chip physical verification and optimization require very fast turn-around time. Last but not least, rapid technology advancement favors generic hotspot detection methodologies to avoid exhaustive pattern enumeration and excessive development/update as technology evolves. To address the above issues, we propose a high performance hotspot detection methodology consisting of: 1) a fast layout analyzer; 2) powerful hotspot pattern identifiers; and 3) a generic and efficient flow with successive performance refinements. We implement our algorithms with industry-strength engine under real manufacturing conditions and show that it significantly outperforms state-of-the-art algorithms in false alarms (2.4X to 2300X reduction) and runtime (5X to 237X reduction), meanwhile achieving similar or better hotspot accuracies. Compared with pattern matching, our method achieves higher prediction accuracy for hotspots that are not previously characterized, therefore, more detection generality when exhaustive pattern enumeration is too expensive to perform a priori. Such high performance hotspot detection is especially suitable for lithography-friendly physical design.


title: Heterogeneous image feature integration via multi-modal spectral clustering

abstract: In recent years, more and more visual descriptors have been proposed to describe objects and scenes appearing in images. Different features describe different aspects of the visual characteristics. How to combine these heterogeneous features has become an increasing critical problem. In this paper, we propose a novel approach to unsupervised integrate such heterogeneous features by performing multi-modal spectral clustering on unlabeled images and unsegmented images. Considering each type of feature as one modal, our new multi-modal spectral clustering (MMSC) algorithm is to learn a commonly shared graph Laplacian matrix by unifying different modals (image features). A non-negative relaxation is also added in our method to improve the robustness and efficiency of image clustering. We applied our MMSC method to integrate five types of popularly used image features, including SIFT, HOG, GIST, LBP, CENTRIST and evaluated the performance by two benchmark data sets: Caltech-101 and MSRC-v1. Compared with existing unsupervised scene and object categorization methods, our approach always achieves superior performances measured by three standard clustering evaluation metrices.


title: Load-Deflection Behavior Prediction of Intact and Corroded RC Bridge Beams with or without Lap Splices Considering Bond Stress-Slip Effect

abstract: AbstractIn the design and performance evaluation of RC structures, different quantities, such as ultimate strength, serviceability, and ductility, need to be considered. Because these quantities deal with both strength and deflection, the nonlinear load-deflection prediction of RC bridges becomes essential. In this paper, an analytical procedure is proposed for predicting the flexural behavior of intact and corroded RC beams with or without lap splices considering the bond stress-slip behavior at the steel–concrete interface. The accuracy of the proposed procedure is verified through several experimental and numerical case studies. Last, the proposed procedure is applied to predict the flexural behavior of intact and corroded T-beams of a RC bridge, and the results are verified through the finite-element analyses.


title: Strategy and Strategic Management Concepts: Are They Recognised by Management Students?

abstract: The purpose of this paper is to assess the acquired knowledge of university management students ::: on strategy and strategic management concepts. Phenomenography was used in the analysis and ::: a ::: collective description was conducted. A sample of students taking management degrees at five ::: universities was adopted. Following data analysis, a model explaining our definition of students is ::: presented. This definition was compared with the existing literature. Our findings do not indicate ::: a ::: single, predominant concept but reflect a diverse set of existing concepts. We conclude that ::: students do present difficulties when it comes to explaining their theoretical definition in real ::: situations applied to organizations. The results contribute towards a better understanding of the ::: universities surveyed, but cannot be generalized. Thus, we recommend future studies replicate this ::: research project at other Higher Education Institutions and confirm the factors making up the ::: construction of the definition of strategy from the perspective of future managers. Given that topics ::: relating to strategy and strategic management are highly relevant to organizations today, it is of ::: similar great importance to identifying just what future managers perceive about these issues. Our ::: model explains the definition of strategy in the vision of young managers about to enter the market. ::: A ::: balance between the current schools of thought on strategy and an approach to the reality of ::: enterprises are presented as challenges for both teachers and researchers in this field.


title: Uni-CAVE: A Unity3D plugin for non-head mounted VR display systems

abstract: Unity3D has become a popular, freely available 3D game engine for design and construction of virtual environments. Unfortunately, the few options that currently exist for adapting Unity3D to distributed immersive tiled or projection-based VR display systems rely on closed commercial products. Uni-CAVE aims to solve this problem by creating a freely-available and easy to use Unity3D extension package for cluster-based VR display systems. This extension provides support for head and device tracking, stereo rendering and display synchronization. Furthermore, Uni-CAVE enables configuration within the Unity environment enabling researchers to get quickly up and running.


A case of eosinophilic enteritis and rheumatoid arthritis


title: COSI: A Framework for the Design of Interconnection Networks

abstract: This article presents a software framework for communication infrastructure synthesis of distributed systems, which is critical for overall system performance in communication-based design. Particular emphasis is given to on-chip interconnect synthesis of multicore designs.


title: A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

abstract: OBJECTIVE:Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. APPROACH:The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. MAIN RESULTS:In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. SIGNIFICANCE:We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.