Reviewerly/exHarmony huggingface dataset:
Improving data collection and processing efficiency by using hierarchies of intelligent agents in multi-agent systems

Abstract The paper considers the challenge of improving data collection and processing efficiency by using various information and measurement systems. A structural scheme of a hierarchical multi-agent control system has been developed, incorporating horizontal (single-level) and vertical (multi-level) connections between intelligent agents. An algorithm is proposed for controlling a heterogeneous group of unmanned aerial vehicles participating in search and rescue operations in an emergency zone based on the hierarchical interaction of intelligent agents, cloning and coalition formation. KPI values have been calculated and the effectiveness of the proposed solution have been proved.
Special issue on recent advances in perceptual learning.

This is an introduction to the special issue "Perceptual Learning." This collection of studies reflects some of the interesting new discoveries being made in the study of perceptual learning. Although much headway has been made toward understanding the basic phenomena, this collection of studies makes clear that there is much that remains to be understood. The study of perceptual learning continues to be a fruitful area of research, and it is our hope that this collection, like the Exeter workshop that it was based on, will continue to inspire future research efforts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Identification of Indian English by Speakers of Multiple Native Languages

Many aspects of speech can provide information about particular speaker’s characteristics. This paper presents a novel method of automatic classification of speakers based upon their regional language accent. Present study uses English spoken as second language by speakers of four South Indian Languages. A new data set is developed by following a specific data collection protocol. The acoustic deviations were studied by analysing the distribution of Mel-frequency Cepstral Coefficients (MFCCs) for the non-native English speech. Using the state-of-the-art methods of Language and accent identification, Indian language are differentiated, and good recovery rates are observed. The results obtained shows that the accent classification in the Indian setting is more effective in the text-independent mode compared to the text-dependent mode, and is also gender dependent. Native language identification has been achieved with an accuracy of $$94\%$$ from a short utterance of 60 s of non-native English speech. It is found that identification of native Malayalam and Kannada speakers is easier than native Tamil and Telugu speakers.
Gavin McInnes’s hate machine

At first, the Proud Boys were a seemingly innocuous white boys club that sprouted from the banter and riffs of online talk show host, Gavin McInnes. But the far right group grew into a nation-wide white supremacist organization. The group came about, thanks to McInnes and his The Gavin McInnes Show (TGMS). The Proud Boys and Gavin McInnes are a prime case study of the problem of free speech and the Internet. Here we see hate speech hiding behind the protective cloak of free speech. The conundrum becomes: How do we deal with fascist politics in the democratic space of the internet? The study conducts a frame analysis of over 32 hours of TGMS, utilizing Stanley’s (2018) rubric of fascist politics. By analyzing McInnes’s online discourse — his hate machine — we obtain a deeper understanding of how fascist politics gently slides into the mainstream and becomes a threat to peaceful political action.
Fraudulent Firm Classification Using Monotonic Classification Techniques

This paper presents a novel method for fraudulent firm classification based on monotonic classification. This paper aims to find a monotonic classifier by using the potential order information in data. The proposed method consists of two stages. First, a feature selection algorithm, which is specially designed for monotonic classification, is applied to evaluate the importance of features. Then, a fraud detection system for predicting fraudulent firms is designed based on a monotonic decision tree algorithm to improve the classification performance. In order to establish predictive indicators for the classification of suspicious companies, the audit office of India has collected detailed one-year unclassified data of 777 companies from 2015 to 2016. Based on the collected dataset, experimental results show that the proposed method can effectively identify the fraudulent firms and improve the quality of an audit work.
Checking Sets of Pure Evolving Association Rules

Extracting association rules from large datasets has been widely studied in many variants in the last two decades; they allow to extract relations between values that occur more “often” in a database. With temporal association rules the concept has been declined to temporal databases. In this context the “most frequent” patterns of evolution of one or more attribute values are extracted. In the temporal setting, especially where the interference betweeen temporal patterns cannot be neglected (e.g., in medical domains), there may be the case that we are looking for a set of temporal association rules for which a “significant” portion of the original database represents a consistent model for all of them. In this work, we introduce a simple and intuitive form for temporal association rules, called pure evolving association rules (PE-ARs for short), and we study the complexity of checking a set of PE-ARs over an instance of a temporal relation under approximation (i.e., a percentage of tuples that may be deleted from the original relation). As a by-product of our study we address the complexity class for a general problem on Directed Acyclic Graphs which is theoretically interesting per se.
Improved Visual Localization via Graph Filtering

