Reviewerly/exHarmony huggingface dataset:
BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems

Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous position-navigation-time tasks within stringent onboard weight and power constraints. We observe that reducing onboard operating voltage can benefit the energy efficiency of both the computation and flight mission, however, it can also result in on-chip bit failures that are detrimental to mission safety and performance. To this end, we propose BERRY, a robust learning framework to improve bit error robustness and energy efficiency for RL-enabled autonomous systems. BERRY supports robust learning, both offline and on-board the UAV, and for the first time, demonstrates the practicality of robust low-voltage operation on UAVs that leads to high energy savings in both compute-level operation and system-level quality-of-flight. We perform extensive experiments on 72 autonomous navigation scenarios and demonstrate that BERRY generalizes well across environments, UAVs, autonomy policies, operating voltages and fault patterns, and consistently improves robustness, efficiency and mission performance, achieving up to 15.62% reduction in flight energy, 18.51% increase in the number of successful missions, and 3.43× processing energy reduction.
Research tools between print and digital publishing: not opposition but integration

In the field of archival publishing, the transition phase in which the relationship between print and digital was in contrast is considered closed and it’s time to move towards a new model of integration. It would be time to start a more mature reflection, in the perspective of imagining a dialectical relationship, not of contrast but capable of overcoming indifference in view of a fruitful integration. For this purpose, this paper provides some ideas and some proposals for work, in the hope that the academic world, Anai and Directorate General Archives will start in collaboration shared work.
CDSBen: Benchmarking the Performance of Storage Services in Cloud-Native Database System at ByteDance

In this work, we focus on the performance benchmarking problem of storage services in cloud-native database systems, which are widely used in various cloud applications. The core idea of these systems is to separate computation and storage in traditional monolithic OLTP databases. Specifically, we first present the characteristics of two representative real I/O workloads at the storage tier of ByteDance's cloud-native database veDB. We then elaborate the limitations of using standard benchmarks such as TPC-C and YCSB to resemble these workloads. To overcome these limitations, we devise a learning-based I/O workload benchmark called CDS-Ben. We demonstrate the superiority of CDSBen by deploying it at ByteDance and showing that its generated I/O traces accurately resemble the real I/O traces in production. Additionally, we verify the accuracy and flexibility of CDSBen by generating a wide range of I/O workloads with different I/O characteristics.
Evaluation of text summarization techniques in healthcare domain: Pharmaceutical drug feedback

Text summarization techniques offer a way to address the significant challenges faced by clinicians and researchers due to the exponential growth of information in healthcare on the internet. By condensing lengthy text into concise summaries, these techniques facilitate faster, easier, and convenient access to relevant information. This is particularly beneficial in use cases such as online user feedback/reviews about drugs, where valuable insights can be obtained that extend beyond clinical trials and observational studies. This paper comprehensively evaluates six widely used text summarization techniques (LSA, Luhn’s Method, Text Rank, T5 Transformer, and Kullback-Leibler, BERT) in extracting key insights, themes and patterns about drugs from online drug reviews. The evaluation considers both quantitative and qualitative aspects, focusing on their applicability to the challenging medical terminology, which is known for its inherent intricacies and complexities. The findings of this study showed the performance of text summarization techniques using metrics such as F1 score, Recall, and Precision, focused on the unigram, bigram, and trigram overlap between the generated text summaries and the reference summaries, utilizing the ROUGE-1, ROUGE-2, and ROUGE-L evaluation methods. It is shown that results showed TextRank to be the most effective text summarization method followed by BERT when working with Medical Terminology in Healthcare &amp; Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms.
EmbedX: A Versatile, Efficient and Scalable Platform to Embed Both Graphs and High-Dimensional Sparse Data

