Reviewerly/exHarmony huggingface dataset:
Forming a decentralized research network: DS4H

There is a trend toward decentralized systems, but these systems are developed without conducting enough software tests. Also, the performance, scalability, and sustainability of the decentralized systems are not taken into account. One of the reasons is the time-consuming testing process. The other is the hardware requirements and the complexity of the software installations of the testing environment. Developer communities need stable and secure research networks to test and develop prototypes before releasing the working versions. Cloud-based blockchain test networks are available, but it allows using a specific framework. Also, users are required to learn how to use each framework. Blockchain test networks use public ledgers, and this is also not preferable in many project implementations because of privacy reasons. Testbeds are inflexible and unrealistic in terms of scalability and performance. There is a need for a physical test network to create a realistic test environment. This study aims to form a sustainable and scalable research environment where decentralized technologies are researched and developed. The method of this study will include three stages to create a decentralized research network. The first stage is to develop the decentralized foundation of the network by using the Quorum blockchain framework and RAFT consensus protocol. Node requirements are simplified to run on low-cost devices. The second stage will be to develop a web-based platform to serve as an efficient way for the developers to deploy smart contracts easily. The final stage will be ensuring the sustainability of the system. Usage policy is formed that includes management of the network, the system usage rules, and regulations. A decentralized governance model is proposed. Smart contracts are developed to manage and ensure the sustainability of the research network. The available test tools were not sufficient and not flexible to use in this system, so a new test tool was developed for this project. The new test tool will verify the research environment with scalability, latency, and performance tests. As a result, the "Decentralized Solutions for Humanity" (DS4H) research network is deployed in four different regions of Turkey. This network is tested with four initial nodes. New nodes are to be added worldwide. The developed tools named Chainex, GoHammer, and Scanner are shared as open-source. Future works will include adding multiple blockchain network support, working on consensus protocols and new decentralized services. Finally, the DS4H research network provides a test and development research environment for blockchain projects with sustainable and low-cost maintenance.
Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning

With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively.
Semialgebras and Weak Distributive Laws

Motivated by recent work on weak distributive laws and their applications to coalgebraic semantics, we investigate the algebraic nature of semialgebras for a monad. These are algebras for the underlying functor of the monad subject to the associativity axiom alone-the unit axiom from the definition of an Eilenberg-Moore algebras is dropped. We prove that if the underlying category has coproducts, then semialgebras for a monad M are in fact the Eilenberg-Moore algebras for a suitable monad structure on the functor id + M , which we call the semifree monad M^s. We also provide concrete algebraic presentations for semialgebras for the maybe monad, the semigroup monad and the finite distribution monad. A second contribution is characterizing the weak distributive laws of the form M T => T M as strong distributive laws M^s T => T M^s subject to an additional condition.
SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios

Due to the arbitrariness of the drone’s shooting angle of view and camera movement and the limited computing power of the drone platform, pedestrian detection in the drone scene poses a greater challenge. This paper proposes a new convolutional neural network structure, SMYOLO, which achieves the balance of accuracy and speed from three aspects: (1) By combining deep separable convolution and point convolution and replacing the activation function, the calculation amount and parameters of the original network are reduced; (2) by adding a batch normalization (BN) layer, SMYOLO accelerates the convergence and improves the generalization ability; and (3) through scale matching, reduces the feature loss of the original network. Compared with the original network model, SMYOLO reduces the accuracy of the model by only 4.36%, the model size is reduced by 76.90%, the inference speed is increased by 43.29%, and the detection target is accelerated by 33.33%, achieving minimization of the network model volume while ensuring the detection accuracy of the model.
Improving Machine Reading Comprehension with Multi-Task Learning and Self-Training

