Reviewerly/exHarmony huggingface dataset:
Development of a cloud platform for gathering, storing and analysis of video data

Today video analysis is very relevant. Amount of video is being increased and there is a need to perform it. Very often there are a number of video data sources on which video is being continuously recorded. This article presents an implementation of a platform which aggregates video from various sources and processes it with use of different services which are based on artificial neural networks and machine learning methods.
Enhancing Quality for VVC Compressed Videos with Multi-Frame Quality Enhancement Model

Versatile Video Coding (VVC) is the most recent video coding standard, released in July 2020 with two major purposes: (1) providing a similar perceptual quality as the current state-of-the-art High Efficiency Video Coding (HEVC) solution at around half the bitrate and (2) offering native flexible, high-level syntax mechanisms for resolution adaptivity, scalability, and multi-view. However, despite of the compression efficiency, the decoded video obtained with VVC compression still contains distortions and quality degradation due to the nature of the hybrid block and transform based coding approach. To overcome this problem, this paper proposes a novel quality enhancement method for VVC compressed videos where the most advanced deep learning-based multi-frame quality enhancement model (MFQE) is employed. In the proposed QE method, the VVC decoded video is firstly segmented into the peak quality and non-peak quality pictures. After that, a Long-short term memory and two sub-networks are created to achieve better quality video pictures. Experimental results show that, the proposed MFQE based VVC quality enhancement method is able to achieve important quality improvement when compared to the original VVC decoded video.
Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios

Revealing the internal workings of a robot can help a human better understand the robot's behaviors. How to reveal such workings, e.g., via explanation generation, remains a significant challenge. This gets even more complex when these explanations are targeted towards children. Therefore, we propose a search-based approach to generate contrastive explanations using optimal and sub-optimal plans and implement it in a scenario for children. In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking. We report results around our explanation generation system that was successfully deployed among seven-year-old children. Our results show trends that the generated explanations were able to positively affect the children's perceived difficulty in learning the zero-sum game.
Probabilistic Inference Using Generators: The Statues Algorithm

We present here a new probabilistic inference algorithm that gives exact results in the domain of discrete probability distributions. This algorithm, named the Statues algorithm, calculates the marginal probability distribution on probabilistic models defined as direct acyclic graphs. These models are made up of well-defined primitives that allow to express, in particular, joint probability distributions, Bayesian networks, discrete Markov chains, conditioning and probabilistic arithmetic. The Statues algorithm relies on a variable binding mechanism based on the generator construct, a special form of coroutine; being related to the enumeration algorithm, this new algorithm brings important improvements in terms of efficiency, which makes it valuable in regard to other exact marginalization algorithms. After introduction of several definitions, primitives and compositional rules, we present in details the Statues algorithm. Then, we briefly discuss the interest of this algorithm compared to others and we present possible extensions. Finally, we introduce Lea and MicroLea, two Python libraries implementing the Statues algorithm, along with several use cases. A proof of the correctness of the algorithm is provided in appendix.
Towards a real time algorithm for parameterized longest common prefix computation

Parameterized matching has proven to be an efficient and useful tool for detecting code duplications. This paper presents a technique for calculating parameterized Longest Common Prefix (plcp) in constant time based on the knowledge about the plcp of the following suffixes. Using this technique, online p-suffix tree construction can be done in worst case time O(log⁡n) per input symbol. Searching for a pattern of length m in the resulting suffix tree takes O(min⁡{mlog⁡(|Σ|+|Π|),m+log⁡n}+mτΠ+tocc) time, where tocc is the number of occurrences of the pattern, and τΠ depends on Π. For constant-sized Π, τΠ=1, for polynomial-sized Π, τΠ=log⁡log⁡|Π|, and for unbounded Π, τΠ=log⁡|Π|.
PlanAlyzer: assessing threats to the validity of online experiments

Online experiments are ubiquitous. As the scale of experiments has grown, so has the complexity of their design and implementation. In response, firms have developed software frameworks for designing and deploying online experiments. Ensuring that experiments in these frameworks are correctly designed and that their results are trustworthy---referred to as *internal validity*---can be difficult. Currently, verifying internal validity requires manual inspection by someone with substantial expertise in experimental design. We present the first approach for statically checking the internal validity of online experiments. Our checks are based on well-known problems that arise in experimental design and causal inference. Our analyses target PlanOut, a widely deployed, open-source experimentation framework that uses a domain-specific language to specify and run complex experiments. We have built a tool, PlanAlyzer, that checks PlanOut programs for a variety of threats to internal validity, including failures of randomization, treatment assignment, and causal sufficiency. PlanAlyzer uses its analyses to automatically generate *contrasts*, a key type of information required to perform valid statistical analyses over experimental results. We demonstrate PlanAlyzer's utility on a corpus of PlanOut scripts deployed in production at Facebook, and we evaluate its ability to identify threats to validity on a mutated subset of this corpus. PlanAlyzer has both precision and recall of 92% on the mutated corpus, and 82% of the contrasts it automatically generates match hand-specified data.
Light field image restoration in low-light environment

