Reviewerly/exHarmony huggingface dataset:
Underwater Image Enhancement Algorithm Based on Illuminance Domain Analysis

Aiming at the problem of poor image quality in conventional underwater vision system, especially in the case of poor water quality, a new underwater image enhancement algorithm based on illuminance domain analysis and improved DCP algorithm and Retinex algorithm is proposed in this paper. This enhancement algorithm estimates the background illumination of the water body of the imported underwater image, compares the estimated value with the set critical illumination value, and adopts the corresponding improved algorithm to obtain the enhanced image, which effectively solves the imaging problem in the poor water quality environment. Uciqe method is used to evaluate the quality of the enhanced image, and more accurate and objective experimental results are obtained. The enhanced algorithm can be applied to underwater fishing, dike inspection, nuclear facilities inspection, treasure hunting, archaeology, fish assessment, ship management and customs inspection.
Integrated high-speed quantum random number generators for mobile, IOT, and edge applications

New technological developments such as IoT, Artificial Intelligence and Blockchain are leading our “Data-driven society”, where data generated by physical devices are shared across multiple platforms to improve everyday services. This unique technological evolution also corresponds to new cybersecurity and data-protection risks and challenges as well as computational limitations, which could ultimately impact users’ experience, safety and privacy. The development of products and infrastructure offering long-term security guarantees and stronger computational capabilities is a global priority. Near-term, quantum technologies provide a radically new toolset to realize stronger encryption systems as well as improved randomized algorithms. One such technology is the development of a reliable high-speed and scalable quantum random number generator. In this article, a System in Package (SiP) integration and packaging process is analyzed to bring this component into the low cost and high-volume arena. The proposed SiP solution, combined with existing surface mount technology, offers numerous benefits, such as scalability, smaller physical size, less parasitic effects and lower cost. We will discuss component performance, with insights on interconnect lengths, the shielding effect and the impact of the encapsulants.
Free Choice Indefinites in Old and Modern Italian

This article deals with a specific meaning of Free Choice Indefinites (FCIs) such as qualunqueand qualsiasi in Old and Modern Italian. Despite the existent synchronic and diachronic studies of FCIs that focus on the prenominalposition as represented in (2) (see Aloni & Port 2013, Chierchia 2006, Stark 2006, Becker 2014,among others), there are no synchronic or diachronic studies focusing on the postnominalposition with the evaluative meaning. It is still unclear what distribution postnominal indefiniteshave in synchrony and diachrony and what the relation between prenominal indefinites with theFC meaning and postnominal indefinites with the evaluative meaning is (see also A&M 2011on cualquiera in Spanish).In this article, I will try to fill the gap with respect to qualunque and describe its distribution, aswell as its syntactic and semantic properties in synchrony and diachrony. My main focus willbe answering the question of how Italian qualunque developed an evaluative meaningcomponent and why this interpretation is only available when qualunque is postnominal.
“OGA, I’m Not Lying”: Discursive Strategies in Selected Police-Suspect Interrogations in Ibadan, Nigeria

Interrogation is a vital tool through which police officers obtain substantial evidence from suspect(s).Previous studies examined the verbal means of language without paying attention to emotions, gestures and voice pitch; and only the language use on the part of the Investigating Police Officers (IPOs) was considered without examining the suspects.Thus, this study identified and discussed the discursive strategies performed by both the IPOs and the suspects during interrogations.Using Jacob Mey's Pragmatic Act Theory as framework, and three purposively selected cases out of the cases in the data collected from the Oyo State Criminal Investigation Department, Iyaganku, Ibadan, data were subjected to pragmatic analysis.Six discursive strategies are identified: smile/grin, frown, shout, question style, repetition and code-switching.The various strategies were cushioned to achieve diverse goals in the interrogation sessions.Smile/grin helped in the establishment of rapport, signaling pretence/sarcasm; frown indicated lie detection and discontent; shout showed anger and discontent; questions for information elicitation (wh-question), precision (polar questions), confirmation (declarative questions); repetition was used for reinforcement, while code-switching was used to signal solidarity and ethnic identity.The police-suspect interrogation is a distinct type of discourse as almost all the interactions tilt towards unraveling secrets to a particular crime.The discursive strategies are characteristic of a policesuspect interrogation and they are used to harness the discourse at hand.
Appendix to "Experiential Observations: capturing the potential content within evidences of experiences"

