Reviewerly/exHarmony huggingface dataset:
Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigation

Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both high probability and flexibility, using only sensor measurement. The optimizer takes action commands from the policy network as initial values and then provides refinement to drive the potentially dangerous ones back into safe regions. With the help of a deep transition model that predicts the evolution of surrounding dynamics and the consequences of different actions, the CBF module can guide the optimization in a reasonable time horizon. We also present a novel joint training framework that improves the cooperation between the Reinforcement Learning (RL) based policy and the CBF-based optimizer both in training and inference procedures by utilizing reward feedback from the CBF module. We observe that the policy using our method can achieve a higher success rate while maintaining the safety of multiple robots in significantly fewer episodes compared with other methods. Experiments are conducted in multiple scenarios both in simulation and the real world, the results demonstrate the effectiveness of our method in maintaining the safety of multi-robot navigation. Code is available at \url{https://github.com/YuxiangCui/MARL-OCBF
The TNT Team System Descriptions of Cantonese, Mongolian and Kazakh for IARPA OpenASR21 Challenge

This paper presents our systems and experimental analyses for the OpenASR21 Challenge. We describe the systems in the constrained condition, constrained-plus condition, and unconstrained condition, and our post-evaluation analyses for the Challenge. The systems in constrained condition are built based on hybrid automatic speech recognition (ASR) technology. For constrained-plus condition, pre-training models and system fusions are incorporated. We pre-train our conformer encoders using the wav2vec 2.0 pre-training method, which is called Ch-w2v-conformer. For unconstrained condition, our end-to-end ASR systems with conformer in optimized loss and long sequence encoders are adopted. We also adapt these challenging public switched telephone network (PSTN) conditions using publicly available data in the shape of wideband dictated speech with a similar accent, respectively. Finally, a series of systems are submitted for this challenge. Our submitted systems have achieved excellent performances for the three conditions.
Grover Search

1. Grover Search: We introduce the basics of discrete quantum walks, describing some of the underlying physics. One of the most important algorithms in quantum computing is Grover’s search algorithm, we show how one can implement this algorithm using a discrete walk on the arcs of a graph.
Averaging

The axioms of quantum physics imply that in general it makes no sense to speak of the long-term behaviour of a quantum walk. In this chapter we introduce a process that allows us to develop a meaningful substitute for a simple average.
Retracted: Research on the Oxidative Stress Response of Human Body Caused by Different Nutritional Supplements and the Improvement Effect of Exercise

[This retracts the article DOI: 10.1155/2022/1355254.].
Blockchain Based Privacy Preserving Recommendation System (BPPRS) Using Digital Signatures

Recommendation system plays an important role in the digital world, since it helps to find users interest. Collaborative filtering is the widely used algorithms in recommendation systems due to its simplicity and efficiency. However, when the user rating data is sparse, which leads to generate unreasonable recommendations for those users who provide no ratings. Faced with these problems, the proposed Blockchain based Privacy Preserving Recommendation System (BPPRS) model is built using a new collaborative filtering algorithm for recommendation by combining the Jaccard Similarity and Triangle Similarity (JSTS). Using the values calculated from JSTS, an item similarity matrix is constructed in order to find the zero rated users so that the item name of these users are recommended to the particular user who are closely similar. The experimental result shows that the proposed model mitigates the sparseness of the information. Further, the BPPRS model is enabled by the use of Blockchain technology to generate the hash value for each of the items recommended to the user; previous hash value is used for current recommended items hash value and stored in different blocks. The encryption of the data ensures the secure recommendation and also the use of MECDSA makes it a better trap door function.
Machine learning and deep learning for breast cancer risk prediction and diagnosis: a Survey

