Reviewerly/exHarmony huggingface dataset:
The Effect of Social Structural Change of Police Size

One of the most important needs in human life is the need for safety. As modern society changes rapidly, crime is also becoming ferocious and intelligent, and as private property increases and individuals change into a self-centered society, exposure to crime can be said to represent vulnerability to crime. The increase in crime rates is a major social problem, and people are living in anxiety and fear that they can become victims because of the increasing number of crimes. Crime is increasing day by day, diversifying, heinous, and organized. In order to protect the people from these crimes and secure safety from various accidents, it is necessary to increase and strengthen the number of police officers, and such measures to increase manpower and strengthen police forces are essential tasks to realize “people's happiness,” one of the government's important policies. In order to secure the safety of the people from crime, it is necessary to increase and strengthen the number of police officers, and such measures to increase manpower and strengthen police forces are essential tasks to realize “people's happiness,” one of the government's important policies. However, there is a question of whether the police force has changed to suit the development of the country or the increasing number of crimes. Police size should be changed to suit the security situation in the long run. However, the fact that political factors such as the tendency of the regime, which is different from the security situation, play a greater role in the change of police size indicates that the police cannot be politically neutral, while at the same time disproving that the police plan is systematic and unplanned. This study attempts to empirically analyze the effect of various independent variables on the determinants of police size, which are dependent variables. The number of police officers in the entire National Police Agency is used as a dependent variable, and socio-economic and political factors at the national level are used
An Improved Deep Reinforcement Learning for Task-oriented Dialogue System

Abstract Nowadays, the need for dialogue systems is felt more than ever. Many services around us need continued support, while the domain of support is limited and specific. Therefore, instead of employing manpower, dialogue systems can help. In this paper, we propose a task-oriented dialogue system based on deep reinforcement learning empowered by several new ideas: first, due to the shortage of training data, we use GAN to generate more training data without human intervention. Second, we propose a new state embedding for representing environment in dialogue system. In this way, we use sentiment analysis and clustering methods. The proposed embedding makes a more accurate and deeper understanding of the environment, which ultimately leads to an increase in system performance. Additionally, we present a new reward function for the agent to better learn dialogues. In order to evaluate this system, we use both user simulator and human judges. With user simulator, we evaluate the average rewards of the proposed system and its improvements. With human judges, we investigate human evaluations about the proposed system. Based on these evaluations promising and comparable results are observed.
DraG4Rec A Symmetrical View of User Preference and Item Popularity

&lt;p&gt;Utilizing user-item interaction dynamics is crucial for providing efficient and precise sequential recommendations (SR). Besides earlier works based on Matrix factorization, Markov Chains, and RNNs, GCNs taking advantage of attention mechanism and Knowledge-enhanced NNs digging heterogenous auxiliary information recently made improvements. However, there still exist some problems: 1) existing works mainly focus on capturing user preference without considering the importance of item popularity; 2) introducing too much auxiliary information denies the independence of interaction behavior; 3) favoring high-order chronological history with heavy memory while ranking score referring different bias is unappreciated. To tackle these problems, we proposed DraG4Rec (Dual rating enhanced attention-based GCN for Recommendation), which makes symmetry of user preference and item popularity manifest great value. Specifically, we first merge ranking score, time, and position encoding for user/item representation in a bipartite graph. Then, an edge-view message-passing mechanism is imported into the attention-based GCN to use implicit 2-order historical information with less memory. Finally, user preference and item popularity are learned jointly. Extensive experiments on dense and sparse real-world datasets demonstrate the superior performance of our framework over state-of-the-art baselines regarding two commonly-used metrics: i.e., Recall and NDCG. The ablation study illustrates the boosting effect of different components. &lt;/p&gt;
Prediction of O3 Concentration Level Using Fuzzy Non-Stationary Method

The composition of air concentration is not constant. It constantly changes with minor changes at any time, so more than one measurement is needed to represent the air concentration level for a full day. The fuzzy non-stationary method can overcome uncertainty in an environment that is not constant or caused by minor temporal changes based on time variables. This study uses a non-stationary fuzzy method to determine the level of O3 concentration based on the input variables of temperature, humidity, and wind speed. The tests were conducted in September, October, and November using four types of implication process interpretation, namely interpretation 1 (classical logic), interpretation 2 (classical logic), interpretation 3 (algebraic), and interpretation 3 (standard). The test results in September showed a tendency for error percentage using the MAPE amount of 19, October's amount of 25, and November's amount of 18.
Sentiment Analysis of the Convict Assimilation Program on Handling Covid-19

