Reviewerly/exHarmony huggingface dataset:
Solar Potential in Saudi Arabia: Spatio-temporal and Plates-inclination Effects on the Performance of Photovoltaic Solar Panels

Inclined and flat-plate photovoltaic (PV) solar panels have been widely used in many countries and regions for generating electric power. For exploiting the available solar energy in a region, prior knowledge of this potential is necessary. This work investigates the performance of solar panels in 82 locations in Saudi Arabia by calculating the annual energy received on inclined-plate with tilt angles from 15&amp;#176; &amp;#8211; 55&amp;#176; inclined to south and flat-plate that continuously follow the daily motion of the sun. &amp;#160;Calculations are performed using a fixed surface albedo of 0.2 and with a near-real value. The analysis indicates that tilt angles of 20&amp;#176;, 25&amp;#176;, and 30&amp;#176; towards the south are the optimum ones depending on the site. These optimum tilt angles define three distinct solar energy zones in Saudi Arabia. The variation of the total energy in each energy zone on a monthly, seasonal, and annual basis is given. Regression analysis for the total energy as a function of time is derived for each zone. Moreover, the spatial distribution of the annual global inclined solar energy in Saudi Arabia is illustrated in a solar map where the total energy is found to vary from 1612 - 2977 kWhm&amp;#8722;2year&amp;#8722;1&amp;#160;for the southward-inclined plates and 2159 &amp;#8211; 4078 kWhm&amp;#8722;2year&amp;#8722;1&amp;#160;for the flat-plates across Saudi Arabia. The correction factor, introduced in a recent publication, is used; it is found that the linear relationship between the correction factor and the ground-albedo ratio is general enough to be graphically representable as a nomogram.
Artificial intelligence and volcanic data management: a dialogue between technique and administration

For years now, proliferation of technological tools has been taking hold, albeit with some resistance, in the public sector as well, since as reflected in the agendas and public policies of EU member States, digitalization is a non-deferrable need, a prerequisite for the implementation of further reforms. This report&amp;#8217;s purpose is to ascertain whether and how, data in IA can support administrations and stakeholders in coping with unforeseen events, such as earthquakes and volcanic eruptions.Among disaster protection risks, volcanic activity is often considered a foreseeable risk because it is thought that phenomena that presage rising magma to surface can be recognized and measured, which is why they are called precursors although this is, however, a simplification that does not take into account the complexity and extreme variability of volcanic phenomena and the difficulty in assessing and interpreting them.Precursor phenomena only as indicators of an ongoing process that if properly and adequately studied, analyzed and monitored, can give an idea of the state of volcano activity and its possible evolutions, allowing for the detection of possible anomalies. For this reason, data and processing by AIs can provide support and decrease errors in the calculation of phenomena even if it could only be a quantitative reduction.Data and their processing can provide a reliable index to support prevention activities. Potential issues for the jurist involve ownership of database management, interoperability, errors in the management of the same knowledge. Grading operated by AI and data has the advantage of being rapid and devoid of operator discretion. Machine learning, in fact, has a capacity of about 98 percent to hit the attribution of a rock of unknown origin.Central node within the discussion is to determine the ownership of data and the AI tools deputed to process them, whether to endow public facilities or outsource this function to the private sector.
Expansion of the NASA Astrophysics Data System to Earth and Space Sciences

The NASA Astrophysics Data System (ADS) is the primary Digital Library portal for Space Science Researchers. In addition to the scientific literature, the ADS has for a long time included in its database non-traditional scholarly resources such as research proposals, software packages, and high-level data products, making them discoverable and easily citable. Over the next three years, in response to NASA's efforts supporting interdisciplinary research and Open Science initiatives, the ADS will greatly expand its coverage of the literature, and will develop a new portal unifying access to the fields of Astrophysics, Planetary Science, Heliophysics, and Earth Science. It will also cover NASA funded research in Biological and Physical Sciences. The planned system will combine a scalable, discipline-agnostic core with a set of discipline specific knowledge centers which will curate and enrich its content using deep subject matter expertise from the NASA Science divisions. In this talk I will provide an overview of the ADS system, its distinguishing features, and then focus on our efforts to support and promote the FAIR principles as part of NASA's Year of Open Science initiatives.
Real-Time Cleaning Activity Support System using Accelerometer and Audio Feedback