Breast cancer is the widest spreading disease among women globally. The prevalence rate of breast cancer continued to rise in the last few decades. The mitotic count is a relevant factor for grading invasive breast cancer. Early analysis is an extremely imperative step in treatment. However, it is not an easy one due to several skepticisms in detection which employ mammograms. Since it is subject to human prone error, requires more time for completion and the nuclei look similar during all stages of mitosis, automatic detection of mitosis is a good solution to overcome these problems. Detailed analysis of breast cancer normally requires medical images of different methods. The sensitivity and specificity of the diagnosis largely depend on the experiences of the radiologists, and uncertain diagnosis is quite frequent because of resolution limitations and the concerns of lawsuits arisen from wrong diagnosis or undetected lesions. In this paper, the top methodologies used for mitosis detection are analyzed. There are many algorithms for classification and prediction of breast cancer: Support Vector Machine (SVM), Decision Tree (CART), k Nearest Neighbors (KNN), Random Forest (RF), and Bayesian Networks (BN). The Wisconsin data set was used to analyze breast cancer as a training set to assess and measure the performance of the three ML classifiers in terms of key frameworks such as accuracy, recall, precision, and ROC. The outcome obtained in this paper provides a critique of the stateofart ML techniques for breast cancer detection. It was also found that the ensemble classifier gives better performance. A preliminary experiment conducted on cascaded RF and Artificial Neural Network (ANN) results in better accuracy than individual classifiers. The paper shows how we can use deep learning technology diagnosis of breast cancer using MIAS Dataset. A deep learning approach is almost used for immense task objective Image processing, Computer Vision, Medical Diagnosis, and Neural Language Processing.
State prediction of MR system by VMD-GRNN based on fractal dimension

Taking the test signals of magneto-rheological vibration system under different states as research objects, four Generalized Regression Neural Network (GRNN) prediction algorithms, based on time series, time series Auto-Regressive (AR) model coefficients, time series box dimensions, and Variational Modal Decomposition (VMD) box dimensions, are designed. Moreover, four Back Propagation Neural Network (BPNN)comparative prediction algorithms, based on the four previous parameters, are also designed. These eight algorithms are applied to predict vibration damping efficiency of the system. The prediction results show that, compared to the BPNN prediction algorithm, the corresponding four GRNN prediction algorithms have the advantages of strong self-learning ability, fast convergence speed, high prediction accuracy, and stable prediction results. Among the eight prediction algorithms, the GRNN prediction algorithm, based on VMD box dimension, forecasts the results with good stability, better self-learning ability, and higher computing speed, which can maximize the prediction accuracy of the system, the minimum prediction error can reach 1.9049% when the parameters K = 4, N = 33, and Spread = 0.601. To sum up, through parameter optimization, the optimal parameter combination scheme of GRNN prediction algorithm, based on VMD box dimension, is obtained, and the best prediction effect is achieved.
A Semiotic Study of the Interplay between People and Buildings

The present paper deals with the semiotic study of the interplay between people and buildings. It studies sign processes, which is any form of activity, conduct, or any process that involves signs, including the production of meaning. It aims at examining the use of semiotics, deepening our understanding of the interplay between people and buildings and providing the basis for personalizing intelligent buildings using intelligent software agents. The data is limited to "Winged Bull", relying on Saussure's model. It is hypothesized that the concerned data is full of signs denoting different things. The present study is qualitative in nature since the researcher depends on herself in analyzing the data in question. It has been observed that "Winged Bull" is informative with signs. That is, each part of it denotes a certain thing enhancing people to think deeply.
Antecedents of Employee Experience: A Systematic Review of Literature

Background:The employee experience literature suggests that to reap the benefits of employee experience, it's important to delve into the antecedents of employee experience and leverage it to achieve individual and organizational level outcomes.Objective: The existing literature lacks consensus on the term 'employee experience' and its antecedents, hence this study aims to conceptualize the term 'employee experience' and study in-depth its antecedents as reported in the literature.Method: The study uses systematic literature review (SLR) methodology along with relational analysis to achieve the objectives identified for this paper. Conclusion:The authors successfully provide a working definition of employee experience for theory and practice and have categorized the 46 antecedents of employee experience (identified from 28 journal articles) into two types, viz.individual and organizational.This comprehensive listing of antecedents, whether empirically examined or not, is expected to aid researchers and practitioners alike in practice, research, and theory building.
Moderating Effect of Investment Opportunity in the Association of Market Response and Capital Expenditure

This study aims to reveal empirical evidence related to the market response to investment spending associated with investment opportunities, considering that investment opportunities is a crucial factor in capital expenditure decisions. This study used data from the Indonesia Stock Exchange in 2016-2021. The sample is only devoted to large-cap companies because this kind of company concerns investors more. The sample does not exclude the business sector, as in previous studies. Following previous research, investment opportunities are measured by Tobin's Q (TQ); a TQ of more than 1 indicates a higher investment opportunity and vice versa. The results show that the market responds negatively to investment spending, and investment opportunities moderate the market's response to investment spending. In addition, it was revealed that the industrial sector strengthened the influence of investment spending on market response. Therefore, management needs to consider investment opportunities before making investment expenditures to avoid getting caught up in overinvesting or under-investing, both of which are detrimental to the company. This study also adds empirical evidence in developing countries where the information gap between management and external parties (markets) is still vast.
RANCANG BANGUN SISTEM INFORMASI PERPUSTAKAAN PADA SMA ATTAQWA 02 BABELAN BERBASIS WEBSITE