Vision-based localization is the problem of inferring the pose of the camera given a single image. One commonly used approach relies on image retrieval where the query input is compared against a database of localized support examples and its pose is inferred with the help of the retrieved items. This assumes that images taken from the same places consist of the same landmarks and thus would have similar feature representations. These representations can learn to be robust to different variations in capture conditions like time of the day or weather. In this work, we introduce a framework which aims at enhancing the performance of such retrieval-based localization methods. It consists in taking into account additional information available, such as GPS coordinates or temporal proximity in the acquisition of the images. More precisely, our method consists in constructing a graph based on this additional information that is later used to improve reliability of the retrieval process by filtering the feature representations of support and/or query images. We show that the proposed method is able to significantly improve the localization accuracy on two large scale datasets, as well as the mean average precision in classical image retrieval scenarios.
Confusion2Vec 2.0: Enriching ambiguous spoken language representations with subwords

Word vector representations enable machines to encode human language for spoken language understanding and processing. Confusion2vec, motivated from human speech production and perception, is a word vector representation which encodes ambiguities present in human spoken language in addition to semantics and syntactic information. Confusion2vec provides a robust spoken language representation by considering inherent human language ambiguities. In this paper, we propose a novel word vector space estimation by unsupervised learning on lattices output by an automatic speech recognition (ASR) system. We encode each word in Confusion2vec vector space by its constituent subword character n-grams. We show that the subword encoding helps better represent the acoustic perceptual ambiguities in human spoken language via information modeled on lattice-structured ASR output. The usefulness of the proposed Confusion2vec representation is evaluated using analogy and word similarity tasks designed for assessing semantic, syntactic and acoustic word relations. We also show the benefits of subword modeling for acoustic ambiguity representation on the task of spoken language intent detection. The results significantly outperform existing word vector representations when evaluated on erroneous ASR outputs, providing improvements up-to 13.12% relative to previous state-of-the-art in intent detection on ATIS benchmark dataset. We demonstrate that Confusion2vec subword modeling eliminates the need for retraining/adapting the natural language understanding models on ASR transcripts.
A Template Attack to Reconstruct the Input of SHA-3 on an 8-Bit Device

We present an enumeration procedure based on a template attack to recover the complete input text of a SHA-3 implementation on an 8-bit microprocessor from a single trace of a power-analysis side channel. This attack targets 600 bytes of triple-redundant internal state in each invocation of the permutation used by SHA-3. We first build templates that can generate for each of these bytes a rank table of all 256 candidates. The templates we obtained for our 8-bit target CPU nearly identified the correct value of most target bytes directly, rather than just gathering information about their Hamming weights. We then search the full intermediate state of the Keccak permutation to eliminate remaining uncertainties about the recovered byte values. From the resulting intermediate states we finally reconstruct both the input and output of SHA-3 and verify the output. In our experimental evaluation of this procedure we achieved success rates higher than 99%.
THE PROBLEM OF SYNTHESIS OF CONNECTIONISM AND SYMBOLISM IN MODELS OF WEAK ARTIFICIAL INTELLIGENCE

The idea of conceptual mutual exclusion of the approaches of connectionism and symbolism in the philosophy of artificial intelligence is proposed. It is shown that the basic concepts of both concepts are complementary in the philosophical and methodological terms. The key idea of the study is the assumption of the need to find ways to consistently synthesis the approaches of connectionism and symbolism.
Fast Training of Contrastive Learning with Intermediate Contrastive Loss

Recently, representations learned by self-supervised approaches have significantly reduced the gap with their supervised counterparts in many different computer vision tasks. However, these self-supervised methods are computationally challenging. In this work, we focus on accelerating contrastive learning algorithms with little or even no loss of accuracy. Our insight is that, contrastive learning concentrates on optimizing similarity (dissimilarity) between pairs of inputs, and the similarity on the intermediate layers is a good surrogate of the final similarity. We exploit our observation by introducing additional intermediate contrastive losses. In this way, we can truncate the back-propagation and updates only a part of the parameters for each gradient descent update. Additionally, we do selection based on the intermediate losses to filter easy regions for each image, which further reduces the computational cost. We apply our method to recently-proposed MOCO, SimCLR, SwAV and notice that we can reduce the computational cost with little loss on the performance of ImageNet linear classification and other downstream tasks.
Tucker-1 Boolean Tensor Factorization with Quantum Annealers

