Reviewerly/exHarmony huggingface dataset:
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules

There have been many compression standards developed during the past few decades and technological advances has resulted in introducing many methodologies with promising results. As far as PSNR metric is concerned, there is a performance gap between reigning compression standards and learned compression algorithms. Based on research, we experimented using an accurate entropy model on the learned compression algorithms to determine the rate-distortion performance. In this paper, discretized Gaussian Mixture likelihood is proposed to determine the latent code parameters in order to attain a more flexible and accurate model of entropy. Moreover, we have also enhanced the performance of the work by introducing recent attention modules in the network architecture. Simulation results indicate that when compared with the previously existing techniques using high-resolution and Kodak datasets, the proposed work achieves a higher rate of performance. When MS-SSIM is used for optimization, our work generates a more visually pleasant image.
Speech Etiquette in Network Communication: Semantics and Pragmatics of Users’ Nominations

The article is devoted to the research of one of the most relevant aspects of etiquette in network communication–nominations participants of communication use to refer to each other. Based on a broad understanding of speech etiquette in social networks–as a means of harmonization of speech activity in various communication situations, the authors consider users’ nominations in social networks as markers of constructive or destructive verbal behavior in online communities. The research is based on linguistic and stylistic analysis, which allows: (1) describing the linguistic characteristics of nominations as lexical units in terms of word formation, grammar, and semantics; (2) revealing the peculiarities of the pragmatics of using the nominations in network communication taking into account the linguistic parameters of these nominations; (3) characterizing their stylistic properties that correlate with the goals of communication (common interests, problem solving, tips, causticity) and the selected manner of verbal behavior (solidarity, outrageous behavior, verbal aggression). To guarantee representativeness, the authors took the material for the research from resources created for groups with different principles of formation: gender (“Strana mam”), age (“4chan”) and territorial ones (“Yubileyniy Kvartal i Shuvalovsky”). The analysis shows that the users’ nominations in online communities demonstrate the participants’ solidarity within the relatively closed we-communities as the dominant setting of communication. Within each community, there is a focus on live communication, which activates the use of the phatic speech genres of everyday communication: appeal (generalized or personal), request, advice, comment, call, offer, insult, and so on. The variety of nominations from the point of view of the addressee (generalizing and personal), as well as in terms of communicative orientation (contactestablishing and contact-destroying), is determined by the set of situations of communication, frequent in different communities, and by communicants’ value orientations. In “Strana mam”, a women’s forum focused on the preservation of family values, solidarity is manifested in the desire for a more intimate communication; this activates the use of diminutives, language play, and vocabulary with a positive connotation. In the territorial “Yubileyniy Kvartal i Shuvalovsky” group, the value of community is cultivated, and solidarity is manifested in the imitation of respectful, often close to business, communication, which entails frequent generalizing nominations with a positive attributive component and the use of interlocutors’ full names as personal nominations. In the teenage “4chan” community, solidarity is manifested in the intentional outrageous communication (by using nominations with obscene words), which demonstrates an open violation of family, moral, spiritual, and other values.
Real-time FPGA implementation of concatenated AES and IDEA cryptography system

&lt;span&gt;The data encryption is one of the most critical issues in the communication system design. Nowadays, many encryption algorithms are being updated to keep pace with the remarkable progress in the communication field. The advanced encryption standard (AES) is a common algorithm that has proved its efficacy. The main drawback of AES is that it uses too simple algebraic structures, since every block is always encrypted in the same way that makes the hacking process possible if the hacker captures the key and the uses S-Box in the input stage. This especially applies to the unwired communication systems where chances of hacking exceed those found in the wired systems. The paper proposes a security enhancement method that is based on utilizing concatenated AES and international data encryption algorithm (IDEA) algorithms. Upon applying the proposed algorithm, the hacking process becomes a great challenge. The paper incorporates the real-time FPGA implementation of the proposed algorithm in the encryption and the decryption stages. Besides, the paper presents a clear analysis of the system’s performance.&lt;/span&gt;
Domain Adaptative Causality Encoder

