Reviewerly/exHarmony huggingface dataset:
Solution of the compromise optimization problem of network graphics on the criteria of uniform personnel loading and distribution of funds

The object of research is a model network schedule for performing a complex of operations. One of the most problematic areas is the lack of a unified procedure that allows finding a solution to the problem of compromise optimization, for which the optimization criteria can have a different nature of the influence of input variables on them. In this study, such criteria are the criteria for the uniformity of the workload of personnel and the distribution of funds. Two alternative cases are considered: with monthly planning and with quarterly planning of allocation of funds and staff load. The methods of mathematical planning of the experiment and the ridge analysis of the response surface are used. The peculiarities of the proposed procedure for solving the problem of compromise optimization are its versatility and the possibility of visualization in one-dimensional form – the dependence of each of the alternative criteria on one parameter describing the constraints. The solution itself is found as the point of intersection of equally labeled ridge lines, which are curves that describe the locally optimal values of the output variables. The proposed procedure, despite the fact that it is performed only on a model network diagram, can be used to solve the trade-off optimization problem on arbitrary network graphs. This is due to the fact that the combination of locally optimal solutions in a parametric form on one graph allows visualizing all solutions to the problem. The results obtained at the same time make it possible to select early dates for the start of operations in such a way that, as much as possible, take into account possible difficulties due to the formation of bottlenecks at certain stages of the project. The latter may be due to the fact that for the timely execution of some operation, it may be necessary to combine two criteria, despite the fact that the possible costs may turn out to be more calculated and estimated as optimal.
COUNTER 5: Lessons Learned and New Insights Achieved

COUNTER (Counting Online Usage of NeTworked Electronic Resources) Release 5 has brought many improvements to reporting usage of e-resources. This session covered the three main developments which are the ability to see both total and unique downloads, the default exclusion of Gold Open Access usage in Standard View reports, and the introduction of the Unique_Title metric for reporting e-book usage. Examples of the manner in which different types of e-journal and e-book usage are reported with the new metrics as well as recommendations for calculating cost per use were also provided. Detailed information on Release 5 can be found in COUNTER Foundation Class YouTube videos and the Release 5 Manual for Librarians.
NDSHA - The New Paradigm for RSHA - An Updated Review

A New Paradigm (data driven and not like the currently model driven) is needed for Reliable Seismic Hazard Assessment RSHA. Neo-Deterministic Seismic Hazard Assessment (NDSHA) integrates earthquake geology, earthquake science, and particularly earthquake physics to finally achieve a New (and needed) Paradigm for Reliable Seismic Hazard Assessment RSHA.Although observations from many recent destructive earthquakes have all confirmed the validity of NDSHA’s approach and application to earthquake hazard forecasting-nonetheless damaging earthquakes still cannot yet be predicted with a precision requirement consistent with issuing a red alert and evacuation order to protect civil populations. However, intermediate-term (time scale) and middle-range (space scale) predictions of main shocks above a pre-assigned threshold may be properly used for the implementation of low-key preventive safety actions, as recommended by UNESCO in 1997. Furthermore, a proper integration of both seismological and geodetic information has been shown to also reliably contribute to a reduction of the geographic extent of alarms and it therefore defines a New Paradigm for TimeDependent Hazard Scenarios: Intermediate-Term (time scale) and Narrow-Range (space scale) Earthquake Prediction.
Deep Learning Self-Organizing Map of Convolutional Layers

The Self-Organizing Convolutional Map (SOCOM) combines convolutional neural networks, clustering via self-organizing maps, and learning through gradient backpropagation into a novel unified unsupervised deep architecture. The proposed clustering and training procedures reflect the model’s degree of integration and synergy between its constituting modules. The SOCOM prototype is in position to carry out unsupervised classification and clustering tasks based upon the distributed higher level representations that are produced by its underlying convolutional deep architecture, without necessitating target or label information at any stage of its training and inference operations. Due to its convolutional component SOCOM has the intrinsic capability to model signals consisting of one or more channels like grayscale and colored images.
The Impact of Augmented Reality on E-learning Systems in Saudi Arabia Universities