In modern online services, it is of growing importance to process web-scale graph data and high-dimensional sparse data together into embeddings for downstream tasks, such as recommendation, advertisement, prediction, and classification. There exist learning methods and systems for either high-dimensional sparse data or graphs, but not both. There is an urgent need in industry to have a system to efficiently process both types of data for higher business value, which however, is challenging. The data in Tencent contains billions of samples with sparse features in very high dimensions, and graphs are also with billions of nodes and edges. Moreover, learning models often perform expensive operations with high computational costs. It is difficult to store, manage, and retrieve massive sparse data and graph data together, since they exhibit different characteristics. We present EmbedX, an industrial distributed learning framework from Tencent, which is versatile and efficient to support embedding on both graphs and high-dimensional sparse data. EmbedX consists of distributed server layers for graph and sparse data management, and optimized parameter and graph operators, to efficiently support 4 categories of methods, including deep learning models on high-dimensional sparse data, network embedding methods, graph neural networks, and in-house developed joint learning models on both types of data. Extensive experiments on massive Tencent data and public data demonstrate the superiority of EmbedX. For instance, on a Tencent dataset with 1.3 billion nodes, 35 billion edges, and 2.8 billion samples with sparse features in 1.6 billion dimension, EmbedX performs an order of magnitude faster for training and our joint models achieve superior effectiveness. EmbedX is deployed in Tencent. A/B test on real use cases further validates the power of EmbedX. EmbedX is implemented in C++ and open-sourced at https://github.com/Tencent/embedx.
Krypton: Real-Time Serving and Analytical SQL Engine at ByteDance

In recent years, at ByteDance, we have started seeing more and more business scenarios that require performing real-time data serving besides complex Ad Hoc analysis over large amounts of freshly imported data. The serving workload requires performing complex queries over massive newly added data items with minimal delay. These systems are often used in mission-critical scenarios, whereas traditional OLAP systems cannot handle such use cases. To work around the problem, ByteDance products often have to use multiple systems together in production, forcing the same data to be ETLed into multiple systems, causing data consistency problems, wasting resources, and increasing learning and maintenance costs. To solve the above problem, we built a single Hybrid Serving and Analytical Processing (HSAP) system to handle both workload types. HSAP is still in its early stage, and very few systems are yet on the market. This paper demonstrates how to build Krypton, a competitive cloud-native HSAP system that provides both excellent elasticity and query performance by utilizing many previously known query processing techniques, a hierarchical cache with persistent memory, and a native columnar storage format. Krypton can support high data freshness, high data ingestion rates, and strong data consistency. We also discuss lessons and best practices we learned in developing and operating Krypton in production.
Real-Time Workload Pattern Analysis for Large-Scale Cloud Databases

Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AW M encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection &amp; Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded query by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AW M enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.
Implement Data Access with Cosmos DB SQL

Accessing data from Azure Cosmos DB is very intuitive when using the NoSQL (SQL) API. SQL is a tried and true data access language that has been implemented in Azure Cosmos DB. But there are some important differences in accessing JSON documents versus a relational store. Queries can be issued from an IDE, an SDK, or from the Azure Cosmos DB Data Explorer. This chapter will explain the structure of queries and familiarize you with common operations such as SELECT, JOIN, and sub-queries. Understanding how to access your data will also help guide your data modeling decisions.
Integration of an RSA-2048-bit public key cryptography solution in the development of secure voice recognition processing applications

The authors initially employs the fast Fourier transform (FFT) approach to transforming voice inputs into digital signals before integrating a speech recognition solution (which includes two models: the hidden Markov model (HMM) and the artificial neural network (ANN)). To achieve standard-tone identification of voice signals and digitally store speech, the authors then incorporated a 2048-bit Rivest-Shamir-Adleman (RSA) encryption method to encrypt and decrypt digital speech. The authors’ building team constructed the program using a 256-bit advanced encryption standard - Galois counter mode (AES-GCM) encryption method to assure the application’s effectiveness. The authors successfully created a voice recognition application according to the HMM of ANN. The collected findings suggest that the authors’ secure speech recognition program (named soft voice - RSA) has improved in terms of safety, keeping speech material secret, and speed. It takes roughly 0.2 s to generate a 2048-bit RSA key pair that exceeds the National Institute of Standards and Technology (NIST) standard, 700-1070 ms to process speech, 1-4 ms to encrypt 2048-bit RSA, 6-8 ms to decrypt 2048-bit RSA.
Artificial intelligence and machine learning