Coronavirus Disease-19 (Covid-19) is an infectious disease caused by the SARS-CoV-2 virus. The rapid spread of this disease has affected 216 other countries and regions, including Indonesia. In minimizing the spread and increasing losses, it is necessary to have several policies made by the Indonesian government in dealing with this. One of the policies taken by the government is the Convict Assimilation Program to prevent the spread of the virus in prisons. The Prisoner Assimilation Program fosters inmates by integrating prisoners into social life. Many media reported on the assimilation program in various media, including news portals, so that it became a forum for the public to express their opinions. News portals can be a source for getting public opinion. Therefore, sentiment analysis can be done to determine the sentiment of any existing public opinion. In this study, the analysis was carried out by applying one of the data mining methods, namely the Support Vector Machine, with positive, negative, and neutral sentiment labeling. The data used is audience comments in Indonesian with a dataset of 404 comments and then resampled so that the number of data becomes 669. The analysis uses the kernel Radial Basis Function (RBF), RBF with Grid Search, Polynomials, and Polynomials with grid search. Kernel RBF and Kernel Polynomial with Grid Search comparing test and training data 80%:20% with the highest accuracy of 95%.
Requirements Conflict Detection and Resolution in AREM Using Intelligence System Approach

Requirements engineering (RE) is the process of defining user requirements that are used as the main reference in the system development process. The quality of the RE results is measured based on the consistency and completeness of the requirements. The collection of requirements from multiple stakeholders can cause requirements conflict and have an impact on the inconsistency and incompleteness of the resulting requirements model. In this study, a method for automatic conflict detection and resolution in the Automatic Requirements Engineering Model (AREM) was developed. AREM is a model that automates the process of elicitation, analysis, validation, and requirements specification. The requirement conflict detection method was developed using an intelligent agent approach combined with a Weighted Product approach. Meanwhile, Conflict resolution is made automatically using a rule-based model and clustering method. Testing the ability of the method to detect and resolve conflicting requirements was carried out through five data sets of requirements from five system development projects. Based on the test results, it is known that the system is able to produce a set of objects that have conflicts in the data requirements. For conflict resolution, experiments were conducted with five conflict resolution scenarios. The experimental results show that the method is able to resolve conflicts by producing the highest completeness value, but the results of conflict resolution also produce a number of soft goals. The success of the method in detecting and resolving conflicts in the model is able to overcome the problem of inconsistencies and incompleteness in the requirements model.
Aktivitas Antikoagulan dari Tumbuhan Kayu Ular (Strychnos lucida R.Br.)

The stem of the snakewood plant (Strychnos lucida) has been used empirically as a malaria drug, relieves fever, treats stomach pain, hemorrhoids, rheumatism, stroke symptoms, and improves blood circulation. Tannins as one of the secondary metabolites of snakewood stems contain gallic acid compounds which are known to thin the blood. The purpose of this study was to determine the anticoagulant activity of the ethanolic extract of snakewood stems on blood group samples, namely A, B, AB, and O. The method used for extraction was maceration, for testing anticoagulants used method modified Lee-White and blood smears, as well as for data analysis carried out descriptively. The results obtained by the Lee-White modification method on blood group samples were visually not clotting. For anticoagulant activity by blood smear method, it was shown that microscopically snakewood bark extract prevented the presence of blood clots which could be observed with unrelated blood cells, intact and separated from each other.Key words: S. lucida; Lee-White modification; blood smear; anticoagulants
REVOCABLE ATTRIBUTE BASE ENCRYPTION WITH DATA INTEGRITY IN CLOUDS

Cloud computing enables enterprises and individuals to outsource and share their data.This way, cloud computing eliminates the heavy workload of local information infrastructure.Attribute-based encryption has become a promising solution for encrypted data access control in clouds due to the ability to achieve one-tomany encrypted data sharing.Revocation is a critical requirement for encrypted data access control systems.After outsourcing the encrypted attribute-based ciphertext to the cloud, the data owner may want to revoke some recipients that were authorized previously, which means that the outsourced attribute-based ciphertext needs to be updated to a new one that is under the revoked policy.The integrity issue arises when the revocation is executed.When a new ciphertext with the revoked access policy is generated by the cloud server, the data recipient cannot be sure that the newly generated ciphertext guarantees to be decrypted to the same plaintext as the originally encrypted data, since the cloud server is provided by a third party, which is not fully trusted.In this paper, we consider a new security requirement for the revocable attribute-based encryption schemes: integrity.
Creating interactive animation media training as a digital literacy for junior high school teachers

The COVID-19 pandemic carried an impact on the world of education. As a policy, an online learning system is carried out, where digital literacy has an important role. Digital literacy skills for a teacher are being able to present interesting learning media. Learning media which is the best alternative is interactive multimedia, which can increase students' learning motivation. Teaching and learning activities in SMP Aswaja are dominated by the conventional method. It is still dominated by lectures and has not used varied learning media, especially the use of digital learning media. To improve the teachers’ understanding and skills in this regard, workshops and training activities for Aswaja Junior High School teachers were carried out which were attended by 27 teachers. The training activities were divided into pre-test, digital literacy and digital learning media materials, training for making digital learning media, and post-test. The average value of the pre-test is 73.33, while the average value of the post-test is 86.3. Paired Sample T-Test analysis showed that there was a significant difference between the pre-test and post-test. Thus the training activities were able to improve understanding and skills in making digital learning media for teachers of SMP Aswaja Kunir Wonodadi.
Training and assistance on the use of village administration management system using OpenSID in Sukatani Village

Sukatani Village, Compreng Sub-District, Subang District in carrying out village administration services still uses Microsoft Word in making village correspondence then the letter number is recorded in the village ledger and the Sukatani village population data is still stored in excel form. The problem is that typos often occur, it takes a long time to search for correspondence files and cannot present population data accurately. This problem can be overcome by using a village administration management system that will be used in Sukatani village. The system was developed using the OpenSID framework which is licensed and free to use and developed for village purposes. In order for the village apparatus to easily use the village administration management system, training and assistance is needed. The training and mentoring is carried out by dividing into two groups and each group is accompanied by a trainer so that the training for village officials can run smoothly and well. The result of this service activity is that the village administration management application and village apparatus can use the application well so that the application can be used continuously to improve village administration services to Sukatani villagers.
Representing Inferences and their Lexicalization

We have recently begun a project to develop a more effective and efficient way to marshal inferences from background knowledge to facilitate deep natural language understanding. The meaning of a word is taken to be the entities, predications, presuppositions, and potential inferences that it adds to an ongoing situation. As words compose, the minimal model in the situation evolves to limit and direct inference. At this point we have developed our computational architecture and implemented it on real text. Our focus has been on proving the feasibility of our design.
SAIL: Self-Augmented Graph Contrastive Learning

This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the locality of graph structure. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL - a novel \underline{S}elf-\underline{A}ugmented graph contrast\underline{i}ve \underline{L}earning framework, with two complementary self-distilling regularization modules, \emph{i.e.}, intra- and inter-graph knowledge distillation. We demonstrate the competitive performance of SAIL on a variety of graph applications. Even with a single GNN layer, SAIL has consistently competitive or even better performance on various benchmark datasets, comparing with state-of-the-art baselines.
Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation

Learning from positive and unlabeled (PU) data is an important problem in various applications. Most of the recent approaches for PU classification assume that the class-prior (the ratio of positive samples) in the training unlabeled dataset is identical to that of the test data, which does not hold in many practical cases. In addition, we usually do not know the class-priors of the training and test data, thus we have no clue on how to train a classifier without them. To address these problems, we propose a novel PU classification method based on density ratio estimation. A notable advantage of our proposed method is that it does not require the class-priors in the training phase; class-prior shift is incorporated only in the test phase. We theoretically justify our proposed method and experimentally demonstrate its effectiveness.
The Informational Physical Model and Fundamental Problems in Physics

