Reviewerly/exHarmony huggingface dataset:
Soiling Mapping Through Optical Losses for Nigeria

Soiling losses and rates are significantly under-reported or underestimated since regional differences, and seasonal variations are overlooked. Accurate prediction of PV soiling losses for a particular location can save revenue losses associated with a solar PV system. This research investigated the effect of soiling on PV performance through optical losses by employing a low-cost soiling station. Low iron glass coupons (5 mm x 5 mm) were exposed on three angles (vertical, tilt-45°, and horizontal) in seven sites across Nigeria to collect annual, seasonal and monthly soiling data. Each coupon was then subjected to optical characterization using a spectrometer and imaging analysis using the SEM/EDX. The finding shows significant optical losses across the country, with all the highest rates recorded on coupons exposed on the horizontal plane, where the maximum loss of 88% was recorded on the Abuja, North Central (ABV) coupon. SEM/EDX finding illustrated minerals with the potential to affect light transmittance, and the pollutant data confirmed the particles. The optical results were further employed to map the soiling distribution across the country. A wide deviation was observed from the data on the Global Solar Atlas.
Research on Tibetan-Chinese Neural Machine Translation Integrating Syntactic Information

In recent years, Neural Networks have gradually replaced other methods in the field of Machine Translation and become the mainstream which have excellent performance in many languages. However, the performance of Neural Machine Translation mainly relies on large-scale parallel corpora, which is not ideal for low-resource languages, especially Tibetan-Chinese Machine Translation. In order to obtain the best translation performance with more external information on the basis of limited corpus, this paper introduces syntactic information, that is, adding part-of-speech(POS) tags as input features in the training process. Experiments verify the effectiveness of this method, which can improve the translation performance to a certain extent.
Towards Transferable 3D Adversarial Attack

Currently, most of the adversarial attacks focused on perturbation adding on 2D images. In this way, however, the adversarial attacks cannot easily be involved in a real-world AI system, since it is impossible for the AI system to open an interface to attackers. Therefore, it is more practical to add perturbation on real-world 3D objects’ surface, i.e., 3D adversarial attacks. The key challenges for 3D adversarial attacks are how to effectively deal with viewpoint changing and keep strong transferability across different state-of-the-art networks. In this paper, we mainly focus on improving the robustness and transferability of 3D adversarial examples generated by perturbing the surface textures of 3D objects. Towards this end, we propose an effective method, named Momentum Gradient-Filter Sign Method (M-GFSM), to generate 3D adversarial examples. Specially, the momentum is introduced into the procedure of 3D adversarial examples generation, which results in multiview robustness of 3D adversarial examples and high efficiency of attacking by updating the perturbation and stabilizing the update directions. In addition, filter operation is involved to improve the transferability of 3D adversarial examples by filtering gradient images selectively and completing the gradients of neglected pixels caused by downsampling in the rendering stage. Experimental results show the effectiveness and good transferability of the proposed method. Besides, we show that the 3D adversarial examples generated by our method still be robust under different illuminations.
Mathematical Model of Information Exchange through the Educational Platforms

Today in developing countries the education system is facing many problems which indicate that it needs serious reforms. A modern education system with a special focus on the development of individuality can bring together its diverse actors and improve its performance. For instance, the rapid advancement of technologies around us allows students to participate in the content generation process. Similarly, teachers and instructors can update their teaching materials with the help of new findings out of research and development activities. Current literature lacks well-designed studies to address the challenges that may arise in considering and delivering such an information exchange model. In this paper, we aim to deliver a mathematical model of information exchange through educational platforms.
Input Feature Engineering for Lstm-Based Vnf Resource Usage Prediction

Virtual resource load prediction in network function virtualization (NFV) is the subject of intense research activity due to its key role in enabling proactive resource adaptation in dynamic NFV environments whose resource demand constantly change. Several Machine Learning (ML)-based approaches have been proposed to forecast the resource load of multiple resource attributes of a virtual network function (VNF) in a service function chain (SFC). One such approach is NFVLearn, a flexible multivariate, many-to-many long short-term memory (LSTM)based model which uses different types of resource load history (CPU, memory, I/O bandwidth) from various VNFs of a SFC to predict future load of multiple resources of a VNF. In this paper, we compare four novel automated input selection frameworks for NFVLearn. Simulations on those frameworks based on graph neural networks (GNNs), Pearson correlation coefficients, Spearman rank correlation coefficients and Kendall rank correlation coefficients demonstrate that models using lesser, highly correlated input features retain high prediction root mean squared error (RMSE) accuracy and coefficients of determination scores by leveraging resource attribute interdependencies from the SFC. Those results show that resource attribute interdependency-based input feature selection frameworks can reduce overhead in the control plane while keeping high accuracy and high fidelity resource load prediction of multiple resource attributes.
Instructional Methods of Paper Crane Contests with Learning Together in Group for Introductory Education to Mechanical Engineering at the National Institute of Technology

Contest style practices in paper crane making have been conducted for an introductory education to mechanical engineering at a national institute of technology. The specific instructions were based on a hypothesis that the successful experience with personal record was effective. The previous paper on the instructions during the first five years indicated that it was desirable to revise the hypothesis with learning together in group. New instructions following the revised hypothesis have been carried out during the second five years. They brought some responses, the recorded progress of the crane works, the point rise in questionnaire, and the increase in the number of descriptions on students′ reports. These numerical results show that the learning quality can be raised higher than before.
Building the Psychological Factors of Personality Towards Social Work

The paper deals with the psychological factors having the essential role in the personality formation. It is noted that the formation of personality is a complex process of cognition, fixation, creative mastering world outlook, ideological, moral attitudes of society, the process of assimilation of social characteristics, knowledge and skills created by society, on the basis of which own vision and assessment of things is developed.
Pengembangan Perangkat Pembelajaran dengan Model Think Pair Share (TPS) Pada Mata Pelajaran IPA

Learning is still dominated by teachers using lectures and teacher learning tools are still not good. This study aims to produce learning tools in the form of lesson plans with the think pair share (TPS) model in the science subject with the theme Kayanya Negeriku for fourth-grade elementary school. This research is development research based on the ADDIE development model which consists of five stages, namely analysis, design, development, implementation, and evaluation, due to time constraints. and conditions that do not support this research only to the development stage. The results of the analysis of the development of learning devices with the think pair share (TPS) model obtained the following results: the content validity of the instrument was 1, 00 with the interpretation of "very high", the reliability of the instrument obtained a practicality coefficient of 100%, and the validation of learning devices with an average of 4,67 with "very good" interpretation. Based on the results of the study, it can be concluded that the validity of the Development of Learning Devices with the Think Pair Share (TPS) Model in the Science Subjects of Kayanya Negeriku Theme Class IV Elementary School is declared valid and suitable for use in the learning process.
HoogBERTa: Multi-task Sequence Labeling using Thai Pretrained Language Representation

Recently, pretrained language representations like BERT and RoBERTa have drawn more and more attention in NLP. In this work we propose a pretrained language representation for Thai language, which based on RoBERTa architecture. Our monolingual data used in the training are collected from publicly available resources including Wikipedia, OpenSubtitles, news and articles. Although the pretrained model can be fine-tuned for wide area of individual tasks, fine-tuning the model with multiple objectives also yields a surprisingly effective model. We evaluated the performance of our multi-task model on part-of-speech tagging, named entity recognition and clause boundary prediction. Our model achieves the comparable performance to strong single-task baselines. Our code and models are available at https://github.com/lstnlp/hoogberta.
A Heterogeneous Collaborative Filtering Model for Social Recommendation

With the rapid development of social networks, many social recommender algorithms based on graph neural networks have been proposed to improve the performance of recommender systems. However, there are two problems with these models. First, many methods fail to consider the heterogeneity between social interaction and user-item interaction. Second, when modeling item representation, they fail to consider high-order neighbor information in the social graph. In order to address the above problems, in this paper, we propose a Heterogeneous Social Collaborative Filtering model based on graph convolutional network (HSCF), which extracts the high-order collaborative signals of nodes in the social space and interaction space, respectively, so that the heterogeneity of node representation can be fully considered. Moreover, when modeling item embedding, we consider the high-order social neighbors’ information to enhancing item representation. Comprehensive experiments on two real-world datasets show that our proposed model outperforms the state-of-the-art baselines.
AUPro: Multi-label Facial Action Unit Proposal Generation for Sequence-Level Analysis

Facial action unit (AU) plays an essential role in human facial behavior analysis. Despite the progress made in frame-level AU analysis, the discrete classification results provided by previous work are not explicit enough for the analysis required by many real-world applications, and as AU is a dynamic process, sequence-level analysis maintaining a global view has yet been gravely ignored in the literature. To fill in the blank, we propose a multi-label AU proposal generation task for sequence-level facial action analysis. To tackle the task, we design AUPro, which takes a video clip as input and directly generates proposals for each AU category. Extensive experiments conducted on two commonly used AU benchmark datasets, BP4D and DISFA, show the superiority of our proposed method.
Cheap Speech Creation