This chapter addresses the topics of artificial intelligence and machine learning. The idea of artificial intelligence is that a computer is doing something that, at least casually, can be regarded as ‘thinking’. However, the sophistication of the program might be a translation of some aspect of the intelligence of the programmer. Machine learning goes beyond this; for machine learning, the program has to take the initiative to improve the performance of the algorithm. The chapter then looks at the difference between classification and clustering, as well as between supervised learning and unsupervised learning. The Receiver Operating Characteristic (ROC) curves reveal how the quality of a prediction depends on the choice of separation threshold. The chapter also considers the structure and activity of artificial neurons, and how they can be combined into artificial neural networks; decision trees; and support vector machines.
Describing Data

This chapter looks at how to describe collected data and it examines the continuous scale level. It explores the calculation of mean, median, and mode as measures of central tendency. Frequency distribution illustrates the central tendency, shape, and variability of data based on observation rather than theory. Meanwhile, theoretical frequency distribution is a type of data description that can be generated by a mathematical equation. The chapter considers three theoretical distributions useful to biologists: the normal, Poisson, and binomial distributions. It also cites the importance of describing the variability of data and central tendency by explaining how to measure variability through range, interquartile range, variance (s<sup>2</sup>), and standard deviation (s).
Imperceptible body transformation in virtual reality: Saliency of self representation

Change blindness (CB) is the perceptual phenomenon whereby people are blind to dramatic changes in their visual environment. In virtual reality (VR) a person's body can be substituted by a life-sized virtual one that moves synchronously with their real body movements as their self-representation. We consider whether CB occurs in VR, and whether there are differences in the case of changes to their own virtual body compared with the body of another. Forty people took part in a Qi Gong lesson in VR led by a virtual instructor. During the lesson both their own and the instructor's face dramatically changed in appearance. Overall, 73% and 85% did not notice the changes to their own and instructor's face respectively. People make iconic inferences about their visual surroundings without sampling detail, and reduced CB in the case of their own body may be a marker for self-representation.
APPLICATION OF MULTIMEDIA TOOLS IN THE TEACHER’S WORK PROCESS FOR IMPLEMENTING POLYTECHNIC EDUCATION

The article deals with issues related to the implementation of multimedia tools in the work process of a teacher in the implementation of polytechnic education during the teaching of physics in a new Ukrainian school. The method of using multimedia tools in the educational process of the teacher’s work in the implementation of polytechnic education in physics lessons is theoretically substantiated. It is noted that in the process of teaching physics, the assigned tasks are effectively implemented, if we take into account modern approaches to polytechnic education of students due to the wide use of a system of tasks of practical content, which will contribute to the development of mental activity of students, which is carried out on the basis of traditional and computer-oriented learning technologies. It is emphasized that the training of students in physics in a modern school is based on the principle of practical orientation, which is implemented, respectively, through general natural and physical components. Possibilities of using multimedia tools in the process of the teacher’s work in physics lessons for the implementation of polytechnic education through the use of multimedia presentations are considered. In particular, the section “Interaction of bodies. Force” in physics lessons in the 7th grade, which, in our opinion, is important in terms of the implementation of polytechnic education during the study of the topic “Pressure of liquids and gases. Pascal’s law. Combined vessels. Manometers”.
MEANS OF ACTIVATING EDUCATIONAL AND COGNITIVE ACTIVITIES IN THE PROCESS OF TEACHING THE COURSE “INFORMATION PROTECTION TECHNOLOGY”

Today’s challenges encourage the use of various means of activating the educational and cognitive activity of students of higher education in the teaching process. In order to activate the educational and cognitive activity of students in the field of training 122 Computer Science in the process of teaching the professional discipline “Information Protection Technologies”, it is proposed to: expand the lecture material with modern topics, in particular, the topic “Pentest”; to deepen the tasks of laboratory work with relevant topics, in particular, “Organization of security in a computer network”, “Asymmetric cryptography and electronic digital signature on the example of the GnuPG system”; to use up-todate software and up-to-date versions of this software for the implementation of laboratory tasks. Thanks to consideration of modern topics, implementation of tasks using current software and current versions of this software, examples of practical use, substantiation of the need for study, etc., approaches to activating the educational and cognitive activity of students have been enriched (in particular, prospects for the further development of the professional labor market and financial motivation). It has been established that students are very interested in applied topics and tasks that are as close as possible to practice, to life situations. Since the questions discussed in the article are always relevant, it is possible to continue research on this topic.
STEM-COMPETENCES OF FUTURE MATHEMATICS TEACHERS AND METHODS OF THEIR FORMATION

