Reviewerly/exHarmony huggingface dataset:
Museums for the new generations: Augmented reality platform with mobile devices

In this text, you will find a new traditional way that the museums can potentiate its strategy of interest to new generations of young people to visit it facilities and increase the impetus for them to know historical and cultural information offered on the galleries themselves, based on experience of immersion virtual environments and other media platforms through augmented reality, with minimal investment costs to users, using their own mobile operating systems iOS or android. All this, in order that new generations of young people interested in returning to the museum to learn more about the history and culture offered the experience and spread it to friends and personally from catching on the experience of visiting the museum, plus there is a post-visit augmented reality accessed from the same web platform museum interest.
Documenting the Creation, Manipulation and Evaluation of Links for Reuse and Reproducibility

Data integration is an essential task in the open world of the Semantic Web. Many approaches have been proposed that achieve such integration by linking related entities across data providers, but they lack the support for in-depth documentation of the involved processes such as the creation, manipulation and evaluation of links. As a consequence, detailed documentation that eases the understanding and reproducibility of underlying processes is needed for a reliable reuse of graphs of identity available in the Semantic Web. We present here an approach to document such links and their processes, building upon a representation we call VoID+. It enables link-publishers to provide data-users with information that better support them in accessing and using links. We show that our approach with the proposed VoID+ ontology allows us to address the relevant competency questions around the reuse of integrated Semantic Web data. We also demonstrate how our approach has been successfully implemented in the Lenticular Lens, a user interface tool that annotates links it discovers, manipulates or validates under user's guidance. Based on a real-life humanities case study, we can show that the ontology amply annotates links in its life-cycle for reliable decision making by data-users.
Analysis of the Susceptibility of Capacitive Touchscreens to External Electric Field Interference

Since the introduction of the iPhone, the touchscreen has become one of the most important human-machine interfaces between users and electronic products. The capacitive touch screen is the most popular touchscreen on the market because of its benefits over a resistive touchscreen. However, since the capacitive touchscreen senses the finger-touch by sensing the capacitance change, it is subjected to the interference of the external electric field, which results in touchscreen malfunction and security issues. This paper will first analyze the working principle of the capacitive touchscreen. The mechanism of false (ghost) touch due to external electric field is explored based on the working principle. The conditions of false touch due to the external electric field will be identified and studied. Experiments are conducted to validate and quantify the analysis above. Different from existing literature, which mostly focuses on demonstrating the results without fully understanding the detail mechanism, this paper focuses on disclosing the fundamentals and identifying critical conditions behind the ghost touch phenomena, which is the major contribution of this paper.
Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.
Cloud computing rising in the field of big data and artificial intelligence

Cloud Computing is a strong, large-scale and complex Computing technology. It lessens the need to maintain an expensive, specialized computer hardware area, as well as expensive software and software. Cloud computing has shown a significant increase in data quality or the production of large amounts of data. Big data processing is a difficult and time-consuming operation that necessitates the use of a large computer system in order to ensure performance. Knowledge creation and analysis are two intertwined activities and this paper investigates the rise of Big Data and Artificial Intelligence (AI) in Cloud Computing research. As data storage and mining methods advance, the preservation of expanding data quantities is characterized by a change in the core of structured results. This shift is reflected in the evolution of structured results. However, one major barrier is that this rate of growth surpasses the ability of data gathering systems and cloud infrastructure platforms to be upgraded. Workloads are really heavy and it is possible that certain cloud computing disputes will be created, which will include the description, characteristics, and categorization of huge data. In addition, analysis problems based on data integrity, heterogeneity and protection.
Composition and Function Analysis of Electronic Blue Army Tactical Deduction System based on OPM

In this paper, by comparing various deduction methods such as on-the-spot deduction, on-map deduction, sand table deduction, war chess deduction, computer deduction, etc., and comprehensively considering the performance of various methods in deduction efficiency, portability and observability, combining with the purpose of electronic blue army tactical deduction (scheme deduction, optimization and optimization), it is determined that the electronic blue army tactical deduction system is mainly composed of electronic sand table and computer simulation system, and its core function is to form a reasonable electronic blue army operational scheme. On this basis, OPM is used to analyze the tactical deduction system of electronic blue army in detail, and the essential components and functions of the system are obtained.
Subtle Attention Guidance for a New Virtual Reality Game