Machine Reading Comprehension (MRC) is an AI challenge that requires machines to determine the correct answer to a question based on a given passage, in which extractive MRC requires extracting an answer span to a question from a given passage, such as the task of span extraction. In contrast, non-extractive MRC infers answers from the content of reference passages, including Yes/No question answering to unanswerable questions. Due to the specificity of the two types of MRC tasks, researchers usually work on one type of task separately, but real-life application situations often require models that can handle many different types of tasks in parallel. Therefore, to meet the comprehensive requirements in such application situations, we construct a multi-task fusion training reading comprehension model based on the BERT pre-training model. The model uses the BERT pre-training model to obtain contextual representations, which is then shared by three downstream sub-modules for span extraction, Yes/No question answering, and unanswerable questions, next we fuse the outputs of the three sub-modules into a new span extraction output and use the fused cross-entropy loss function for global training. In the training phase, since our model requires a large amount of labeled training data, which is often expensive to obtain or unavailable in many tasks, we additionally use self-training to generate pseudo-labeled training data to train our model to improve its accuracy and generalization performance. We evaluated the SQuAD2.0 and CAIL2019 datasets. The experiments show that our model can efficiently handle different tasks. We achieved 83.2EM and 86.7F1 scores on the SQuAD2.0 dataset and 73.0EM and 85.3F1 scores on the CAIL2019 dataset.
Methods of Future Science Teachers Training to Use Smart-Technologies in the Professional Activity

The article deals with one of the possible ways of modernization of pedagogical education in Ukraine in order to train teachers of the new generation and to provide conditions for the formation and development of modern alternative models of teachers’ professional and personal growth on the principles of SMART-education concept. The problem of future science teachers training to use SMART-technologies in the professional activity as the condition for realization of the SMART-concept in education is actualized. The advantages of SMART-technologies use in the study of objects and phenomena of wildlife have been substantiated. They allow the formation of holistic thinking of both students and schoolchildren by combining figurative and logical thinking and strengthen the emotional and value perception of wildlife objects. Methods of formation of future science teachers’ readiness to use SMART-technologies in the educational process, which involves the realization of a dual goal: the formation of the ability to use the resource SMART-concept in education to implement their own lifelong education and providing quality professional activities during teaching natural sciences school course have been highlighted. The foundation of the proposed methodology constitutes module “Methods of using SMART-technologies”, which is a component of the discipline “Methods of Biology teaching”. The content and means of its studying by students, tasks for the organization of independent work, tests, criteria and indicators of the level of students’ abilities and skills formation to use the SMART-complex (motivational-value, cognitive, activity-constructive, reflexive and evaluative) have been developed. The effectiveness of the proposed methods on the basis of determined criteria has been proved experimentally.
Use of YouTube As A Medium for Teaching English Writing Skill During Covid-19 Pandemic

A popular video sharing site, YouTube, could be used as an alternative teaching medium in online learning activities during the COVID-19 pandemic. It is useful to help students to keep on learning and get materials well in the difficult circumstance caused by the pandemic. This study aims to describe how Youtube is used as a learning medium in English teaching, especially the writing skill at MTs Ma'aarif NU 01 Kemranjen during the COVID-19 pandemic. This study uses a qualitative approach. Data are collected through the triangulation of interview, observation, and documentation. The data are then analysed by applying Miles and Huberman’s (1994) model of data reduction, data display, and verification. The results of this study reveal that Youtube is seen to be a good learning medium to cope with the learning difficulties in the unprecedented Covid-19 crisis. The asynchronous nature of the Youtube videos enabled students to repeat the video as much as they want to. The implementation of Youtube in online learning was done in three stages: 1) preparation, the teacher makes and uploads the videos; 2) implementation, the teacher shares the link and provides brief explanation on WhatsApp; and 3) evaluation, the teacher gives quizzes to ensure that the materials are well-received. Meanwhile, the obstacles in the implementation are poor Intenet network, insufficient facilities, and unmotivated students. The pedagogical implication from this study is that Youtube is a good fit for online learning, it is highly recommended for teaching during the pandemic.&#x0D;
Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data

The vision of Industrie 4.0 includes the automated reduction of anomalies in flexibly combined production machine groups up to a zero-failure ideal.Algorithmic real-time detection of production anomalies may build the basis for machine self-diagnosis and self-repair during production.Several real-time anomaly detection algorithms appeared in recent years.However, different algorithms applied to the same data may result in contradictory detections.Thus, real-time anomaly detection in Industrie 4.0 machine groups may require a benchmark ranking for algorithms to increase detection results' reliability.This paper makes a qualitative research contribution based on ten expert interviews to find design principles for such a benchmark ranking.The experts were interviewed on three categories, namely timeliness, thresholds and qualitative classification.The study's results can be used as groundwork for a prototypical implementation of a benchmark.
Improving the value of lobster selling with grading method using machine vision technology