Light field (LF) imaging provides rich spatial and angular information, but is problematic in low-light environment as the images suffer from low contrast and visibility. In this paper, we present a learning-based method to enhance low-light LF images. A high-dimensional convolutional neural network (CNN) is introduced to extract the spatio-angular features from the LF. The network operates directly on the four-dimensional LF data rather than on individual sub-aperture images, avoiding the loss of geometric information. Color compensation is then performed on the enhanced LF images coming from the high-dimensional CNN to reduce color distortion. The experimental results show that the proposed method achieves noticeable improvement compared with state-of-the-art low-light image restoration techniques in both visual inspection and objective assessments.
Lazy Stream Programming in Prolog

In recent years, stream processing has become a prominent approach for incrementally handling large amounts of data, with special support and libraries in many programming languages. Unfortunately, support in Prolog has so far been lacking and most existing approaches are ad-hoc. To remedy this situation, we present lazy stream generators as a unified Prolog interface for stateful computations on both finite and infinite sequences of data that are produced incrementally through I/O and/or algorithmically. We expose stream generators to the application programmer in two ways: 1) through an abstract sequence manipulation API, convenient for defining custom generators, and 2) as idiomatic lazy lists, compatible with many existing list predicates. We define an algebra of stream generator operations that extends Prolog via an embedded language interpreter, provides a compact notation for composing generators and supports moving between the two isomorphic representations. As a special instance, we introduce answer stream generators that encapsulate the work of coroutining first-class logic engines and support interoperation between forward recursive AND-streams and backtracking-generated OR-streams. Keywords: lazy stream generators, lazy lists, first-class logic engines, stream combinators, AND-stream / OR-stream interoperation, Prolog extensions
Machine Learning Model for a Dynamic Path Planning Problem

Abstract Due to an advancement in Industry 4.0 technology, various autonomous systems have been developed in order to increase the operational efficiency. This paper considers an application of Industry 4.0 technology to an autonomous transportation operation. The paper focuses on applying a machine learning technique to a dynamic path planning problem where real-time randomized obstacle locations are considered. The routes or the solutions from the dynamic path planning problem are determined by an A-star algorithm, which are then used to build machine learning models based on an artificial neural network. The models were developed to discover the relationship between the input and output of the dynamic path planning problem. The structure of the network which is defined by the number of intermediate layers and the number of nodes is provided, where the overall accuracy is used to evaluate the setting efficiency. The proposed methodology was tested with a problem that consists of 7 types of paths, and the number of randomized obstacles fluctuated from 1 to 8. The paths were generated based on a layout of a consumer product warehouse. The proposed model succeeded in predicting the robot paths with 98.5% prediction accuracy.
On maps which preserve semipositivity and quantifier elimination theory for real numbers

Assume that [Formula: see text] is a superoperator which preserves hermiticity. We give an algorithm determining whether [Formula: see text] preserves semipositivity (we call [Formula: see text] positive in this case). Our approach to the problem has a model-theoretic nature, namely, we apply techniques of quantifier elimination theory for real numbers. An approach based on these techniques seems to be the only one that allows to decide whether an arbitrary hermiticity-preserving [Formula: see text] is positive. Before we go to detailed analysis of the problem, we argue that quantifier elimination for real numbers (and also for complex numbers) can play a significant role in quantum information theory and other areas as well.
Upper bound on the classical capacity of a quantum channel assisted by classical feedback

We introduce various measures of forward classical communication for bipartite quantum channels. Since a point-to-point channel is a special case of a bipartite channel, the measures reduce to measures of classical communication for point-to-point channels. As it turns out, these reduced measures have been reported in prior work of Wang et al. on bounding the classical capacity of a quantum channel. As an application, we show that the reduced measures are upper bounds on the classical capacity of a point-to-point quantum channel assisted by a classical feedback channel. Some of the various measures can be computed by semi-definite programming.
Complex Distributions Emerging in Filtering and Compression