Gaze and attention guidance is the most important challenge in virtual reality (VR) games because they affect comprehension, playability and immersion in the interactive environment. In this work, we propose an intuitive game-design approach to subtly guide the user’s attention in a new custom VR game. The results of the survey show the potential of this approach to enhance the VR experience that leading to a high rate of comprehension and focus on game mechanics.
Automatic Processing of Planning Problems: Application on Representative Case Studies

Automatic planning is a full-fledged discipline in artificial intelligence. It allows describing and solving planning problems in many fields such as robotics, project management, internet navigation, crisis management, logistics and games. The PDDL language, planners and validators associated with this language allow the description, resolution and validation of planning problems. In this paper, we recommend a formal process based on the PDDL language that favors the obtaining of reliable PDDL descriptions. Our process is successfully experimented on a set of representative case studies like the Hanoï Towers and the Sliding puzzle game.
Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media

Vaccines are one way to prevent the coronavirus from entering the human body, although it is not 100% accurate. However, the implementation of vaccination in Indonesia is still controversial. People give their opinions directly or through social media such as Twitter. Retrieval of tweets using the Twitter API and using python. The data obtained is then preprocessed using case folding, cleaning, tokenizing, filtering, and stemming. After that, the model was evaluated using the Naive Bayes method. Naïve Bayes is a classification method that can predict the probability of a class to produce decisions based on learning data. Currently, nave Bayes is one of the methods to find accuracy in sentiment analysis that is often used and is the best. The results of this study obtained an accuracy of 79%.
Optoelectronic Reservoir Computer for Early Stage Alzheimer’s Disease detection

An optoelectronic Reservoir Computer is proposed and implemented numerically and on physical hardware for early-stage Alzheimer’s disease detection for the first time. The approach yields classification accuracy of 85% surpassing state-of-the-art performances.
Validating the Product Hypothesis

In Chapter 3 , we discussed different techniques for identifying your customer. Whether you are delivering a fixed- scope project or iteratively building a new product, your business will not be possible without having customer needs at the center of your delivery model. Customers define which businesses succeed and which businesses fail. In addition, we discussed Lean UX techniques for identifying proto-personas and understanding their needs. We also covered multiple techniques for user segmentation and analysis, including empathy maps and customer journeys.
Teachers Adapting to Curricular Change: Basis for Teacher Education Curriculum Review

Implementing the flexible mode of instruction at all levels is a challenge to the educational system, especially to teachers who deliver subjects that are highly skill-based. This two-phase study investigated the strategies that enabled teachers to adapt to curricular change. Phase one of the study involved a phenomenological inquiry into the new demands of flexible delivery of TLE/TVL among secondary level teachers in the Philippines and the competencies that enabled them to meet these emerging demands. Data were gathered through semi-structured individual interviews with five teachers at the secondary level. Phase two of the study utilized the preliminary findings to review how the new teacher education curricula for TLE/TVL promote the necessary competencies to train teachers who are adaptable to curriculum change. Findings revealed that flexible learning modes demand teachers to explore creative and resourceful ways of delivering the same core practices such as teaching, assessment, and establishing connections with clients and stakeholders. Teachers meet these challenges through a combination of institutional training, resources, and personal learning initiatives. Further, opportunities to develop the competencies that teachers need to adapt to the curricular change are provided in the new teacher education curriculum. With the importance of self-learning and expansion of research skills development in the TLE/TVL teacher education curricula was then recommended. Keywords: online, modular, blended, technology and livelihood education, technical and vocational education
Analysis of Teachers’ Perceptions on the Mathematical Competencies

The purpose of this study is to analyze teachers' perceptions about the mathematical competency of the 2015 revised curriculum. To this end, 42 high school mathematics teachers were asked to describe mathematical competencies in their own words. It was analyzed using semantic network analysis. In addition, the questionnaire was also conducted on the competencies that were considered important or not important in mathematics classes, and the reasons were analyzed and categorized by competencies. As a result of comparing teachers' responses to competencies and the description of the curriculum, information processing showed high agreement, but creativity and integration, attitude and practice showed low agreement. Also, teachers lacked understanding of some sub-elements of each competency. Teachers recognized problem solving as the most important competency in school mathematics. creativity and integration and information processing were mainly mentioned as competencies that were not considered important in class, but the reasons were different. Based on the main results, implications for mathematics teaching and learning were presented.
Response to the rejoinder to “The impact of infectious diseases on remittances inflows to India” by Prof. Stark