Material annex to Experiential Observations: capturing the potential content within evidences of experiences, contribution to the special issue on Cultural Heritage of the Semantic Web Journal. The materials include the description of the test scenario, the feedback survey, a report, questions and answers, and sample artefacts.
Sarcasm Target Identification with LSTM Networks

The earlier work on sarcastic texts mainly concentrated on detecting the sarcasm on a given text. With the spread of cyber-bullying with the use of social media, it becomes also essential to identify the target of the sarcasm besides detecting the sarcasm. In this study, we propose a deep learning model for target identification on sarcastic texts and compare it with other work on English. The results show that our model outperforms the related work on sarcasm target identification.
Forecast of Coronavirus Distribution in a Number of Countries of Europe and Asia: Dynamic and Stochastic Approach

Abstract The simplest example of a dynamic stochastic approach to the study of the distribution of the coronovirus is considered. The distribution of coronavirus outside of china is considered as a modeling object. The choice of the developed model is due to the fact that the volterra equations are used to describe the dynamics of quantities that do not go beyond the range of positive values. The investigated process and the model parameter in the work are scalar in nature. As a result of modeling, the authors predicted the spread of coronavirus in a number of countries in Europe and Asia
Technologies for organizing research activities of students at the university

Scientific and technological progress, constant processes of reformation determine the need of employers for highly qualified graduates who are ready to carry out independent professional activities, quickly and non-standardly solve emerging issues, therefore, higher education institutions in the conditions of a competency-based approach strive to make development of students' competence more practice-oriented. Research activity is one of the most important tools in implementation of this process. The purpose of the article is to analyze the experience of a higher educational institution in the implementation of technologies for organizing research activities of students at the university. The article presents educational technologies that contribute to the development of research processes. Students’ motivation engaged in research activities are analyzed. The study allowed us to identify students’ motivation to participate in research activities and adjust the situation for the better. The achieved results can be used at universities for the preparation of students of various areas of training.
Influence of Plasmodium falciparum Parasitaemia on White Blood Cell among children (6-59 Months). A Case Study of Comprehensive Health Centre Bulumkutu, Maiduguri, Borno, State – Nigeria

This study was conducted to assess the influence of Plasmodium falciparum parasitaemia on Platelate (children) (6-59), at Bulumkutu Comprehensive Health Centre Maiduguri, Borno State, between August to December, 2018.A total of 210 children were enrolled in the study which consisted of 88 (41.9%) patients with positive P. falciparum malaria and 122 (58.10%) negative malaria.Hematological parameters were analyzed using sysmex haematology auto-analyser (2011), while the Giemsa stained slides thick and thin blood films were prepared from the stock solution, and tested for Plasmodium falciparum malaria and count of malaria parasite density.A positive correlation was observed between the parasite density and white blood cell of (children tested positive) as index of leucocytosis (r2 = 0.831, P = 0.001) and positive correlation was observed between parasite and white cells of males females subjects respectively, where (r 2 = 0.735, p = 0.001) for males and (r2 = 0.710, p = 0.001) for females.
Genetic-based Pruning Technique for Ant-Miner Classification Algorithm

Ant colony optimization (ACO) is a well-known algorithm from swarm intelligence that plays an essential role in obtaining rich solutions to complex problems with wide search space. ACO is successfully applied to different application problems involving rules-based classification through an ant-miner classifier. However, in the ant-miner classifier, rule-pruning suffers from the problem of nesting effect origins from the method of greedy Sequential Backward Selection (SBS) in term selection, thereby depriving the opportunity of obtaining a good pruned rule by adding/removing the terms during the pruning process. This paper presents an extension to the Ant-Miner, namely the genetic algorithm Ant-Miner (GA-Ant Miner), which incorporates the use of GA as a key aspect in the design and implementation of a new rule pruning technique. This pruning technique consists of three fundamental procedures: an initial population Ant-Miner, crossover to prune the rule, and mutation to diversify the pruned classification rule. The GA-Ant Miner performance is tested and compared with the most related ant-mining classifiers, including the original Ant-Miner, ACO/ PSO2, TACO-Miner, CAnt-Miner, and Ant-Miner with a hybrid pruner, across various public available UCI datasets. These datasets are varied in terms of instance number, feature size, class number, and the application domains. Overall, the performance results indicate that the GA-Ant Miner classifier outperforms the other five classifiers in the classification accuracy and model size. Furthermore, the experimental results using statistical test prove that GA-Ant Miner is the best classifier when considering the multi objectives (i.e., accuracy and model size ranks).
SciviK: A Versatile Framework for Specifying and Verifying Smart Contracts