Effective monitoring and support of cleaning activities is one of the significant challenges in maintaining a clean and healthy environment for infection prevention. Therefore, in this study, we focus on cleaning activities to propose a real-time activity support system. Users' activities are recognized with a machine learning technology based on accelerometer data acquired with mop-mounted sensors. We developed a real-time support system where a robot provides audio feedback for motivating the user. It was confirmed that the proposed system will contribute for maintaining clean environment by encouraging users to establish a lifestyle in the new normal era.
Defense mechanism and adversarical attacks A Review

As AI as well as DL approaches rapidly advance, it is more important than ever to guarantee the safety and reliability of the implemented methods. In recent years, it has become common knowledge that DL algorithms are susceptible to security breaches when presented with hostile data. Even while people may not notice any wrongdoing, the DL models may act in unexpected ways due to the faked samples. Adversarial assaults have been successfully implemented in real-world, physical circumstances, adding more evidence to their viability. As a result, adversarial attack and response approaches have emerged as a focus of intense study in the fields of machine learning and cybersecurity in recent years. First, we layout the theoretical foundations, algorithmic mechanisms, and practical uses for adversarial attack approaches in this study. Then, we detail a few ongoing research projects on defensive tactics that span the field's vast frontier. Subsequently, we examine a number of outstanding issues and difficulties, with the intention of stimulating more study into this vital subj ect.
Toward ensuring better learning performance in reinforcement learning

To solve a sparse reward problem, this paper proposes a design method for an efficient reward function in the training process of reinforcement learning. Reinforcement learning is a type of machine learning, and its learning process progresses through interactions between environments and agents. In this process, the reward function obtains rewards and performs actions to obtain them. A poorly designed reward function does not provide appropriate, valuable feedback and causes inefficient learning. We propose a dynamic target-oriented method in which a reward function provides larger values as the current state approaches the target state to ensure a better learning performance. We define reward functions based on the method and evaluate them using the CARLA driving simulator, comparing them with reward functions based on other methods. We show that the proposed reward functions can obtain better training results.
From Wide to Deep: Dimension Lifting Network for Parameter-efficient  Knowledge Graph Embedding

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters.
Enabling Distributed and Optimal RDMA Resource Sharing in Large-Scale Data Center Networks: Modeling, Analysis, and Implementation

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability issues in solving a large-scale RDMA resource scheduling problem. In this paper, we present how to optimally share the RDMA resources in large-scale data center networks with a distributed manner. First, we propose Distributed RDMA NUM (DRUM) to model the RDMA resource scheduling problem as a new variation of the NUM problem. Second, we present distributed algorithms to efficiently solve the large-scale, interdependent RDMA resource sharing problem for different RDMA use cases. Through theoretical analysis, the convergence and parallelism of proposed algorithms are guaranteed. Finally, we implement the algorithms as a kernel-level indirection module in the real-world RDMA environment, so as to provide end-to-end resource sharing and performance guarantee. Through extensive evaluations by large-scale simulations and testbed experiments, we show that our method significantly improves applications’ performance under resource contention, achieving <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.7-3.1\times $ </tex-math></inline-formula> higher throughput, and in a dynamic context, the largest performance improvement reaches 98.1% and 64.1% in terms of latency and throughput, respectively.
La memoria verborum en el Congestorium artificiosae memoriae de Johannes Romberch

From classical rhetoric onwards, it has been acknowledged that memory can help to remember words as well as content, that is, memoria verborum versus memoria rerum. Although the volume of the treatises on ars memorativa had been progressively increasing, there is no integral explanation for the uses and applications of memoria verborum. Likewise, in modern studies, this method has sometimes been confused with the formation of images from their signifier (imagines vocales or a verbo). In the present article a definition of this resource in the light of the encyclopaedic work of Johannes Romberch, author of the Congestorium artificiosae memoriae (Venice, 1520) is offered.
Structural relations among early childhood teachers' social support, organizational health, job crafting, and teaching flow