The purpose of this paper was to investigate the impact of Augmented Reality on e-learning systems at colleges in Saudi Arabia. In this research, Augmented Reality could reenact real environment by computerized overlays that learners can interact with and without much of a stretch access. What is more, Augmented Reality helps consumers to explore alternative learning avenues around learning content. Setting that aside, there has not been sufficiently thorough research on the evaluation of Augmented Reality in the context of teaching. The primary objective of this research is to examine possible standard factors identified with the successful use of unparalleled scale. This prototype highlights the essential factors that affect the implementation of AR via the quantitative approach to Augmented Reality knowledge assortment and evaluation. The research finds the principal coefficients for the attainment of Augmented Reality: IT infrastructure, IT agility, interaction stability, self-learning ability, curriculum, student background, ease of use and Usefulness. The after-effects of this analysis includes useful debates to create up a perfect fate of Augmented Reality and help handle the enhancement of instruction and e-learning with competitive societies and frameworks in the Kingdom of Saudi Arabia as well as other countries.
Smoothed Shock Filtering: Algorithm and Applications

This article presents the smoothed shock filter, which iteratively produces local segmentations in image’s inflection zones with smoothed morphological operators (dilations, erosions). Hence, it enhances contours by creating smoothed ruptures, while preserving homogeneous regions. After describing the algorithm, we show that it is a robust approach for denoising, compared to related works. Then, we expose how we exploited this filter as a pre-processing step in different image analysis tasks (medical image segmentation, fMRI, and texture classification). By means of its ability to enhance important patterns in images, the smoothed shock filter has a real positive impact upon such applications, for which we would like to explore it more in the future.
Recontextualization of Ideologies: A Critical Study of Tabooed Representation in Pakistani TV Dramas

Taboo is a religiously and socially prohibited practice. In a society, known for its conservative approach and attitude, several subjects have long not been discussed in a public discourse. This study explores how the media revolution of the 21st century is changing this situation by compromising and recontextualizing these taboos, as depicted through a text or talk. In recent years, the country's private TV industry has depicted some highly unpleasant topics in its dramas. The critical perspective of Ernesto Laclau and Chantal Mouffe (1985) probes deeply into the discourse of TV dramas to uncover the hidden ideologies behind the words in broader social and cultural contexts. To show this change, this study analyzes six Pakistani dramas dealing with hitherto tabooed topics to reveal the pros and cons of the growing ideological trends in Pakistani TV Dramas. First, the study outlines the significance of drama in shaping and recontextualizing ideologies. Second, it outlines certain salient taboo features applicable to almost every play. Finally, it suggests a few improvements in a bid to make things better.
Industry 4.0 Quantum Strategic Organizational Design Configurations. The Case of 3 Qubits: One Reports to Two

In this work we explore how the relationship between one subordinate reporting to two leaders influences the alignment of the latter with the company’s strategic objectives in an Industry 4.0 environment. We do this through the implementation of quantum circuits that represent decision networks. This is done for two cases: One in which the leaders do not communicate with each other, and one in which they do. Through the quantum simulation of strategic organizational design configurations (QSOD) through 500 quantum circuit simulations, we conclude that in the first case both leaders are not simultaneously in alignment, and in the second case that both reporting nodes need to have an alignment probability higher than 90% to support the leader node.
Mining Sports Articles using Cuckoo Search and Tabu Search with SMOTE Preprocessing Technique

Sentiment analysis is one of the most popular domains for natural language text classification, crucial for improving information extraction.However, massive data availability is one of the biggest problems for opinion mining due to accuracy considerations.Selecting high discriminative features from an opinion mining database is still an ongoing research topic.This study presents a two-stage heuristic feature selection method to classify sports articles using Tabu search and Cuckoo search via Lévy flight.Lévy flight is used to prevent the solution from being trapped at local optima.Comparative results on a benchmark dataset prove that our method shows significant improvements in the overall accuracy from 82.6% up to 89.5%.
A copyright ‘human-centred approach’ to AI?