The growing adoption of smart contracts on blockchains poses new security risks that can lead to significant monetary loss, while existing approaches either provide no (or partial) security guarantees for smart contracts or require huge proof effort. To address this challenge, we present SciviK, a versatile framework for specifying and verifying industrial-grade smart contracts. SciviK's versatile approach extends previous efforts with three key contributions: (i) an expressive annotation system enabling built-in directives for vulnerability pattern checking, neural-based loop invariant inference, and the verification of rich properties of real-world smart contracts (ii) a fine-grained model for the Ethereum Virtual Machine (EVM) that provides low-level execution semantics, (iii) an IR-level verification framework integrating both SMT solvers and the Coq proof assistant. We use SciviK to specify and verify security properties for 12 benchmark contracts and a real-world Decentralized Finance (DeFi) smart contract. Among all 158 specified security properties (in six types), 151 properties can be automatically verified within 2 seconds, five properties can be automatically verified after moderate modifications, and two properties are manually proved with around 200 lines of Coq code.
The effects of knowledge management capabilities on Research outcome and Teaching effectiveness in King Saud University

The aim was to investigate effects of KMC on research outcome(RO) and teaching effectiveness(TE) in KSU. The final sample consisted of 320 University staff, from Humanities Colleges. Four faculties were selectively chosen. They were Colleges of Languages and Translation, Education, Sports Science and Physical Activity and Arts. PCA was conducted to measure the relationship between the study variables. SEM was employed to determine a valid causal model. Means, standard deviations, correlation, skewness and kurtosis among variables indicated that variables were highly correlated. The results revealed that KA has significant direct effects on both RO &amp; TE, KS has significant direct effects on both RO &amp; TE, KU has significant direct effects on both RO &amp; TE,TI has significant direct effects on both RO &amp; TE, and OC has significant direct effects on both RO &amp; TE.
Cohort profile: St. Michael’s Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing

Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electronic health records (EHR), but granular information extracted from unstructured EHR data is limited. The St. Michael's Hospital TB database (SMH-TB) was established to address gaps in EHR-derived TB clinical cohorts and provide researchers and clinicians with detailed, granular data related to TB management and treatment.We collected and validated multiple layers of EHR data from the TB outpatient clinic at St. Michael's Hospital, Toronto, Ontario, Canada to generate the SMH-TB database. SMH-TB contains structured data directly from the EHR, and variables generated using natural language processing (NLP) by extracting relevant information from free-text within clinic, radiology, and other notes. NLP performance was assessed using recall, precision and F1 score averaged across variable labels. We present characteristics of the cohort population using binomial proportions and 95% confidence intervals (CI), with and without adjusting for NLP misclassification errors.SMH-TB currently contains retrospective patient data spanning 2011 to 2018, for a total of 3298 patients (N = 3237 with at least 1 associated dictation). Performance of TB diagnosis and medication NLP rulesets surpasses 93% in recall, precision and F1 metrics, indicating good generalizability. We estimated 20% (95% CI: 18.4-21.2%) were diagnosed with active TB and 46% (95% CI: 43.8-47.2%) were diagnosed with latent TB. After adjusting for potential misclassification, the proportion of patients diagnosed with active and latent TB was 18% (95% CI: 16.8-19.7%) and 40% (95% CI: 37.8-41.6%) respectively.SMH-TB is a unique database that includes a breadth of structured data derived from structured and unstructured EHR data by using NLP rulesets. The data are available for a variety of research applications, such as clinical epidemiology, quality improvement and mathematical modeling studies.
CHILDREN FROM MIGRANT FAMILIES IN A RUSSIAN SCHOOL: ADAPTATION PROBLEMS IN THE CONTEXT OF INTERNATIONAL APPROACHES

Nearly 36 million of school age children grow in the families of international migrants, and that index continues to grow. It is an important strategic priority to educate such children. Education is an investment into the future of the whole world. Problems of the education of children from migrants’ families are described in the context of the importance of achieving Sustainable Development Goals named in the 2030 Agenda for Sustainable Development. There is a comparative analysis of the experience of Russia and foreign countries: how they organize the access of migrants’ children to pre-school, primary and secondary education, their linguistic, social and cultural adaptation.
Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.
A Hybrid Blockchain-based Zero Reconciliation Approach for an Effective Mobile Wallet