Quantum annealers are an emerging computational architecture that have the potential to address some challenging computational issues that will be left unresolved as we approach the end of the Moore's Law era of computing. D-Wave quantum annealers are designed to solve a challenging set of problems - quadratic unconstrained binary optimization problems. This makes them a natural fit for solving problems with binary or Boolean variables. Here, we explore the use of a quantum annealer to solve Boolean tensor factorization. The goal of Boolean tensor factorization is to represent a high-dimensional tensor filled with Boolean values as a product of Boolean matrices and a Boolean core tensor. We show that a particular Boolean tensor factorization problem (called Tucker-1 factorization) can be decomposed into a sequence of quadratic unconstrained binary optimization problems that can be solved with a D-Wave 2000Q quantum annealer. While quantum annealers specifically and quantum computers in general are at a fairly early stage in their development, they are currently capable of solving these Boolean tensor factorization problems. Our results show that for fairly small tensors, we are frequently able to obtain an accurate (sometimes exact) factorization using quantum annealing.
The Challenges of Semantic Interoperability in the Era of eScience on the Web

This article is an exploratory study that intends to present semantic interoperability initiatives in the area of information science and computer science. It discusses these initiatives, focusing on open science and eScience, aiming at pointing out similarities and differences in the methodologies used for an intelligent retrieval of data in heterogeneous environments that do not benefit a universal language. We will illustrate seminal initiatives regarding the compatibility of languages in information science and the alignment and semantic mapping in the area of computer science, emphasizing what they have in common and their differences, showing that those seminal initiatives are still relevant nowadays. In this perspective, we want to bring up discussions about methodologies that supports a kind of “intermediate language” that allows the compatibility of research resources, semantically interconnecting their contents without forcing the use of a single language, but of a switching language, such as a metalanguage that guarantees to keep the differences of each community of speech.
An Empirical Study of Prioritization Decision on the Establishment of Distribution Public Big Data Systems

The contemporary society can be described as the era of Fourth Industrial Revolution or the Data Age. Efforts to make objective and scientific decisions through big data continue. In particular, big data-based decision-making is becoming increasingly important in the distribution industry as there are not only various stakeholders but also many factors affecting the industry such as policies, climate and consumer trends. As a result, the Korean government and local governments are making efforts to establish a system to collect and utilize data that can help stakeholders in the distribution industry to make good decisions. However, at the moment of the 4th Industrial Revolution and the Data Age, although the importance of data has been emphasized, public hearings to gather expert opinions have been regarded as a common way to verify government policies in the distribution sector. This paper aimed to draw up a task that should be prioritized for the establishment of the public data system for the distribution industry in Korea. Using Delphi, AHP, ANP, and hierarchical construct model methods, the general public re-verified the opinions presented by experts in the field to devise policy-making methods for experts and the general public to make up together. Through this, it suggested the need for experts and the general public to share their opinions on the protection and utilization of personal information that would be sensitive to the general public and the co-prosperity of stakeholders in the distribution industry such as small business owners and large corporations.
CornerNet-Lite: Efficient Keypoint based Object Detection.

Keypoint-based methods are a relatively new paradigm in object detection, eliminating the need for anchor boxes and offering a simplified detection framework. Keypoint-based CornerNet achieves state of the art accuracy among single-stage detectors. However, this accuracy comes at high processing cost. In this work, we tackle the problem of efficient keypoint-based object detection and introduce CornerNet-Lite. CornerNet-Lite is a combination of two efficient variants of CornerNet: CornerNet-Saccade, which uses an attention mechanism to eliminate the need for exhaustively processing all pixels of the image, and CornerNet-Squeeze, which introduces a new compact backbone architecture. Together these two variants address the two critical use cases in efficient object detection: improving efficiency without sacrificing accuracy, and improving accuracy at real-time efficiency. CornerNet-Saccade is suitable for offline processing, improving the efficiency of CornerNet by 6.0x and the AP by 1.0% on COCO. CornerNet-Squeeze is suitable for real-time detection, improving both the efficiency and accuracy of the popular real-time detector YOLOv3 (34.4% AP at 30ms for CornerNet-Squeeze compared to 33.0% AP at 39ms for YOLOv3 on COCO). Together these contributions for the first time reveal the potential of keypoint-based detection to be useful for applications requiring processing efficiency.
Analyses of Characteristics of Japanese Forehead Shapes for Masculine / Feminine using Deep Learning

