Reviewerly/exHarmony huggingface dataset:
Pengaruh Pembelajaran Daring Terhadap Minat Belajar Siswa Pada Mata Pelajaran Bahasa Indonesia Kelas XII SMA Negeri 1 Selesai Langkat

The purpose of this study was to find out and analyze the effect of online learning on students' learning interest in Indonesian subjects in of Grade XII SMA Negeri 1 Selesai. The number of samples were 35 students of Grade XII MIA 4 taken from 322 of the total population. The purposive sampling technique was crried out in this study. The method used in this research is descriptive method with a quantitative approach. The simple linear analytical analysis method used in this study.The results of this study obtained that tcount value was -5.785 while the ttable value was 1.693 which means that Ha is accepted and Ho is rejected and it can be said that online learning had a negative effect on student learning interest. The percentage of Online Learning variable influence on the Student Learning Interest variable was 0.655 or 65.50%. While the remaining 35.50% was influenced by other factors. While the value of the correlation coefficient rcount = 0.658, and it can be stated that the relationship between the Online Learning variable and the Student Learning Interest variable had a strong relationship level.
A Descriptive Study On The Vocabulary Mastery of Tenth Year Students Of SMK Al Maksum Stabat

This study deals with the discussion of vocabulary. The problem in this research is "how far is the ability of tenth graders of Al Maksum Stabat Vocational High School in understanding and remembering vocabulary. The purpose of this research is to get an overview of students' ability to master vocabulary and to find out what difficulties students face in mastering vocabulary. The population of this study was tenth grade students of SMK Al Maksum Stabat which consisted of 120 students from three parallel classes. For the sample, only 40% of the population was taken, namely 50 students. This research instrument uses an objective test. This research method is field research. After the data was analyzed, it was found that the average or mean value of the students was 78.51. It can be concluded that the ability of the tenth grade students is "good", and it is suggested that the English teacher give a lot of practice, especially with regard to vocabulary.
Planning for Madrasah Diniyah Awaliah Al-Ikhlas Mosque RW-06 Maharatu Village, Marpoyan Damai District - Pekanbaru City

Planning a development requires more detailed details, so that in the field implementation there is no change. The Management of Al-Ikhlas Mosque RW-06 wants to establish the construction of the Early Early Madrasah (MDA). This is done because it is in accordance with Law No. 20 of 2003 concerning the National Education System and followed up or the adoption of PP No. 55 of 2007 as the basis for the need for religious education in Indonesia. Prior to the construction of Madrasah Diniyah Awaliah (MDA), detailed planning is needed in the form of designs (pictures). The results of the planning carried out are Siteplan Images, 1st and 2nd floor plans Front view, Side, Rear Pieces A-A and B-B, Cost Budget Plan (RAB) with a physical value of the building Rp.2,754,207,000, - including the value of VAT, Scheduling (Curve S) implementation time is ± 4 months in the work and adjusted to general and technical specifications. For the planning data manager, it is used as a basis or guide for doing further work.
Web Development and Assistance in Pancuran Gading Village, Kampar Regency

Pancuran Gading Village is one of the villages in the Tapung sub-district, Kampar Regency, Riau province. Therefore, it is necessary to develop and assist the Pancuran Gading Village website, Kampar Regency. Currently Pancuran Gading Village will be used as a pilot village by the Ministry of Villages. Therefore it is necessary to improve existing facilities and one of the pilot villages is a digital village. The problem faced by partners is that there is no village website yet. The village website is a media of information for residents or society in general. The methods for implementing this service are preparation and planning, field surveys and interviews, data collection, website design and development, implementation of mentoring and website handover, evaluation and final reporting. The result of this service is in the form of a web profile for Pancuran Gading Village. And then provide assistance regarding the use of the website..
Revealing Lying Behavior of the Main Character of 13 Reasons Why (Season 2) TV Series through Violated Maxims

Bhattacharjee (2017) mentions that sometimes people lie to inflate their image and to cover up bad behavior. The capacity for dishonesty is essential as the need to trust others, which is ironically puzzling to detect. Finally, it would be enunciated that lie is human. Similar to Bhattacharjee, DePaulo et al., (1996) state that people lie continually about their feelings, actions, plans, whereabouts, achievements, and knowledge. This phenomenon fascinates the researchers to carefully probe it in one of TV series entitled 13 Reasons Why (Season 2). In this TV series, the main character, Clay Jansen seems to do some lies due to his capacity in court as the witness of Hannah’s suicide. He indeed does not want to engage in the trial but Hannah’s spirit is haunted him to uncover the case. Employing Cooperative Principles (CP) proposed by Grice (1975) and premises of motivations (instrumental, relational, and identity) of deception by Buller and Burgoon (1996), this investigation is conducted to reveal lying behavior of the main character and his motivations of doing it. Under descriptive qualitative method, the analysis results that all four maxims are violated. It also reveals that Clay employs three lie motivations which disclose his lying behavior. Instrumental motivation is applied to avoid punishment and to protect source of information. Then, relational motivation is exploited to redirect the social interaction and to protect interlocutors’ feeling. Last, identity motivation is operated to protect his private feeling and to project a favorable image.
Automatic Pill Recognition and Recommendation System Using Deep Learning techniques

