Reviewerly/exHarmony huggingface dataset:
Rumah Cerdas Mandiri (RCM): Program Edukasi Berkelanjutan Wilayah Nginden Jangkungan Surabaya

Nginden Jangungan Village, which is located in Sukolilo sub-district, Surabaya, has an area with low socioeconomic conditions whis is contrast to its surroundings of good private universities, expensive housing area, shopping block2, hotels, luxurious places of worship, and an international hospital. Thus, this area with poor conditions is like a poverty within the metropolitan of Surabaya. For people living in this poor area contrasting condition with its modern surroundings has made them think that they are inferior compared to their superior surroundings. Therefore, as early as possible, the younger generation of this group, must be equiped with proper education making them able and confident that they can be successful in the future. Therefore, it is deemed necessary to provide extra learning activities for children, namely learning assistance for the National Examination subjects, as well as efforts to increase children's self-confidence which is monitored using Behavior Check List (BCL). All of these activities are carried out in a program called Rumah Cerdas Mandiri (RCM) which is located in one of the RW halls in the Nginden Jangkungan Village, Surabaya. The result of this RCM program is that the academic ability of children is improving well although there is some of them that need to be improved. In addition, based on the results of the BCL analysis, it shows that children have self-confidence, courage to express themselves and increased curiosity. These two direct outputs are very important as a trigger for their future success
Solving Chemistry Problems Involving Some Isomers of Benzene Ring

This paper presents an algorithm for understanding and solving the chemistry problems involving some isomers. The research into this problem involves three aspects. The first part is to design the method of understanding the problem text. This method adopts the syntactic-semantic model and it first constructs a pool of syntactic-semantic models. The second part is to design the method of extracting knowledge items from the problem figure and the method of combining the knowledge items from text and figure. The third part is to develop a solving procedure with the aid of a knowledge base. Based on the solution provided by the proposed algorithm, a diagnostic procedure is to judge whether the handwritten answer is correct. The experimental results show that the proposed algorithm has a good performance.
Capable of Classifying the Tuples with Wireless Attacks Detection Using Machine Learning

A wireless attack is a malicious action against wireless systems and wireless networks. In the last decade of years, wireless attacks are increasing day by day and it is now a very big problem for modern wireless communication systems. In this paper, the author’s used the Aegean Wi-Fi Intrusion Dataset (AWID3). There are two versions of this dataset, one is over 200 million tuples of full data and one is 1.8 million tuples of reduced data. As our dataset has millions of tuples and over 100 columns, it is easy to become overwhelmed because of its size. The authors used the reduced version and predicted if an attack was one of four types using the k-nearest-neighbors classifier. All of the attack types we used had a distribution that highly favored the non-attack class. Our best results were for the attack “arp” type where we attained the best accuracy with recall. The author’s primary goal of the paper was to attain the highest accuracy possible when creating a model that is capable of classifying the 4 attack types and detecting and classifying wireless attacks using Machine Learning models on the AWID3 dataset. One of the goals that supported this main objective was determining a way to avoid the curse of dimensionality.
Design and Implementation of the Adaptive Archive Allocation System

In view of the language barriers existing in proper names, professional terms, measurement and tradeoff in China’s Imperial Maritime Customs archives, this paper lists the Chinese-English comparison table of proper names, professional terms in China’s Imperial Maritime Customs archives. Based on the list of this Chinese-English comparison table, this paper tries to realize the optimal allocation between the adaptive archive allocation system and the system users from the aspects of the design and implementation of the adaptive allocation system, with the purpose of efficient exploration and exploitation research of these archives with high quality from multiple perspectives. The adaptive system in this paper is a simulation system built with Python, but with actual data, invoking Python correlation package and the POMDP model.
The Human Factor Capabilities in Security Operation Center (SOC)

The need for Cybersecurity Operation Center to protect the information system against cyberattacks has become increasingly common and a must to face the new sophisticated and advanced threat. The socio-technical system of the SOC contains three building blocks Human resources, technologies, processes. The literature review, announcements, and publications of the cybersecurity industry reveal that the human factor is considered the weakest link in cybersecurity and inside the Security Operation Centers (SOC). It represents the most important factor to guarantee the best performance. Many researchers shed light on the human factor challenges inside the SOC environment and how these challenges could be addressed, reduced or mitigated. However, these research papers do not consider the complexity, unpredictability and interdependent and evolving nature of the SOC systems. This study aims to explore the human challenges inside the complex and interdependent socio-technical SOC system. To this end, we employed survey-based questionaries with 40 SOC analysts and 5 experts alongside the daily observation of SOC analysts and interviews with SOC experts. The finding of this study will help SOC managers and SOC designers better understand the challenges faced by the SOC analysts and take into account the interdependent and evolving nature of the Security Operation Centers.
Optimal Multi-robot Placement Based on Capability Map for Medical Applications