This study examines if democracy reduces poverty in 40 Sub-Saharan African countries for the period 1999–2018. For this purpose, we employ the Generalised Method of Moments. The results show that democracy is not directly associated with poverty reduction in sub-Saharan Africa. However, this observation hides important non-linearities and an interesting pattern of policy complementarities. Indeed, democracy is associated with poverty reduction in countries where economic growth is strong and human capital high. The robustness tests carried out do not change these results. This means that poor economic growth and weak human capital not only have a direct negative effect on the well-being of SSA countries, but also prevent the poor in those countries from benefitting the gains of democracy. Therefore, in order to reduce poverty in SSA, policy makers should continue the process of democratization while simultaneously adopting policies of economic development and human capital building. Democracy in isolation is useless for the poor in SSA.
A Time-Domain Perspective of Quantum Electro-Optic Sampling

The development of a fully time-domain perspective of electro-optic sampling provides insights into the geometry of the time-domain detector and outlines possible ways for quadrature sampling of quantum fields.
Short-Term Power Prediction of a Photovoltaic Power Station Based on the SSA-CEEMDAN-FCN Model

Photovoltaic power generation is greatly affected by weather factors. To improve the prediction accuracy of photovoltaic power generation, complete ensemble empirical mode decomposition with an adaptive noise algorithm (CEEMDAN) is proposed to preprocess the power sequence. Then, the full convolutional network (FCN) model optimized based on the sparrow search algorithm (SSA) is used to predict the short-term photovoltaic power. SSA can more reasonably determine the parameters of FCN and improve the prediction performance of FCN. Therefore, the FCN model optimized by the SSA algorithm is used to establish prediction models for subsequences and predict each subsequence, respectively. Finally, the predicted value of each subsequence is superimposed. Taking the actual data of a photovoltaic power station in Jiangsu province of China as an example, by comparing some different common prediction models, it is proved that the proposed method is reasonable and feasible.
Textual notes to D.S. Merezhkovsky “Non-War Diary. 1914–1916” (based on archival materials from RO IRLI)

The author of the article for the first time produces a detailed textual analysis of the collection of D.S. Merezhkovsky “Non-War Diary. 1914–1916” (1917), relying on archival materials from the RO IRLI (Pushkin House). For most of the articles that made up this book, various documents have been preserved: preparatory notes and extracts, draft and white manuscripts, typescripts, newspaper clippings. Their value is determined not only by the tasks of textual criticism, but also by the history of literature and interpretive poetics. As a result, it became possible to outline in more detail the pre-press history of a number of articles and fill in the gaps in our understanding of them. On specific examples, we were able to characterize the logic of finalizing texts by a writer (from a manuscript to a newspaper publication and then to a collection); clarify ideas on the problem of distinguishing between censored / editorial and author’s excluded fragments in Merezhkovsky’s articles; expand knowledge of the sources he used. Documentary evidence of the author’s direct participation in the compositional organization of the book is given, and the dynamics of structural solutions is revealed. Thanks to additional facts, we supported the conclusion about the creative nature of the changes introduced in the preparation of the “Non-War Diary. 1914–1916” taking into account the changing socio-political background and “fluctuations” in the views of Merezhkovsky. Finally, the analytical review of archival materials from the RO IRLI helped expand the historical and literary context of Merezhkovsky’s speeches and reveal the writer’s hidden polemics with his contemporaries.
Regularized Contrastive Learning of Semantic Search

Semantic search is an important task which objective is to find the relevant index from a database for query. It requires a retrieval model that can properly learn the semantics of sentences. Transformer-based models are widely used as retrieval models due to their excellent ability to learn semantic representations. in the meantime, many regularization methods suitable for them have also been proposed. In this paper, we propose a new regularization method: Regularized Contrastive Learning, which can help transformer-based models to learn a better representation of sentences. It firstly augments several different semantic representations for every sentence, then take them into the contrastive objective as regulators. These contrastive regulators can overcome overfitting issues and alleviate the anisotropic problem. We firstly evaluate our approach on 7 semantic search benchmarks with the outperforming pre-trained model SRoBERTA. The results show that our method is more effective for learning a superior sentence representation. Then we evaluate our approach on 2 challenging FAQ datasets, Cough and Faqir, which have long query and index. The results of our experiments demonstrate that our method outperforms baseline methods.
Overview of the NLPCC2022 Shared Task on Speech Entity Linking

