Reviewerly/exHarmony huggingface dataset:
Graph-XLL: a Graph Library for Extra Large Graph Analytics on a Single Machine

Graph libraries containing already implemented algorithms are highly desired since users can conveniently use the algorithms off-the-shelf to achieve fast analytics and prototyping, rather than implementing the algorithms with lower-level APIs. Besides the ease of use, the ability to efficiently process extra large graphs is also required by users. The popular existing graph libraries include the igraph R library and the NetworkX Python library. Although these libraries provide many off-the-shelf algorithms for users, the in-memory graph representation limits their scalability for computing on large graphs. Therefore, in this paper, we introduce Graph-XLL: a graph library implemented using the WebGraph framework in a vertex-centric manner, with much less memory requirement compared to igraph and NetworkX. Scalable analytics for extra large graphs (up to tens of millions of vertices and billions of edges) can be achieved on a single consumer grade machine within a reasonable amount of time. Such computation would cause out-of-memory error if using igraph or NetworkX.
ApproxML

Machine learning (ML) has gained a pivotal role in answering complex predictive analytic queries. Model building for large scale datasets is one of the time consuming parts of the data science pipeline. Often data scientists are willing to sacrifice some accuracy in order to speed up this process during the exploratory phase. In this paper, we propose to demonstrate ApproxML, a system that efficiently constructs approximate ML models for new queries from previously constructed ML models using the concepts of model materialization and reuse . ApproxML supports a variety of ML models such as generalized linear models for supervised learning, and K-means and Gaussian Mixture model for unsupervised learning.
Global and Local Multi-scale Feature Fusion for Object Detection and Semantic Segmentation

Feature fusion approaches have been widely used in object detection and semantic segmentation to improve accuracy. Global feature fusion integrates semantic information and detail spatial information. Combining the fine feature maps in the bottom-up stage and the coarse feature maps in the top-down stage is very effective in the network where it is necessary to understand the contextual information of a given image. In this paper, we propose a method to integrate multiple feature maps in the local region as well as global feature fusion. Local multi-scale feature fusion integrates neighboring feature maps from different levels and scales to get a more diverse range of receptive fields with less computation while keeping detail appearance information. Experimental results demonstrate that the proposed network, which is based on the global and local feature fusion, achieves competitive accuracy with real-time inference speed in semantic segmentation and object detection tasks over the previous state-of-the-art methods.
Three Reasons Why: Framing the Challenges of Assuring AI

Assuring the safety of systems that use Artificial Intelligence (AI), specifically Machine Learning (ML) components, is difficult because of the unique challenges that AI presents for current assurance practice. However, what is also missing is an overall understanding of this multi-disciplinary problem space. In this paper, a model is given that frames the challenges into three categories which are aligned to the reasons why they occur. Armed with a common picture of where existing issues and solutions “fit-in”, the aim is to help bridge cross-domain conceptual gaps and provide a clearer understanding to safety practitioners, ML experts, regulators and anyone involved in the assurance of a system with AI.
The Development of an Integrated Corpus for Malay Language

Generally, a corpus serves as the source of data for various types of research. As such, there are several Malay corpora being developed to support the needs of the researchers. However, the various corpora of Malay text are distributed and not integrated, where some words are not included or missing in some corpora. The focus of this paper is to develop an integrated corpus that will combine four most comprehensive Malay corpora. The intention is to provide comprehensive coverage of Malay corpora which would be beneficial for any relevant work.
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks

Generative Adversarial Networks (GANs) have achieved outstanding results in generation of realistic data, particularly for image data. Autonomous driving systems are equipped with a large suite of sensors to obtain robustness and redundancy. Ultrasonic sensors are commonly used because of their low-cost and reliability of near-field distance estimation. However, machine learning algorithms are not commonly used for ultrasonic data, as it requires extensive datasets whose creation is time-consuming, expensive and inflexible to hardware and environmental changes. On the other hand, there exists no method to simulate these signals deterministically. Thus, we present a novel approach for synthetic ultrasonic signal simulation using conditional GANs (cGANs). To the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics sensors. The performance of cGANs allows us to bring the environment simulation to a high quality close to realistic data. By using our setup and environmental parameters as condition, the proposed approach is flexible to external influences. Due to its low complexity and smaller time effort needed for data generation, the proposed method outperforms other simulation algorithms such as finite element method. We verify the outstanding accuracy and realism of our method by applying a detailed statistical analysis and comparing the generated data to an extensive amount of measured signals.
WEB-VC: Visual Cryptography for Web Image

The conventional encryption techniques of visual cryptography are extended to web page. The key point is that Cascading Style Sheet blend modes can simulate stacking operators. The multiply blend mode can be regarded as Boolean OR operator, and the difference blend mode can be regarded as exclusive-OR (XOR) operator. Thus, the secrets can be revealed in a web browser by overlapping shares together without any additional computation. We named this scheme Web-VC. The decoding procedure of Web-VC is just like usual visual cryptography, while digital shares can be used in this case. Web-VC has strong ability of resisting compression and transcoding without losing the secret. Another contribution of this work is the method of XOR-based flip visual cryptography.
Storage Service Reliability and Availability Predictions of Hadoop Distributed File System

Hadoop is a de facto standard for Big Data storage and provides a complete arrangement of components for Big Data elaboration. Hadoop Distributed File System (HDFS), the fundamental module of Hadoop, has been evolved to deliver fault-tolerant data storage services in cloud. This work proposes a precise mathematical model of HDFS and estimates its data storage service availability and reliability. In this connection, a stochastic Petri net (SPN)-based dependability modelling strategy is adopted. In addition, a structural decomposition technique has been advocated to address the state space complexity of the said model. The proposed model is useful to measure crucial quality of service parameters, namely storage service reliability and availability for emerging distributed data storage systems in the context of cloud.
Full-Reference Metric Adaptive Image Denoising

The performance of image denoising algorithms generally depends largely on the selection of the parameters. We address the problem of optimizing the denoising parameters to achieve maximum denoising performance. Most existing methods for no-reference denoising parameter optimization either use the estimated image noise or individual no-reference image quality evaluation metrics. In the paper, we introduce a natural image statistics based on the generalized Gaussian distribution and an elastic net regularization regression model. Consequently, we propose their use for performing no-reference parameter optimization in the state-of-the-art BM3D denoising algorithm, adaptively depending on which of the following most widely used full-reference image quality evaluation metrics is optimized: FSIM, SSIM, MS-SSIM, without any knowledge of the noise distribution.
Mutual Noise Estimation Algorithm for Video Denoising

This paper presents a novel algorithm to estimate spatio-temporal noise variance in videos. The algorithm uses mutual information from the spatial and temporal noise statistics to detect homogeneous blocks in a video frame and estimate the noise. The experimental results show the accuracy and robustness of the algorithm over videos with low to high spatial and temporal complexities, and varying levels of noise power and noise correlations.
Bipolar Gan: Double Check the Solution Space and Lighten False Positive Errors in Generative Adversarial Nets

Generative Adversarial Nets (GAN) and its variations gain their popularity both in application scenarios and the research front. In this paper, we proposed a novel approach which is compatible with previous methods. It can improve the quality of generated images using both Authenticity Discriminator and Falsity Discriminator to double check the solution space. Our experiments exhibited the feasibility of our solution and expressed its ability to improve recently proposed methods.
Locating object - of - interest &amp; preventing Stampede using crowd management

Congestion in urban areas is becoming a serious problem in modern society. Large scale events at locations such as movie theatres or sports stadiums, etc which attract thousands of spectators can be expected to cause more inconvenience in future as compared to the present day conditions. Crowd management is important as with the increase in number of individuals a number of problems can occur, like, the risks of people or objects getting lost and stampede in areas of high crowd density. It is essential for people to live safely and securely in such large cities. Real-time analysis and rapid response is necessary for the security and safety. Using Artificial Intelligence we will be able to track people and object of interest almost immediately. In cases of people or costly objects being lost it becomes imperative to find its location in real-time, and this is where this implementation has an upper-hand on all its predecessors. In places of high density of population it is very common for children to get separated from their parents. Even at places where CCTV cameras are installed it is very time seeking to search for a particular object in millions of objects.
An Interval Type-2 Fuzzy Based DTC of IMD Using the Hybrid Duty Ratio Control

This paper provides an interval type-2 fuzzy based DTC (IT2FDTC) of the induction motor drive (IMD) in which a hybrid duty ratio control (HDRC) technique is used to activate a two-level inverter. Moreover, it is implemented with the help of DSPACE-1104 controller in real-time application using 2HP induction motor and compared its overall performance with a conventional proportional integral DTC (CPIDC) using SVM technique. The IT2FDTC can be provided less torque and flux ripples with fast dynamic performance of IMD over a CPIDTC. A three-dimensional Mamdani type-2 fuzzy controller along with centroid method is used in IT2FDTC. Hence, the proposed method of study not only control the gains of PI effectively, but also control the duty ratios of modulating wave to further improve the dynamic performance and current harmonic profile of IMD.
Improving Comparative Radiography by Multi-resolution 3D-2D Evolutionary Image Registration

Comparative radiography has a crucial role in the forensic identification endeavor. A proposal to automate the comparison of ante-mortem and post-mortem radiographs has been recently proposed based on an evolutionary image registration method. It considers the use of differential evolution to estimate the parameters of a 3D-2D registration transformation that automatically superimposes a bone surface model over a radiograph of the same bone. The main drawback of this proposal is the high computational cost. This contribution tackled this high computational cost by incorporating multi-resolution and multi-start strategies into its optimization process. We have studied the accuracy, robustness and computation time of the different configurations of the proposed method with synthetic images of patellae, clavicles and frontal sinuses. A significant improvement has been obtained in comparison to the state-of-the-art method in term of the robustness of the optimization method and computational cost with a drop in accuracy smaller than the 0.5% of the pixels of the silhouette of the bone or cavity.
Proposal of Expert System to Predict Financial Frauds Using Data Mining

The phrase ‘Money Laundering’ is a terrible movement to disguise the black money that converts as the white money. This criminal activity poses a severe threat to the monetary institutions and also to the nation. There are three phases in Money Laundering Placement, Layering, and Integration. The advancement of competent mechanism to detect suspicious transaction is a critical problem because of financial transaction made in global market. The system we are developing targets on how monetary institutions can obtain better results in anti-money laundering initiatives. To overcome this criticality we propose an efficient approach to find out if the transactions are legal or illegal by adopting Hashbased technique and Graph Theoretic Approach.
Design of Spatial Inverted Pendulum System

Pendulum-based systems which are highly non-linear and unstable have become one of the most widely studied subject of control theory. The close interest of researchers on inverted pendulum problem arises from its strong representation ability with real engineering applications. This study focuses on the design and production of an experimental setup in which a spatial inverted pendulum can be balanced by means of a planar mechanism in RRRRP configuration. A mechanism with two different motion inputs (rotational and linear) that no studies were performed on before was prototyped. This system is highly unstable and shows non-linear dynamic behavior. The mechanical parts that forms the system were manufactured by a 3D printer and a CNC milling machine. At the end of study, a four-degree of freedom spatial inverted pendulum experimental setup has been established on which the control works can be carried out, by installing the electronic and electro-mechanical equipment.
End-To-End Convolutional Network for Video Rain Streaks Removal

Existing video rain streaks removal methods utilize various manual models to represent the appearance of rain streaks, and only use convolutional neural network (CNN) as a post-processing part to compensate the artifacts like misalignment caused by traditional de-raining operations. However, these manual models only work for some particular scenes because the distribution of rain streaks is complex and random. Moreover, since CNN network and previous traditional de-raining operations cannot be trained jointly, the output of CNN network may still contain artifacts. To address these problems, we propose an end-to-end video rain streaks removal CNN network called EEVRSR net. Experimental results of both synthetic and real data demonstrate that the proposed EEVRSR net achieves better performance in both speed and effectiveness over state-of-the-art methods.
Towards Risk-Aware Cost-Optimal Resource Allocation for Cloud Applications

Low-cost transient resources can be leveraged to reduce the cost of running applications on the cloud. However, they are susceptible to unilateral revocations by the provider, making them a risky choice for applications with strict performance requirements. Effective use of transient resources to minimize cloud costs without impacting the application performance remains an ongoing challenge for cloud consumers. In this paper, we address this challenge by proposing an approach for risk-aware, cost-optimal cloud resource allocation based on the concept of diversification. Contract diversification mitigates the risk to application performance due to resource revocation by procuring the required capacity as a mix of transient and non-transient resources. Resource diversification mitigates the risk further, by procuring the transient capacity as a heterogeneous mix of instance types. As supported by experimental results, the proposed approach achieves meaningful cost savings and risk mitigation.
Reinforcing the Robustness of a Deep Neural Network to Adversarial Examples by Using Color Quantization of Training Image Data

Recent works have shown the vulnerability of deep convolutional neural network (DCNN) to adversarial examples with malicious perturbations. In particular, Black-Box attacks without information of parameter and architectures of the target models are feared as realistic threats. To address this problem, we propose a method using an ensemble of models trained by color-quantized data with loss maximization. Color-quantization can allow the trained models to focus on learning conspicuous spatial features to enhance the robustness of DCNNs to adversarial examples. The proposed method can be adapted to Black-Box attacks with no need of particular attack algorithm for the defense. The results of our experiments validated the effectiveness for preventing decrease in the test accuracy with adversarial perturbation.
Programming with Affect: How Behaviour Trees and a Lightweight Cognitive Architecture Enable the Development of Non-Player Characters with Emotions