Money-related exchanges are highly dependent upon mobile or banking wallet system, where end-to-end enhancement is necessary to make the entire process efficient. This paper proposes a hybrid blockchain-based wallet design to give a quicker exchange between end-to-end nodes and control transaction failure. To make the blockchain adaptable for the wallet, we need to enable faster transactions and ensure security; thus, any kind of forgery cannot occur. Moreover, to make it highly available and make the transaction faster, we have to redesign the legacy consensus. In the proposed architecture, all nodes do not need to participate in the consensus. Only the participating peers are sufficient, thus we can get faster transaction experience. This paper has redesigned the legacy wallet architecture to make it compatible with the blockchain-based platform by using Corda. We have shown the transaction prerequisites such as validity consensus, uniqueness consensus, and overall efficiency and shown a qualitative comparison with legacy architecture.
Environmental variables database from a Miocene marine stratigraphic section: A multivariate statistical analysis

The data presented here are related to the research article “Miocene Atlantic transgressive-regressive events in northeastern and offshore Patagonia: A palynological perspective” (Guler et al. 2021; https://doi.org/10.1016/j.jsames.2021.103239). A total of 60 drilled cutting samples from a 580 m-thick subsurface stratigraphic section (YPF.Ch.PV.es-1 borehole) in Península Valdés, Chubut Province, Argentina, collected every 10 m, were processed for palynological analysis. The quantitative data were statistically evaluated. In detail, the database contain: 1) raw palynological data – proxy data – from counting under transmitted light microscope; 2) four paleoenvironmental variables selected to conduct a multivariate analysis: terrestrial/marine ratio, acritarchs, outer neritic dinocyst taxa and warm-water dinocyst taxa; 3) transformed variables used for the Principal Component Analysis (PCA) and 4) the principal component scores obtained, stratigraphically ordered from the top to bottom of the borehole. Data from future studies in new sites combined with here presented data, can be useful to refine paleoenvironment models applied to basin analysis.
Encryption as an obstacle for criminal investigation and evidence collection

Encryption has become an integral part of modern life. It is without a doubt of great importance for the realization of some of the basic human rights in the ubiquitous technical environment, for the daily use of numerous online services, as well as for the functioning of the Internet in general. On the other hand, the authorities in charge of detecting and proving crime are increasingly facing obstacles when accessing encrypted content. Namely, encryption represents a kind of challenge in the implementation of both general and special evidentiary actions. This paper is dedicated to understanding the technical aspects of this challenge. The author explains the basic principles on which encryption technology is based, pointing out the difference between symmetric and asymmetric encryption, between encryption of stored data and data encryption in transit, and between server-based and user-based encryption, and its implication for investigation of crime.
Enhancing Local Dependencies for Transformer-Based Text-to-Speech via Hybrid Lightweight Convolution

Owing to the powerful self-attention mechanism, the Transformer network has achieved considerable successes across many sequence modeling tasks and has become one of the most popular methods in text-to-speech (TTS). The vanilla self-attention excels in capturing long-range dependencies but suffers in modeling stable short-range dependencies that are quite important for speech synthesis where the local audio signals are highly correlated. To address this problem, we propose the hybrid lightweight convolution (HLC), which is responsible for fully exploiting local structures of a sequence, and combine it with the self-attention to improve the Transformer-based TTS. The experimental results show that our modified model obtains better performance in both objective and subjective evaluations. At the same time, we also demonstrate that a more compact TTS model may be built through the combination of self-attention and proposed hybrid lightweight convolution. Besides, this method is also potentially adaptable for other sequence modeling tasks.
Artificial Fish Swarm Algorithm Based on Tabu Search

In this paper, through the analysis of artificial fish swarm algorithm, the algorithm is effectively improved. Tabu search is added into the artificial fish swarm algorithm, and Tabu search table is set, so that the artificial fish swarm algorithm can effectively avoid falling into the local optimization when searching for the optimal solution, which speeds up the later convergence speed and improves the performance of the algorithm. The simulation results show that compared with Tabu search algorithm and artificial fish swarm algorithm, the improved artificial fish swarm algorithm has obvious improvement in convergence speed, calculation accuracy and jumping out of local optimal ability.