Reviewerly/exHarmony huggingface dataset:
KARAKTERISTIK KEPENULISAN ARTIKEL PADA JURNAL AGRISEP UNIVERSITAS BENGKULU TAHUN 2015-2019 MENGGUNAKAN KAJIAN BIBLIOMETRIKA

The purpose of this study is to know the characteristics of authorship and articles in the Journal AGRISEP at University of Bengkulu in 2015-2019 by using a study of bibliometrics based on several indicators. This research uses a descriptive quantitative study. The object is articles in the AGRISEP Journal of University of bengkulu in 2015-2019 with a total of 91 articles. Data were analyzed by using frequency tabulations. The results of this research show that the characteristics of writing and articles in the Journal of University of Bengkulu AGRISEP in 2015-2019 is vary greatly. It shows that the authors were male (54.08%). Most of the authors work as Lecturers (50.43%.). University of bengkulu contributed the most articles with almost 41 articles. Article was written by 3 authors (52.75%). Most of the authors have Master educational background. Articles use Indonesian language (96.70%.). The highest author's productivity is Sriyoto. The length of articles contain in this journal was 11-15 pages (56.15%). Articles contain 3 keywords were 51.14%, and the abstract length is around 150-199 words (35.16%). The most type of literature is Journal with 373 citations (36.93%). Based on these characteristics, it can be seen that the articles have met the written guidelines and articles that have been determined as well as an overview of the development of the articles in the AGRISEP Journal of Bengkulu University in 2015-2019.
Case-Based Reasoning for Personalized Recommender on User Preference through Dynamic Clustering

The existence of user data sparsity is inevitable for that the user interacts only with items of interest in the recommender system, and the abundance of cyber resources increases the scale of data makes the system data more sparse eventually, they seriously affect recommender system performance. Many researches have highlighted such problems for applying supplements to those vacancies explicitly or implicitly, to balance computing complexity and the system efficiency. CBR-recommender is proposed to learn user preference from what user really interacts to keep the integrity and virginity of such personalized information, accompanied with Covering algorithm to partition the features of rare items into some specific domains, implement high personalized requirements. Our experiments results indicates that the new system has shorter running time in the research of large-scale recommendation for computing user preference dynamically, and can perform better results to meet the individual needs with high user satisfaction.
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter

Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labelling tasks due to their respective strengths. However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labelling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer. Compared with the existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT. Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech tagging, show that LEBERT achieves the state-of-the-art results.
Quantum random number generator using vacuum fluctuations

Experimental implementation of a quantum random number generator based on vacuum fluctuation is presented in this paper.A Y-splitter is used in optical setup of the quantum random number generator.The generation of random numbers in real time with a speed of 300 Mb/s is demonstrated.The conditional minimum entropy is used to estimate the randomness.A cryptographic hashing function is used for post-processing.The resulting sequence has passed DieHard and NIST statistical tests successfully.
Comparison of Data Mining Methods Using C4.5 Algorithm and Naive Bayes in Predicting Heart Disease

Heart disease is a condition of the presence of fatty deposits in the coronary arteries in the heart which changes the role and shape of the arteries so that blood flow to the heart is obstructed. Data mining methods can predict this disease, some of the methods are C4.5 Algorithm and Naive Bayes which are often used in research.The data set in this research was obtained from the uci machine learning repository site, where the dataset has 3546 records and 13 attributes.The accuracy value of the NaÃ¯ve Bayes algorithm has a high value of 81.40% compared to the C4.5 algorithm which only has an accuracy value of 79.07%. Based on the calculation results, it can be concluded that the NaÃ¯ve Bayes Algorithm is a very good clarification because it has a value between 0.709 - 1.00.From conclusion above, the NaÃ¯ve Bayes algorithm has a higher accuracy value than the C4.5 algorithm so the researchers decided to use the NaÃ¯ve Bayes algorithm in predicting heart disease.
Incidencia de los Simuladores de negocio en el proceso de Aprendizaje