Multi-robot systems have become important in various applications. These robotic platforms consisting of several robotic arms collaborating in a common workspace are a suitable tool to perform complex tasks, such as surgical applications. However, the placement of each robotic arm has an impact on the whole system kinematics and dynamic performance. In this paper, we present a novel technique to optimize the base location of the robot manipulators based on the capability map to develop a bi-cobot system dedicated to medical applications. This technique is implemented to simultaneously identify the base placement of the two collaborative robots while optimizing their kinematic criteria and the system compactness. The obtained results are evaluated using a cobot simulation platform for the execution of minimally invasive surgical tasks.
Analytic Tools in Distribution Theory

This chapter is the true introduction, at a level as elementary as possible, to the core of the book. In the first section the reader will find the needed definitions and basic results that can be translated to the $$ \mathcal{C}^{\omega} $$ category from the last section of the preceding chapter.
Strategies for Enhancing Generalization Ability of Communication Event Co-reference Resolution

There are many different expressions for the same event. The goal of event co-reference resolution is to build a calculation model for the similarity of event expressions to achieve the unity of data. Based on the Roberta pre-trained model, aiming at the problem of unbalanced distribution of difficult and easy cases in data, the effectiveness of various methods to enhance the generalization ability of the model is explored, including different data input methods, data enhancement, different loss functions, adversarial learning, contrastive learning. The best data input and model training methods are finally selected. On the CCKS2021 event co-reference resolution task for communication field, the f1 value of single model reaches 0.80 in test dataset 1 and 0.89 in test dataset 2.
Secure Distributed Learning

Like the common setting of cooperative learning, each participant securely maintains a private dataset on its local device. All participants want to jointly train a global machine learning model on the union of all their dataset, such that they can make full use of the advantage of each dataset. Maybe, they need a central server to coordinate the learning task in a high-efficient way.
UHD (2160/HDR) and HD (1080/BT.709) Simultaneous Workflows at the CBC

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As video content evolves from traditional television into more complex and varied forms of media, more and more of our content creators capture in higher resolution, frame rate, and dynamic range instead of traditional high-definition (HD) to give themselves creative latitude in post-production. Generating ultrahigh-definition (UHD) content comes with a lot of challenges for a traditional HD infrastructure. For example, which version(s) should be archived: The source, ungraded asset, the HD version, the social media version(s) like square and vertical, or a combination of these? If content is shot in UHD, for example, and then cropped in post, should the final, cropped HD product be the one we archive, or the UHD original? If we archive the original, how do we store information about the cropping parameters? If color and dynamic range are wider in the source than the final render (e.g., SLog3/SGamut3), which 3D look-up table is the right one to use? Should 3D LUT also be archived with the source asset? How do we link a specific asset to a specific 3D LUT for posterity? Also, public broadcasters have legal obligations with regard to content preservation, but these do not necessarily specify the format(s) for archival purposes, so we need to balance obligations, cost, and future value of assets in the process of designing such workflows</i> .
Exploring Dual-Element Sustainable Tourism on Hainan Island Based on Deep Learning: Early Warning and Forecasting

As an international tourism island, tourism sustainability of Hainan Island is an important parameter for achieving strategic development. In this study, the sustainable tourism destination environment and sustainable tourism experience have been carefully considered as drivers and pull factors, respectively. A state-space model is used to map ecological, social and natural carrying capacity into the same space to assess the changes in environmental sustainability on Hainan Island from 2009 to 2019. Through Natural Language Processing (NLP), the sentiment index of the tourism comments, which was posted on the Ctrip website, is analyzed by the sentiment classification. And combined with the the Cifuentes formula, the changes of tourist experience carrying capacity is perceived. Finally, the multisource Long and Short-term Memory Neural Network(MLSTM) model is used to predict the future tourism sustainability of Hainan Island. Hainan Island as a marine tourism destination, this article can provide a multifaceted approach to the study of sustainable tourism development on the island.
Load and Frequency Regulation Power Market Trading Model based on Block Chain