본 연구는 유아교사가 인식한 사회적지지, 조직건강, 교수몰입 및 잡 크래프팅의 관계를 살펴보고 사회적지지와 조직건강이 잡 크래프팅을 매개로 교수몰입에 미치는 효과를 분석하는 것을 목적으로 한다. 어린이집 또는 유치원의 담임교사로 재직 중인 유아교사 200명을 대상으로 설문조사를 통해 자료를 수집하여 본 연구분석에 사용하였다. 연구결과는 다음과 같다. 첫째, 유아교사의 사회적지지, 조직건강, 잡 크래프팅, 교수몰입 간에는 정적 상관이 있는 것으로 나타났다. 둘째, 유아교사의 사회적지지, 조직건강과 교수몰입관계에서 잡 크래프팅은 부분매개 역할을 하는 것으로 나타났다. 본 연구에서는 유아교사가 교수활동에 몰입하여 유아교육의 질적 효율성을 높이기 위해서 조직 내에서는 사회적지지를 강화하고 조직건강을 증진시키며, 유아교사의 잡 크래프팅을 활성화하는 방안을 마련할 필요가 있음을 시사한다.The purpose of this study is to examine the structural relationship among early childhood teachers' social support, organizational health, job crafting, and teaching flow, and to analyze the mediating effect of job crafting on early childhood teachers' social support and organizational health on teaching flow. Data were collected through a questionnaire survey targeting 200 early childhood teachers currently working as teachers in preschool and kindergarten. The research results are as follows. First of all, there was a positive correlation among early childhood teachers' social support, organizational health, job crafting, and teaching flow. Secondly, job crafting played partial mediating roles in the relationship between early childhood teachers' social support, organizational health, and teaching flow. The results of study implies that in order to increase the qualitative efficiency of early childhood education by immersing early childhood teachers in teaching activities, it is necessary to prepare plans to strengthen social support within the organization, improve organizational health, and activate early childhood teachers' job crafting.
A qualitative exploration of early childhood teachers' perceptions, experiences, and needs for education for sustainable development

본 연구는 유아교사의 지속가능발전교육에 대한 인식과 유아교육기관에서의 지속가능발전교육 경험 및 요구를 살펴보는 데 목적이 있다. 이를 위하여 유아교사 10명과 개별 또는 집단으로 심층 면담을 진행하였다. 면담의 내용은 녹음 및 녹화 후 전사하여 질적 연구의 분석 과정을 거쳐 분석하였다. 연구 결과는 다음과 같다. 첫째, 유아교사들은 지속가능발전교육이 다음 세대가 지구에서 아름다운 환경을 누리며 행복하게 살아갈 수 있도록 하는 교육으로 반드시 필요하나 본인들의 앎이 얕은 수준에 있다고 인식하고 있었다. 둘째, 유아교사는 유아교육기관에서 일상생활 속에서 기본생활습관을 지도하며, 자연 속에서 놀이하는 시간을 통해, 다양한 지식을 전달하고 배울 수 있도록 하는 단위 활동을 통해, 가정 및 지역사회와 연계하는 활동을 통해 지속가능발전교육을 경험하고 있다고 하였다. 셋째, 유아교사는 지속가능발전교육을 실천하며 교사의 인식과 이해 부족, 개정 누리과정과의 연결에 대한 혼란, 학부모의 부정적인 인식과 사회적인 지원 부족으로 인한 어려움을 느끼고 있었다. 이에 유아교사는 체계적인 지속가능발전교육 프로그램과 교수 자료, 실질적인 내용의 교사 교육, 사회 전반적인 인식 전환 노력과 지원을 요구하고 있었다. 이러한 결과는 지속가능발전교육의 효과적인 실행과 유아교사의 전문성 향상을 위한 토대가 될 것이라 사료된다.The purpose of this study is to examine early childhood teachers' perceptions on Education for Sustainable Development(ESD) and their experiences and needs of ESD in early childhood education institutions. For this, in-depth interviews were conducted with ten early childhood teachers individually or as a group. The contents of the interviews were recorded, transcribed and analyzed through the process of qualitative research. The finding of the study are as follows. Firstly, early childhood teachers recognized that ESD was necessary for the next generation to dwell in beautiful environment and live happily on the earth, however that their knowledge was at a shallow level. Secondly, early childhood teachers were practicing ESD at early childhood education institutions by teaching basic habits in daily life, through play time in the nature, unit activities conveying and learning various knowledge, and activities connected with home and local communities. Thirdly, while practicing ESD, early childhood teachers felt difficulties from teachers’ lack of awareness and understanding, confusion in affiliating with the revised Nuri Curriculum, parents' negative perception and lack of social support. Accordingly, early childhood teachers were demanding systematic education programs for ESD, teaching materials, teacher education with practical contents, and efforts and support to change the general perception of the society. These results are considered to be the foundation for the effective implementation of ESD and the improvement of the expertise of early childhood teachers.
Cycle-enhance: low-light image enhancement based on CycleGan