A simple filter for marking patterns in a binary sequence produces an output with similar statistics to cooperative systems such as spin glasses and neural networks, providing a potential tool for understanding the statistics in those systems as well.
Whale Optimization Algorithm (WOA)

Whale optimization algorithm (WOA) is one of the popular swarm-based algorithms (SA), inspired by nature. The WOA was determined by mimicking the pattern of navigation of whales while encircling preys. The humpback whale is considered in this context. These set of whales can recognize and surround the location of the prey strategically. Identifying the exact position in the a priori search space in whale is usually not imaginable. It is assumed by the WOA that the target prey is the best candidate solution. This model was implemented in MATLAB by considering 30 search agents (n = 30) and the maximum number of iterations was set to 1000. The best solution obtained by WOA is [3.0015 1.9969] and the best optimal value of the objective function found by WOA is 0.00015167. The parameter space and the objective space presented, demonstrate the problem solving capability of WOA in complex domain.
Mapping Property of Bilateration and Its Application to Human-Following Robot

Objective of this paper is twofold. The first one is to study the mapping property and unified form of the component equations of the unknown node in bilateration, and the second one is to introduce the concept model for human-following robot based on bilateration. Bilateration needs only two known nodes and two distances’ data. Because of the simple sensor arrangement in bilateration, it needs less computation and uses less number of unavoidable erroneous distances compared to the trilateration.
Research on Automatic Target Detection and Recognition System Based on Deep Learning Algorithm

Automatic target detection and recognition is the cornerstone of the intelligent unmanned systems to realize higher-level tasks. In this paper, the deep learning algorithm of Faster R-CNN was studied in depth, and the target detection model is designed combining the RPN network and the fast R-CNN. The target detection and recognition device with the ability of image acquisition and intelligent processing was also designed. Combining the device with the Faster R-CNN model, the automatic target detection and recognition system was developed. At last, the VGG-16 model was adopted for training the detection model, and the system was used for target detection experiments. The results show that the recognition accuracies of the system for the visible light images of trucks and tanks are 89.7% and 90.3%, respectively, and that for infrared images of tanks is 63.7%. Therefore, a good recognition effect has been achieved. This work provides a reference for the application of deep learning algorithms in the field of automatic target detection and recognition.
Observable Cyber Risk on Cournot Oligopoly Data Storage Markets

With the emergence of global digital service providers, concerns about digital oligopolies have increased, with a wide range of potentially harmful effects being discussed. One of these relates to cyber security, where it has been argued that market concentration can increase cyber risk. Such a state of affairs could have dire consequences for insurers and reinsurers, who underwrite cyber risk and are already very concerned about accumulation risk. Against this background, the paper develops some theory about how convex cyber risk affects Cournot oligopoly markets of data storage. It is demonstrated that with constant or increasing marginal production cost, the addition of increasing marginal cyber risk cost decreases the differences between the optimal numbers of records stored by the oligopolists, in effect offsetting the advantage of lower marginal production cost. Furthermore, based on the empirical literature on data breach cost, two possibilities are found: (i) that such cyber risk exhibits decreasing marginal cost in the number of records stored and (ii) the opposite possibility that such cyber risk instead exhibits increasing marginal cost in the number of records stored. The article is concluded with a discussion of the findings and some directions for future research.
Convolutional neural network based blind automatic modulation classification robust to phase and frequency offsets

In this study, the authors propose robust automatic modulation classifiers using convolutional neural networks (CNNs). CNNs can identify features directly from a received signal when appropriately trained. They trained the classifier by taking the magnitude, phase, real, and imaginary parts of the received signal for CNN-based automatic modulation classification, which is insensitive to phase and frequency deviation. Also, in order to make the proposed classifier more robust, they generated signal-to-noise ratio, phase offset, and frequency offset in the training signal set. They compared the proposed methods with conventional automatic modulation classification methods using higher-order cumulants, order statistics, and a CNN-based method. The simulation results show that the proposed methods outperform the conventional methods not only in the additive white Gaussian noise channel but also in the channels having severe phase or frequency offsets.
Train the Multi-Layer Perceptrons Based on Crow Search Algorithm

This paper presents a new approach for training the Multi-Layer Perceptron (MLP) based on the crow search algorithm (CSA) optimization algorithm. The main objective of this approach is to reduce the error to its minimum level and increase the rate of the classification. The benchmark of the proposed approach implementation achieved using more than one standard datasets for classification to ensure the quality of the result, and the performance also compared with other optimization algorithms such as Particle Swarm optimization (PSO), Genetic Algorithm (GA), and Ant Colony optimization (ACO). The outcomes indicated that the crow search algorithm was the best since it produces the highest accuracy rate and solves the optimization problem efficiently.
AlloyMC: Alloy meets model counting