Load and frequency regulation is important for the power system stability. With the expansion of renewable energy capacity in the power grid and the development of the power market, it is necessary to encourage participation of both power source and load in the load and frequency regulation through spot trading in power market. In this paper a load and frequency regulation power market trading model based on block chain is proposed. In this trading model, the power source and load propose their regulation will and ability. If the power grid has the requirement for load and frequency regulation, the regulation proposed by power source and load can be purchased. The trading process based on block chain is also illustrated. The trading is making deals through smart contract. The consensus is completed depending on validation nodes. And an application is used to illustrated and verify the effectiveness of the proposed trading model.
A Hierarchical Incentive Mechanism for Coded Federated Learning

Federated Learning (FL) is a privacy-preserving collaborative learning approach that trains artificial intelligence (AI) models without revealing local datasets of the FL workers. One of the main challenges is the straggler effects where the significant computation delays are caused by the slow FL workers. As such, Coded Federated Learning (CFL), which leverages coding techniques to introduce redundant computations to the FL server, has been proposed to reduce the computation latency. In order to implement the coding schemes over the FL network, incentive mechanisms are important to allocate the resources of the FL workers and data owners efficiently in order to complete the CFL training tasks. In this paper, we consider a two-level incentive mechanism design problem. In the lower level, the data owners are allowed to support the FL training tasks of the FL workers by contributing their data. To model the dynamics of the selection of FL workers by the data owners, an evolutionary game is adopted to achieve an equilibrium solution. In the upper level, a deep learning based auction is proposed to model the competition among the model owners.
SS-PT: A Stance and Sentiment Data Set from Portuguese Quoted Tweets

Our contribution presents the first ever stance and sentiment annotated corpus in Portuguese. Using data from Twitter, we annotated a data set with over 15,000 tweets. In building the corpus, we made both random and supervised searches to maximize balance. Using four annotators, we have classified each tweet on four categories: support, against, neutral or inconclusive. Furthermore, we have annotated each tweet for sentiment: positive, negative, neutral, sarcasm or inconclusive. Our annotators yield strong inter-annotator agreement. In addition, we test a baseline model using a pre-trained BERT model for Portuguese. Results suggest that our data set is in line with its counterparts in other languages.
PERAN PRODUSER DALAM PROSES PRODUKSI WEB SERIES PERDANA WAHANA HONDA

Wahana Honda is one of the businesses engaged in the distribution of Honda motorcycles and the service of Honda motorcycles is one of the services of Wahana Honda. This research uses descriptive research method with a qualitative approach. The method used is a case study by conducting in-depth interviews with informants. The results of this study indicate that the role of producers in the Wahana Honda Prime web series process consists of 3 stages, in pre-production which of course it is related in making themes, story ideas, cinematographic processes in the production and editing of images. In the production process the producer is fully responsible for controlling, supervising, and coordinating to all the crew involved for the smooth running of their production, carrying out production in accordance with the script and shooting script made by the producer. In post production there are 2 editing processes, namely offline and online. The producer ensures and supervises the production work in accordance with the client's direction and deliver the results in accordance with the agreed time.
Jointly Super Resolution and Degradation Learning on Unpaired Real-World Images

Recently super-resolution methods based on CNN have achieved amazing success. However, the effects of these methods on real-world images are not available. The main reason is that most of them use bicubic downsampling by default to obtain degraded low-resolution images, while the degradation process of real-world images is unknown. In our work, we argue that image degradation and super-resolution are tightly coupled. In order to complete this cycle, we propose a framework to jointly learn the degradation and super-resolution of real-world images. At the same time, in order to stabilize learning and optimize performance, we have combined a variety of image content losses. Our framework can not only achieve real-world super-resolution, but also generate paired unknown degraded datasets for other super-resolution methods. The experiments on the NTIRE2020 real-world SR dataset show the effectiveness of our model.
A Hybrid Approach to Analyze Cybersecurity News Articles by Utilizing Information Extraction &amp; Sentiment Analysis Methods