The article discusses the general principles of STEM education and the ways of forming STEM competence of future mathematics teachers, in particular, solving applied problems, implementing STEM projects, implementing elements of STEM technologies in mathematics classes, holding STEM weeks. The analysis of scientifi c and methodological sources showed that the implementation of STEM education is a creative process aimed at improving the quality of education, it is the result of the activity of all subjects of the educational process. Activities within the framework of STEM education are the creation of stable connections between the educational institution, society, professional activity and the whole world. Since the main subject of the implementation of STEM education is the teacher, that is, the implementation of innovation is possible through the organization of activities, fi rst of all, of teachers of higher education institutions, and later – teachers of general secondary and vocational (vocational-technical) education institutions. The research proved that there is currently an urgent need for training and retraining of mathematics teachers who will be able to work in this direction and transfer the implementation of STEM education from an individual to a mass level.
Multi-task and Multi-team Work Order Scheduling Using Non-dominated Sorting Genetic Algorithm II

Work orders in the maintenance field and in general resource-constrained problems have integrated multi-objective scheduling in order to produce an efficient job routine. One of the current drawbacks of today’s scheduling apparatuses often introduces conflicting schema and illogical arrangements. In this research, non-dominated sorting genetic algorithm version II (NSGA-II) is proposed for work order scheduling applications, using a set of five-step genetic-oriented workflow. The set includes data (chromosome) representation, high-fitness parents selection, adjusted binary crossover, mutation, and survival selection. NSGA-II is investigated in conjunction with multi-team and multi-task directives or MT $$^2$$ WOS-NSGA-II. The scheduling results show noteworthy improvements compared to traditional NSGA-II with binary n-crossover, having minimum timing and resource constraints violation, and reducing execution hours by approximately 300 units.
The task-oriented Smart Tourism Chatbot Service

The smart tourism service provides tourists with a travel planner service before the trip and a tour guide service during the trip. Smart tourism chatbot service can easily and conveniently provide smart tourism services to tourists along with smart tourism apps. In this paper, we develop the smart tourism platform and propose the task-oriented smart tourism chatbot service that efficiently provides tourism information provided by smart tourism apps to users. The smart tourism platform consists of the smart tourism chatbot and smart tourism information systems. The smart tourism chatbot system identifies the intention of the user's question and searches for the tourism information ID in the tourism information knowledgebase. The smart tourism information system provides tourism information of the smart tourism app to the user using the searched tourism information ID. The proposed smart tourism chatbot system applies the tourism information NER (Named Entity Recognition) model to accurately understand the intention of the user's question to the existing DST (Dialogue State Tracking) model-based chatbot system. We create tourist information named entities of the NER model based on the values of the domain and slot in the DST model defined in the 4W1H method. The NER model determines the domain, slot, and value to identify the intention of the user's question, and the DST model manages the dialogue state and retrieves the tourism information ID from the tourism information knowledgebase.
The Ability of Students in Identifying Regular and Irregular Verb in English Text at the Eleventh Grade Student’s of SMA Kristen Rantepao

In order to achieve this goal the authors use a quantitative method. The population of this study was the eleventh grade students of SMA Kristen Rantepao. The total number of eleventh graders is 5 classes. In finding a sample, the writers use cluster sampling, where the writer only take some individuals who are part of the population. The writer chose class XI.1 as a sample consisting of 20 students as respondents. In collecting data, the writer uses written test. The results of the analysis show that the ability of eleventh graders at SMA Kristen Rantepao in identifying regular and irregular verbs in the text is classified as low, where students get an average score of 21.75%. The classification of students’ scores that have been presented shows that there are no students who get very good scores, there are 9(45%) students get good scores, there are 2 (10%) students get good scores, there are 5(25%) students get a poor scores and there are 4 (20%) students get a very poor score. Based on the results above, the writer concludes that the ability of eighth graders (VIII) SMA Kristen Rantepao in identifying regular and irregular verbs in the text is still poor, it can be seen from the average score of 21.25%.