Lobster is a product of the fishery sector which has a very high selling value, fishermen in coastal areas, especially in the Pangandaran area, sell the catch on traditional markets and are exported overseas. The expensive lobster selling value is inversely proportional to the income received by fishermen, the problems faced are influenced by several factors, namely whether, fishing regulations, and technology used, lobster sales will increase if the sales process if fishermen can sort and sort lobster sized large, which has a very high export value, the problems faced by fishermen currently sell directly to the fish market or buyers without going through the grading process. The grading system can be done manually but this causes the lobster to become not fresh because of the fairly long sorting process. The sorting and grading system method is a must so that a sold lobster possesses export value and quality, some large fishermen use a very large and fairly expensive sorting tool and are only owned by fishermen with large capital as well as catching patterns and large-scale lobster production. In small fishermen limited to the number of catches and transport capacity.
An Infrastructure for Collaborative Ontology Development

Collaborative development of a shared or standardized ontology presents unique issues in workflow, version control, testing, and quality control. These challenges are similar to challenges faced in large-scale collaborative software development. We have taken this idea as the basis of a collaborative ontology development platform based on familiar software tools, including Continuous Integration platforms, version control systems, testing platforms, and review workflows. We have implemented these using open-source versions of each of these tools, and packaged them into a full-service collaborative platform for collaborative ontology development. This platform has been used in the development of FIBO, the Financial Industry Business Ontology, an ongoing collaborative effort that has been developing and maintaining a set of ontologies for over a decade. The platform is open-source and is being used in other projects beyond FIBO. We hope to continue this trend and improve the state of practice of collaborative ontology design in many more industries.
Self-Punishment and Reward Backfill for Deep <i>Q</i>-Learning

Reinforcement learning (RL) agents learn by encouraging behaviors, which maximizes their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, leading the agent to experience ambiguity in terms of whether those actions are effective, an issue known as the credit assignment problem. In this brief, we propose two strategies inspired by behavioral psychology to enable the agent to intrinsically estimate more informative reward values for actions with no reward. The first strategy, called self-punishment (SP), discourages the agent from making mistakes that lead to undesirable terminal states. The second strategy, called the reward backfill (RB), backpropagates the rewards between two rewarded actions. We prove that, under certain assumptions and regardless of the RL algorithm used, these two strategies maintain the order of policies in the space of all possible policies in terms of their total reward and, by extension, maintain the optimal policy. Hence, our proposed strategies integrate with any RL algorithm that learns a value or action-value function through experience. We incorporated these two strategies into three popular deep RL approaches and evaluated the results on 30 Atari games. After parameter tuning, our results indicate that the proposed strategies improve the tested methods in over 65% of tested games by up to over 25 times performance improvement.
Emotional intelligence and conflictological competence in the conditions of digitalization

In modern society, under the influence of the widespread active introduction of innovative technologies (robotization, digitalization, etc.), ordinary employees of organizations are changing. In addition, life imposes new requirements on the heads of organizations. Managers not only solve the usual tasks, but also face the problem of the need to change the usual leadership style: psychological methods are replacing directive management methods. The emotional intelligence of the manager is especially appreciated, which means the ability to observe and be aware of their own experiences and emotions of other people, manage their own feelings and emotions of another, and build interaction on this basis. Another important quality of a manager that is in demand today is conflictological competence. Robotization and digitalization prevent some traditionally arising conflicts, but new conflicts related to the virtual nature of work also arise in organizations: the rigidity and unambiguity of verbal means of communication, provoking conflict; the conflict caused by the reduction of jobs in the organization; the digital divide, which consists in differences in digital literacy and digital competence of the younger and older generations of employees.
Error Recognition Model: High-mathability End-user Text Management