Distortion-free enhancement on images captured under low-light conditions has always been a challenging problem in computer vision. To address these problems, this paper proposes an end-to-end network, which can learn the map way of low-light images to normal-light images from unpaired low-light and normal-light datasets. The network is consisted of dual branches, the upper branch is a refinement branch focusing on noise suppression, and the lower branch is a global reconstruction branch based on light-weight Transformer. The discrimination network adopts the multi-scale discrimination structure of feature pyramid to enhance the global consistency and avoid local overexposure. Qualitative and quantitative experimental results show that the proposed method can effectively suppress the generation of artifacts and noise amplification of enhanced images.
A New Similarity Measure for Multi Criteria Recommender System

In the information age, the biggest problem for a person who desires to buy something online is how to get enough information to make a decision and how to make the right decision with that vast information. Recommender Systems give a piece of advice about the products, information, or services that the user might be interested. A Recommender System works much better for users when it has more information. In Collaborative Filtering, where user’s preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. The vast majority of recommender systems, independently of their type, use a single criterion to evaluate each item and derive their recommendations. Ratings on multiple criteria, on the other hand, convey richer information about user preferences, notably in systems where recommendations are based on the opinion of others. In order to take full advantage of multi-criteria ratings in various applications, new recommendation techniques are required. The current work does not explain what will be a better similarity measure for cold start users, which rated very few movies. Which similarity measure performs well at high-ranked users with having a fair number of overall ratings. So simply, we can say that most of the approaches are applied only to highly ranked users. We divided the users into three groups using the normal distribution probability. We calculated the similarities among users based on multi-criteria ratings. We also considered when the new user came into the system and how new recommendations would be made to him. It is easy to find similarities among users when they have enough ratings and on the basis of those similarities but challenging to find similar users for new users or cold start users. We divided the users into three groups: the first group rated less than 5 items, the second group more than 5 and less than 15, and the third group more than 15 items. We used distance metrics such as Manhattan distance, Euclidean distance and Chebyshev distance to compute the distance among users based on similarities in the number of ratings to check which distance metrics work well on which group of users. The other measure we used is Pearson correlation.
Mobile Application Development for Human Veterinary Resources Management in a Low Density Population Context: Promoting Students Engagement by Working with the Community

This paper describes a mobile application, developed in an educational context, by the students of the Degree in Computer Engineering of the Instituto Politécnico de Bragança, allowing them to develop skills, based on real-world community problems solving, promoting by this way its engagement, and, at the same time, provide a solution to an effective need of the local community. The developed application has as goal to support the Human Veterinary Resources Management in a Low Density Population Context.
A Comprehensive Review on Deep Learning Approaches for Question Answering and Machine Reading Comprehension in NLP

Natural Language Processing (NLP) deals with the development of methodologies capable of interacting with computer through human language. NLP improves machine’s comprehension of human language, allowing for human-computer communication based on linguistics. Recent years have seen phenomenal success of NLP models in language and grammatical tasks such as information extraction, translation, classification and reasoning. This accomplishment is mainly due to the influence of transformers, which inspired design ideas such as BERT, SQuAD 2.0 and others. These large-scale models produced unique results, despite the higher computational cost. As a result, current NLP systems use transfer learning, pruning, knowledge filtration and quantization to accomplish reasonable performance. Furthermore, Information Retrievers (IR) are created to extract precise data files from large datasets, addressing the large data assertion made by language models. Major contribution of this study is to understand the application of deep learning methods in NLP for automated question answering and obtaining a comprehension of essay text. Context-based NLP issues that are presented along with existing solutions. The challenges of using NLP in comprehension are examined, as well as research community methods for extracting answers from paragraphs. Further direction of this research is to develop novel deep learning models for QA and text comprehension that can overcome the demerits of existing approaches.
1st workshop on exploring disruptive technologies from the local community perspective

The use of disruptive technologies by the private and public sectors is increasing. With the release of new systems, many have wondered how artificial intelligence may change the world for better or worse. For this, European Commission has created instructions on how artificial intelligence and blockchain should and should not be used in the public sector. On the other hand, private companies are free to utilise these technologies. Many challenges and barriers must be overcome to have the public adopt these technologies. This one-day workshop will explore disruptive technologies from a local community perspective. We will use a case of artificial intelligence and blockchain-enabled electronic ID (eID) for the initial framing and exploration of this problem and compare the differences when the software solution is deployed locally, nationally or internationally. These differences and issues can be looked at, for example, through the lens of trust, usability, security, and privacy.