As we look back on Professor Eugene Volokh's predictive article about cheap speech, it is worth examining what other elements of the speech and media landscape, as well as the supporting legal infrastructure, have changed over that same period. This Essay focuses on the substantial reduction in the cost of speech creation, as opposed to distribution. After briefly discussing the accuracy of many of Volokh's most important predictions, it examines innovative technological changes that have enabled a larger number and more diverse range of people to engage in speech creation because of the rapidly shrinking costs of doing so by employing user-friendly interfaces. It provides examples of speech of profound public concern that has resulted from such changes. The Essay then traces the corresponding evolution in First Amendment doctrine and legal scholarship that has expanded the concept of what types of speech creation are covered by the free speech clause. Finally, it addresses some negative externalities of cheap speech creation, and how the law might confront the challenges presented by such costs without sacrificing speech creation's critical expressive value.
Improvement over Montgomery Modular Multiplication

This paper describes an improvement over Montgomery Modular Multiplication proposed by Peter L. Montgomery in []. Montgomery in [] has proposed a method of computing \(z = x.y \, mod \, N\) (\(N > 1\)) which is much faster and does not require any division by N. Central to his method is one algorithm called Reduction algorithm which is required to be employed 4 times in order to compute z. The improvement that we propose still uses the same Reduction algorithm of [], but in the improved version we are only required to employ the Reduction algortihm twice in order to compute z.
Professional Judgments of Principled Network Expansions

As data increase in both size and complexity, it is essential to determine optimal solutions for processing and interacting with network representations by human analysts. In this paper, we explore different ways of growing networks from a small set of “seed” nodes to create tractable sub-networks for human analysis. In particular, we define a method for generating different network expansions for preference testing and statistical analysis in a paired-comparison task. To illustrate this method, we generate a range of expansions from a database of politically exposed persons and conduct the preference study with professional criminal network analysts. The resulting statistical analysis demonstrates an avoidance of purely random generated networks and preference for naive network expansion methods (depth-limited traversals based on one or two “hops” from the seed nodes) over more statistically rigorous methods (e.g., edge- and node-based sampling). From these results and inferential analysis, we propose practice considerations and define future research.
Policies for Enhancing Public Trust and Avoiding Distrust in Digital Government During Pandemics: Insights from a Systematic Literature Review

The coronavirus outbreak (COVID-19) has demonstrated the importance of the state capacity and public policy-making process in managing both the pandemic and the resulting crisis. Trust and/or distrust in the relationship between citizens and authorities can determine the success or failure of states in combating pandemics. The goal of this study is to provide insight into trust and distrust in digital government during pandemics by creating an overview of the scattered knowledge. Accordingly, the chapter creates an overview of the factors influencing trust and distrust in digital government in pandemics. The results showed that factors affecting distrust are mostly associated with problems in the interactions between citizens and public authorities, whereas factors affecting trust address governments’ policy responses and public compliance. The level of trust is a dynamic condition that can either be strengthened or broken. A single factor can result in trust for one person and distrust for another person. Surprisingly, trust and distrust can coexist at the same time. Governments must pursue a balance between trust- and distrust-related factors in times of pandemics to derive the dual benefits of trust and distrust.
OmniDRL: An Energy-Efficient Deep Reinforcement Learning Processor With Dual-Mode Weight Compression and Sparse Weight Transposer

In this article, we present an energy-efficient deep reinforcement learning (DRL) processor, OmniDRL, for DRL training on edge devices. Recently, the need for DRL training is growing due to the DRL’s distinct characteristics that can be adapted to each user. However, a massive amount of external and internal memory access limits the implementation of DRL training on resource-constrained platforms. OmniDRL proposes four key features that can reduce external memory access by compressing as much data as possible and can reduce internal memory access by directly processing compressed data. A group-sparse training (GST) enables a high weight compression ratio (CR) for every DRL iteration by selective utilization of weight grouping and weight pruning. A group-sparse training core is proposed to fully take advantage of compressed weight from GST by skipping redundant operations and reusing duplicated data. An exponent-mean-delta encoding additionally compresses the exponent of both weight and feature map for higher CR and low memory power consumption. A world-first on-chip sparse weight transposer enables the DRL training process of compressed weight without off-chip transposer. As a result, OmniDRL is fabricated in a 28-nm CMOS technology and occupies a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.6\times 3.6$ </tex-math></inline-formula> mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> die area. It shows a state-of-the-art peak performance of 4.18 TFLOPS and a peak energy efficiency of 29.3 TFLOPS/W. It achieved 7.42-TFLOPS/W energy efficiency for training robot agent (Mujoco Halfcheetah, TD3), which is 2.4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> higher than the previous state of the art.
Personalized Neural Architecture Search

Existing approaches for Neural Architecture Search (NAS) aim at efficiently maximizing individual or sets of objectives (e.g. high accuracy or a low number of parameters) by exploiting Reinforcement Learning (RL), evolutionary algorithms, or Bayesian optimization. Most multi-objective NAS algorithms assume that all objectives are fully known and require them to be broadly explored to successfully approximate the Pareto front, which results in computational expensive search algorithms. To address this problem, we propose an interactive machine learning approach based on preference elicitation which enables end-users to explore and find a custom loss function and can be directly used for State-of-the-Art single-objective black-box optimization. We integrate our approach into State-of-the-Art single objective NAS algorithms and evaluate it against multi-objective approaches on the NATS-Bench benchmark dataset. Furthermore, we show that diverse end-user preferences can be successfully approximated in terms of loss functions, leading to suitable neural architectures.
An Interactive Computer Assisted Design System: Architecture and Design

With the development of information technology, more and more design work relies on computer assisted design systems. Since, computer assisted design systems are mostly pure tool systems that passively accept user design input and lacking intelligent assistance capabilities, this paper proposes an interactive computer assisted design system architecture. It decomposes the logic of assisted design system into goal-driven approach and operation-driven approach. Through the monitoring, collection and analysis of user operations and system status, different auxiliary approach are used in different states in the process.
Information seeking behavior of Members of the Omani Parliament

This study aims to investigate the information-seeking behavior of Members of the Omani Parliament in terms of their thoughts, perceptions, attitudes, motivations, techniques, preferences, ways, tools, and problems encountered by them towards accessing information. A qualitative approach was adopted for data collection by an open interview with MPs of Shura conical. This study has revealed that MPs of the council were looking for information for parliamentary work and they used internet searches, ask the staff of the information service department and search on government publications to obtain information. The result also showed The MPs prefer government sources to do their job, and they did not rely on the parliamentary library as a source of information. Besides, MPs facing some administrative challenges from the Government to obtain information. Finally, the study presented a set of recommendations for improving the information work in the Shura council.
Topic Models for Re-Id from Curbed Video Traces

The problem of pedestrian re-identification is important. It is an essential pre-requisite for most of the video based surveillance studies. The re-identification will be the concrete platform for the several post-high level activities of surveillance (like recognition, analysis, tracking, tracing, monitoring, etc.). The re-identification includes, recognizing the pedestrian footprints over the frames of occluded video traces. The video traces, maybe captured from a single or network of surveillance cameras, housed at different places in non-overlapping fashion. Driven by the recent developments in topic models and its application in video/image domain, the proposed problem is tackled by finding the basic building blocks for topic model, called as words. In experimental analyses the newly introduced person re-identification method is compared with several popular re-identification methods based on re-identification perspective. Several experiments were carried-out on well-known public re-identification datasets, and the superiority of the proposed novel re-identification technique is proved with the rigorous analysis on these datasets.
A Bottom-up Saliency Detection Method

A bottom-up saliency detection method is presented to handle image in this paper. An image is first filtered by a bandpass filter to decide the seeding region, and k-means with initial seeds selected from the seeding region is applied to extract several low-level image features. Next seven image statistics are computed using pixels of each cluster, and entropy is calculated for each feature in each cluster. Then a unique feature (so-called entropy order sum) is defined by adding the contribution of each individual feature, and the relation between entropy order sum and saliency is investigated. Finally, salient clusters are detected by defining a hit rate of saliency between the entropy order sums of the various clusters.
Evolution of Models and Methods in the Field of Resilient Computing

This paper examines the development of computer models and methods based on a comparative analysis conducted by S. Lem in relation to two evolutions: biological and technological in his scientific work “Summa Technologiae”. The conclusions made by the author were decades ahead of time and received many confirmations in the development of the computer world. The analysis of the integration process of the computer world into the natural one continues the comparison of evolutions and shows examples of resilient, i.e., elastic development of computer models and methods in modern conditions. This development is gaining the greatest importance in containing the risks of accidents in critical infrastructures that solve the problems of energy and food security of mankind. An example of such a development is considered to improve checkability, which is important for the safety of critical systems built on FPGAs.