SMA Attaqwa 02 Babelan is a high school with its address in Babelan District, Bekasi Regency. In ongoing library activities, it is still carried out in a conventional way, which is still taking notes in books and members come directly to the library to find out information about books. Where this activity is considered to still have problems, including limited library operating hours so that students have to go to the library physically to borrow books. The book stock is limited and the current system is not informative, so that when you come to the library to borrow the book you need, the book has not been returned by the previous borrower. The purpose of this study is to analyze the system that is currently running, in order to overcome the problems faced at Attaqwa 02 Babelan High School, by designing a Library Information System Design at Attaqwa 02 Babelan High School Based on Websites. The research framework that will be carried out in solving the problems discussed is identifying, conducting information searches based on theoretical foundations, collecting data using observation and interview methods, analyzing to find solutions to the problems faced by SMA Attaqwa 02 Babelan.
SISTEM INFORMASI MANAJEMEN ARSIP SURAT GMKI CABANG SALATIGA BERBASIS WEB DENGAN FRAMEWORK LARAVEL

The Indonesian Christian Student Movement (GMKI) is a student organization that was founded on February 9, 1950. GMKI has approximately 90 branches spread throughout the country, including in Salatiga City. As one of the branches, GMKI Salatiga Branch refers to the Internal Working Mechanism (MKI) as a policy document that regulates correspondence procedures within the organization. In its application, the correspondence mechanism is still carried out in a conventional way and has not utilized technology optimally. This study aims to produce a web-based mail archive management information system with the Laravel framework. The author uses a qualitative descriptive approach in writing. The design method in this study uses the prototype method. Then, for testing the system the author uses the black box testing method. The construction of this web-based information system can make it easier for the Branch Management Board (BPC) of GMKI Salatiga Branch in the process of making template letters and digitally filing letters from anywhere as long as they are connected to the internet.
PERANCANGAN SISTEM INFORMASI DATA SISWA DAN GURU PADA TK DARMA BAKTI DEPOK BERBASIS JAVA

Darma Bakti Kindergarten is one of the levels of early childhood education in the form of formal education that provides educational programs for children with an age range of 4-6 years. Data collection or data management, especially student and teacher data, are currently partly not computerized, namely still recording data in ledgers and some using computerization, namely using Microsoft Word and Microsoft Excel. However, it allows data in the ledger to be prone to loss and tearing, data input errors, repetition of data input (Data Double), loss of data, data searches that take a long time, ineffective data reporting because the reported data process must be typed one by one. first. The purpose of this research is to build a data information system for students and teachers using the Java programming language and MySQL database. At this writing the author uses qualitative methods, and the information system design method used is the waterfall method. With the design of this information system, the problem of data collection in TK Darma Bakti Depok can be resolved.
Improving Communication Processes between Teachers and Students in Higher Education Institutions During the Pandemic

The article is devoted to determining the features of the scientific and communicative communication between students and teachers in the context of distance learning during the Covid-19 pandemic. The article aims at determining the benefits of using online educational platforms for scientific and communicative interaction between students and teachers. The main scientific research method is a survey conducted within the Kyiv National University of Culture and Arts. During the survey, which was conducted during 2020-2021, the effectiveness of the educational platform "Mentimeter" was investigated. Study results. The online platform "Mentimeter" motivates students to acquire new knowledge and carry out communication processes within this platform. The platform provides students with the educational materials necessary to obtain professional knowledge within the specialty. Teachers use Mentimeter to organize the educational process and ensure two-way communication between students and teachers. During the online survey, information was obtained on the communication effectiveness and the possibility of their application during distance lectures. Students noted that such a platform establishes effective communication between teachers and students under quarantine restrictions. This practice can significantly improve learning efficiency and lead to the potential for improving teaching methodology in the future.
ACHIEVEMENT STUDENT SUPPORT SYSTEM USING WEIGHTED PRODUCT METHOD