There is little support in game development tools for implementing emotion and mood in non-player characters. In this paper, we present a tool that integrates behaviour trees into an emotion modeling framework, based on ALMA, to add emotion and mood to NPCs. We introduce two new nodes to traditional behaviour trees, the Emotion Adder, which triggers emotions in NPCs and the E-Selector, which incorporates the NPC's mood in the decision-making process. This EMoBeT framework arms developers with a tool that will assist them in integrating and manipulating psychologically valid moods and emotions of NPCs using the familiar behaviour tree model.
Ideal Combination Feature Selection Model for Classification Problem based on Bio-Inspired Approach

Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. However, most of the feature selection methods that were introduced work individually that sometimes caused less optimal feature being selected, subsequently degrading the consistency of the classification accuracy rate. The aim of this paper is to exploit the capability of bio-inspired search algorithms, together with wrapper and filtered methods in generating optimal set of features. The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. The next step is to define an optimized feature set for classification task. Performance metrics are analyzed based on classification accuracy and the number of selected features. Experiments were conducted on nine (9) benchmark datasets with various sizes, categorized as small, medium and large dataset. Experimental results revealed that the ideal combination is a feature selection model with the implementation of bio-inspired search algorithm that consistently obtains the optimal solution (i.e. less number of features with higher classification accuracy) on the selected dataset. Such a finding indicates that the exploitation of bio-inspired algorithms with ideal combination of wrapper/filtered method can contribute in finding the optimal features to be used in data mining model construction.
Towards Computer-Generated Cue-Target Mnemonics for E-Learning

A novel method to generate memory aids for general forms of knowledge is presented. Mnemonic phrases are constructed using constraints of phonetic similarity to learning material, grammar, semantics, and factual consistency. The method has been implemented in Python using the CMU Pronouncing Dictionary, the CYC AI knowledge base, and Kneser-Ney 5-gram probabilities built from the large-scale COCA text corpus. Initial tests have produced encouraging output.
CONTEMPORARY PROBLEMS OF DISTANCE COURSE IN THE HIGHER EDUCATION SYSTEM OF RUSSIA

In the article, three interrelated problems that are directly or indirectly implemented in the process data, are identified on the basis of a very thoughtful analysis and generalization of various data on the features of distance course of the higher education system of Russia. First, it is a cultural and psychological problem. Secondly, it is methodical, thirdly – administrative and organizational. Based on the generalization of the available data, some aspects of solving these problems are revealed in the work. The main focus is on the choice of the most demanded disciplines, for example, English and others. At the same time, it indicates a high activity of enthusiasts, professionals who develop online courses. The concepts of hybrids of such courses are introduced, since different software programs are used in their development. There is a lack of financial and resource support in creating online courses, as well as realization time. This article does not claim to be a comprehensive disclosure of the problem; it only gives some initial data that can serve as the beginning of a more in-depth study of this topic.
Challenges and Prospects of Shaping the Leading Positions of the Plekhanov Russian University of Economics in Training Students for Specialization ‘Management’

The article provides results of the education and scientific center 'Management' work (OSCM), a new form of managing the profile chairs and interaction with faculties. The choice of this organizational form in the Russian Plekhanov University of Economics was correct, which is proved by May 2018 Decrees of the President of the Russian Federation. Shaping the leading positions of the university in training students for specialization 'Management' is ensured by integration and coordination of joint efforts of the OSCM managerial team, lecturers and employees of chairs, academic schools, research institutions and other structural divisions. Dynamics of the OSCM organizational structure shown by the authors is stipulated by focusing on resolving yearly program objectives of university development. Founding key chairs and research labs provides an opportunity to integrate the teaching process with the real practice of management, which is confirmed by international and professional-public accreditation of education programs. There are 20 key professional education programs of training bachelors and masters of management, 6 of them were accredited by the European Council on Business Education and 13 have professional- public accreditation. The demand for training programs for specialization 'Management' is provided by the whole set of measures, including system career advising of Moscow school leavers, partnership with the Regional Center of career advising and employment based in the Zemskiy gymnasia in Balashikha, the use of advanced education technologies and two-language programs, etc. The academic novelty of the article is connected with its critical comprehension of challenges and prospects of shaping leading positions in training students for specialization 'Management' on the basis of OSCM work analysis, studying the impact of current reforms on the university functioning. The authors put forward a number of recommendations dealing with the goal of attaining leading positions in training students for specialization 'Management'.