A copyright ‘human-centred approach’ to AI? Alexandre L Dias Pereira Alexandre L Dias Pereira Email: jacques.dewerra@unige.ch Search for other works by this author on: Oxford Academic Google Scholar Associate Professor, Faculty of Law, University of Coimbra, Portugal. Author Notes GRUR International, Volume 70, Issue 4, April 2021, Pages 323–324, https://doi.org/10.1093/grurint/ikab006 Published: 13 March 2021
Privacy-Enhanced Robust Image Hashing with Bloom Filters

Robust image hashes are used to detect known illegal images, even after image processing. This is, for example, interesting for a forensic investigation, or for a company to protect their employees and customers by filtering content. The disadvantage of robust hashes is that they leak structural information of the pictures, which can lead to privacy issues. Our scientific contribution is to extend a robust image hash with privacy protection. We thus introduce and discuss such a privacy-preserving concept. The approach uses a probabilistic data structure -- known as Bloom filter -- to store robust image hashes. Bloom filter store elements by mapping hashes of each element to an internal data structure. We choose a cryptographic hash function to one-way encrypt and store elements. The privacy of the inserted elements is thus protected. We evaluate our implementation, and compare it to its underlying robust image hashing algorithm. Thereby, we show the cost with respect to error rates for introducing a privacy protection into robust hashing. Finally, we discuss our approach's results and usability, and suggest possible future improvements.
Evacuation training using scenario-based augmented reality game

"Evacuation training is important as disaster education that covers how to survive disasters. However, traditional evacuation training does not provide realistic simulated evacuation experience (SEE). To provide the such, we developed game-based evacuation training (GBET), where trainees are required to reach a shelter in the real world within a time limit while making decisions against virtual disaster situations presented as digital contents (e.g. video and single-choice question) on GPS-enabled smartphones or tablets. However, the GBET was insufficient in the audiovisual reality. To provide a more realistic SEE, we created an evacuation training using scenario-based augmented reality (AR) game that integrates marker-based AR and scenariobased game. Although only applicable in indoor activities, the evacuation training (the extended GBET system) presents AR that expresses disaster situations (e.g. flood and fire) by superimposing threedimensional computer graphics onto the real-time view through a handheld head-mounted display."
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference

Quantization enables efficient acceleration of deep neural networks by reducing model memory footprint and exploiting low-cost integer math hardware units. Quantization maps floating-point weights and activations in a trained model to low-bitwidth integer values using scale factors. Excessive quantization, reducing precision too aggressively, results in accuracy degradation. When scale factors are shared at a coarse granularity across many dimensions of each tensor, effective precision of individual elements within the tensor are limited. To reduce quantization-related accuracy loss, we propose using a separate scale factor for each small vector of ($\approx$16-64) elements within a single dimension of a tensor. To achieve an efficient hardware implementation, the per-vector scale factors can be implemented with low-bitwidth integers when calibrated using a two-level quantization scheme. We find that per-vector scaling consistently achieves better inference accuracy at low precision compared to conventional scaling techniques for popular neural networks without requiring retraining. We also modify a deep learning accelerator hardware design to study the area and energy overheads of per-vector scaling support. Our evaluation demonstrates that per-vector scaled quantization with 4-bit weights and activations achieves 37% area saving and 24% energy saving while maintaining over 75% accuracy for ResNet50 on ImageNet. 4-bit weights and 8-bit activations achieve near-full-precision accuracy for both BERT-base and BERT-large on SQuAD while reducing area by 26% compared to an 8-bit baseline.
PONZI SCHEME AND ITS PREVENTION:INSIGHTS FROM MALAYSIA

Ponzi scheme is a fraudulent investing schemes which promises high rates of return with little risk to investors. The scheme has led to substantial amount of financial leakages in recent years. Despite warnings by the law enforcement agencies and various alerts by regulators, many have unfortunately fallen for such schemes. Hence, the current measures are limited in preventing the fraudulent schemes. Therefore, the present study aims to present key insights into the modus operandi, profiles of fraudsters and victims as well as prevention measures undertaken by regulators and law enforcement agency in Malaysia. This study employs a qualitative methodology involving semi-structured interviews and document reviews to collect data. Interviews with regulators, enforcers, and victims revealed that the emergence of digitally promoted modus operandi in recent years posed new challenges to both regulators and enforcers to detect and prevent Ponzi schemes. Additionally, education is the best approach to ensure effective preventive strategy which needs to be supported by a specific regulation on Ponzi scheme. With a new law, better enforcement could lead to more effective prevention of the fraud. Findings of this study is expected to provide recent evidence on Ponzi schemes and valuable insights for future development of preventive measures.
FINANCING OF SMALL AND MEDIUM SCALE ENTERPRISES AND ITS GROWTH IMPACT IN NIGERIA.

