Reviewerly/exHarmony huggingface dataset:
Trajectory optimization of UAV based on Hp-adaptive Radau pseudospectral method

&lt;p style='text-indent:20px;'&gt;Unmanned Aerial Vehicles (UAVs) have been extensively studied to complete the missions in recent years. The UAV trajectory planning is an important area. Different from the commonly used methods based on path search, which are difficult to consider the UAV state and dynamics constraints, so that the planned trajectory cannot be tracked completely. The UAV trajectory planning problem is considered as an optimization problem for research, considering the dynamics constraints of the UAV and the terrain obstacle constraints during flight. An hp-adaptive Radau pseudospectral method based UAV trajectory planning scheme is proposed by taking the UAV dynamics into account. Numerical experiments are carried out to show the effectiveness and superior of the proposed method. Simulation results show that the proposed method outperform the well-known RRT* and A* algorithm in terms of tracking error.&lt;/p&gt;
THE EFFECTIVENESS OF INSTAGRAM FILTER TO IMPROVE STUDENTS’ VOCABULARY

Vocabulary becomes an important aspect to learn because it is the tool to recognize words' meaning and sentences, and to create sentences, paragraphs and texts.In order to improve students' vocabularies, media are needed to help students memorize them.The aim of the research is to notice how effective Instagram filter in enhancing students' vocabulary comprehension.For this reason, teacher should find an innovative media for teaching vocabulary to students in most interesting way.Lately, Instagram becomes the most widely used social networking platform.Most people spend a lot of time scrolling on Instagram just to have fun, find some information, or even to get new friends.This platform is served with feed, stories, reel, IG TV and IG filter.This study employed IG filter to be used as a media to teach vocabulary to students.This is based on what the researchers observed that most students in schools, in this case is high school, like to play with Instagram filters.This what makes the researchers interested in using Instagram filter as a media for teaching vocabularies.This research is designed as quasi experiment which is held in one of senior high schools in Jepara.The findings reveal that IG filter is effective and successful to be used as a media for teaching vocabulary and to improve students' comprehension in vocabulary.The average score was in good category (86.33).While the N-gain score was 58.72% which is categorized as quite effective.It means that Instagram filters is quite effective as a learning media.
An ethical analysis of the 2016 data scandal: Cambridge Analytica and Facebook

This paper analyzes the ethics behind the actions of the 2016 Data Scandal on the example of 2 major sides, Facebook and Cambridge Analytica. Subsequent events such as bankruptcy of Cambridge Analytica and a significant drop in the stock prices of Facebook (a fall of 24%, equivalent to $134 billion.) were an integral part of this research paper to explore the role of the attitudes of the business entities over bankruptcy in these kinds of scandals. Thereby, a comparison technique has been employed to analyze the ethical dimension of the bankruptcy of Cambridge Analytica, and how the attitude of Facebook provided a chance of survival and recovery within this process. The outcome of the research clearly identifies that even in the corporative entities bypassing or violating the ethical standards can be observed. Albeit, there is a strong correlation between the degree of ethical standards and the sustainability of the businesses from the aspect of customers, partners, and the government. The article continued with the analysis of the significance of the immediate implementation of the ethical standards and deterrent defense with a manner of “bona fide” in these types of scandals to handle the crisis. The research concluded with an ethical analysis of data analysis and data mining from the Kantian definition of autonomy, Jurgen Habermas’s definition of privacy in the era of digitalization.
Entanglement witnesses from mutually unbiased measurements

A new family of positive, trace-preserving maps is introduced. It is defined using the mutually unbiased measurements, which generalize the notion of mutual unbiasedness of orthonormal bases. This family allows one to define entanglement witnesses whose indecomposability depends on the characteristics of the associated measurement operators. We provide examples of indecomposable witnesses and compare their entanglement detection properties with the realignment criterion.
Using code-switching as the teaching strategy in classroom for low English proficiency learners

This qualitative study is aimed to find the function of teachers’ code-switching (CS) to support low proficiency learners in learning English and to determine the benefits of CS practiced by the teachers in the EFL learning. The data of this study were obtained from two English teachers at Junior Islamic Boarding School of Darul Ihsan, Aceh Besar. The observation sheet and questionnaire were used to collect the data. Two English classes were observed in two sessions respectively. All of the conversation in the classroom was recorded and the code-switching parts were then transcribed and analyzed. The results showed that the teachers used CS for clarification, explaining difficult grammatical terms, building connections between teacher and students, quoting specific terms that is not available in English, adding specification, repeating message which previously said in English, explaining message qualification, and emphasizing a particular issue. The results of the questionnaires showed several benefits of implementing CS for students, such as enjoying communication during learning activities, feeling satisfied and comfortable in the learning situation, feeling less tense and rigid, focusing more on the lesson, improving their ability to understand new words, and helping them understand difficult concepts and grammar in English.
Empowering Natural Language Interfaces to Databases with Aggregations

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.
EmbAssi: embedding assignment costs for similarity search in large graph databases

The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation is NP-hard and challenging in practice. We introduce methods for answering nearest neighbor and range queries regarding this distance efficiently for large databases with up to millions of graphs. We build on the filter-verification paradigm, where lower and upper bounds are used to reduce the number of exact computations of the graph edit distance. Highly effective bounds for this involve solving a linear assignment problem for each graph in the database, which is prohibitive in massive datasets. Index-based approaches typically provide only weak bounds leading to high computational costs verification. In this work, we derive novel lower bounds for efficient filtering from restricted assignment problems, where the cost function is a tree metric. This special case allows embedding the costs of optimal assignments isometrically into $\ell_1$ space, rendering efficient indexing possible. We propose several lower bounds of the graph edit distance obtained from tree metrics reflecting the edit costs, which are combined for effective filtering. Our method termed EmbAssi can be integrated into existing filter-verification pipelines as a fast and effective pre-filtering step. Empirically we show that for many real-world graphs our lower bounds are already close to the exact graph edit distance, while our index construction and search scales to very large databases.
Graph Contrastive Clustering

Recently, some contrastive learning methods have been proposed to simultaneously learn representations and clustering assignments, achieving significant improvements. However, these methods do not take the category information and clustering objective into consideration, thus the learned representations are not optimal for clustering and the performance might be limited. Towards this issue, we first propose a novel graph contrastive learning framework, which is then applied to the clustering task and we come up with the Graph Constrastive Clustering~(GCC) method. Different from basic contrastive clustering that only assumes an image and its augmentation should share similar representation and clustering assignments, we lift the instance-level consistency to the cluster-level consistency with the assumption that samples in one cluster and their augmentations should all be similar. Specifically, on the one hand, the graph Laplacian based contrastive loss is proposed to learn more discriminative and clustering-friendly features. On the other hand, a novel graph-based contrastive learning strategy is proposed to learn more compact clustering assignments. Both of them incorporate the latent category information to reduce the intra-cluster variance while increasing the inter-cluster variance. Experiments on six commonly used datasets demonstrate the superiority of our proposed approach over the state-of-the-art methods.
Effective Bitext Extraction From Comparable Corpora Using a Combination of Three Different Approaches

Parallel sentences extracted from comparable corpora can be useful to supplement parallel corpora when training machine translation (MT) systems.This is even more prominent in low-resource scenarios, where parallel corpora are scarce.In this paper, we present a system which uses three very different measures to identify and score parallel sentences from comparable corpora.We measure the accuracy of our methods in low-resource settings by comparing the results against manually curated test data for English-Icelandic, and by evaluating an MT system trained on the concatenation of the parallel data extracted by our approach and an existing data set.We show that the system is capable of extracting useful parallel sentences with high accuracy, and that the extracted pairs substantially increase translation quality of an MT system trained on the data, as measured by automatic evaluation metrics.
Remote Microcontroller Scanner Design for STM32 Microcontrollers Used to Distance Education Form

The novel coronavirus disease (Covid-19) is a global problem that has caused many changes in people's lives. One of the problems is distance education, that has caused a difficulty form of study. Students often do not have required study components, because they are in the home and teacher does not have complete control over them. This paper described remote control and monitor system for the microcontroller technique subject, where the teacher gets possibilities for monitoring each student’s microcontroller program. The subject is focused to the basic peripherals and functionalities of STM32 microcontrollers and internet communication based on the ESP8266 chip. Therefore, the article contains of monitoring module design, remote control application and simple test of the remote-control scanner for STM32 microcontrollers focused to the distance form of study.
Continuity Of Educational Practice Programs From The Position Of A Modular Approach

Modernization of education in the logic of the third generation federal state educational standards requires changes in the construction of educational practices from the standpoint of strengthening their role in the formation of a graduate's competence model and the implementation of continuity within the educational module.The article presents an analysis of building practice programs within the framework of a modular approach, identifies a number of key tasks, the solution of which makes it possible to effectively build an educational program, identifies the positive results of implementing educational practices from this position.The authors presented the experience of developing and implementing a modular program of educational practices in line with the competence-based approach to teaching students: the implementation of the continuity of submodules in the training module "Practices", the logical sequence of the studied submodules, the introduction of a system of certification procedures as a necessary measure to promote the formation of sustainable learning motivation for any type of activity.
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion

A typical pipeline for multi-object tracking (MOT) is to use a detector for object localization, and following re-identification (re-ID) for object association. This pipeline is partially motivated by recent progress in both object detection and re-ID, and partially motivated by biases in existing tracking datasets, where most objects tend to have distinguishing appearance and re-ID models are sufficient for establishing associations. In response to such bias, we would like to re-emphasize that methods for multi-object tracking should also work when object appearance is not sufficiently discriminative. To this end, we propose a large-scale dataset for multi-human tracking, where humans have similar appearance, diverse motion and extreme articulation. As the dataset contains mostly group dancing videos, we name it "DanceTrack". We expect DanceTrack to provide a better platform to develop more MOT algorithms that rely less on visual discrimination and depend more on motion analysis. We benchmark several state-of-the-art trackers on our dataset and observe a significant performance drop on DanceTrack when compared against existing benchmarks. The dataset, project code and competition server are released at: \url{https://github.com/DanceTrack}.
A Study of Online Platforms Learning During the Covid-19 Pandemic in College

Nearly a billion individuals have been quarantined since the COVID-19 epidemic began. As a result, several nations, including Indonesia, have enacted legislation prohibiting all forms of schooling and requiring governments and allied agencies to develop alternate ways of instruction for each educational institution. On the other hand, universities that have not yet developed an infrastructure for online learning may encounter obstacles. Many professors and students are wary of implementing online learning methodologies due to the ingrained culture of the traditional educational system. The study's objective was to ascertain how teaching and learning activities were conducted a year after Al Washliyah Labuhanbatu University was closed due to an epidemic. The study employed a mixed-method approach, collecting quantitative and qualitative data using questionnaires. The mixed-method strategy was supplemented with a Google Forms-based online questionnaire distributed to 100 students. Over 75% of instructors include video conferencing, learning management systems, and instant messaging in their presentations. During Covid-19 at Al Washliyah University, teachers agreed that combining the three platforms reduced obstacles to online learning and increased lecturers' and students' familiarity with the usage of online learning materials.
Towards a typology of questions for requirements elicitation interviews

Interviewing is known to be one of the most common requirements elicitation techniques. Interviews are driven by a series of questions asked for the purpose of receiving responses that can help understanding the domain and the needs of stakeholders. However, what constitutes a successful choice and ordering of questions continues to be more of an art than a systematic process. We review literature from a broad range of disciplines in which interviewing is widely applied, in order to identify a set of categories for characterizing interview questions. The resulting typology aims at offering an initial coding language for qualitatively analyzing interview content. Such coding language can be further validated for its reliability to enable standardization and community-wide reuse. We offer examples of how such an instrument would help researchers develop and evaluate both descriptive and normative theories of interviewing.
Keystroke Dynamics Classification Based On LSTM and BLSTM Models

By adopting keystroke dynamics, authentication applications can integrate advanced identity proofing technology for detecting fraud and prevent unauthorized access. However, understanding a user's keystroke dynamics behavior in real applications is a challenging task regarding that this behavior is notably changing over time. To mitigate this problem, we apply, in this paper, the long short-term memory (LSTM) model that recognizes a continuous sequences of keystroke dynamics to identify users of public datasets. We also consider the bidirectional long short-term memory (BLSTM) as it maintain information about the future data. Hence, collecting information about intra-class variations of the keystroke dynamics from both past and future data, is an interesting solution to our problem. The obtained results are promising since we obtained an accuracy rate over than 60% for both architectures when dealing with public databases.
Methodology for Constructing and Using a Mathematical Model for Assessing Labor Costs for the Development of Distance Learning Courses