This article is some review of results that were obtained at 2007-2021 years development of &amp;ldquo;The Information as Absolute&amp;rdquo; concept and the informational physical model, which is based on the concept; including a number of fundamental physical problems are briefly considered in framework of the conception and the model. Recently in physics there are several publications, that present lists of the problems. However, those lists are essentially incomplete, for at least two reasons. Firsts of all, a number of phenomena are studied traditionally by philosophy, and so corresponding problems are usually considered to be &amp;ldquo;metaphysical&amp;rdquo;. However, they relate also to some concrete physical phenomena. For example, physics evidently studies Matter, and so the metaphysical problems &amp;ldquo;what is ontology of Matter&amp;rdquo;, &amp;ldquo;what is &amp;ldquo;Space&amp;rdquo;, &amp;ldquo;Time&amp;rdquo; and a few other physical phenomena and notions as well, are really a Meta-physical problem &amp;ldquo;what does physics study?&amp;rdquo; There are other fundamental physical problems, which are not considered as such in physics, and are absent in the &amp;ldquo;fundamental problems lists&amp;rdquo;. Those include the problems, which really exist, yet are incorporated into standard physical theories, and so are fundamental &amp;ldquo;implicitly&amp;rdquo;, which in physics are &amp;ldquo;solved by default&amp;rdquo;. Note, though, that a number of &amp;ldquo;Meta-physical&amp;rdquo;, and concrete fundamental, problems more in detail are considered in the paper &amp;ldquo;The Informational Conception and Basic Physics&amp;rdquo;, https://arxiv.org/abs/0707.4657, v5 (2021), so this paper is, in certain sense, an expanded conclusion of this paper, which includes, correspondingly, more in detail consideration of some more general physical problems, and, besides, in this [v3] version, the problem &amp;ldquo;what are Gravity and Electric Forces&amp;rdquo; is essentially clarified. Besides, the concrete problem &amp;ldquo;What is Life&amp;rdquo;, and the rational cosmological model, where a few vague points in standard cosmology rather probably are rationally clarified, while the fundamental problem &amp;ldquo;matter &amp;ndash; antimatter asymmetry&amp;rdquo; in Matter is solved practically for sure, are considered, and one of recently published rather complete &amp;ldquo;lists of fundamental problems&amp;rdquo; is commented in Appendix.
Special Issue on Recent Advances in Machine Learning and Applications

Digital technologies for cyber-physical systems are rapidly advancing, and the ubiquity of the Internet of Things (IoT) has created significant challenges for academic, industrial, and service applications due to high dimensionality, noise contamination, incompleteness, inconsistency, and massive amounts of data [...]
A Study on Perceived Appreciation, Psychological Capital and Work Life Balance Among Work From Home Employees

Senior executives have grown increasingly open to the concept of introducing Work from Home for all or a portion of their workforces since the Covid-19 issue. Work from Home offers benefits such as enhanced work-life balance, decreased travel time, increased flexibility, increased perceived autonomy, reduced occupational stress, improved productivity, lower turnover rate, and increased job satisfaction. On the contrary, it faces difficulties such as multitasking, social isolation, diminished job motivation, extra costs, distraction, and restricted communication. The study's goal was to investigate Work from Home Employees' Perceived Appreciation, Psychological Capital, and Work Life Balance. A sample of 31 Work from Home employees aged 25 to 40 years old was gathered. Standardised scales were used to measure Perceived Appreciation, Psychological Capital &amp; Work Life Balance. The results found out that Work Life Balance is significantly positively correlated with Perceived Appreciation &amp; further, dimensions of Psychological Capital Hope &amp; Optimism are significantly positively correlated with Perceived Appreciation. It is suggested that companies should introduce programs for employees to help them balance work and family responsibilities and build on employees' psychological capital. Furthermore, creating an appreciation plan that fits the company’s values &amp; mission is beneficial for employee’s engagement, motivation &amp; retention.
Pengaruh Model Example Non Example Berbantukan Media Powtoon terhadap Hasil Belajar Matematika Siswa Kelas IV SD

This study aims to determine the effect of the Example Non Example model with the help of Powtoon media on the mathematics learning outcomes of fourth-grade students at SDN 01 Taman. This type of research is experimental quantitative research. This research method Experiment Design with Pretest-Posttest Control Group Design. This study took two classes as samples: class IV A as the experimental class and class IV B as the control class. Sampling using Simple Random Sampling. The technique of collecting data on mathematics learning outcomes uses interviews, observations, tests and documentation. From the data analysis using the t test, it is known that ttable = 2.069 for 5% with dk = 25, if tcount = 5.784 &gt; ttable = 2.069 then H0 is rejected and H1 is accepted. Thus it can be said that the influence of the Example Non Example model with the help of Powtoon media affects the mathematics learning outcomes of fourth-grade students at SDN 01 Taman