Reviewerly/exHarmony huggingface dataset:
Computer Vision to Improve Security Surveillance through the Identification of Digital Patterns

The need to have good security, either in the streets, at home or at workplaces, cannot be overemphasized. Due to its significance, security experts continue to improve the mechanism and the tools used to manage security issues. The revolution in computing and information technology has significantly affected how people deal with security. Computer vision application has been developed for security purposes, especially, by improving surveillance systems. Computer vision can manage face detection, motion detection, person identification, tracking, access control, and interpretation of movement. Most of these tasks can be used to improve security surveillance. Computer integrated systems can be used to identify strange behavior and aid security management. Forensic science involves the analysis of images to establish patterns. As a result, strategies aimed at utilizing computer vision focus on the improvement of image computation power of computer systems. Surveillance processes based on the use of cameras involve different phases: environmental design, discovery of movements, analysis and description of behaviors, organizing objects in motion, tracing as well as discovery of individuals. This paper seeks to analyze how computer systems can be trained to identify digital patterns in order to help in surveillance processes including tracking of strange behavior and crime.
A Distributed Task Scheduling System Suitable for Massive Environments

In a million-scale host-scale environment, the resource scheduling/management system becomes more and more important. How to improve resource utilization and reduce the cost of infrastructure while ensuring the performance of business operations in the data center is the core issue we must consider. And because of the complex relationship between tasks, the corresponding DAG (DAG: Directed Acyclic Graph) is often formed, making the distribution and management of tasks and resources more difficult. In order to solve the problems of tedious task scheduling and low resource utilization in a large-scale host machine environment, this article describes a general task and machine model based on the actual situation of various companies, and based on this model, implements a model suitable for massive data task scheduling scheme.
Negative Sampling in Semi-Supervised learning

We introduce Negative Sampling in Semi-Supervised Learning (NS3L), a simple, fast, easy to tune algorithm for semi-supervised learning (SSL). NS3L is motivated by the success of negative sampling/contrastive estimation. We demonstrate that adding the NS3L loss to state-of-the-art SSL algorithms, such as the Virtual Adversarial Training (VAT), significantly improves upon vanilla VAT and its variant, VAT with Entropy Minimization. By adding the NS3L loss to MixMatch, the current state-of-the-art approach on semi-supervised tasks, we observe significant improvements over vanilla MixMatch. We conduct extensive experiments on the CIFAR10, CIFAR100, SVHN and STL10 benchmark datasets.
Hierarchical Verification for Adversarial Robustness

We introduce a new framework for the exact point-wise $\ell_p$ robustness verification problem that exploits the layer-wise geometric structure of deep feed-forward networks with rectified linear activations (ReLU networks). The activation regions of the network partition the input space, and one can verify the $\ell_p$ robustness around a point by checking all the activation regions within the desired radius. The GeoCert algorithm (Jordan et al., NeurIPS 2019) treats this partition as a generic polyhedral complex in order to detect which region to check next. In contrast, our LayerCert framework considers the \emph{nested hyperplane arrangement} structure induced by the layers of the ReLU network and explores regions in a hierarchical manner. We show that, under certain conditions on the algorithm parameters, LayerCert provably reduces the number and size of the convex programs that one needs to solve compared to GeoCert. Furthermore, our LayerCert framework allows the incorporation of lower bounding routines based on convex relaxations to further improve performance. Experimental results demonstrate that LayerCert can significantly reduce both the number of convex programs solved and the running time over the state-of-the-art.
Prediction of Ventricular Hypertrophy of Heart Using Fractional Calculus

In this paper we have studied Left and Right ventricular hypertrophy of heart from ECGs using fractional calculus. An ECG is a rough or unreachable curve which is continuous everywhere but nondifferentiable at some points or all points where classical calculus fails.Our purpose of this paper is to find left and right fractional derivatives at those non-differentiable points and then predict LVH and RVH by calculating the phase transition values (absolute difference of left and right fractional derivatives). Our investigation shows that for LVH patients the phase transition values at the non-differentiable points of V1, V2, V5 and V6 leads are higher than those for normal ECG. For RVH ECG the phase transition values at S are smaller than those of R in V1 leads which are just opposite to the case of normal ECGs. Fractal dimension and Hurst Exponents of V1, V2, V5 and V6 leads of the ECGs have been calculated for both problematic and normal ECGs. All such measures may help doctors to diagnose LVH and RVH from ECG more accurately than the technique they use.
Software Environment for Studying Intelligent Anytime Heuristic Search Methods

Anytime algorithms are widely used in real-time intelligent systems because they offer a tradeoff between solution quality and solving time. This property of them is valuable when the first solution can be obtained much faster than obtaining an optimal solution with the classical algorithm. In this paper, we describe several frameworks for making anytime-algorithms from classical heuristic searches and introducing a C++ implementation of these algorithms useful for demonstrating to the students the concept of an anytime heuristic search, its use, properties and tuning. These algorithms are studied by students who are studying in the master's program in the field of "Applied mathematics and Informatics" when studying methods and systems of artificial intelligence (discipline "Methods and software tools for decision support").
Eye Tracking to Evaluate the Usability of an Online Pneumoconiosis Education Booklet in a Sample of South Asian Construction Workers

Pneumoconiosis is an incurable disease. An online educational booklet for South Asian construction workers has been developed to provide them with knowledge of the disease and its preventive measures. As eye tracking has long been applied to assess users' preferences with regard to the content of commercial products, this approach would likely offer a promising opportunity for an objective assessment of the usability of the developed booklet. This was a mixed-methods study that combined eye tracking and interviews to explore the usability of the educational booklet among South Asian construction workers. Twelve construction workers were invited to read the booklet individually on a laptop computer. An eye-tracking device was placed under the computer screen to collect eye tracking data of the participants to measure their attention to the contents of the booklet. Afterward, the participants were invited to attend a semistructured interview to explore their acceptance of and satisfaction with the booklet. Results showed that participants gazed more often and for a longer duration at the working environment that increases pneumoconiosis risk and the preventive measures for pneumoconiosis, and they paid more attention to the pictures. The workers reported that the booklet was useful and informative, and they were satisfied with its design and layout.
A New Repeated Pixel Value Difference-Based Steganographic Scheme with Overlapped Pixel

The steganographic scheme based on pixel value difference (PVD) embeds secret information by modifying the value of pixel pair within the block. The embedding capacity, visual quality, and performance of the PVD-based algorithm depend on the correlation between the pixel pair. To improve the capacity, quality, and performance of PVD-based algorithm, a new steganographic scheme has been proposed which uses overlapped pixels of a block to embed secret data within the R,G,B color components of interpolated pixel of a color image. At first, the RGB color components of the host image are partitioned into non-overlapped pixel blocks. After that, image interpolation is applied to enlarge each block. Then the secret message is embedded within the interpolated pixels using PVD algorithm. It has been observed that the new approach provides 2.22 bpp payload with 43.47 (dB) PSNR. The proposed scheme has been tested through different steganographic attacks. The experimental results show that the proposed algorithm has better embedding capacity compared to the conventional PVD-based approach.
Visualization of group discussion using correspondence analysis and LDA in Ideathon

Ideathon has attracted attention in recent years but the results of ideathons are not stable; good ideas are not often generated. Therefore, it is necessary to analyze and support the idea creation process in the ideathon. This research focuses on group discussions and analyzes the idea creation process by visualizing the discussions. Visualization is performed by combining correspondence analysis with LDA (Latent Dirichlet Allocation). From the visualization results, divergence of the discussion is evaluated quantitatively using cosine similarity. It is also revealed that group discussion with better evaluation includes higher divergence.
Multi-Indicator Image Quality Assessment of Smartphone Camera Based on Human Subjective Behavior And Perception

As smartphones are widely used in daily lives, manufacturers and customers are increasingly concerned about the performance of smartphone cameras. However, due to the unique distortion types, there are relatively few image quality assessment (IQA) methods for smartphone images. In this paper, we propose a smartphone photo quality assessment model, which scores from four quality aspects: color, texture, noise and exposure. Based on human observation behaviors of different indicators, two novel image cropping methods viz. SalGAN-crop based on saliency prediction and SSIM-crop based on structural similarity are proposed. Different features are after-wards extracted by simulating human subjective perception, and the predicted scores are finally given by AdaBoost regression analysis. Experimental results reveal that our model can provide more accurate scores than traditional methods.
Spectral Frank-Wolfe Algorithm: Strict Complementarity and Linear Convergence

We develop a novel variant of the classical Frank-Wolfe algorithm, which we call spectral Frank-Wolfe, for convex optimization over a spectrahedron. The spectral Frank-Wolfe algorithm has a novel ingredient: it computes a few eigenvectors of the gradient and solves a small-scale SDP in each iteration. Such procedure overcomes slow convergence of the classical Frank-Wolfe algorithm due to ignoring eigenvalue coalescence. We demonstrate that strict complementarity of the optimization problem is key to proving linear convergence of various algorithms, such as the spectral Frank-Wolfe algorithm as well as the projected gradient method and its accelerated version.
Angularly Consistent Light Field Video Interpolation