Distance learning plays a significant role in solving the problem of the sustainable development of society. Unfortunately, the development and implementation of distance courses are still carried out intuitively, which does not allow practical implementation of effective mathematics methods and slows down the introduction of breakthrough technologies. The aim of the research was to develop a new model and a methodology for assessing the labor costs of designing distance learning courses by university teachers based on a comprehensive assessment of the courses’ quality. The suggested model originally extends the constructive cost evaluation model already used in IT industry. The developed criteria make it possible to assess labor costs in relation to the quality of course development. The paper provides and analyzes such calculations on the example of courses in the theory of algorithms and programming.
Tourism Vocational High School Students' Online EFL Learning Experience during COVID-19 Pandemic

This study aimed to explore the tourism vocational high school (VHS) students' experience of EFL learning during the COVID-19 pandemic. A qualitative study was utilized to obtain 20 students' experience. The data were obtained from grades 10 and 11 students of tourism VHS in Yogyakarta. A semi-structured interview was conducted, and verbatim transcripts were analyzed using inductive and thematic analysis approaches. Three major themes were identified from the students' experiences, namely (a) how they conducted the EFL teaching and learning, (b) language learning strategies, and (c) perceptions of online EFL learning. It was found that students have made use of all available resources. This study suggests that students have used appropriate language learning strategies for online EFL learning, such as cognitive, metacognitive, resource management, and social strategies. Their perceptions toward EFL online learning were mainly positive. However, students still prefer face-to-face learning to online learning.
Artificial Intelligence Machine Translation Based on Fuzzy Algorithm

In order to study machine translation more in-depth, it is particularly important for the research of artificial intelligence with fuzzy algorithms to convert an unfamiliar language into a mature language. The neural network translation model has been developed in recent years and has achieved rich research results. Aiming at the current lack of accuracy of neural machine translation (NMT), which may cause ambiguity, this paper takes English machine translation as an example and proposes an artificial intelligence machine translation optimization model based on fuzzy theory. On the basis of NMT model translation, first the semantics of English machine translation is classified, a semantic selection model is built, then the analytic hierarchy process is used to determine the semantic order of English machine translation, and the corresponding fault-tolerant operation is carried out to the error-prone errors, weight the semantics, and introduce the fuzzy theory to arrange the English semantics of English machine translation. Finally, the performance of the model is analyzed through specific application experiments. The results show that the accuracy of the machine translation selection permutation model is improved by nearly 4.5% and can reach more than 90% compared with other models, and the timeliness is better than other models, which is improved by nearly 15%, which has obvious advantages.
Composite Enclaves: Towards Disaggregated Trusted Execution

The ever-rising computation demand is forcing the move from the CPU to heterogeneous specialized hardware, which is readily available across modern datacenters through disaggregated infrastructure. On the other hand, trusted execution environments (TEEs), one of the most promising recent developments in hardware security, can only protect code confined in the CPU, limiting TEEs’ potential and applicability to a handful of applications. We observe that the TEEs’ hardware trusted computing base (TCB) is fixed at design time, which in practice leads to using untrusted software to employ peripherals in TEEs. Based on this observation, we propose composite enclaves with a configurable hardware and software TCB, allowing enclaves access to multiple computing and IO resources. Finally, we present two case studies of composite enclaves: i) an FPGA platform based on RISC-V Keystone connected to emulated peripherals and sensors, and ii) a large-scale accelerator. These case studies showcase a flexible but small TCB (2.5 KLoC for IO peripherals and drivers), with a low-performance overhead (only around 220 additional cycles for a context switch), thus demonstrating the feasibility of our approach and showing that it can work with a wide range of specialized hardware.
Extracting the power of dependent types

Most existing programming languages provide little support to formally state and prove properties about programs. Adding such capabilities is far from trivial, as it requires significant re-engineering of the existing compilers and tools. This paper proposes a novel technique to write correct-by-construction programs in languages without built-in verification capabilities, while maintaining the ability to use existing tools. This is achieved in three steps. Firstly, we give a shallow embedding of the language (or a subset) into a dependently typed language. Secondly, we write a program in that embedding, and we use dependent types to guarantee correctness properties of interest within the embedding. Thirdly, we extract a program written in the original language, so it can be used with existing compilers and tools.