Reviewerly/exHarmony huggingface dataset:
MULTIPLE OBJECT TRACKING USING A TRANSFORM SPACE

Abstract. This paper presents a method for multiple object tracking (MOT) in video streams. The method incorporates the prediction of physical locations of people into a tracking-by-detection paradigm. We predict the trajectories of people on an estimated ground plane and apply a learning-based network to extract the appearance features across frames. The method transforms the detected object locations from image space to an estimated ground space to refine the tracking trajectories. This transform space allows the objects detected from multi-view images to be associated under one coordinate system. Besides, the occluded pedestrians in image space can be well separated in a rectified ground plane where the motion models of the pedestrians are estimated. The effectiveness of this method is evaluated on different datasets by extensive comparisons with state-of-the-art techniques. Experimental results show that the proposed method improves MOT tasks in terms of the number of identity switches (IDSW) and the fragmentations (Frag).
Morphosyntax and Semantics in the NooJ Italian Dictionary of Simple Words

The main topic of this paper is to describe how to transform effectively the “lexical matter” of a language (not only Italian) into a formal and taxonomic morphosyntactic classification of the simple words stored and tagged inside NooJ electronic dictionaries. The ultimate goal is to build electronic dictionaries for NooJ that can be used effectively in Natural Language Processing (NLP) and Automatic Textual Analysis (ATA). To achieve this task, we will start from the following assumption: nouns inflection codes may prove useful not only to describe morphological behaviours and features, but also to “predict” syntactic combinations inside sentences. This means that inside NooJ finite-state automata (FSA) and transducers (FST), nouns inflection codes can account for Lexicon-Grammar (LG) co-occurrence and selection restriction rules, thus also providing for several aspects of formal semantics (FS). The method we intend to outline here can therefore become a descriptive and applicative standard, reusable for all those languages in which word inflection has a value not only morphological (as for gender, number and possibly case) but also syntactic. As is known, such a formal descriptive approach is absent in paper dictionaries, in which there is a tendency to “flatten” the morphosyntactic description of words in favour of a list of words which uses rely heavily on the linguistic competence of readers and speakers.
The Confidence Level of Applied Foreign Languages Students In English Public Speaking

This study aims to determine: (1) the confidence level of Diponegoro University Applied Foreign Languages English Students Class of 2019 – 2020 with public speaking in English; (2) the reason for the insecurity of Diponegoro University Applied Foreign Language English Students Class of 2019 – 2020 with public speaking in English; (3) solutions to the lack of confidence for English students of Diponegoro University Applied Foreign Languages Class of 2019 – 2020 with public speaking in English. This research uses a quantitative approach with an exploratory type. Determination of research subjects using simple random sampling techniques. The sample in this study was 54 students of applied foreign language class 2019-2020 specializing in English at Diponegoro University. The instrument used is a questionnaire. The questionnaire used in this study is closed and answered directly by the respondent. The measuring instrument that will be used in this study is to use a modified Likert scale with four alternative answers, namely strongly agree (SS), agree (S), disagree (TS), and strongly disagree (STS). The results showed that the highest number of 54 respondents Students of Applied Foreign Languages at Diponegoro University is Lack of Preparation with average 3.18, while reason chosen with the lowest number of respondents is A Feeling of Incapacity with average 2. The solutions for three highest reasons. There are lack of preparation, the solution is break down the practice into smaller, more doable parts in order to reduce stress and increase performance, the practice should be done in small, manageable pieces. Then stage fright, rather than focusing on the audience, concentrate on the topic. People are more interested in new knowledge than in how it is delivered. They may be unaware of the students' anxiousness. And the last is inferiority or low self-esteem, appreciate what the students achieved. Give pat at the back following the speech or presentation
The Network EPES8–2

The article presents the EPES8–2 network with two round functions using the same algorithm for encryption and decryption. INTRODUCTION: Currently, block encryption algorithms based on the Feystel network are widely used. Block encryption algorithms based on the Feystel network include encryption algorithms such as DES, GOST 28147-89, as well as block encryption algorithms such as CAST 256, DFC, E2, LOKI97, which participated in the competition announced by NIST. The advantage of this network is that a single algorithm is used for encryption and decryption, only the encryption round keys are used in reverse order for decryption.
Collective Signature Schemes Using Problem of Finding Roots Modulo

Digital signature schemes in general and representative collective digital signature schemes, in particular, are often built based on the difficulty of the discrete logarithm problem on the finite field, of the discrete logarithm problem of the elliptic curve, of the problem of factor analysis, of the problem of finding the roots modulo of large primes or a combination of the difficult problems mentioned above. In this paper, we use the new difficult problem, which is to find the with root in the finite ground field GF(p) to build representative collective signature schemes, but the chosen modulo p has a special structure distinct p=Nt_0 t_1 t_2+1, where N is an even number and t0, t1, t2 are prime numbers of equal magnitude, about 80 bits. The characteristics of the proposed scheme are: i) The private key of each signer consists of 2 components (K_1, K_2), randomly selected, but the public key has only one component (Y) calculated by the formula Y= K_1^ (w_1) K_2^ (w_2); w_1 = t_0 t_1 and w_2 = t_0 t_2; and ii) The generated signature consists of a set of 3 components (e, S1, S2). We use the technique of hiding the signer's public key Y, which is the coefficient λ generated by the group manager, in the process of forming the group signature and representative collective signature to enhance the privacy of all members of the signing collective.
Diagnosis Technology of Substation Abnormal State Based on Cloud Edge Collaboration

Abstract The substation monitoring system based on cloud edge collaborative computing is an important application field of power Internet of things (IoT) technology, which can effectively improve the data processing capacity of the system. First of all, the problems of computing range, computing power, delay performance, task priority, information transmission and cross are analysed when cloud and edge computing are applied in the abnormal state monitoring of substation. The application functions of the two computing modes in the monitoring system are studied. Secondly, the system of abnormal state detection and the diagnosis process of substation based on cloud edge collaborative computing are designed. Then, the design of abnormal state diagnosis system for a substation in Laver Park of Lianyungang is proposed. Finally, it is verified that the design has the ability to reduce the data transmission and raise the level of fault processing through the abnormal state diagnosis of transformer oil chromatogram in substation.
BESKlus : BERT Extractive Summarization with K-Means Clustering in Scientific Paper

This study aims to propose methods and models for extractive text summarization with contextual embedding. To build this model, a combination of traditional machine learning algorithms such as K-Means Clustering and the latest BERT-based architectures such as Sentence-BERT (SBERT) is carried out. The contextual embedding process will be carried out at the sentence level by SBERT. Embedded sentences will be clustered and the distance calculated from the centroid. The top sentences from each cluster will be used as summary candidates. The dataset used in this study is a collection of scientific journals from NeurIPS. Performance evaluation carried out with ROUGE-L gave a result of 15.52% and a BERTScore of 85.55%. This result surpasses several previous models such as PyTextRank and BERT Extractive Summarizer. The results of these measurements prove that the use of contextual embedding is very good if applied to extractive text summarization which is generally done at the sentence level.
DEVELOPING THE PROFESSIONAL COMPETENCE OF FUTURE SURDOPEDAGOGS THROUGH THE OPTIMIZATION OF INDEPENDENT EDUCATION

Properly organized independent work creates the basis for students to acquire learning experience, life skills, social and professional attractiveness, opportunities for personal and self-esteem. Independent education forms professional competencies.
Intelligent Identification of Embedded System Equipment State Based on Internet of Things

IoT (Internet of things) technology will be more widely used in the manufacturing industry, which will bring new trends to the development of the manufacturing industry. With the continuous advancement of IoT technology in the power field, the integration of IoT and smart grid has become a new topic. In the process of monitoring and troubleshooting the whole EPSE (Embedded Power System Equipment), it is very important to conduct a comprehensive analysis and routine testing on specific equipment and systems, so that the power system can work stably. In this paper, the knowledge sharing and transfer model of EPSE based on IoT is analyzed, and an intelligent identification method of EPSE state based on the GA-C_HMM (Genetic Algorithm-Coupled Hidden Markov Model) algorithm is proposed. After the GA parameters are optimized, the state model library is constructed by fitting each state data of the equipment to C_HMM, and the state of the equipment is determined by calculating the maximum probability value of the signal to be diagnosed. The experimental results show that when the recognition time is 12 hours, the recognition accuracy rate of the existing recognition method is 69.7%, and that of the recognition method in this paper is 98.3%, which shows that the recognition accuracy rate of the recognition method adopted in this study is higher than that of the existing methods and the recognition ability is stronger.
Utilization of e-book 3-dimensions information systems based on google analytics

The use of information and communication technology has become a necessity for people to search for information and information published in 3-dimensional e-book media, which has been widely used in information retrieval. This paper aims to determine the use of 3-dimensional e-Books produced by the Directorate of Repository, Multimedia, and Scientific Publishing-National Information Research Agency. The method used in this research is the Google Analytics method, an application that provides information on the results of data collection (data mining) using the web using page tag techniques. With Google Analytics, the collected data related to acquisition and user behavior on the web can be analyzed to be used as a goal, reference, and reliable data. The results showed that there were 304 (three hundred and four) titles of 3-dimensional E-Books produced, which were grouped into 12 categories. Users of 3-dimensional E-Books are spread across 12 (twelve) countries. The two countries that use the most are Indonesia, with 2,718 users (94.05%), followed by America with 72 users (2.49%). Meanwhile, the two most widely used 3-dimensional e-book categories were the tourism category with 625 (31.2%) and animal husbandry and agriculture 421 (21.0%). Considering that users of this 3-dimensional e-book are scattered in various countries, it is recommended that the production of this 3-dimensional e-book can be continued.
Detection of Shilling Attack in a Recommender System

In recent times commendation programs have become an integral part of our daily life, from grocery compliments to recommendation movies. People would not like to devote their time to find the best thing on the list according to their needs. The level of recommendations is fundamental for users to recommend anything. The Joint Recommendation Program recommends items for customer engagement and is a widely used and proven way to provide recommendations. Based on user ratings, it recommends that particular item. Since this recommendation program is based on ratings, it is straightforward for attackers to create false profiles and inject biased profiles in very large numbers. These types of attacks are called shin attacks divided into push (hacker attack) and nuke attack (opposite object). This attack is detected using aggregation, separation, element extraction, and possible methods. Key Words: Shilling Attacks, Recommender Systems, Ratings, Nuke, Push
Fraud detection under the unbalanced class based on gradient boosting

Credit fraud modeling is an important topic covered by researchers. Overdue risk management is a critical business link in providing credit loan services. It directly impacts the rate of return and the bad debt percentage of lending organizations in this sector. Credit financial services have benefited the general public as a result of the development of the mobile Internet, and overdue risk control has evolved from the manual judgment that relied on rules in the past to a credit model built using a large amount of customer data to predict the likelihood of customers becoming delinquent. When creating a credit rating model, the emerging nature of the credit samples makes the minority class sample score very few; that is, when a large number of actual samples are obtained, this causes machine learning models to be biased towards the majority class when training. Traditional data balancing methods can reduce the bias of models to the majority category when the data is relatively unbalanced rather than excessive. Gradient boosting algorithms (XGBoost and CatBoost) are proposed in this paper to model highly unbalanced data to detect credit fraud. To find hyperparameters and determine the accuracy of the minority class as an optimization function of the model, Bayesian optimization is used to increase the model's accuracy for the minority class. The paper was tested with real European credit card fraud data. The results were compared to traditional machine learning (decision trees and logistic regression) and the performance of the bagging algorithm (random forest). For comparison, the traditional data balancing method (Oversample) is used
The Role of Auxiliary Stages in Gaussian Quantum Metrology

The optimization of the passive and linear networks employed in quantum metrology, the field that studies and devises quantum estimation strategies to overcome the levels of precision achievable via classical means, appears to be an essential step in certain metrological protocols achieving the ultimate Heisenberg-scaling sensitivity. This optimization is generally performed by adding degrees of freedom by means of auxiliary stages, to optimize the probe before or after the interferometric evolution, and the choice of these stages ultimately determines the possibility to achieve a quantum enhancement. In this work we review the role of the auxiliary stages and of the extra degrees of freedom in estimation schemes, achieving the ultimate Heisenberg limit, which employ a squeezed-vacuum state and homodyne detection. We see that, after the optimization for the quantum enhancement has been performed, the extra degrees of freedom have a minor impact on the precision achieved by the setup, which remains essentially unaffected for networks with a larger number of channels. These degrees of freedom can thus be employed to manipulate how the information about the structure of the network is encoded into the probe, allowing us to perform quantum-enhanced estimations of linear and non-linear functions of independent parameters.
Comparative analysis of resampling techniques on Machine Learning algorithm

Generally, classification algorithms in the field of data science assume that the classes of training data are equally distributed. However, datasets on real problems often have an unbalanced class distribution. Unbalanced dataset classes make up the majority class and the minority class. In general, minority classes are more attractive and more important to identify. In this case, the correct classification for the minority class sample is more valuable than the majority class. The unbalanced class distribution causes the classification algorithm to have difficulty in classifying minority class samples correctly. If the performance of the algorithm model is good for the majority class sample but bad for the minority class then this imbalance problem is a crucial thing to be addressed. Many solutions are offered for this problem, namely by oversampling techniques in the minority class and/or undersampling techniques in the majority class. In this study, the authors tried various sampling techniques and tested them on various machine learning classification algorithms to find out the combination of resampling techniques and algorithms that have high recall in classifying minority class samples and still considering the majority class classification.
Analisis Kebutuhan Buku Ajar Bahasa Inggris dalam Materi Pembelajaran Tingkat SMK Jurusan Teknik Komputer Jaringan

The systematization of books (teaching materials) so that students can use them to achieve learning objectives. Teachers must have the ability to develop teaching materials according to the needs of students. Lack of textbooks in teaching at the SMK level, especially English textbooks. The purpose of this study was to identify some data and the match between the content in English textbooks and the needs of students at SMK 1 Gondang, Nganjuk. This study uses a qualitative method with a descriptive approach. Collecting data through interviews and questionnaire surveys on students and literature review. The population and sample in this study were grade X students and English textbooks for SMK. It can be seen that the needs of SMK students majoring in Network Computer Engineering related to learning English are in accordance with the existing KD SK in accordance with the knowledge learned in Network Computer Engineering. Based on the results of interviews, it was stated that the needs of SMK students majoring in TKJ in English are those who introduce the industrial world a lot, the ability to communicate in the general and industrial environment, and are familiar with vocabulary, especially terms, procedures, and the competence of SMK majoring in Network Computer Engineering in general. The description above shows that students' expectations and learning situations can build English language learning in meeting the needs of students effectively. The needs of SMK students majoring in TKJ for those related to learning English are in accordance with the existing KD SK and in accordance with the knowledge learned in TKJ.
Rhetorical Move Analysis of Science and Engineering Abstracts Rejected in a Scopus-Indexed Journal

The role of an appropriate writing of an abstract becomes significantly important as it acts not only as the representative of the whole content of the paper, but also helps journal reviewers to decide whether the article is deemed worthy to be published. While myriad research on rhetorical move analysis of research article abstracts has been conducted, an inadequate amount of them has probed onto rejected research article abstracts, specifically using a cross-disciplinary lens. This study aims to uncover the rhetorical organization and linguistic features of science and sngineering abstracts by utilizing Hyland’s (2000) rhetorical structure. The method of this research is used as the nature of this research rooted from discourse analysis. A total of eighteen rejected abstracts were retrieved from Indonesian Journal of Science and Technology (IJoST). The findings revealed that all of Hyland’s moves were found in both dataset where the move occurrences were identical to one another. However, significant differences existed in step occurrences, particularly in Step 4 of Move 1, Step 1*, 1, and 2 of Move 3, and Step 1 and 2 of Move 5. Engineering studies considered Introduction, Purpose, and Method as obligatory moves, while science studies viewed Purpose as a conventional move of the three. Pattern-wise, science applied two configurations, while engineering used three. Regarding the linguistic features, present tense and active voice were dominant across the disciplines. Furthemore, it was observed that the conventionality of abstract writing had not been properly performed in the rejected abstracts. This research is hoped to provide an insightful source on rejected research article abstracts to future researchers.
A Study of Speech Recognition with Deep Learning

The development of deep learning and the continuous progress of artificial intelligence have contributed to the rapid development of speech recognition. Among them, the end-to-end structure is the more important part of the whole speech recognition. This paper introduces two end-to-end speech recognition methods, the attention model and the CTC loss function, describes the practical application of deep learning in speech recognition and suggests improvements to the two models. Finally, the practical usefulness of speech recognition is demonstrated by analyzing the application of trigger word detection and sentiment analysis in artificial intelligence in teaching and learning.
Utilization of interactive multimedia to improve the reading ability of students with mild intellectual disabilities

The current study is a literature study that describes interactive multimedia in learning to read for students with mild intellectual disabilities.The research used written sources from various peer-reviewed journal articles of good quality.Articles are selected according to the research topic collected from the Google Scholar database.Researchers reviewed 15 journal articles published in the 2015-2020 period with the topic of the role of interactive multimedia in learning to read for students with mild intellectual disabilities.The study results show that interactive multimedia, such as Kinect, Computer Assisted Instruction, Marbel mobile application, and pop-up books can increase the effectiveness of learning and reading skills for students with mild intellectual disabilities.In addition, interactive multimedia can support interesting interactions and a fun learning experience between teachers, students, and learning.