Cybersecurity is becoming indispensable for everyone and everything in the times of the Internet of Things (IoT) revolution. Every aspect of human society — be it political, financial, technological, or cultural — is affected by cyber-attacks or incidents in one way or another. Newspapers are an excellent source that perfectly captures this web of cybersecurity. By implementing various NLP techniques such as tf-idf, word embedding and sentiment analysis (SA) (machine learning method), this research will examine the cybersecurity-related articles from 18 major newspapers (English language online version) from six countries (three newspapers from each country) collected within one year from April 2018 till March 2019. The first objective is to extract the crucial events from each country, which we will achieve by our first step — ‘information extraction.’ The next objective is to find out what kind of sentiments those crucial issues garnered, which we will accomplish from our second step — ‘SA.’ SA of news articles would also help in understanding each ‘nation’s mood’ on critical cybersecurity issues, which can aid decision-makers in charting new policies.
Computation of sandwiched relative α-entropy of two n-mode Gaussian states

A formula for the sandwiched relative [Formula: see text]-entropy [Formula: see text] for [Formula: see text], of two [Formula: see text]-mode Gaussian states [Formula: see text], [Formula: see text] in the boson Fock space [Formula: see text] is presented. This computation extensively employs the [Formula: see text]-parametrization of Gaussian states in [Formula: see text] introduced in Ref. 10.
Artificial Intelligence and the Blockchain in Smart Agriculture: Emergence, Opportunities, and Challenges

Agriculture is one of the most important sectors and has been given the least priority in many countries for a long time, even though everyone needs food to live. This has been due to several reasons, such as undervaluation and unavailability of workforce in the sector, lack of support from governments, and unavailability of modern tools and techniques. In recent years, we have witnessed a steady growth in agricultural production across the globe. Careful examination of this trend reveals that there is a complete paradigm shift in agriculture that has been enabled and facilitated by advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and blockchains. This chapter discusses the emergence, opportunities, and challenges of AI and blockchain technology in the agricultural sector and examines how these technological interventions can help solve many of the prevailing issues in the sector. It also throws light on some of the future research dimensions and trends using AI and the blockchain in smart farming, which will act as research threads for academicians and other researchers in the agricultural sector.
Belief selection through argumentation

Dung's argumentation framework proved to be a powerful framework for reasoning about inconsistent beliefs. We revisit preference-based argumentation frameworks (PAF) for handling inconsistent beliefs encoded in classical propositional logic. We first define rational properties that should be fulfilled by the defeat relation. They are based on inconsistency and entailment. Then, using these two notions as a common thread we define three families of defeats between arguments, and two families of preference relations. We show that PAF with entailment-based preference relations satisfy interesting properties that are not satisfied by PAF with a general preference relation. Lastly, we apply our framework in datamining pattern selection.
Leader’s Intellectual Stimulation Training and Church Leadership Performance

This study sought to investigate the influence of intellectual stimulation training on graduate’s performance focused on the Pan Africa Christian University (PAC) Transformational Church Leadership (TCL) diploma program graduates of 2016 and 2017, in selected churches in Kenya.
ATDD: Fine-Grained Assured Time-Sensitive Data Deletion Scheme in Cloud Storage

With the rapid development of general cloud services, more and more individuals or collectives use cloud platforms to store data. Assured data deletion deserves investigation in cloud storage. In time-sensitive data storage scenarios, it is necessary for cloud platforms to automatically destroy data after the data owner-specified expiration time. Therefore, assured time-sensitive data deletion should be sought. In this paper, a fine-grained assured time-sensitive data deletion (ATDD) scheme in cloud storage is proposed by embedding the time trapdoor in Ciphertext-Policy Attribute-Based Encryption (CP-ABE). Time-sensitive data is self-destructed after the data owner-specified expiration time so that the authorized users cannot get access to the related data. In addition, a credential is returned to the data owner for data deletion verification. This proposed scheme provides solutions for fine-grained access control and verifiable data self-destruction. Detailed security and performance analysis demonstrate the security and the practicability of the proposed scheme.
Research on cross-platform integration method based on natural language technology

With the gradual improvement of my country's informatization construction, people need more intelligent and accurate information retrieval and automatic question answering and other services in the field of artificial intelligence. In order to continuously improve the performance of the algorithm to provide more efficient and comfortable services, a large number of researchers have invested in the research of natural language processing.Text matching is the core and basic problem in the field of natural language processing. It has experienced from the early traditional text matching methods based on statistics to the deep text matching methods in recent years. This paper studies several popular deep learning text matching methods, including single-semantic text matching, multi-semantic text matching and attention mechanism text matching. On the basis of the currently widely used algorithms, a multi-channel matching pyramid model, a text matching model of cyclic attention mechanism and a model stacking integration algorithm of dynamic parameters are proposed, and the integration is realized by using natural language processing technology.