In this paper, we present an overview of the NLPCC 2022 Shared Task on Speech Entity Linking. This task aims to study entity linking methods for spoken languages. This speech entity linking task includes two tracks: Entity Recognition and Disambiguation (track 1), Entity Disambiguation-Only (track 2). 20 teams registered in the challenging task, and the top system achieved 0.7460 F1 in track 1 and 0.8884 in track 2. In this paper, we present the task description, dataset description, team submission ranking and results and analyze the results.
Aggregation using Genetic Algorithms for Federated Learning in Industrial Cyber-Physical Systems

Industry and academics are interested in industrial cyber-physical systems (ICPS). Complexity makes it hard to grasp these systems design and functioning. By offering FedGA-ICPS, a federated learning framework based on genetic algorithms, we can solve ICPSś performance and decision support. To simulate the structure and behavior of these systems, we use ICPS. FedGA-ICPS investigates the performance of the ICPS sensors by offering locally integrated learning models. Genetic algorithm then speeds up and improves federated learning aggregation. Transfer learning is used to disseminate model parameters across restricted entities. Fashion MNIST’s initiative achieved notable outcomes.
What UAE Software Students Think About Software Testing: A Replicated Study

Software testing is vital to improve software quality. However, software tester role is stigmatized, partly due to misperception and partly due to the treatment of the testing process within the software industry. The present study analyses this situation aiming to explore what might inhibit an individual from taking up a software testing career. In order to investigate this issue, we surveyed 132 senior students pursuing degrees in information systems, information and communication technology, computer science, computer engineering, software engineering, and other closely-related disciplines at three universities in the United Arab Emirates: two publicly funded and one top-notch private university. The students were asked to describe the PROs and CONs of taking up a career in software testing and to ponder the likelihood that they would take up the career themselves. The study identified 7 main PROs and 9 main CONSs for pursuing a testing career, and indicated that the role of software tester is perceived as a social role, which may require as many soft skills as technical prowess. The results also show that UAE software-related students have a stronger negative attitude towards software testing compared to their counterparts in other countries where similar investigations have been carried out in the past three years.
Heatmap Regression for Lesion Detection Using Pointwise Annotations

In many clinical contexts, detecting all lesions is imperative for evaluating disease activity. Standard approaches pose lesion detection as a segmentation problem despite the time-consuming nature of acquiring segmentation labels. In this paper, we present a lesion detection method which relies only on point labels. Our model, which is trained via heatmap regression, can detect a variable number of lesions in a probabilistic manner. In fact, our proposed post-processing method offers a reliable way of directly estimating the lesion existence uncertainty. Experimental results on Gad lesion detection show our point-based method performs competitively compared to training on expensive segmentation labels. Finally, our detection model provides a suitable pre-training for segmentation. When fine-tuning on only 17 segmentation samples, we achieve comparable performance to training with the full dataset.
THE EXPEDITION OF GEOLOGICAL EXPLORATION BY THE GEOLKOM OF THE VSNX USSR (1925) TO ASSESS THE GOLD RESERVES OF THE ALDAN RIVER BASIN

The expedition of the Geolkom (Geological Committee) of the VSNX USSR carried out its work in Yakutia in 1925 under the guidance of the famous geologist V.N. Zverev. The main goal of the expedition was to justify the genesis and nature of gold placers in the Aldan River basin, to define the depth of occurrence of gold-bearing layers and the boundaries of their strike in plan. Such information was essential for a comprehensive and reliable assessment of the potential of a new, practically unexplored before, large alluvial gold deposit in the east of the country. The expedition to the newly discovered Aldan gold-bearing province was to play a decisive role in determining the future status and funding of the new gold mining enterprise. The importance of the goal being fulfilled became the reason for the accelerated preparation of the expedition, so its organization was unscheduled, carried out outside the programs and with special funds of the Geological Committee of the VSNX USSR. Based on the study of surviving archival sources, it was established that the expedition members carried out thorough geological, exploration and topographic work on gold deposits in the area of the middle Aldan River, including its right tributary basins. One of the main tasks of the expedition ruled by V.N. Zverev was the calculation of explored reserves and the assessment of forecast gold resources in the explored area. In general, the expedition fulfilled its tasks, managed to form a theoretically substantiated idea of the genesis of placer deposits of Aldan gold, provided materials that make it possible to bring the necessary scientific base under the general plan of exploration work for future placer gold mining along the Aldan River basin.