Specifying and analyzing desired properties of software systems can play an important role in the development of more dependable systems. Alloy is a mature tool-set that provides a first-order, rela- tional logic with transitive closure for writing the specifications, and a fully automatic backend based on propositional satisfiability (SAT) solvers for analyzing them. Alloy’s intuitive notation and sup- port for modern solvers make it a particularly effective specification and analysis tool, which has been applied in several domains, including verification, security, and synthesis. This paper introduces a new backend for Alloy, which complements SAT solvers, and provides a new method to assist Alloy users to more effectively use the tool-set, specifically in scenarios where multiple solutions to the same formula are desired. We add to the Alloy backend support for model counting, i.e., computing the number of solutions to the given formula. We extend the Alloy grammar to add a new com- mand for model counting, and extend the Alloy GUI to customize it. Our implementation, called AlloyMC, supports two state-of-the-art model counters: the approximate model counter ApproxMC and the exact model counter ProjMC. AlloyMC runs on Linux, Mac, and Windows. To use AlloyMC, users just download and run its integrated JAR file with no need to install dependencies (e.g., model counters and their dependent libraries). The AlloyMC source code, the JAR file, and the data set are available publicly.
A Study on Satisfaction with Involuntary University Online Lecture by Corona 19

In this study, the satisfaction and immersion of the class were investigated in 254 learners who took online classes for one semester at Seoul A University. In the case of University A, self-directed learning and learning commitment showed relatively high numbers, but negative responses were made to interactive communication related to “discussion and discussion” and “question and response”. Conversely, he pointed out that the advantage of online classes is that students can listen repeatedly. In the arts and physical education classes, the degree of learning commitment was the largest, and in the engineering and natural classes, the amount of learning persistence was low. By grade level, the 2nd grade responded the most negatively to online classes, and the 3rd grade responded most positively.BR After Corona 19 is over, the experience of online classes is expected to make a huge difference in the way the classes are taught.
Robust online tracking via sparse gradient convolution networks

Convolution networks trained offline have recently exhibited promising performance in object tracking tasks. However, offline training is time-consuming and their performance heavily rely on the category of auxiliary training sets. In this paper, we propose a sparse gradient convolution network without pretraining for object tracking. This approach combines shallow convolutional networks and traditional methods (gradient features and sparse representations) to avoid the offline training. In the first frame, we utilize the sparse representation method to learn a series of gradient-based local patches served as fixed filters, and they are used to convolving the input image in the subsequent frames to encode local structural information. Then, we stack all the local structure features to construct global spatial structure features, and the inner geometric layout information is preserved. Moreover, sparse coding and online updating are used to overcome issues related to target appearance variations. Qualitative and quantitative evaluations based on a challenging benchmark dataset demonstrate the effectiveness of the proposed algorithm against several state-of-the-art tracking methods.
Spectrum-Adapted Polynomial Approximation for Matrix Functions with Applications in Graph Signal Processing

We propose and investigate two new methods to approximate f(A)b for large, sparse, Hermitian matrices A. Computations of this form play an important role in numerous signal processing and machine learning tasks. The main idea behind both methods is to first estimate the spectral density of A, and then find polynomials of a fixed order that better approximate the function f on areas of the spectrum with a higher density of eigenvalues. Compared to state-of-the-art methods such as the Lanczos method and truncated Chebyshev expansion, the proposed methods tend to provide more accurate approximations of f(A)b at lower polynomial orders, and for matrices A with a large number of distinct interior eigenvalues and a small spectral width. We also explore the application of these techniques to (i) fast estimation of the norms of localized graph spectral filter dictionary atoms, and (ii) fast filtering of time-vertex signals.
Quantum Zeno dynamics induced atomic entanglement in a hybrid atom-cavity-fiber system

Quantum entanglement is important quanum resources in quantum information sicence.Here we propose an approach to preparing atomic quantum entanglement in a hybrid atom-cavity-fiber system.Using quantum Zeno dynamics method, the system evolution states are always split into a series of Zeno invariant subspaces consisting of dark and bright states.By choosing the initial state of the system, the bright states are all neglected and only dark states are kept to build the effective Hamiltonian.By tuning the system parameters, two-atom multiple-dimensional entanglement, twoatom Bell state, and three-atom GHZ state can be realized by one step.Our proposal provides a way to perform quantum information processing with dark states.