Reviewerly/exHarmony huggingface dataset:
Unlocking Doors: A TinyML-based Approach for real-time Face Mask Detection in Door Lock Systems

In response to the rapid spread of coronaviruses, including COVID-19 and seasonal common cold viruses, this article introduces a proposed system for enhancing door lock systems using TinyML technology for real-time face mask detection. The research project focuses on developing a machine learning model based on the YOLOv5 architecture to classify individuals based on their mask-wearing behavior correctly, incorrectly, or not at all in high-risk spaces prone to the transmission of coronaviruses, such as healthcare facilities, laboratories, and public settings. The study outlines the hardware and software tools utilized, including the Raspberry Pi 4, camera hardware, and the YOLOv5 machine learning model. The model is trained using a dataset containing three different classes and converted to a TFLite format for efficient implementation on the Raspberry Pi. Evaluation results demonstrate a mean Average Precision (mAP) of 0.99 and an inference rate of 10FPS for a 128-frame size input. This proposed system offers practical implications for enhancing door lock systems and promoting public health and safety during outbreaks of coronaviruses, including COVID-19 and other seasonal coronaviruses, providing a valuable approach to decrease the spread of these diseases and mitigate transmission risks in high-risk spaces, thereby contributing to the overall reduction of public health threats.
Pengaruh Pemberian Tes Lisan Sebelum Pembelajaran terhadap Pencapaian Hasil Belajar Bahasa Inggris Mahasiswa Program Studi Pendidikan Agama Islam STIT Palapa Nusantara Lombok

This research aims to investigate the influence of administering an oral test before learning on the achievement of English language learning outcomes among students at STIT Palapa Nusantara Lombok. In the teaching and learning process, administering an oral test before learning can have benefits such as enhancing learning motivation, stimulating mental preparation, and identifying students' learning deficiencies and needs. The method used in this research is an experimental method with a Posttest-Only Control Design. The study was conducted on students of the Islamic Education Study Program in the even semester of the academic year 2022/2023 at STIT Palapa Nusantara Lombok. The experimental group received treatment in the form of an English oral test before learning, while the control group did not receive this treatment. Data were obtained through the oral test before learning and the learning outcome test (final test) given to both groups. Data analysis was performed using instrument validity and reliability tests, descriptive statistics, normality test, homogeneity test, and mean difference test. The data description results showed different average values between the experimental group and the control group. The statistical analysis showed that administering an oral test before learning has a significant influence on the achievement of English language learning outcomes among students at STIT Palapa Nusantara Lombok. This research is expected to provide a better understanding of the influence of administering an oral test before learning on the achievement of English language learning outcomes among students. The results of this research can serve as a reference in the development of effective English language learning strategies at STIT Palapa Nusantara Lombok and other higher education institutions.
Application of Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) for Stock Forecasting

Background: Stock price forecasting assists investors to anticipate risks and opportunities in making prudent investments and maximizing returns. Objective: This study aims to identify the most accurate model for stock forecasting. Methods: This paper utilized the daily closing stock price of Unilever Indonesia, Tbk (UNVR) from January 1, 2018 to July 31, 202. Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), was utilized in this study. Mean Absolute Scaled Error (MASE) and Median Absolute Percentage Error (MdAPE) are used to compare forecasting accuracy. Results: Following conducting each model, we assessed that the best models are DSARIMAX (0,1,[4]) ([3],1,1)5(1,1,0)253, regarding MASE and MdAPE corresponding to approximately 1.423 and 0.111. The scope of this study has limitations to a test set for one-month forecast periods. Conclusion: As stock prices rise, investors require precise forecasts. Models of forecasting must perform well. This analysis shows how the DSARIMA generate forecasts stock prices more accurately. This investigation evaluated the closing stock price of UNVR. Both MASE and MdAPE assess prediction. After analyzing each model, DSARIMAX (0,1,[4])([3],1,1)5(1,1,0)253 has the lowest MASE and MdAPE values, 1.423 and 0.111, respectively. The procedure lasted one month. Research may combine forecasts and improve their accuracy.
Conceptional Reconstruction of High School English Teaching from the Perspective of Deep Learning