Medication stability is necessary to avoid problems in day-to-day Human care.According to recent studies, prescribing errors are one of the most serious problems in fitness.It is LASA mistakes, which are the most common.Prescription tablet images can be accurately identified based on their visible appearance, ensuring patient safety and allowing patients to use modern healthcare devices.Several business studies have addressed the tablet identity problem, with solutions primarily based on content-based image retrieval (CBIR) and image analysis.The few-shot mastery issue, on the other hand, regularly inhibits correct tablet popularity in everyday life.Existing LASA prevention strategies are not without flaws.This project aims to develop technologies that can help users correctly distinguish prescription tablets from photographs.We recommend an automatic type device for tablet images using deep learning in this project.The proposed type was implemented using the deep learning set of rules of the DCNN and RPN.The choice of futurization for the molecules is an essential step in developing deep learning systems for tablet type and era.This version exceeded typical computer imaginative and visionary solutions in terms of identifying pills and capsules, as well as preventing medication errors.The project's outcome can be utilized in the real world to help patients recognize tablets and capsules, as well as to prevent medicine errors caused by look-alike pills, with an accuracy of more than 90%.
Frequency-based Hilbert emotional feature space for emotion detection using machine learning

Abstract Human emotions are high-dimensional and heterogeneous. It is very challenging to study and develop efficient machine learning techniques under these conditions. We define an emotion as a high-dimensional feature vector and treat its features as observations that are drawn from a low-dimensional Hilbert space. We also project every emotional feature vector onto a frequency domain and capture their directional frequencies. Our approach extracts the origins of their spatial frequencies using circularly-connected frequencies in the frequency domain, and constructs a Hilbert emotional feature space (HEFS). We validate the intrinsic features in the proposed HEFS using the classification performance of random forest (RF) classifiers as a quantitative measure. We also use precision scores, confusion matrices, and receiver operating characteristic curves to quantify the classification performance and validate the significance of the modeled frequencies. Our finding is that human emotions can be detected with high-performance scores of RF using the proposed feature vectors.
Speaker Recognition Based on Mutated Monarch Butterfly Optimization Configured Artificial Neural Network

Speaker recognition is the process of extracting speaker-specific details from voice waves to validate the features asserted by system users; in other words, it allows voice-controlled access to a range of services. The research initiates with extraction features from voice signals and employing those features in Artificial Neural Network (ANN) for speaker recognition. Increasing the number of hidden layers and their associated neurons reduces the training error and increases the computational process's complexity. It is essential to have an optimal number of hidden layers and their corresponding, but attaining those optimal configurations through a manual or trial and the process takes time and makes the process more complex. This urges incorporating optimization approaches for finding optimal hidden layers and their corresponding neurons. The technique involve in configuring the ANN is Mutated Monarch Butterfly Optimization (MMBO). The proposed MMBO employed for configuring the ANN achieves the sensitivity of 97.5% in a real- time database that is superior to contest techniques.
Comparative Analysis between Elgamal and NTRU Algorithms and their implementation of Digital Signature for Electronic Certificat

The emergence of electronic certificates, which are official documents in the form of digital files transmitted via the internet, facilitates the exchange of information. However, internet use has risks, such as data theft for fabricating and modifying information. This problem can be solved by applying a digital signature. This problem can be solved by applying a digital signature. The main concern in this research is how to perform a comparative analysis between asymmetric cryptographic Elgamal and NTRU (Nth-Degree Truncated Polynomial Ring) algorithms and their implementation of a digital signature as an effort to improve information security in electronic certificates. The stages of the research method are divided into the key generation process, signing, and verification. In the signing and verification process, the SHA-512 hash function is also used for hashing messages to be encrypted-decrypted and QR Code as the signature. Comparison of performance of NTRU with Elgamal algorithms required running at a pdf extension with security levels 80,128,192, 256 bits will be obtained from the templates.office.com website. The results obtained that the El Gamal algorithm is better than the NTRU algorithm, but at a higher security level, the NTRU algorithm is better than the Elgamal algorithm. In the verification experiment that has been carried out, it can be concluded that by using SHA-512 as a hash function, the N parameter used for NTRU must be greater than or equal to 512 to avoid error results from verification.
Social Media, Hate Speech and the Anglophone Crisis in Cameroon: The Case of Some Selected Posts and Comments on Facebook by Cameroonians