In facial reconstruction by plastic surgery, masculine and feminine are one of the factors that affect the degree of perfection. It is useful to analyze the characteristics for creating a natural shape as a male or female. In this study, 3D models were created by measuring the facial shapes of Japanese men and women (75 men, 75 women). 100 cross-sectional images of the forehead were made for each person from the 3D model. These images and the gender were used as teaching signals, and the CNN was trained by them. Next, we visualized the characteristics found by the learned CNN using Grad-CAM, and performed curvature analysis on the characteristic parts. As a result, the male characteristic was the swell of superciliary arch and the female characteristic was the roundness of the frontal tuber.
The idea of free education in the national pedagogical thought of the late 19th - early 20th centuries

The article considers the influence of the idea of free education on the development of pedagogical theory and practice in Ukraine in the late XIX - early XX centuries. The peculiarities of free education principles usage to substantiate the concept of the national school are shown, the connection of the educational policy of the 1920s and the attempts to reform school education in this period with the approaches of theorists of free education are revealed.
Neural modelling of solar radiation variability

Abstract Artificial neural networks are increasingly used in engineering and technical sciences, especially to solve problems under process uncertainty. The mathematical model presented in this article describes cloud variability. The application of the model can increase the efficiency of solar systems because the response time of the solar panel to changing weather conditions is crucial. The model involves an artificial neural network that serves to determine the degree of daily cloud coverage based on three data – the month, daily solar radiation sum and total harmonic distortion factor (THD). The THD factor is determined for daily solar radiation courses using a Fast Fourier Transform. Approaching the daily variability of solar radiation as a sine wave allows employing the THD factor in an unconventional and innovative way. The modelling data have been derived from the measurements of the meteorological station of the Institute of Mechanical Engineering of the Warsaw University of Life Sciences. MATLAB Software (2019a) was used for data processing and network modelling. The model is verified using the mean square error. The performed analysis provides promising results and conclusions.
Blockchain Technology and Its Emerging Applications

This chapter describes blockchain technology by introducing what this technology is, the history behind it, and various applications of this approach in financial and nonfinancial sectors. Several key features of using this technology are explained, with the “how” and “why” behind the technology, which is having a big impact in the digital world. Finally, this chapter provides real-world examples that utilize this method. Blockchain technology has reached the height of popularity with the advancement of Bitcoin and other digital currencies; but there are various other sectors where it could also be used. Blockchain is a combination of files that are the same on many computers and software that writes and verifies the files. Depending on the technology being used, a file contains various kinds of information, with a stamp that verifies the information. Each transaction is verified by a majority of the parties involved, and once entered, information can never be erased. Since a majority of the people or computers need to verify a transaction, there is much less chance of this technology being hacked. In other words, blockchain is much more secure than other storage systems. No one oversees this technology, and the transactions processed in it are very transparent.
An Interactive Computer System with Gesture-Based Mouse and Keyboard

Researchers around the world are now focused on to make our devices more interactive and trying to make the devices operational with minimal physical contact. In this research, we propose an interactive computer system which can operate without any physical keyboard and mouse. This system can be beneficial to everyone, especially to the paralyzed people who face difficulties to operate physical keyboard and mouse. We used computer vision so that user can type on virtual keyboard using a yellow-colored cap on his fingertip, and can also navigate to mouse controlling system. Once the user is in mouse controlling mode, user can perform all the mouse operations only by showing different number of fingers. We validated both module of our system by a 52 years old paralyzed person and achieved around 80% accuracy on average.
The automated special software for component testing of the satellite communication station

Abstract Research tests of satellite communication station components are distinguished by a special variety of tasks to be solved, associated both with a wide range of components of the station, and a variety of estimated technical and functional characteristics. The whole set of characteristics can be subdivided into three categories: frequency and quality characteristics of components, operation parameters of the station and parameters of electromagnetic compatibility. These measurement operations of these parameters should be carried out using an automated measuring complex, which includes a measuring signal generator, vector spectrum, signal and network analyzers and operated under control of a specialized software. In the paper the architecture, logic algorithms and some application results of the software are shortly presented. The special software consists of the individual modules for control technical characteristics of low-noise and power amplifiers, cable assemblies, transceiver signal path element, and documenting the results. The program also includes modules that implement measurement instrumentation control used to assess the operational parameters and electromagnetic compatibility of the communication equipment. The developed specialized software allows automatically performing the entire cycle of operations related to the analysis of frequency and spectral characteristics, as well as quality characteristics of the devices under test in the automatic mode.