As one of the important subjects in senior high school, English is not only a tool for communication, but also has rich cultural significance. High school English teaching should not only make students realize its instrumental significance, but also connect English with social life to guide students to establish correct values and achieve all-round development. The new college entrance examination reform puts more emphasis on competence education and the overall development of students. Meanwhile, the examination-oriented education in senior high school English teaching is still outstanding, and theory of "focus only on scores" is still prevalent. The theories of senior high school English teaching lag behind the development of the times and the call of the country. Deep learning emphasizes the acquisition of students’ English ability, attaches importance to the cultivation of students' values, and supports the comprehensive development of students, which is complementary to course education. It also has enlightening significance for the reform of English teaching in senior high schools. The paper focuses upon the conceptional reconstruction of senior high school English teaching through the perspective of deep learning theory, and discusses the positive impact of deep learning on the improvement of senior high school students’ English learning initiative, the accumulation of humanistic quality and the formation of values.
Penyeleksian Penerimaan Teleservice Representative dengan Penerapan Metode ARAS dan Pembobotan ROC

Company Refers to a business entity or organization established by one or several individuals with the intention of producing or selling goods or services with the aim of making a profit. In helping to increase company sales by promoting new products or services to customers and establishing better relationships with existing customers, a teleservice representative is needed. A teleservice representative is a customer service professional who works via a remote communication device such as a telephone, email or text message. . To achieve this, they must have the criteria of Experience, Good Negotiation, Communication Articulation, Interpersonal and Confidence. The presence of a Teleservice Representative is very important for the company in ensuring that the quality of customer service is continuously improved and customers are satisfied with the company's products or services. So companies must make the right selection of teleservice representatives because teleservice representatives have a very vital role for the company because they are a direct line in providing superior customer service and guaranteeing customer satisfaction. To solve problems in the selection of teleservice representatives, an information system is needed, namely a decision support system (SPK). In this study, the Additive Ratio Assessment (ARAS) method and ROC weighting are applied, which are methods that can be used to make decisions and produce values on selection weights in the selection of teleservice representatives. so that the alternative that produces the selection of teleservice representatives, which can be seen in table 10, is alternative A4, namely Nisa Fadillah with a value of 1.0000
A Systematic Literature Review on Blockchain Technology in Software Engineering

Blockchain technology is gaining increasing interest among software developers as a distributed and decentralized ledger for tracking the origin of digital assets. However, the application of blockchain in software engineering requires further attention. In this study, we aim to address the current challenges and explore the need for specialized blockchain practices in software engineering. Through a systematic literature review, we identify the various applications of blockchain technology in software engineering. Additionally, we conduct a thorough analysis of existing obstacles and propose potential solutions. Gathering and evaluating requirements using blockchain-based requirements engineering approaches will enhance the quality and reliability of data in software development projects. This, in turn, will improve the overall quality and dependability of software, as well as increase user interest and productivity.
Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation

Nowadays, interactive recommendation systems (IRS) play a significant role in our daily life. Recently, reinforcement learning has shown great potential in solving challenging tasks in IRS, since it can focus on long-term profit and can capture the dynamic preference of users. However, existing RL methods for IRS have two typical deficiencies. First, most state representation models use left-to-right recurrent neural networks to capture the user dynamics, which usually fail to handle the long and noisy sequential data in real life. Second, an IRS always needs to handle millions of items, leading to a large discrete action space in RL settings, which has not been fully addressed by the inefficient existing works. To bridge these deficiencies, in this paper, we propose attention-based tree recommendation (ATRec), an efficient tree-structured policy with attention-based state representation for IRS. ATRec uses an attention-based state representation model to effectively capture the user’s dynamic preference hidden in the long and noisy sequence of behaviors. Moreover, to improve the learning efficiency, we propose an efficient tree-structured policy representation method, in which a complete tree is devised to represent the policy, and a novel parameter-sharing strategy is introduced. Extensive experiments are conducted on three real-world datasets and the results show the proposed ATRec obtains 42.3% improvement over some of the state of the arts methods in the hit rate and 21.4% improvement in the mean reciprocal rank of the top 30 ranked items. Additionally, the learning and decision efficiency can also be improved at an average of 35.5%.
Personal Characteristics and Organizational Career Management as Predictors of Organizational Commitment: An Empirical Investigation