Automated discovery of causal relationships from text is a challenging task. Current approaches which are mainly based on the extraction of low-level relations among individual events are limited by the shortage of publicly available labelled data. Therefore, the resulting models perform poorly when applied to a distributionally different domain for which labelled data did not exist at the time of training. To overcome this limitation, in this paper, we leverage the characteristics of dependency trees and adversarial learning to address the tasks of adaptive causality identification and localisation. The term adaptive is used since the training and test data come from two distributionally different datasets, which to the best of our knowledge, this work is the first to address. Moreover, we present a new causality dataset, namely MedCaus, which integrates all types of causality in the text. Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7% improvement, on the tasks of identification and localisation of the causal relations from the text.
The Capabilities of Haematology Analysers for Assessing the Bodyʼs Physiological and Pathological Conditions (Review)

Haematology analysers have become an intrinsic part of contemporary medical practice and are used by specialists in various fields of medicine to diagnose diseases and predict their course and outcome. Moreover, the readings of these devices are currently in demand in experimental biology and medicine, toxicology, and veterinary medicine. This review examines the capabilities of modern models of haematology analysers and prospects for their use. New technical approaches combined with already known methods and statistical calculation parameters allow us to significantly expand the range of analyser output. It is shown that the introduction of programs for statistical calculations of a large number of indices and parameters of the distribution of cell populations by their volumes opens up new prospects for describing and evaluating not only pathological, but also physiological states of the body. We analysed both Russian and foreign literature on the use of erythrocyte and platelet indices to diagnose cardiovascular and other pathologies. Taking into account the indicators of corpuscular volume based on histograms and analysing statistical parameters of blood cell distribution enhance our understanding of the structure of blood cell populations, significantly increase the information content of research and can serve as an additional criterion for quantitative assessment of the bodyʼs conditions and diagnosis of diseases. Statistical characteristics such as asymmetry coefficient, kurtosis, and standard deviation of empirical erythrograms and leukograms allow us to assess the degree of anisocytosis and cellular heterogeneity, as well as the ratio of different populations. Studying the volume characteristics of blood cells based on histogram analysis significantly improves the effectiveness of using haematology analysers in evaluating various pathological and physiological conditions of the body.
Decomposing lexical and compositional syntax and semantics with deep language models.

The activations of language transformers like GPT2 have been shown to linearly map onto brain activity during speech comprehension. However, the nature of these activations remains largely unknown and presumably conflate distinct linguistic classes. Here, we propose a taxonomy to factorize the high-dimensional activations of language models into four combinatorial classes: lexical, compositional, syntactic, and semantic representations. We then introduce a statistical method to decompose, through the lens of GPT2's activations, the brain activity of 345 subjects recorded with functional magnetic resonance imaging (fMRI) during the listening of ~4.6 hours of narrated text. The results highlight two findings. First, compositional representations recruit a more widespread cortical network than lexical ones, and encompass the bilateral temporal, parietal and prefrontal cortices. Second, contrary to previous claims, syntax and semantics are not associated with separated modules, but, instead, appear to share a common and distributed neural substrate. Overall, this study introduces a general framework to isolate the distributed representations of linguistic constructs generated in naturalistic settings.
Advance Security: Anomaly Detection in Mobile Crowd Sensing Using Machine Learning Techniques

New technologies increasing with new requirements made the public available with many mobile comfortable and social network savvy to collect scaling data for various purposes as crowdsourcing with heterogeneous and cross space data. In order to secure sensing, mobile crowdsensing applications have to address a security threat, such as jamming, fake sensing attacks, and other threats during transmissions. Previous study on a particular part of the issue. But we need an innovative protection solution to achieve the advantages of the new sensing paradigm. This thesis will explore advanced mobile crowd-sensing protection through machine-learning techniques such as SVM (Support Vector Machine) help and the ANN (Artificial Neural Network). We demonstrated using full-blown implementation techniques and experimental evaluation, using precision and false alarm rate as metrics. In this work, the Artificial Neural Network's accuracy and false alarm rate as compared with SVM. Specifically, the proposed ANN achieved an average of 96.4% and more than 7% of the usual incorrect positive rate utilizing 10-fold cross-validation. We strongly recommend the deployment of trustworthy mobile crowdsensing applications using Artificial Neural Network.
Analysis of Breast Cancer Diagnosis and Prognosis Using Machine Learning Algorithms