Since the outbreak of the Anglophone crisis in 2016, media professionals and ordinary people in Cameroon have been constantly producing hate speech via all forms of media, especially the social media platform called Facebook. This is believed to have far reaching consequences on the targeted individuals, groups and on national unity at large. This paper investigates how hate speech is represented in some posts and comments by Cameroonians on Facebook during Anglophone crisis. Data was gotten from some popular Facebook ‘groups’, ‘pages’ and the ‘personal walls’ of some Cameroonian activists. The paper reveals that hate speech for the most part on Facebook during the Anglophone crisis in Cameroon is based on ethno-political groupings, represented through the use of inclusive/non-inclusive pronouns, and words and expressions that excite hatred such as terrorists, Ambafools/Ambafrogs; Anglofou/Anglofools; Graffi; Cam no go; Bamileke Marquissard; La Republic/LRC; Francofools; and CPDM monsters. These words and expressions are further intensified through the use of deictic expressions and several demeaning adjectives, metaphors and similes. To solve this problem, the paper proposes some alternative words and expressions that can be used by Cameroonians both online and offline to reduce the rate of hate speech in Cameroon. The paper firmly holds that if work is done for the social and cultural transformation of all the people of Cameroon, then the usage of hate speech in Cameroon during and after the Anglophone crisis will be reduced.
An empirical study on English-Mizo Statistical Machine Translation with Bible Corpus

Machine Translation (MT) is the process of automatically converting the text or speech in one natural language to another language with the help of a machine. This work presents a Bidirectional Statistical Machine Translation (SMT) system of an extremely low resource language pair Mizo-English, built in a low resource setting. A total of 30800 sentences are collected from the English Bible dataset and manually translated to Mizo by a native linguistic expert to generate the English-Mizo parallel dataset. After subjecting to various pre-processing steps, the parallel dataset is used to build our MT system using MOSES tools. Our framework uses different tools, such as GIZA++ for creating the Translation Model (TM) and IRSTLM to determine the probability of the target model. The quality of our MT system is evaluated using two automatic evaluation metrics: BLEU and METEOR. Our MT systems are also manually evaluated using two parameters: adequacy and fluency.
Towards Auto Contract Generation and Ensemble-based Smart Contract Vulnerability Detection

Smart contracts (SC) are computer programs that are major components of Blockchain. The "intelligent contract" is made up of the rules accepted by the parties concerned. When the transactions started by the parties obey these established rules, then only their transactions will be completed without the involvement of a third party. Because of the simplicity and succinct nature of the solidity language, most smart contracts are written in this language. Smart contracts have two limitations, which are vulnerabilities in SC and that smart contracts can't be understood by all stakeholders, especially non-technical people who are involved in the business, since they are written in a programming language. Hence, the proposed paper used the XGBoost model and BPMN (Business Process Modeling Notation) tool to solve the first and second limitations of the SC respectively. Attackers are drawn to attention because of the popularity and fragility of the Solidity language. Once smart contracts have been launched, they can’t be changed. If that smart contract is vulnerable, attackers may then cash it. BPMN is used to represent business rules or contracts in graphical notation, so everyone involved in the business can understand the business rules. This BPMN diagram can be converted into a smart contract template through the BPMN-SOL tool. A few publications and existing tools exist on smart contract vulnerability detection, but they require more time to forecast and interpretation of vulnerability causes is also difficult. Thus, the proposed model experimented with several deep learning approaches and improved F1 score results by an average of 2% using the XGBoost model based on the ensemble technique to detect vulnerabilities of SCs, which are: Denial of Service (DOS), Unchecked external call, Re-entrancy, and Origin of Transaction. This paper also combined two important features to construct a data set, which are code snippets and n-grams.
Human-like Attention-Driven Saliency Object Estimation in Dynamic Driving Scenes

Identifying a notable object and predicting its importance in front of a vehicle are crucial for automated systems’ risk assessment and decision making. However, current research has rarely exploited the driver’s attentional characteristics. In this study, we propose an attention-driven saliency object estimation (SOE) method that uses the attention intensity of the driver as a criterion for determining the salience and importance of objects. First, we design a driver attention prediction (DAP) network with a 2D-3D mixed convolution encoder–decoder structure. Second, we fuse the DAP network with faster R-CNN and YOLOv4 at the feature level and name them SOE-F and SOE-Y, respectively, using a shared-bottom multi-task learning (MTL) architecture. By transferring the spatial features onto the time axis, we are able to eliminate the drawback of the bottom features being extracted repeatedly and achieve a uniform image-video input in SOE-F and SOE-Y. Finally, the parameters in SOE-F and SOE-Y are classified into two categories, domain invariant and domain adaptive, and then the domain-adaptive parameters are trained and optimized. The experimental results on the DADA-2000 dataset demonstrate that the proposed method outperforms the state-of-the-art methods in several evaluation metrics and can more accurately predict driver attention. In addition, driven by a human-like attention mechanism, SOE-F and SOE-Y can identify and detect the salience, category, and location of objects, providing risk assessment and a decision basis for autonomous driving systems.
Universal Composability is Robust Compilation