Purpose: The objective of this study was investigate the effect of organizational career management and demographic variables on organizational commitment in Jordanian universities. Theoretical framework: Organizational Career management and organizational commitment become vital concepts in the field of human resource management and organizational behavior. Nevertheless, in Arabian countries such as Jordan, these concepts have not received adequate consideration from specialists. Design/methodology/approach: The researcher designed a questionnaire based on the objectives and hypotheses of the study. The researcher disseminated 468 questionnaires. 445 the collected data and test the hypotheses. The data were analysed using frequencies, percentage, mean rating, one sample t-test and ANOVA. Findings: Study results showed a positive and significant correlation between organizational career management and organizational commitment, from the standpoint of administrative staff in Jordanian Universities’. Research, Practical &amp; Social implications: Some managerial implications for Jordanian Universities’ Managers and HR practitioners have emerged based on the findings of this study. Originality/value: Many organizations use programs to develop employee career path employees, but these programs are fragmented and do not exist in an integrated process linking the path of individuals and the organization. This study adapt comprehensive model and explore its relation with organizational commitment.
Planning for Attacker Entrapment in Adversarial Settings

In this paper, we propose a planning framework to generate a defense strategy against an attacker who is working in an environment where the defender can operate covertly. Here the objective of the defender is to covertly guide the attacker to a trap state from which the attacker cannot achieve their goal. Further, the defender is constrained to achieve its goal within a K number of steps, where K is calculated as a pessimistic lower bound within which the attacker is likely to not suspect any foul play in the environment. Such a defense strategy is highly useful in real-world systems like honeypots or honeynets where an unsuspecting attacker interacts with a simulated production system while assuming it is the actual production system. Typically, the game between an attacker and a defender is captured using game theoretic frameworks. However, our problem formulation allows us to capture it as a much simpler infinite-horizon discounted MDP where the optimal policy to the MDP gives the defender's strategy against the actions of the attacker. Through empirical evaluation, we show the merits of our problem formulation.
Fine-Grained Complexity Analysis of Multi-Agent Path Finding on 2D Grids

Multi-Agent Path Finding (MAPF) is a fundamental motion coordination problem arising in multi-agent systems with a wide range of applications. The problem's intractability has led to extensive research on improving the scalability of solvers for it. Since optimal solvers can struggle to scale, a major challenge that arises is understanding what makes MAPF hard. We tackle this challenge through a fine-grained complexity analysis of time-optimal MAPF on 2D grids, thereby closing two gaps and identifying a new tractability frontier. First, we show that 2-colored MAPF, i.e., where the agents are divided into two teams, each with its own set of targets, remains NP-hard. Second, for the flowtime objective (also called sum-of-costs), we show that it remains NP-hard to find a solution in which agents have an individually optimal cost, which we call an individually optimal solution. The previously tightest results for these MAPF variants are for (non-grid) planar graphs. We use a single hardness construction that replaces, strengthens, and unifies previous proofs. We believe that it is also simpler than previous proofs for the planar case as it employs minimal gadgets that enable its full visualization in one figure. Finally, for the flowtime objective, we establish a tractability frontier based on the number of directions agents can move in. Namely, we complement our hardness result, which holds for three directions, with an efficient algorithm for finding an individually optimal solution if only two directions are allowed. This result sheds new light on the structure of optimal solutions, which may help guide algorithm design for the general problem.
Complete Search of Sliding Tile Puzzles on a Personal Computer [Extended Abstract]

The 4x4 and 8x2 Sliding Tile Puzzles have more than ten trillion solvable states, making complete brute force search very challenging: best existing solutions take weeks to run or require expensive hardware. We propose and implement a set of optimizations of the frontier search algorithm, that are efficient on a modern personal computer. We run a number of complete searches, each taking about 3 days on our hardware, for both puzzles in single-tile and multi-tile metrics, verifying previously known results about the radiuses of the puzzles. We also discover that the diameter of the 4x4 Puzzle is 80 single-tile moves, and the radius of the 8x2 Puzzle is 57 multi-tile moves.
W-restrained Bidirectional Bounded-Suboptimal Heuristic Search

In this paper, we develop theoretical foundations for bidirectional bounded-suboptimal search (BiBSS) based on recent advancements in optimal bidirectional search. In addition, we introduce a BiBSS variant of the prominent meet-in-the-middle (MM) algorithm, called Weighted MM (WMM). We show that WMM has an interesting property of being W-restrained, and study it empirically.
Understanding Natural Language in Context

