Reviewerly/exHarmony huggingface dataset:
Implementasi Model Project based learning Sebagai Upaya Mengembangkan Multiple Intelligences Siswa

This research is motivated by the statement that every human being has multiple intelligences called multiple intelligences, meaning that every human being has their own superior intelligence. This intelligence should be developed in schools. Unfortunately, not all teachers know the right method to develop this intelligence. Therefore, the purpose of this study is to find out what aspects of multiple intelligences develop during the implementation of project based learning at MI Al Fithrah Surabaya and the implications of implementing project based learning. The implementation of Project based learning learning is only limited to gadget addiction material which is carried out in class 3C MI Al Fithrah Surabaya. The research approach used is descriptive qualitative with the case study method. The instruments used were observation, interviews and documentation. The results showed that during the implementation of project based learning learning all aspects of students' multiple intelligences developed, namely logical mathematical intelligence, linguistic intelligence, kinesthetic intelligence, musical intelligence, interpersonal intelligence, intrapersonal intelligence, naturalist intelligence, visual spatial, except for existential intelligence which was not develop. Mathematical logical intelligence and linguistic intelligence can develop very well. In addition, the impact of the implementation of the project based learning learning model is that there are five products produced by students, namely: Madasi Medicine (Siri Leaf Eyes), Gadget Addiction Danger Videos, Puppet Games, Wormat (Tomato Carrots), Gadget Diet Benefit Posters, as well as increased academic and non-academic achievements of students.&#x0D; &#x0D;
Custom Bell inequalities from formal sums of squares

Bell inequalities play a key role in certifying quantum properties for device-independent quantum information protocols. It is still a major challenge, however, to devise Bell inequalities tailored for an arbitrary given quantum state. Existing approaches based on sums of squares provide results in this direction, but they are restricted by the necessity of first choosing measurement settings suited to the state. Here, we show how the sum of square property can be enforced for an arbitrary target state by making an appropriate choice of nullifiers, which is made possible by leaving freedom in the choice of measurement. Using our method, we construct simple Bell inequalities for several families of quantum states, including partially entangled multipartite GHZ states and qutrit states. In most cases we are able to prove that the constructed Bell inequalities achieve self-testing of the target state. We also use the freedom in the choice of measurement to self-test partially entangled two-qubit states with a family of settings with two parameters. Finally, we show that some statistics can be self-tested with distinct Bell inequalities, hence obtaining new insight on the shape of the set of quantum correlations.
A data package for abstractive opinion summarization, title generation, and rating-based sentiment prediction for airline reviews

Customer reviews are valuable resources containing customer opinions and sentiments toward the product. The reviews are informative but can be quite lengthy or may contain repetitive information calling for opinion summarization systems that retain only the significant opinion information from the review. Abstractive summarization is a form of text summarization that generates a summary mimicking a human-written summary [1]. When pretrained language models are finetuned for abstractive review summarization, there usually occurs a problem known as the 'domain shift', because the source and target domains exhibit data from varying distributions [2]. This issue results in performance degradation of the model at the target end. This paper contributes a data package comprising of an annotated abstractive summarization dataset (annotated_abs_summ) of airline reviews having 500 reviews and abstractive summary pairs, a dataset (review_titles_data) consisting of 7079 reviews and review title pairs for review title generatioon or domain adaptive training [3] to address the domain shift problem for abstractive opinion summarization and, an annotated reviews dataset (annotated_sentiment) for rating-based sentiment classification. All datasets have been collected from the Skytrax Review Portal via web scraping using Python programming language. The datasets have several potential use cases. The abstractive summarization dataset can serve as a benchmark dataset for airline review summarization. The dataset for domain adaptive training can be used as a standalone dataset for review title generation. The dataset for sentiment analysis is multipurpose having columns like user rating and recommendation value, that can be used for statistical analysis like finding correlation between these data items as well as for other Natural Language Processing (NLP) tasks like predicting rating or recommendation value from the customer reviews. The datasets can be extended using various data augmentation techniques [4,5]. Moreover, the datasets are related and can be collectively used to develop a multi-task learning model [6] for better learning efficiency and improved performance.
PENGGUNAAN PLATFORM EDMODO BERBASIS E-LEARNING TERHADAP HASIL BELAJAR MATEMATIKA SISWA

This study aims to determine the effectiveness of using the edmodo platform based on e-learning­ on students mathematics learning outcomes on number pattern material. The research was carried out at SMP Negeri 1 Botupingge in the odd semester of the 2021/2022 academic year. This type of research is an experimental research using the quasi-experimental method. The research design used was a non-equivalent control group design involving 2 classes as the research sample, namely the experimental class and the control class which were given different treatment. The sample was selected using a purposive sampling technique with the number of students in each class as many as 25 students. The data collection technique used is the provision of tests including pre-test and post-test in the form of essays to obtain data on student learning outcomes. The results of the analysis with the t-test at a significance level of 0.05 indicate a difference between the learning outcomes of students who are taught using Edmodo and Google Meet. The average student learning outcomes using Edmodo is 77.64, which is higher than the average student learning outcomes using Google Meet at 67.72. Based on the results of the average N-Gain calculation, the gain value is 0.61. This shows that the effectiveness of using the Edmodo platform is in the medium category. This study measures student learning outcomes in the cognitive domain using the Edmodo platform, so it is hoped that who will conduct researcher further research on the use of the Edmodo platform in the affective domain or in other educational units.
Studi Kinerja Inlet Persegi Panjang sebagai Drainase Jalan

High rainfall condition, especially in tropical countries, often cause flooding or inundation on road section, especially urban road. Puddles that do not enter or are hampered can cause damage to the road. This can be overcome by the inlet design of the road drainage channel that is suitable for the conditions in the field. By paying attention to the distance between inlets, dimensions, and inlet types adjusted to the rainwater discharge and the width of the existing road. Street Inlet is a hole on the sides of the road that serves to accommodate and distribute rainwater runoff along the road leading into the drainage channel.The research was carried out on a prototype that describes the condition of the highway with street inlet modifications such as conditions in the field. The method of analyzing surface runoff in the use of rational methods, inlet dimension analysis is used in the applicable hydraulic rules. The input data used are rainfall data, type of road, type of inlet street, rain runoff or inundation, drainage channel conditions, linear regression. This study discusses the performance of the road inlet to reduce inundation due to rain runoff (with a model of a rectangular inlet on the shoulder of the road). In the research carried out the type of inlet that will be used is the gutter inlet which has a horizontal opening.
Research on Matching Players Before and After Occlusion for Field Sports

In field sports, to realize tactical analysis, many techniques for athlete detection, identification and tracking by analyzing videos have been developed. However, many techniques have the same problems that the accuracy of detection, identification and tracking will decline in quality while athletes are occluding. In this research, the authors focus on the fact that the features of each athlete before and after occlusion are approximated, and then developed the method for identifying athletes with their features before and after occlusion. In the results, we were able to solve the problem of degrading detection, identification and tracking accuracy in the occlusion interval.
Research on Removing Shadow of Field Sport Players Using GAN

In recent years, the utilization of ICT for field sports has been promoted in Japan. And then, a lot of researches have been conducted on tactical analysis using positions and trajectories of players obtained from play video data. However, in many of them, an area of an athlete may be misidentified as area containing shadow or area of only shadow, which leads to a problem of acquiring accurate positions of athletes. Therefore, in this research, a method to remove the shadows of players from soccer play videos by using pix2pix, a type of GAN, to generate shadow-free videos is proposed and implemented. As a result, it was verified through experiments that the shadows of athletes could be removed accurately. Furthermore, the effectiveness of the proposed method was also confirmed by tracking accuracy comparison of an existing person tracking method with and without shadow removal.
An Efficient Convolutional Neural Network Accelerator

This paper proposes a three-dimensional tree architecture. This architecture consists of 32 tree architectures. Each tree architecture is responsible for all operations of a kernel, so that each kernel can be processed in parallel. The inner product operation in each kernel can also use the characteristics of the tree architecture to achieve parallel operations. Operations in two different dimensions achieve the goal of parallel processing.
Overshoot Reduction Inspired Recurrent RBF Neural Network Controller Design for PMSM

With the rapid development of computer science, neural network (NN) has received widespread attention. However, it is mainly used as a black box in identification and prediction, which is highly complicated. Its explainability should be mentioned when it is used in real applications. So, this paper derives the explainable control scheme to make sure its theoretical rigorousness. Then a straightforward explanation of the neural network controller using a block diagram is given to help researchers to reproduce the method. Since the detailed design of the neural network should concern the principle of permanent magnet synchronous motor (PMSM), this analyses the real PMSM system and finds the control performance characteristic, especially in spontaneous disturbances. Unlike the black-box neural network, the recurrent radial basis function neural network (RRBF) controller method is proposed, guided by overshoot. Because various PMSMs have various performances of speed changing, three key functions of RRBF are proposed to check which variant is the most suitable for reducing the overshoot. Finally, the RRBF method is implemented in STM32, and its effectiveness in the experiment is checked.
Geochemical elements as provenance and paleoenvironmental indicators in siliciclastic sediments of Neogene Gwandu formation exposed in Birnin Kebbi Area, Northwestern Nigeria