Constitution of the Republic of Indonesia No 12 of 2012 that higher education in part of the national education system has a strategic role in educating the life of the nation. Students are people who try to develop themselves through the educational process on a particular path, level and type of education. The Weighted Product method is chosen because the method of completion uses multiplication to connect the attribute value, where the value must be raised with the value of the attribute weight in question. Criteria that have been determined include average value, discipline, attendance, extracurricular, and Non-Academic. The results of this study there are 6 alternatives, that A2 alternative with the value 0.145790734 as students with the lowest value and alternative A3 0.212196782 as students with the best value.
Analisis Pola Asuh Anak Dari Dampak Lingkungan Terhadap Keterampilang Berhitung Siswa

This study discussed the Analysis of Child Parenting Patterns from Environmental Impacts on Students' Counting Skills at SD Negeri 1 Sunanggalih. This study aimed to determine the form of parenting and the impact of the school environment on numeracy skills. The resource persons in this study consisted of 22 people in the form of 10 parents, 10 students and 2 teachers. This research was a qualitative research with data collection methods using the instruments of observation, interviews, and documentation. The data analysis technique was used the following steps: data reduction, data presentation, and conclusion drawing. The results showed that there were two forms of parenting applied by parents, namely democratic parenting and permissive parenting. The school environment had a great influence on students' numeracy skills
INFORMATION SOCIETY AND INTERDISCIPLINARY DIDACTICS OF INFORMATICS

The article discusses topics of intensifying the information society forming process in the continuous education system by introducing interdisciplinary methods of ICT field courses (against the background of the Covid-pandemic). Based on the topics of ICT disciplines to be studied within the scope of our project, object-oriented modeling of real objects and events was carried out. An object-role model (ORM - conceptual model) was built in the automated mode as the basis of the information base of the system. Representation of teachers' knowledge was carried out in a computer model using objects and predicates, based on categorical analysis and logical-algebraic methods. A web portal of system customer services based on cloud technology was created. The teacher can reveal the semantic connections between the topics and effectively plan integrated learning, and the student can actively participate in the learning process with high motivation, based on the use of modern digital technologies, including virtual reality.
DMPA-HHO: A Hybrid MPA-HHO optimizer with Dynamic Opposite Learning

Abstract A new hybrid algorithm is proposed by incorporating Harris Hawks Optimization with Marine Predators algorithm and dynamic Opposition-based learning, namely DMPA-HHO. In the algorithm, the problem is addressed that Harris Hawks Optimization (HHO) tends to fall into local optima and low accuracy of the solution. Dynamic Opposite Learning (DOL) improves the swarm diversity and swarm quality, and enhances the global search capability and search accuracy. HHO and the Marine Predators Algorithm (MPA) are blended to enhance the progressive rapid dives of the Harris hawk flock, effectively improving the algorithm's exploitation capabilities. DMPA-HHO uses the FADs’ effect of the MPA to increase the possibility of individuals escaping from the local optimum solution when the search falls into the local optimal solution. Compared with others on several benchmark functions, the DMPA-HHO algorithm has a better search accuracy and a stronger ability to avoid trapping in local optima.
REFRAMING AGING: INTRA- AND INTERGENERATIONAL DIGITAL CONVERSATIONAL AGENTS TO SUPPORT OLDER ADULTS

Abstract Digital conversational agents (DCAs) have become extraordinarily ubiquitous. Researchers envision the prospects of using DCAs to monitor health among older adults. However, older adults show hesitation to engage with DCAs. It is possible older adults prefer receiving human assistance rather than getting help from a machine. Another potential explanation is that communicative cues of DCAs such as voice need to be further optimized to invoke behavioral engagement. To understand how DCAs can better support older adults, we develop an experiment with three scenarios in which an agent (a human, an embodied DCA, a mixed presence of human and DCA) shares active aging information. We manipulate the agent’s voice in terms of age (older voice, younger voice). We investigate how the interplay of agent categories and intragenerational/intergenerational voice cues affect older adult participants’ evaluation of information and intention to adopt DCAs. Our study will contribute to DCAs design for older clients.
Recent Applications of Artificial Intelligence in Computer Network Considering Big Data Methods

Recent applications of artificial intelligence in computer network consider big data methods is studied in this research. The development of computer network technology has also been better developed and used globally because of its high performance and many development advantages. The biggest advantage of artificial intelligence technology is to be able to dig and analyze data better, to be able to analyze different users finely, so as to better assist in decision-making. Therefore, this paper discusses the novel suggestions for the AI based model.