Recent years have seen an increasing number of applications that have a natural language interface, either in the form of chatbots or via personal assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana (Microsoft). To use these applications, a basic dialog between the assistant and the human is required. While this kind of dialog exists today mainly within static robots that do not make any movement in the household space, the challenge of reasoning about the information conveyed by the environment increases significantly when dealing with robots that can move and manipulate objects in our home environment. In this paper, we focus on cognitive robots, which have some knowledge-based models of the world and operate by reasoning and planning with this model. Thus, when the robot and the human communicate, there is already some formalism they can use -- the robot’s knowledge representation formalism. In this paper we describe an approach for translating natural language directives into the robot's formalism, allowing much more complicated household tasks to be completed. We do so by combining off-the-shelf SoTA large language models, planning tools, and the robot knowledge of the state of the world and of its own model. This results in much more accurate interpretation of directives in natural language.
Online Planning for Constrained POMDPs with Continuous Spaces through Dual Ascent

Rather than augmenting rewards with penalties for undesired behavior, Constrained Partially Observable Markov Decision Processes (CPOMDPs) plan safely by imposing inviolable hard constraint value budgets. Previous work performing online planning for CPOMDPs has only been applied to discrete action and observation spaces. In this work, we propose algorithms for online CPOMDP planning for continuous state, action, and observation spaces by combining dual ascent with progressive widening. We empirically compare the effectiveness of our proposed algorithms on continuous CPOMDPs that model both toy and real-world safety-critical problems. Additionally, we compare against the use of online solvers for continuous unconstrained POMDPs that scalarize cost constraints into rewards and highlight the limitations of the default exploration scheme.
Landmark Progression in Heuristic Search

The computation of high-quality landmarks and orderings for heuristic state-space search is often prohibitively expensive to be performed in every generated state. Computing information only for the initial state and progressing it from every state to its successors is a successful alternative, exploited for example in classical planning by the LAMA planner. We propose a general framework for using landmarks in any kind of best-first search. Its core component, the progression function, uses orderings and search history to determine which landmarks must still be achieved. We show that the progression function that is used in LAMA infers invalid information in the presence of reasonable orderings. We define a sound progression function that allows to exploit reasonable orderings in cost-optimal planning and show empirically that our new progression function is beneficial both in satisficing and optimal planning.
Problematika Pembelajaran Jarak Jauh Mata Pelajaran Bahasa Daerah (Jawa) Di Masa Pandemi Covid 19

Javanese language subjects are based on a cultural framework that includes three forms of culture, starting from what appears on the surface in the form of physical culture, then social systems, to the system of ideas that exist in Javanese culture. In this case, the Javanese language functions as a means of communication as well as of preserving and developing Javanese cultural values. Thus the learning approach is more directed to a communicative and pragmatic approach by emphasizing the affective aspect. Unggah-ungguh basa is a manifestation of the Javanese cultural value system as a form of excellence in the Javanese language. This is a point of emphasis so that anyone who learns Javanese is expected to be able to speak Javanese skillfully, behave politely, and have good character. The current problem, with the pandemic, is that most of the learning is carried out remotely. Of course, in the implementation of distance learning, there are many problems or obstacles that must be accepted. Basically distance learning is a type of learning where students are far from the educator, so that education cannot be done face-to-face and the delivery of messages from educators to students must be done through the media. Distance learning during the pandemic is an alternative to teaching and learning activities that are usually carried out offline. Utilizing technology that is always evolving in the world of education. For this reason, the aspects studied both pragmatically and theoretically in this study are the problems in the implementation of distance learning of Javanese language, namely the readiness of teachers and students, the obstacles faced when distance learning takes place in schools.
Extended Classification of Complement Clause Possibilities for Thinking Verbs

This paper investigates further elaboration of syntactic properties of Thinking verb. All data required for this study are taken in Corpus of Contemporary American English. This paper aims to analyse the complement clause possibilities in Thinking verbs and the sentence structure based on the data found in COCA. The research is conducted through qualitative procedures by categorizing a certain construction of the sentences and interpreting certain context related to the data. The feature of complement clause in Thinking verb has special properties; the ‘That’ is constituted in all verb types. ‘WH’ and ‘ING’ exist in Thinking verbs (except Conclude), Solve and Believe subtypes. ‘Judgment TO’ is found in Think, Assume, Remember, Know (except teach), Conclude and Believe (except Doubt) subtypes. ‘Modal (FOR) TO’ is generally formed in Think subtype (except Consider), Assume subtype (except Suppose), Ponder subtype, Remember subtype, Know subtype (except Sense), Conclude subtype (except Infer), Solve and Believe subtypes. The extended classification of Complement Clause possibilities in Thinking verbs resulted the expand of sentence classification as constituent part, Complement clause possibilities are enriched into some verb types and the data are mostly found in complex sentence.