No Abstract
Belief Propagation Meets Lattice Reduction: Security Estimates for Error-Tolerant Key Recovery from Decryption Errors

In LWE-based KEMs, observed decryption errors leak information about the secret key in the form of equations or inequalities. Several practical fault attacks have already exploited such leakage by either directly applying a fault or enabling a chosen-ciphertext attack using a fault. When the leaked information is in the form of inequalities, the recovery of the secret key is not trivial. Recent methods use either statistical or algebraic methods (but not both), with some being able to handle incorrect information. Having in mind that integration of the side-channel information is a crucial part of several classes of implementation attacks on LWEbased schemes, it is an important question whether statistically processed information can be successfully integrated in lattice reduction algorithms.We answer this question positively by proposing an error-tolerant combination of statistical and algebraic methods that make use of the advantages of both approaches. The combination enables us to improve upon existing methods – we use both fewer inequalities and are more resistant to errors. We further provide precise security estimates based on the number of available inequalities.Our recovery method applies to several types of implementation attacks in which decryption errors are used in a chosen-ciphertext attack. We practically demonstrate the improved performance of our approach in a key-recovery attack against Kyber with fault-induced decryption errors.
Enhancing Quality and Security of the PLL-TRNG

Field Programmable Gate Arrays (FPGAs) are used more and more frequently to implement cryptographic systems, which need random number generators (RNGs) to be embedded in the same device. The main challenge related to the implementation of a generator running inside FPGAs is that the physical source of randomness, such as jittered clock generator, is implemented in the configurable logic area, i.e. in the close vicinity of noisy running algorithms, which can have significant impact on generated numbers or even serve to attack the generator. A possible approach to prevent such influence is the use of Phase-Lock Loops (PLLs), which are separated from the re-configurable logic area inside the FPGA chip. In this paper, we propose a new architecture of the PLL-based TRNG including a method to avoid correlation in the output through control of timing in the sampling process, as well as new embedded tests based on the enhanced stochastic model. We also propose a workflow to help find the best parameters, such as output bitrate and entropy rate. We show that bitrates of around 400 kb/s or more can be achieved, while guaranteeing min-entropy rates per bit higher than 0.98 as required by the latest security standards.
Solidity Smart Contract Vulnerabilities, Attack Scenarios, and Mitigation—A Survey

Blockchain technology has risen phenomenally in the last decade, making it the preferred platform for the transfer of digital assets. Unfortunately, the landscape of smart contracts deploying the blockchain technology is still rife with vulnerabilities. The adoption of blockchain based smart contracts is on the uprise. It is being adopted in the fields of health care, insurance, education, and governance. This paper discusses potential vulnerabilities and exploits affecting Ethereum Solidity Smart Contracts on a daily basis. These vulnerabilities are discussed along with their attack scenarios and mitigation techniques. Solidity program snippets are provided for better understanding of the scenarios where exploits can take place.
Atrició fonològica i desestabilització de l’estàndard en la televisió valenciana

&#x0D; &#x0D; &#x0D; In the context of the Valencian linguistic conflict, the native language undergoes a progressive reduction of the phonological repertoire that tends to eliminate any phoneme that does not exist in the dominant language. This ongoing process exceeds the traits and territory of the well-known apitxat. This adaptation of the phonological system to that of Spanish is accompanied by lexical impoverishment, semantic and grammatical calque of the dominant language. In such a situation, mass media can be a tool not only to increase language use, but also for standardization, that is, the transmission of a normative and genuine model of language. In this contribution, we analyse a corpus of news from the Valencian public television À Punt, in a period of great prominence of the COVID-19 pandemic, to find out what impact the attrition of phonemes has on the speech of professional broadcasters. From a sociophonic point of view, we focus on the pronunciation diverging from the normative Valencian, especially when caused by the approach to Spanish, in order to determine the model of language that this broadcaster transmits to the Valencian society.&#x0D; &#x0D; &#x0D;
Character-based Writer Verification of Ancient Hebrew Square-script Manuscripts: On Edge-direction Feature

Handwriting significantly contributes to the task of the writer identification and verification of modern and historical documents. This work developed a writer verification system for ancient Hebrew square-script manuscripts, mainly based on the edge-direction feature. Two configurations within the proposed system are carried out, i.e., character-based edge-direction feature extraction and extraction techniques of handwriting shape representation that may drive the system performance. A classification-based verification approach, utilizing Support Vector Machine (SVM) as the classifier, is employed to evaluate the performance of the two configurations. This study has confirmed that the skeleton-based shape representation technique outperforms the edge detection technique used in the predecessor approach. Furthermore, a character-based writer verification system provides the corresponding scholars and experts with an alphabetical investigation to identify the uniqueness of each writer&#x2019;s handwriting.
A Multi-objective 3D Offline UAV Path Planning Problem with Variable Flying Altitude

This study focuses on a 3D multi-objective collision-free offline UAV path planning problem by considering the variability in flying altitude over an urban environment that is replete with static obstacles. The environment is decomposed into several equal-sized ground cells with an infinite flying altitude. The UAV can adjust the altitude to fly above obstacles or bypass them by choosing another way. Various cells may have different flying risks at different altitude levels. This study aims to find the most efficient and safe trajectory toward the destination while maximizing the number of visited obstacle-free cells. This aim could be followed in real-world surveillance and disaster tracking operations, where the users may want to collect data even en route to the destination. This problem is formulated as a multi-objective mixed-integer non-linear mathematical model in which minimizing the flying distance, the required energy, the maximum path risk, and the number of not-visited obstacle-free cells are the objective functions. An enhanced Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is developed to solve the problem. Computational results indicate the superiority of the developed algorithm in solving several real-world data sets.
A Comparative Analytical Study of Breast Cancer Prediction Techniques Using Deep Learning Approaches for Research Novices

Breast cancer is when the cells in the breast tissue alter and proliferate uncontrollably, resulting in tumors and lumps. The pathways that connect the lobules to the papilla, or the lobules themselves, are the starting point for the development of most breast cancers. First, our study includes a thorough analysis of DL-based breast cancer prediction. Second, we provide a comparative study of the datasets best suited for breast cancer prediction. Third, we provide a comparative analysis of the most suitable core DL approaches for breast cancer prediction. Then, use the Statistical Variation Tool ANOVA parameters to refine the analysis of the best core forecasting algorithm, extract the accuracy gains, and generate statistical evidence for the best forecasting algorithm. In this study, the LDA AE algorithm provided a high accuracy of 98.27% using the gene expression dataset. The study concludes with an overview of current research and breast cancer detection and classification challenges.
Sistem dan Prosedur Akuntansi Pembelian Persediaan Bahan Baku Sparepart di PT. Bengkalis Dockindo Perkasa

This study aims to find out about the accounting information systems and procedures for purchasing spare parts raw materials that are applied to PT. Bengkalis Dockindo Perkasa whether it is in accordance with standard operating procedures. This study uses a qualitative descriptive analysis method because the data concerning accounting and other data support this research. Based on the results of the research, the company has a minimum stock to repair ships and manufacture other products, but there are some raw materials that are not available in the warehouse due to requests from consumers, so the company must purchase raw materials in an indented manner through the selected supplier. The existence of an accounting information system that regulates the inventory purchasing process should be able to take into account when and how many companies will purchase spare parts raw material inventory. Due to uncertain orders from the field, it is difficult to estimate the company's raw material purchases.Keywords: Accounting information system, purchase of raw material inventory.
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders

In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system introduced in Qiang et al. (2020) in the following ways: For the subtask of simplification candidate selection, it utilizes a RoBERTa transformer language model and expands the size of the generated candidate list. For subsequent substitution ranking, it introduces a new feature weighting scheme and adopts a candidate filtering method based on textual entailment to maximize semantic similarity between the target word and its simplification. Our best-performing system improves LSBert by 5.9% accuracy and achieves second place out of 33 ranked solutions.
JADES: New Text Simplification Dataset in Japanese Targeted at Non-Native Speakers

The user-dependency of Text Simplification makes its evaluation obscure. A targeted evaluation dataset clarifies the purpose of simplification, though its specification is hard to define. We built JADES (JApanese Dataset for the Evaluation of Simplification), a text simplification dataset targeted at non-native Japanese speakers, according to public vocabulary and grammar profiles. JADES comprises 3,907 complex-simple sentence pairs annotated by an expert. Analysis of JADES shows that wide and multiple rewriting operations were applied through simplification. Furthermore, we analyzed outputs on JADES from several benchmark systems and automatic and manual scores of them. Results of these analyses highlight differences between English and Japanese in operations and evaluations.