This paper describes an analysis of Wisconsin diagnostic breast cancer (WDBC) and Wisconsin prognostic breast cancer (WPBC) datasets using machine learning algorithms (MLA). The datasets contain cell nucleus attributes collected through fine needle aspiration (FNA) of the suspicious breast tissue specimen. Features were extracted through digital image processing of the FNA collected specimen. The datasets were transformed into a lower dimension using principal component analysis followed by training of MLA such as support vector machine with multiple kernels like linear, polynomial, radial basis function and sigmoid, k-nearest neighbor, linear discriminant analysis, decision tree, logistic regression, and Gaussian Naive Bayes. The accuracy comparison between machine learning classifiers for different train/test data splits for WDBC and WPBC and also the cross-validation was performed to ensure more accurate results.
Life Insurance-Based Recommendation System for Effective Information Computing

Due to the rapid advancements in information and communication technologies, the digital data is exponentially growing on the internet. The insurance industry with tough competition has emerged as information rich domain based on health, assets, and life insurance for public. Customers expect to receive personalized services that match their needs, preferences, and lifestyles. But a large portion of population is still unfriendly to the insurance selection. Major reasons could be the time and complexities involved in selection of suitable policies. This paper presents the state of the art of the research done in insurance recommendation systems at national and international levels. Multi-criteria decision-making methods are compared with collaborative filtering and data mining techniques. Their suitability to the field of life insurance recommendation is analyzed. The paper identifies the lack of public dataset of customers and life insurance policies and highlights the need for a personalized, neutral, and unified model for effective information computing for life insurance recommendations.
A Study of Throughput Prediction using Convolutional Neural Network over Factory Environment

In this paper, using the time-series throughput data generated from a simulated factory scenario, we study throughput prediction using convolutional neural network (CNN). Different with image or numerical recognition using CNN, in which the distribution of the prediction target during training stage usually has the similar level, the distribution of the throughput data concentrates only on several values. This centralized distribution may degrade the prediction accuracy. Therefore, we will propose a new CNN prediction method employing target vectorization which can mitigate the centralization of distribution. This method makes training process of CNN hold more possibility and improves the prediction accuracy of the throughput.
Effect of entanglement embedded in environment on quantum non-Markovianity based on collision model

Abstract By means of collision models (CMs) where the environment is simulated by a collection of ancillas consisting of two entangled qubits, we investigate the effects of entanglement in the environment on the non-Markovianity of an open quantum system. Two CMs are considered in this study, in the first one the open quantum system S directly collides with the environment, while in the second one the system interacts with two intermediate qubits which, in turn, are coupled to the environment. We show that it is possible to enhance the non-Markovianity by environment entanglement in both models. In particular, in the second model, we show that the initial state of the auxiliary qubits can also affect the non-Markovianity of the system and there exists the optimal combination of the initial environmental state and the initial state of auxiliary qubits. In this case, the non-Markovianity can be greatly enhanced.
Repurposing GANs for One-shot Semantic Part Segmentation

While GANs have shown success in realistic image generation, the idea of using GANs for other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural parts of objects during their attempt to reproduce those objects? And can image synthesis serve as an “upstream” representation learning task? In this work, we test these hypotheses and propose a simple and effective approach based on GANs for fundamental vision tasks: semantic part segmentation and landmark detection. With our approach, these tasks only require as few as one labeled example along with an unlabeled dataset, rather than thousands of examples. Our key idea is to leverage a trained GAN to extract a pixel-wise representation from the input image and use it as feature vectors for a segmentation network. Our experiments demonstrate that this GAN-derived representation is “readily discriminative” and produces surprisingly good results that are comparable to those from supervised baselines trained with significantly more labels. We believe this novel repurposing of GANs underlies a new class of unsupervised representation learning, which can generalize to many other tasks. More results are available at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><uri>https://RepurposeGANs.github.io/</uri></i> .
Robert A. Rescorla (1940–2020).

Memorializes Robert Arthur Rescorla (1940-2020), emeritus professor of psychology at the University of Pennsylvania. Rescorla was the world's most distinguished scholar in animal learning and a great teacher. Rescorla thought of himself as primarily an experimen talist, and his experiments on Pavlovian conditioning and instrumental learning would win any prize for the aesthetics of experimental design. Most of his nearly 200 empirical articles included multiple experiments with replications. Among Re scorla's most beautiful experiments were the studies of extinc tion performed in the last decade of his career. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Machine translation in healthcare

Machine translation has enormous potential to improve communication across language barriers in the healthcare setting. We first explain what machine translation (MT) is, and why it has the potential to be useful in the health domain. We provide a brief account of the history of machine translation, covering the three main paradigms (rule-based, statistical and neural) and describe where the current state-of-the-art lies in relation to the goal of ‘fully automatic high-quality MT’. We identify different models of usage for MT (assimilation, post-editing) link these models with evaluation methods, and discuss their application to translation in health. We then describe a selection of research projects applying MT to the health domain, focusing most attention on two that we have personal acquaintance with: HimL and MedicalMT. Finally we present an outlook for the future of MT in the health domain.
How to generate perfect mazes?