In this paper, we address the problem of temporal interpolation of sparsely sampled video light fields using dense scene flows. Given light fields at two time instants, the goal is to interpolate an intermediate light field to form a spatially, angularly and temporally coherent light field video sequence. We first compute angularly coherent bidirectional scene flows between the two input light fields. We then use the optical flows and the two light fields as inputs to a convolutional neural network that synthesizes independently the views of the light field at an intermediate time. In order to measure the angular consistency of a light field, we propose a new metric based on epipolar geometry. Experimental results show that the proposed method produces light fields that are angularly coherent while keeping similar temporal and spatial consistency as state-of-the-art video frame interpolation methods.
Research of Medical Insurance Based on the Combination of Blockchain and Credit Technology

With the rapid development of Internet, artificial intelligence and big data, blockchain technology and credit technology came into being, and the mode of combining the blockchain and credit happens generally. However, this model is rarely involved in the field of medical insurance. In this article, it researches on the relationship of blockchain and credit technology, and the application of combining blockchain and credit technology in medical insurance in our county based on the retrospective, Tamper Proof, and decentralized characters of blockchain and the service of credit report, credit evaluation and Credit information consultation. It would solve the problems of incomplete and asymmetric information among medical institutions, patients, and medical insurance organizations, and also the problems of dishonesty in medical insurance.
Time varying quality estimation for HTTP based adaptive video streaming

Rate adaptation of video can result in varying Quality of Experience (QoE) over time. For instance, the latest HTTP-based video streaming approaches rely on rate adaptation techniques to overcome the negative effects of time varying channel conditions. Therefore, in order to have a satisfactory user’s QoE in these situations, it is necessary to evaluate and predict subjective quality variations over time, termed here as Time Varying Subjective Quality (TVSQ). It is a challenge to model TVSQ due to hysteresis effects and non-linear responses of our Human Visual System (HVS). This study proposes a moving average filter based time varying quality metric, which accounts for the recency and primacy effects of our HVS. The performance of the proposed method is evaluated over a publicly available TVSQ database, which consists of 15 x 300-seconds videos evaluated by 25 subjects for time varying quality. The results show a good performance with respect to the state of the art. The simple calculations and the accuracy of the proposed method enable us to use this approach in real-time HTTP based streaming applications.
Machine Common Sense.

Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI). There is a wide range of strategies that can be employed to make progress on this challenge. This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions. The basic idea is that there are several types of commonsense reasoning: one is manifested at the logical level of physical actions, the other deals with the understanding of the essence of human-human interactions. Existing approaches, based on formal logic and artificial neural networks, allow for modeling only the first type of common sense. To model the second type, it is vital to understand the motives and rules of human behavior. This model is based on real-life heuristics, i.e., the rules of thumb, developed through knowledge and experience of different generations. Such knowledge base allows for development of an expert system with inference and explanatory mechanisms (commonsense reasoning algorithms and personal models). Algorithms provide tools for a situation analysis, while personal models make it possible to identify personality traits. The system so designed should perform the function of amplified intelligence for interactions, including human-machine.
When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous

We aim to enable robots to visually localize a target person through the aid of an additional sensing modality -- the target person's 3D inertial measurements. The need for such technology may arise when a robot is to meet person in a crowd for the first time or when an autonomous vehicle must rendezvous with a rider amongst a crowd without knowing the appearance of the person in advance. A person's inertial information can be measured with a wearable device such as a smart-phone and can be shared selectively with an autonomous system during the rendezvous. We propose a method to learn a visual-inertial feature space in which the motion of a person in video can be easily matched to the motion measured by a wearable inertial measurement unit (IMU). The transformation of the two modalities into the joint feature space is learned through the use of a contrastive loss which forces inertial motion features and video motion features generated by the same person to lie close in the joint feature space. To validate our approach, we compose a dataset of over 60,000 video segments of moving people along with wearable IMU data. Our experiments show that our proposed method is able to accurately localize a target person with 80.7% accuracy using only 5 seconds of IMU data and video.
Data Integrity and Recovery Management Under Peer to Peer Convoluted Fault Recognition Cloud Systems

Data is expanding beyond the speed of light and hence a lighting speed devices and networks are required for a better and more reliable system for data storage. Cloud computing is a boon for larger, remote data storage at a virtual location. Cloud computing encounters major challenges such as security, connectivity and backup recovery. In this paper, a novel methodology is proposed and concentrated on backup and recovery management to provide a fault tolerance and recognition cloud system with a higher order of reliability and scalability. The methodology proposes an approach of data segmentation and token generation for data split-up using tailing terminology of appending cloud storage location or addresses. Thus the faulty cloud server missing a location segment is recognized within a smaller broadcasting limit and fetches backup from secure neighboring sources.
Evaluation Mechanism of Learning Achievement Based on Intelligent Learning Platform