We examined the extent to which SMEs financing influenced economic growth in Nigeria through the use of time-series data from 1999-2018.After a literature search, other factors possessing the potential to influence the dependent variable ASGDP was added to the research model.Relying on Ordinary Least Squares estimation using Eviews 10.0, findings shows that lending rate reduces ASGDP by 7% and gross capital formation reduces ASGDP by 5%.On the other hand, surprisingly, credit to SMEs did not retain the massive effect on growth as seen in previous studies.We can attribute this to our choice of adopting SME contribution to GDP as our target variable.Electricity distribution increased ASGDP by 4.6%.Policy recommendations to the Federal Government and the apex bank are; capital inflows vis-à-vis affordable local loans to SMEs at single digit (interest rate) is necessary to enhance the performance of SMEs and growth simultaneously.Electricity tariffs should be heavily subsidized for small and medium scale enterprises to decrease their overhead cost.
On the Interaction Between Distance Functions and Clustering Criteria in Multi-objective Clustering

Multi-criterion algorithms for clustering have gained some traction due to their ability to cater to a diverse range of cluster properties. Here, we investigate the interaction between the clustering criteria employed in a multi-objective algorithm and the distance functions on which these criteria operate. We do so by contrasting the multi-criterion evolutionary algorithm $$\varDelta $$ -MOCK with a bi-objective version of the evolutionary multi-view clustering approach MVMC, which uses a single clustering criterion but can incorporate multiple dissimilarity matrices. Using a benchmark suite representing a diverse range of cluster properties, we illustrate that comparable results to $$\varDelta $$ -MOCK can be achieved using MVMC with two complementary distance functions. We then establish the mathematical equivalence of $$\varDelta $$ -MOCK's connectivity objective to a compactness criterion operating on a redefined distance function. We conclude by discussing the implications of our findings for future work on the representation of clusters in multi-objective evolutionary clustering.
Accessible Augmented Reality to support chemistry teaching

Learning technologies are innovating the Education, offering resources that enrich the teaching-learning process. Among the various resources offered, Augmented Reality (AR) stands out, where real and virtual worlds are mixed. However, AR brings visual-based interactions only, blocking any experiences from students with visual limitations. Therefore, this work explored the accessibility guidelines in order to match the experience of a visually impaired user with a non-impaired user while interacting with AR. Thus, to verify if this is possible an AR application, Accessible Chemistry, was developed. Based on assisted technologies, it aimed to teach concepts of atoms connections and compounds formations to students with low vision. Finally, a predictive evaluation was made to validate it"s accessibility.
Usability of data-oriented user interfaces for cultural heritage: A systematic mapping study

This study surveys the state of the art in usability and user experience strategies applied to applications that deal with large amounts of data in the field of cultural heritage, highlighting the most prominent aspects and underlining the under-explored. In these applications, large amounts of data need to be wisely presented to help final users at drawing conclusions and making decisions. While sophisticated technology may be used to improve the user experience, it should not be applied to the detriment of usability, which is critical for the success of these applications. We performed a systematic mapping study to classify the literature retrieved in the four largest scientific databases by a structured search string. We classify applications according to purpose, intended users, the way they address and evaluate user experience and usability, among others, and include the analysis of combined results through maps. Findings reveal the contradiction that while most articles are intended for the education and tourism of the general public, only half of the studies evaluate usability. Moreover, there is a significant research gap in user interfaces for systems in the context of preventive conservation, for research, assessment and decision assistance. This is the first systematic mapping study combining usability and cultural heritage, especially for data-oriented applications. It shows that more research is necessary to assist conservators and researchers and to address usability from early stages of development.
Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty

Complex systems are usually affected by various sources of uncertainty, and it is essential to account for mechanisms that ensure the proper management of such disturbances. This paper introduces a novel approach to solve symbolic regression problems, which combines the potential of Grammatical Evolution to obtain solutions by describing the search space with context-free grammars, and the ability of Modal Interval Analysis (MIA) to handle quantified uncertainty. The presented methodology uses an MIA solver to evaluate the fitness function, which represents a novel method to manage uncertainty by means of interval-based prediction models. This paper first introduces the theory that establishes the basis of the proposed methodology, and follows with a description of the system architecture and implementation details. Then, we present an illustrative application example which consists of determining the outer and inner approximations of the mean velocity of the water current of a river stretch. Finally, the interpretation of the obtained results and the limitations of the proposed methodology are discussed.
How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.