Discussion, evaluation and error recognition, in natural language digital texts, is one of the most neglected areas in the digital world, despite the fact that text management is the most prevalent computer related activity.Millions of erroneous text-based documents of different types are in circulation, without us being aware of how fragile, damaged and harmful they are.It is well accepted in programming and even in other end-user activities, that error recognition plays a crucial role in teaching, learning and in real-world problemsolving processes.In the present paper, we introduce the High-mathability Error Recognition Model, which consists of the processes used in discussion and concept-based problemsolving and we also provide examples of the utilization of the model.We argue that error recognition and correction, and the assessment of problems in text management are as important as in other fields of informatics and computer sciences.In our study experimental groupsstudying with the Error Recognition Modeland control groupsstudying with low-mathability tool-centered approacheswere compared.It was found that the Error Recognition Model is more effective in digital text management, than for the tool-centered methodologies, in two error types: the typographic and layout-breaking error categories and a strong compensation effect was found in the syntax error category.
Influence of Summarizing Short Stories Towards Students’ Narrative Writing Ability

The objectives of the research are to know and describe the average score of students’ narrative writing ability, and to know and describe whether there is an influence of Summarizing Short Stories towards students’ narrative writing ability at SMP Negeri 8 Bandar Lampung in 2020/2021. Most of the students get difficulties in improving their language skills, especially in writing. Writing is a problem in expressing and developing ideas or thoughts into written form. Narrative text is one of kinds of text that should be learned by the students. To solve the problem above, the writer used the technique in teaching narrative writing that is Summarizing Short Stories. In this research, the writer chooses the title: “The Influence of Summarizing Short Stories towards Students’ Narrative Writing Ability at the Second Semester of the Eighth Class at SMP Negeri 8 Bandar Lampung in 2020/2021”. In this reseach, the writer analyzed the result by using t-test formula. The research was conducted at the eighth class of SMP Negeri 8 Bandar Lampung in 2020/2021. The students of VIIIa were as a sample that were taught by using Summarizing Short Stories. The result of the research showed that there was significant influence of using Summarizing Short Stories towards students’ narrative writing ability. It is shown by the distribution list with df = (n1 + n2 – 2) = (23 + 22 – 2) = 43 obtained ttable t0.95 or significance level of 5% and ttable t0.99 or significance level of 1% was equal to 1.68 and 2.42. there were lower than tcount = 3.84 and the average score of students’ narrative writing ability in experimental class ( =66.58) was higher than the students’ score in control class (: 56.14) at the second semester of the eighth class of SMP Negeri 8 Bandar Lampung in 2020/2021.
A pedestrian detection algorithm for low light and dense crowd Based on improved YOLO algorithm

The real-time pedestrian detection algorithm requires the model to be lightweight and robust. At the same time, the pedestrian object detection problem has the characteristics of aerial view Angle shooting, object overlap and weak light, etc. In order to design a more robust real-time detection model in weak light and crowded scene, this paper based on YOLO, raised a more efficient convolutional network. The experimental results show that, compared with YOLOX Network, the improved YOLO Network has a better detection effect in the lack of light scene and dense crowd scene, has a 5.0% advantage over YOLOX-s for pedestrians AP index, and has a 44.2% advantage over YOLOX-s for fps index.
A Modeling Approach for Blockchain-inspired Business Models: An Extension of the e3 -Value Method

Blockchain enables new ways of organizing economic activities.Blockchain promises to reduce costs and time associated with intermediaries and strengthen trust in an ecosystem of actors.The impact of this promising technology is reflected in an emerging stream of research and various companies exploring the potential uses of blockchain technology and building new blockchain-inspired business models.While there are promising use cases of this new technology, research and practice are still in their early stages in terms of changing existing and creating new business models.We propose an extension of the e 3 -value method to capture and represent the characteristics of blockchain-inspired business models.The approach is particularly useful for the value-based analysis of blockchain-inspired business models and the fundamental economic evaluation of these.It provides the foundation for capturing, communicating, innovating and experimenting with blockchain-inspired business models.We demonstrate the practicality of the extended e 3 -value method by evaluating and applying it to an existing blockchain business model.