Abstract In recent years, blended learning has received widespread attention in the field of college teaching. However, how to effectively evaluate the blended learning has not yet formed a mature theory. For this reason, on the basis of clarifying the current status of blended learning evaluation, this paper combines the individual achievement goals of students, and uses the analytic hierarchy process to explore a set of learning performance evaluation systems that use smart learning platforms as auxiliary tools. After years of teaching practice, the evaluation system is scientific and feasible, and it can effectively improve students’ learning results.
The translate and line properties for 2-primitive elements in quadratic extensions

Let [Formula: see text] be integers and [Formula: see text] be any prime power [Formula: see text] such that [Formula: see text]. We say that the extension [Formula: see text] possesses the line property for [Formula: see text]-primitive elements if, for every [Formula: see text], such that [Formula: see text], there exists some [Formula: see text], such that [Formula: see text] has multiplicative order [Formula: see text]. Likewise, if, in the above definition, [Formula: see text] is restricted to the value [Formula: see text], we say that [Formula: see text] possesses the translate property. In this paper, we take [Formula: see text] (so that necessarily [Formula: see text] is odd) and prove that [Formula: see text] possesses the translate property for 2-primitive elements unless [Formula: see text]. With some additional theoretical and computational effort, we show also that [Formula: see text] possesses the line property for 2-primitive elements unless [Formula: see text].
Lattice-based group signature scheme without random oracle

Group signature is a cryptographic primitive where any member can anonymously sign a message on behalf of the population they belong to. Several group signatures were proposed based on number-theoretic assumptions. All these schemes are insecure in the presence of quantum computers. Group signatures based on lattice assumptions are believed to be quantum-resistant. In the past few years, group signatures based on lattice assumptions have been proposed and most of them are proved to be secure in random-oracle model. This paper presents a lattice-based group signature scheme without using random-oracle. Our scheme is based on correlation-intractable function ensembles for all evasive relations which are constructed recently based on well-defined assumptions. Security of our scheme is proved based on correlation-intractable function ensembles and hardness of Short Integer Solution and Learning With Errors problem.
Clinical Concept Normalization on Medical Records Using Word Embeddings and Heuristics.

Electronic health records contain valuable information on patients' clinical history in the form of free text. Manually analyzing millions of these documents is unfeasible and automatic natural language processing methods are essential for efficiently exploiting these data. Within this, normalization of clinical entities, where the aim is to link entity mentions to reference vocabularies, is of utmost importance to successfully extract knowledge from clinical narratives. In this paper we present sieve-based models combined with heuristics and word embeddings and present results of our participation in the 2019 n2c2 (National NLP Clinical Challenges) shared-task on clinical concept normalization.
LP Structures Theory Application to Building Intelligent Refactoring Systems

An approach of automatized object-oriented code refactoring is described that applies lattice-based algebraic structures for type hierarchy representation and optimization. A distinctive feature of these algebraic structures is their ability to model aggregation not as a relation between two independent sets of types and attributes, but as a relation between two specific types. The property makes it possible to perform a more careful optimization of type hierarchy. The described approach focuses on redundant attributes removal and on the relocation of identical attributes into their common superclasses (“Pull Up Field” technique). In this paper it is demonstrated how the adopted algebraic structures can be extended to model a wide range of type hierarchies. Also, they are shown to be able to perform transformations allowing for external constraints. Such constraints may represent some additional knowledge of the type hierarchy or of the refactoring process itself. The described approach employs only the fundamental ideas of object-oriented programming. Supplemented with language-specific features it may be used as a basis for building intellectual systems that facilitate object-oriented code refactoring.
Unique English: An English Learning Application for Remedial Program Using Gamification Approach

Literacy and Numeracy Screening (LINUS) that implemented in Malaysia as an educational program to improve literacy and numeracy among Year 1 to Year 3 pupils in all primary schools has been terminated in 2019. However, school-level remedial program still exists and its related application is limited in the market as there is no centralized syllabus for English remedial program yet. Therefore, an English learning application for remedial program called Unique English is designed with Multimedia Mobile Content Development to overcome this problem by using previous LINUS centralized constructs. An application testing has been carried out in S.K. Pintas Raya, Parit Raja which involves pupils in remedial program and received satisfactory results with 83.2 % positive feedbacks obtained thus it can be concluded that Unique English is suitable to be implemented in remedial program.