This paper discusses the relationship between two frameworks: universal composability (UC) and robust compilation (RC). In cryptography, UC is a framework for the specification and analysis of cryptographic protocols with a strong compositionality guarantee: UC protocols remain secure even when composed with other protocols. In programming language security, RC is a novel framework for determining secure compilation by proving whether compiled programs are as secure as their source-level counterparts no matter what target-level code they interact with. Presently, these disciplines are studied in isolation, though we argue that there is a deep connection between them and exploring this connection will benefit both research fields. This paper formally proves the connection between UC and RC and then it explores the benefits of this connection. For this, this paper first identifies which conditions must programming languages fulfil in order to possibly attain UC-like composition. Then, it proves UC of both an existing and a new commitment protocol as a corollary of the related compilers attaining RC. Finally, it mechanises these proofs in Deepsec, obtaining symbolic guarantees that the protocol is indeed UC. Our connection lays the groundwork towards a better and deeper understanding of both UC and RC, and the benefits we showcase from this connection provide first evidence of scalable mechanised proofs for UC.
Prediction model of teacher candidate student graduation status: Decision Tree C4.5, Naive Bayes, and k-NN

This study aims to determine the prediction model of the graduation status of prospective teacher students at IAIN Bone in terms of attributes, accuracy levels, and differences in the level of accuracy produced in the attributes of decision tree C4.5, Naïve Bayes, and k-NN data mining algorithms. This research uses a quantitative approach by adopting the Data Mining method. This research was conducted at IAIN Bone. The data collection process in this study used documentation techniques in the form of data on alumni of the Tarbiyah Faculty of IAIN Bone. The data analysis used was a descriptive analysis using decision tree C4.5, Naive Bayes, and k-NN data mining algorithms assisted by the RapidMiner application. The results of this study show that (1) model prediction of the graduation status of prospective teacher students in IAIN Bone in terms of attributes generated in the Decision Tree C4.5 and Naïve Bayes data mining algorithms consist of gender, age, Semester 1 IP, Semester 2 IP, Semester 3 IP, Semester 4 IP, and GPA, while the attributes produced in k-NN data mining algorithm consists of gender, regional origin, number of siblings, age, IP Semester 1, IP Semester 2, IP Semester 3, IP Semester 4, and GPA; (2) model prediction of graduation status of iain bone teacher candidate students in terms of the accuracy rate generated in the Decision Tree C4.5 data mining algorithm of 93.90%, Naïve Bayes by 90.24%, and k-NN of 92.07%; and (3) there was no significant difference between the accuracy rate produced by decision tree's data mining algorithm. C4.5 and Naïve Bayes (p-value = 1.00); Decision Tree C4.5 and k-NN (p-value = 1.00); as well as Naïve Bayes and k-NN (p-value = 1.00) in predicting the graduation status of iain bone teacher candidate students.
Analysis of language errors in student thesis

This study aims to describe the forms of spelling errors in the thesis of IAIN Bone students. In addition, it also describes the form of improvement and efforts to reduce language errors. This research is a qualitative descriptive. The subject of this research is the thesis of PGMI Study Program Student, IAIN Bone who graduated in 2020. The free speech technique and note-taking technique were used in collecting research data. Then, the data were analyzed using the Miles and Huberman analysis model which consisted of three stages, namely data reduction, data display, and verification. This study resulted that the spelling errors found in the student's thesis were grouped into three types, namely: a) typographical errors, that included capitalization and italics; b) incorrect use of comma punctuation; and c) word writing errors, which include writing prepositions di- and ke-, non-standard words, affixes di-, word combinations, words with affixes, numbers, and particle -pun. These errors can be reduced by a) providing understanding to students regarding good and correct Indonesian language rules in Indonesian General Courses; b) improving the understanding of the supervisors regarding the rules of the Indonesian language; and c) fostering a positive attitude towards the Indonesian language.
Information Governance for Enhancing the Performance of Higher Education Institutions

The essence of information has become more crucial for organizations, particularly in the digital age, where the creation of information is at exponential growth. The absence of proper classification of data and information hampered the quality in terms of consistency and reliability of the information. As a consequence, there is data redundancy, inaccuracy, and uncertainty. Hence, an information governance framework would be developed based on empirical evidence gathered in the research. Face to face interviews were conducted for data collection and in depth interviews carried out in ensuring a comprehensive understanding on the issue. Keywords: Information governance, information, sustainability, risk eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by E-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under the responsibility of AMER (Association of Malaysian Environment-Behavior Researchers), ABRA (Association of Behavioral Researchers on Asians), and cE-Bs (Centre for Environment-Behavior Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia.