Reviewerly/exHarmony huggingface dataset:
SmartDG: Modelling System-Level Requirements for Component-Based Robotic Software Systems

System-level requirements and non-functional properties pose a significant modelling challenge in software-intensive robotic systems where the software components are developed by different vendors. It is often a manual and empirically non-verifiable proposition to check all non-functional claims made by a software component and to compose a system with confidence. System-level observance of non-functional properties is key to establish their compliance with system-level requirements, which at present is mostly solely a subjective judgement of the system builder. A crucial next step in system composition policy should be to enforce encapsulation of system-level properties within the functional body of the component. A software component should be capable of informing its peers how system-level properties are modified across services that it provides. Tracking system-level properties across components is a desired mechanism to verify compliance of system-level requirements. Monitoring these properties is desired during (i) component development for debugging, (ii) system composition for adequate component selection, and (iii) at run-time for diagnosis and enforcement. This paper presents Smart Dependency Graphs “SmartDG”, an addition to the RobMoSys compliant SmartMDSD toolchain, to model system-level dependencies between robotic software components. In SmartDG methodology, component developers implement transfer functions for dependency objects defined by domain experts, which enables system builders to trace system-level properties via dependency graphs. SmartDG provides a structural template to model system-level requirements to allow for an empirically verifiable composition of a robotic software system.
Integrated learning in the study of chemistry course

The article defines integration and integrated learning, outlines the basic principles of integrated learning, methodological features of integrated lessons. The features of the structure of integrated lessons are explained, as well as the main purpose of integration.
Annotation and Evaluation of Utterance Intention Tag for Interview Dialogue Corpus

In this paper, we proposed the utterance intention tags for an interview dialogue corpus. We constructed an interview dialogue corpus with the tags we designed. Three or five annotators annotated the tags to the interview dialogue corpus (the total number of 49999 utterances) consisting of 30 dialogues. We conducted an evaluation experiment using Fleiss's kappa value to evaluate the reliability of the proposed tags. When three annotators annotated 18 different tags to the corpus, we obtained the kappa value of 0.55.
Building Community Collaboration in the Development of a Standard

COUNTER is the membership organization responsible for the Code of Practice, which enables publishers and vendors to count and report the use of electronic resources in a standard way. This article outlines how COUNTER members from the library and publishing communities worked together to develop Release 5 of the Code of Practice, and how they collaborate to support its adoption and use. It describes how COUNTER members continue to cooperate on new developments such as the reporting of Open Access usage which cannot be attributed to institutions.
Low Latency Execution Guarantee Under Uncertainty in Serverless Platforms

Serverless computing recently emerged as a new run-time paradigm to disentangle the client from the burden of provisioning physical computing resources, leaving such difficulty on the service provider’s side. However, an unsolved problem in such an environment is how to cope with the challenges of executing several co-running applications while fulfilling the requested Quality of Service (QoS) level requested by all application owners. In practice, developing an efficient mechanism to reach the requested performance level (such as p-99 latency and throughput) is limited to the awareness (resource availability, performance interference among consolidation workloads, etc.) of the controller about the dynamics of the underlying platforms. In this paper, we develop an adaptive feedback controller for coping with the buffer instability of serverless platforms when several collocated applications are run in a shared environment. The goal is to support a low-latency execution by managing the arrival event rate of each application when shared resource contention causes a significant throughput degradation among workloads with different priorities. The key component of the proposed architecture is a continues management of server-side internal buffers for each application to provide a low-latency feedback control mechanism based on the requested QoS level of each application (e.g., buffer information) and the worker nodes throughput. The empirical results confirm the response stability for high priority workloads when a dynamic condition is caused by low priority applications. We evaluate the performance of the proposed solution with respect to the response time and the QoS violation rate for high priority applications in a serverless platform with four worker nodes set up in our in-house virtualized cluster. We compare the proposed architecture against the default resource management policy in Apache OpenWhisk which is extensively used in commercial serverless platforms. The results show that our approach achieves a very low overhead (less than 0.7%) while it can improve the p-99 latency of high priority applications by 64%, on average, in the presence of dynamic high traffic conditions.
THE ANALYSIS OF STUDENTS’ E-LEARNING READINESS ON ENGLISH LEARNING PROCESS

The process of English Learning before and during Covid-19 pandemic is very difference. The learning process which is conducted at school has changed into distance learning process. All schools are required to implement e-learning to support the distance learning process. This study was aimed at analyzing students’ e-learning readiness toward the implementation of e-learning at SMK Negeri 1 Kubutambahan. A mixed-method research design utilizing concurrent embedded model which is a quantitative method as a primer method was employed in this study. A questionnaire of students’ e-learning readiness and an observation sheet were used to collect the data. The obtained data were then analyzed quantitatively and qualitatively. The result of the study showed that the readiness of SMK Negeri 1 Kubutambahan students in using e-learning in the context of distance learning is classified on ready category which still needs some improvement. From the four aspects of readiness assessed, there is one aspect that belongs to unready category that requires a little improvement, namely the technological aspect. Meanwhile, the other three aspects, namely aspects of innovation, people, and self-development belong to the ready category which requires a little improvement in its implementation.
Context Understanding in Computer Vision: A Survey

Contextual information plays an important role in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. Appearance context information, e.g., colors or shapes of the background of an object can improve the recognition accuracy of the object in the scene. Semantic context (e.g. a keyboard on an empty desk vs. a keyboard next to a desktop computer) will improve accuracy and exclude unrelated events. Context information that are not in the image itself, such as the time or location of an images captured, can also help to decide whether certain event or action should occur. Other types of context (e.g. 3D structure of a building) will also provide additional information to improve the accuracy. In this survey, different context information that has been used in computer vision tasks is reviewed. We categorize context into different types and different levels. We also review available machine learning models and image/video datasets that can employ context information. Furthermore, we compare context based integration and context-free integration in mainly two classes of tasks: image-based and video-based. Finally, this survey is concluded by a set of promising future directions in context learning and utilization.
Pengaruh Media Google Classroom terhadap Hasil Belajar Siswa Mata Pelajaran IPS Kelas VIII SMP Terpadu Ibnu Muay Babelan Bekasi

This study aims to determine the effect of Google Classroom Media on student learning outcomes in social studies subjects in class VIII SMP Terpadu Ibnu Muay Babelan Bekasi. The type of research conducted is quantitative. This research is ex post facto. This research was conducted in class VIII SMP Terpadu Ibnu Muay Babelan Bekasi. This research is quantitative associative with a sample size of 30 students. This data collection was carried out using the Google Classroom Media scale and documentation of even semester report cards. Validity test using content validity test (expert judgment). The reliability test uses Cronbach's Alpha which produces a reliability index of 0.968. The item correlation test is used to determine the consistency of respondents' answers. The data analysis technique was carried out by descriptive statistical analysis while to determine the relationship between the two variables, namely Google Classroom Media and student learning outcomes obtained through calculations with product moment correlation. The results showed that there was a positive and significant relationship between Google Classroom Media and student learning outcomes. Social Studies subject for class VIII of the Integrated Junior High School of Ibnu Muay Babelan Bekasi for the 2020/2021 academic year. This is indicated by the Pearson Correlation value on SPSS of 0.497 and the P value of 0.00 &lt;0.05 which indicates a positive and significant relationship.
Segmentation of Lines and Words of Handwritten Devanagari Text using Connected Components with Statistics Method

The pre-processing activities for handwritten Devanagari text recognition includes an significant step called Segmentation. The segmentation accuracy of Devanagari text characters depends entirely on the accurately segmented lines and words in the handwritten documents. The process of segmenting lines and words correctly leads to many issues. More detailed information is lagging on the segmentation of lines and words from Devanagari text documents, whereas it is available more for other script documents in the literature. Here, we accomplished the task of segmenting the lines and words using Connected Components with Statistics Method on PHDIndic_11 dataset. Experimentation using above mentioned method resulted in line segmentation accuracy of 91.91% and word segmentation accuracy of 72.89% which outperforms over Global threshold and Otsu’s optimum threshold methods.
Effectiveness of Video-Assisted Teaching on Knowledge and Attitude Regarding Attention Deficit Hyperactivity Disorder among Primary School Teachers in Gurugram, Haryana, India: A Pre-Experimental Study

The study was conducted in selected schools of Gurugram, Haryana. Sixty (60) primary school teachers were selected from three schools of Gurugram by using total enumeration sampling technique. Self-structured knowledge questionnaire and attitude rating scale was used to assess the knowledge and attitude of primary school teachers regarding ADHD. The findings revealed that in the pre-test, majority 41(68.3%) of teachers had inadequate knowledge and 19(31.7%) had moderate knowledge and 28(46.7%) had unfavourable attitude and 32(53.3%) had Neutral attitude whereas in the post-test, 15(25%) had moderate knowledge and majority 45(75%) had adequate knowledge and 13(21.7%) had neutral attitude and majority 47(78.3%) had favourable attitude regarding ADHD. Paired “t” test was used to observe differences between pre and post-test mean scores and were found statistically significant at 0.05 level. The study concluded that educational material like video assisted teaching had shown significant improvement in knowledge and attitude of primary school teachers.
Efficient In-Memory Evaluation of Reachability Graph Pattern Queries on Data Graphs

Graphs are a widely used data model in modern data-intensive applications. Graph pattern matching is a fundamental operation for the exploration and analysis of large data graphs. In this paper, we present a novel approach for efficiently finding homomorphic matches of graph pattern queries, where pattern edges denote reachability relationships between nodes in the data graph. We first propose the concept of query reachability graph to compactly encode all the possible homomorphisms from a query pattern to the data graph. Then, we design a graph traversal-based filtering method to prune nodes from the data graph which violate reachability conditions induced by the pattern edges. We use the pruned data graph to generate a refined query reachability graph which serves as a compact search space for the pattern query answer. Finally, we design a multiway join algorithm to enumerate answer tuples from the query reachability graph without generating an excessive number of redundant intermediate results (a drawback of previous approaches). We experimentally verify the efficiency of our approach and demonstrate that it outperforms by far existing approaches and a recent graph DBMS on evaluating reachability graph pattern queries.
Enhanced Local Ternary Pattern method for Face Recognition

Biometrics is a term used to determine an individual's identification based on physiological or behavioral traits. Such physiological or behavioral characteristics differ from person to person. For this reason, it is more secure and popular to authenticate the person using biological characteristics than other conventional authentication methods. The Local Binary Pattern (LBP) face recognition system is widely used but is noise sensitive. For the purpose of improving the performance, a descriptor of a local texture called Local Ternary Pattern (LTP) is introduced, which is more discriminating in uniform regions and less noise sensitive. The proposed method called Enhanced LTP (ELTP), uses pre-processing technique. Here, the input image is pre-processed using Gamma Correction and Histogram Equalization. The LTP is applied on pre-processed image to get the finalized feature vectors. Experimentation is conducted on the standard datasets ORL, UMIST and VTU (VISA) face datasets. It is proved that ELTP shows better accuracy than other face recognition methods.
HandArch: A deep learning architecture for LIBRAS hand configuration recognition

Despite the recent advancements in deep learning, sign language recognition persists as a challenge in computer vision due to its complexity in shape and movement patterns. Current studies that address sign language recognition treat hand pose recognition as an image classification problem. Based on this approach, we introduce HandArch, a novel architecture for realtime hand pose recognition from video to accelerate the development of sign language recognition applications. Furthermore, we present Libras91, a novel dataset of Brazilian sign language (LIBRAS) hand configurations containing 91 classes and 108,896 samples. Experimental results show that our approach surpasses the accuracy of previous studies while working in real-time on video files. The recognition accuracy of our system is 99% for the novel dataset and over 95% for other hand pose datasets.
Pengaruh Pembelajaran Daring terhadap Prestasi Belajar IPS Siswa Kelas VIII SMPN 20 Bekasi

Based on the research conducted by researchers at the Department of Economic Education, Panca Sakti University, Bekasi, this study aims to analyze: 1) Knowing the impact of online learning on the world of education, 2) There are limitations of learning media in online learning that affect teaching and learning, and 3) Knowing the success rate of online learning for social studies lessons at SMPN 20 Bekasi. The results of this study indicate that there is a significant influence between online learning (X) on social studies learning achievement (Y). Based on data processing and data analysis assisted by the SPSS version 16 program, a correlation coefficient (r) of 0.242 means that the influence of online learning on social studies learning achievement has a significant effect, as indicated by R Square = 0.128 which means that 12,8% of variable variation learning achievement can be affected by online learning.
Drilling Lexico-Semantic Knowledge in Portuguese from BERT

We compiled a set of patterns that denote lexico-semantic relations in Portuguese, created templates where one argument is fixed and the other is replaced by a mask, and then used BERT for predicting the latter. For most relations and measures, when assessed in a test of lexico-semantic analogies, BERT predictions outperformed those of earlier methods computed from static word embeddings, either with a pattern alone or with a combination of patterns. There is still a large margin of progression, but this suggests that BERT can be used as a source of lexico-semantic knowledge in Portuguese.
QUIZIZZ ATAU KAHOOT, GAMIFIKASI DALAM PEMBELAJARAN BAHASA INGGRIS

Gamification is the comprehensive use of game features with the aim of improving student’s performance and motivation in learning. The purpose of this present research is to conduct a systematic review of previous relevant research using the Quizizz and Kahoot! in learning English during 2021. The research method employed a Literature Study through a search on the Google Scholar page. Synthesis of literature data was analyzed from various components of students’ needs in learning English using Quizziz and Kahoot! The results point out that the online learning application Quizizz and Kahoot! have various advantages for learning English, among others, increasing the students’ ability and motivation in participating in the process of learning English actively and communicatively.
A Method For Multiclass Lymphoma Classification Based on Morphological and Non-Morphological Descriptors

Lymphoma is one of the most common types of cancer and its treatment can be more effective if the disease variant is correctly diagnosed. Many works have been done using computer vision and machine learning to classify the images. This work presents lymphoma based on histological a method using simple descriptors and a decision tree-based ensemble classifier, aiming to maintaing the interpretability of the data and understand what information in most important to the classification task. We use morphological and non morphological descriptors extracted from the cells nuclei, a feature selection method based on principal component analysis (PCA), and a gradient boosting decision tree (GBDT) method for multiclass classification. Our approach achieves an average accuracy of 0.932. this result is close to those obtained in the state of the art, while it uses simpler descriptors and better interpretable classification models.
Theoretical Educational Practices of Siraja Javanese Script Interactive Learning Media Based On 2D Application

One of the cultural heritage of the ancestors of the Indonesian nation which is priceless is traditional script. The preservation of Javanese script has been carried out by the government, one of them is by including it in the education curriculum. Javanese script is one of the basic competencies that is less understood and sought after, most students consider Javanese script difficult to learn from both pronunciation and form. Based on observations made at SD Negeri Kleco 1 that the learning method used is the conventional method, not using instructional media. This study aims to determine the feasibility of the media and the influence of the use of interactive learning media on student learning outcomes in Javanese language. This research uses the R&amp;D method with the ADDIE model. This study uses a Matching pretest and posttest control group design. Media validity of 21 items was declared valid with an average value of a coefficient of 0.87 and perfect reliability with a result of 0.943. The material content validity of 13 items was declared valid with an average value of a coefficient of 0.91 and strong reliability with a result of 0.745. Student questionnaire assessment results are categorized as acceptable with an average value of 80%. The value of learning outcomes of the control class is smaller than that of the experimental class, which is 65.73 &lt; 76.97. The conclusion of this research is that SIRAJA learning media is appropriate and influential in improving student learning outcomes in Javanese language, especially Javanese script material.
A Count Sketch Maximal Weighted Residual Kaczmarz Method with Oblique Projection for Highly Overdetermined Linear Systems

Motivated by the count sketch maximal weighted residual Kaczmarz (CS-MWRK) method presented by Zhang and Li (Appl.Math.Comput., 410, 126486), we combine the count sketch tech with the maximal weighted residual Kaczmarz Method with Oblique Projection (MWRKO) constructed by Wang, Li, Bao and Liu (arXiv: 2106.13606) to develop a new method for solving highly overdetermined linear systems.The convergence rate of the new method is analyzed.Numerical results demonstrate that our method performs better in computing time compared with the CS-MWRK and MWRKO methods.
Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks

Current recommendation works mainly rely on the semantic information of meta-paths sampled from the heterogeneous information network (HIN). However, the diversity of meta-path sampling has not been well guaranteed. Moreover, changes in user’s reading preferences and paper’s audiences in the short term are often overshadowed by long-term fixed trends. In this paper, we propose a paper recommendation model, called COMRec, where the diversity and dynamics are jointly exploited in HIN. To enhance the semantic diversity of meta-path, we propose a novel in-out degree sampling method that can comprehensively capture the diverse semantic relationships between different types of entities. To incorporate the dynamic changes into the recommended results, we propose a compensation mechanism based on the Bi-directional Long Short-Term Memory Recurrent Neural Network (Bi-LSTM) to mine the dynamic trend. Extensive experiments results demonstrate that COMRec outperforms the representative baselines.
The School–Enterprise Cooperation in Vocational Education in China

This chapter explores the school–enterprise cooperation in vocational education in China.
GAME MECHANICS OF GAMIFICATION IN CAREER GUIDANCE AND PROFESSIONAL SELF-DETERMINATION OF CHILDREN FROM DIFFERENT AGE GROUPS IN THE EDUCATION SYSTEM

The authors of the article argue that at present there is an objective need to update and introduce into practice new models of career guidance and professional self-determination of children from different age groups - preschoolers, junior schoolchildren, adolescents - using modern digital resources in the process of lifelong education. The purpose of this study is to develop and experimentally test vocational guidance game mechanics in the formation of children’s digital footprint in the process of vocational guidance. The article describes the developed game mechanics in the form of a quest, which are used in the career guidance process applying gamification resources.