A perfect maze is a maze where any two cells can be joined by a unique path. In the literature, there exist eleven maze generation algorithms as compiled by Buck in 2015 in his book “ Mazes for Programmers ”. Each algorithm creates mazes differently. Our aim is to analyze how perfect mazes are generated. For this, we use the simple measures introduced by Buck, as well as the physical based measures introduced by McClendon in 2001. We introduce a new measure that helps us establish a ranking for perfect mazes. We also propose two new maze generation algorithms, called Prim & Kill and Twist & Merge . According to our measure, these two algorithms generate mazes differently than the existing algorithms do.
Extended IMD2020: a large‐scale annotated dataset tailored for detecting manipulated images

Image forensic datasets need to accommodate a complex diversity of systematic noise and intrinsic image artefacts to prevent any overfitting of learning methods to a small set of camera types or manipulation techniques. Such artefacts are created during the image acquisition as well as the manipulating process itself (e.g., noise due to sensors, interpolation artefacts, etc.). Here, the authors introduce three datasets. First, we identified the majority of camera models on the market. Then, we collected a dataset of 35,000 real images captured by these cameras. We also created the same number of digitally manipulated images. Additionally, we also collected a dataset of 2,000 ‘real-life’ (uncontrolled) manipulated images. They are made by unknown people and downloaded from the Internet. The real versions of these images are also provided. We also manually created binary masks localising the exact manipulated areas of these images. Moreover, we captured a set of 2,759 real images formed by 32 unique cameras (19 different camera models) in a controlled way by ourselves. Here, the processing history of all images is guaranteed. This set includes categorised images of uniform areas as well as natural images that can be used effectively for analysis of the sensor noise.
Requirement Engineering and Its Role in a Blockchain-Enabled Internet of Things

This chapter aims to reach a complete understanding of the problems of Internet of Things (IoT), and to have a clear set of IoT requirements via applying the proposed Requirements Engineering (RE) model for Blockchain-enabled IoT, to satisfy customers and stakeholders. The key problems of IoT are ambiguities in requirements, communication gap between stakeholders, which causes poor requirement analysis, lack of IoT domain knowledge, lack of knowledge of RE methods, poor management of Functional Requirements (FRs) and secure Non-Functional Requirements (NFRs), and conflicts in RE practices due to RE methods’ variability. To overcome these challenges, we have found that RE can provide a functional process for Blockchain-enabled IoT. The proposed model is a closed collaborated Blockchain network of IoT customers, IoT business personnel, IoT requirements engineers, and IoT developers to provide a transparent common communication platform, where customers and stakeholders can understand the concepts and terminology of IoT to easily describe their needs, thereafter, their abstract requirements. Then these requirements are analyzed, and refined, by the requirements analysts and only after verification, validation, and mutual consensus, the requirements are added to the Blockchain-enabled IoT. The consensus mechanism in Blockchain ensures mutual trust within nodes, and is used for verifying IoT requirements. The RE model for Blockchain-enabled IoT is a promising solution to improve the transparency, trust, adaptability, traceability, effectiveness, efficiency, and overall quality of the RE process for IoT.
New graph-based features for shape recognition

Shape recognition is a main challenging problem in computer vision. Different approaches and tools are used to solve this problem. Most existing approaches to object recognition are based on pixels. Pixel-based methods are dependent on the geometry and nature of the pixels, so the destruction of pixels reduces their performance. In this paper, we construct a graph that captures the topological and geometrical properties of the object. Then, using the coordinate and relation of its vertices, we extract features that are robust with respect to noise, rotation, scale variation and articulation. To evaluate our method, we provide different comparisons with state-of-the-art results on various known benchmarks, including Kimia’s, Tari56, Tari1000, Tetrapod and articulated datasets. We provide the analysis of our method against different variations. The results confirm the advantage of this method in different datasets, especially in the presence of noise.