The traditional learning methods applied in the administration area in the various Ecuadorian universities have not been fully efficient or effective in their practical applicability. Limiting the opportunity to be competitive to the professionals graduated in this career. Faced with this reality, the need for a study to reorient the educational processes in that area becomes imperative. Experiments carried out in this research were based on classroom observation of two groups of students from the same university in two teaching areas. A traditional or controlled and the other under the inclusion of advanced technology such as simulators, in the learning processes. The exploration was developed under the modality of the qualitative approach applied to both students and teachers in the administrative area.The results demonstrated the incidence at the cognitive, participative, organizational and attitudinal levels, manifested both in the students and in the teaching staff. The students faced the case with dynamism and responsibility. Like the teaching staff, who felt safe, comfortable and took the role of protagonist in their classes. For greater security it is suggested to reproduce new experiences mediated with similar technological resources in the teaching of other subjects related to the area of financial administration and to provide the laboratories with simulators and licensed software.
Breast Cancer Histopathological Image Classification using Deep Convolutional Neural Network

Deep learning has come up with the intense class of models which have potential applications in the field of image classification, video recognition, object recognition, natural language Processing and speech recognition. Mainly, Deep convolutional Neural Network is one of the deep learning models that is used for image classification, that extracts the feature from the images and use these extracted features to classify images (2D or 3D images). In this paper, DCNN is used to classify mammogram images obtained from medical imaging process to detect the benign and malignant cells. The outcome of the study is to bring out the idea behind computing techniques incorporated with medical diagnostics, helping medical professional to take advantage of computer aided diagnostics, ultimately improving the time spent by pathologist to inspect the stained tissues in-turn increasing the survival rates.
Optimizing the walk coin in the quantum random walk search algorithm through machine learning

This paper examines the stability of the quantum random walk search algorithm, when the walk coin is constructed by generalized Householder reflection and additional phase shift, against inaccuracies in the phases used to construct the coin. The optimization of the algorithm is done by numerical methods - Monte Carlo, neural networks, and supervised machine learning. The results of numerical simulations show that, with such a construction of the Householder reflection, the algorithm is more stable to inaccuracies in the specific values of these phases, as long as it is possible to control the phase difference between the phase shift and the phase involved in the Householder reflection. This paper explicitly shows as an example, how achieving a properly designed phase difference would make quantum random walk search on a hypercube more stable for coin register consisting of one, two, and three qubits.
Peripheral blood smear pathologist tool

Microscopic evaluation of a peripheral blood smear is one of the most valuable test for the diagnosis and differential diagnosis of disease inclusive of clinical history and physical examination. Despite advances in haematology automation and application of molecular techniquesits diagnostic relevance is enormous.
“I Don't Know Exactly but I Know a Little”: Exploring Better Responses of Conversational Agents with Insufficient Information

Despite the increasing presence of conversational agents (CAs) in our daily lives, the lack of information and technology behind them prevents CAs from answering many questions. One of the most typical problems facing conversational user interfaces today is that they often disappoint people by giving the same answer (e.g., "I don't know"). In this work, we focused on situations in which CAs do not provide a proper answer because of a lack of information. Under these situations, we aimed to find more effective answer strategies for CAs to provide people better user experiences. We tested four different response strategies using different degrees of inferences and information as ground. We found differences in usability and user experience depending on how CAs respond. Our results will help designers understand how people feel about the way CAs respond and create better CA responses in situations where it is difficult to provide accurate answers.
Image De-Noising techniques: a comparative study

Image processing techniques can be used to distinguish images or enhance its quality. The occurrence of noises is common during electronic transmissions between devices, and the restoration of the images damaged during these transmissions is a challenging endeavor. This paper detail noises that are present in corrupt images and noise reduction techniques that can be used to minimize or eliminate noises from the images. Additionally a comprehensive survey is provided and summarises the recent implemented noise reduction techniques for noise removal from images and based of quality preservation purposes. This visualization will assist the development process of new techniques.
PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks

In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic methodologies are not widely applicable for addressing regression problems using CNNs, as regression involves learning unconstrained continuous and, in many cases, multi-variate target variables. We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems. PROPEL is fully differentiable and, hence, can be easily incorporated for end - to-end training of existing CNN regression architectures using existing optimization algorithms. The proposed method is flexible as it enables learning complex unconstrained probabilities while being generalizable to higher dimensional multi-variate regression problems. We utilize a PROPEL-based CNN to address the problem of learning hand and head orientation from uncalibrated color images. Our experimental validation and comparison with existing CNN regression loss functions show that PROPEL improves the accuracy of a CNN by enabling probabilistic regression, while significantly reducing required model parameters by 10×, resulting in improved generalization as compared to the existing state-of-the-art.
Investigation and prediction of open source software evolution using automated parameter mining for agent-based simulation

Abstract To guide software development, the estimation of the impact of decision making on the development process can be helpful in planning. For this estimation, often prediction models are used which can be learned from project data. In this paper, an approach for the usage of agent-based simulation for the prediction of software evolution trends is presented. The specialty of the proposed approach lies in the automated parameter estimation for the instantiation of project-specific simulation models. We want to assess how well a baseline model using average (commit) behavior of the agents (i.e., the developers) performs compared to models where different amount of project-specific data is fed into the simulation model. The approach involves the interplay between the mining framework and simulation framework. Parameters to be estimated include, e.g., file change probabilities of developers and the team constellation reflecting different developer roles. The structural evolution of software projects is observed using change coupling graphs based on common file changes. For the validation of simulation results, we compare empirical with simulated results. Our results showed that an average simulation model can mimic general project growth trends like the number of commits and files well and thus, can help project managers in, e.g., controlling the onboarding of developers. Besides, the simulated co-change evolution could be improved significantly using project-specific data.
Specificity of social adaptation of university teachers approaching retirement age

The article presents a sociological analysis of a topical research problem related to the study of the specifics of social adaptation of university teachers of pre-retirement age. A focused interview method was used (a qualitative strategy was used). The respondents were university professors aged 58 to 64 (women and men, respectively, of pre-retirement age). The research was of a pilot nature; it pursued the goal of collecting preliminary primary information on the problem of social adaptation of teachers of pre-retirement age working in higher educational institutions. As a result of the study, the following results were achieved: respondents are much more concerned about the difficulties of adapting to the status of a «teacher of pre-retirement age» than to the changes in their health status; the overwhelming majority of respondents would like to continue their professional activities when they reach retirement age; the main factor influencing the fulfillment of the obligatory scientometric indicators by teachers is a favorable moral and psychological climate in the work collective and cooperation between members of the teaching staff of different age groups. The potential of personnel development in the higher education system is, among other things, in the successful social and professional adaptation and cooperation of teachers with different teaching experience.
Evolutionary Algorithms for Multi-Objective Optimization of Drone Controller Parameters

Drones are effective for reducing human activity and interactions by performing tasks such as exploring and inspecting new environments, monitoring resources and delivering packages. Drones need a controller to maintain stability and to reach their goal. The most well-known drone controllers are proportional-integral-derivative (PID) and proportional-derivative (PD) controllers. However, the controller parameters need to be tuned and optimized. In this paper, we introduce the use of two evolutionary algorithms, biogeography-based optimization~(BBO) and particle swarm optimization (PSO), for multi-objective optimization (MOO) to tune the parameters of the PD controller of a drone. The combination of MOO, BBO, and PSO results in various methods for optimization: vector evaluated BBO and PSO, denoted as VEBBO and VEPSO; and non-dominated sorting BBO and PSO, denoted as NSBBO and NSPSO. The multi-objective cost function is based on tracking errors for the four states of the system. Two criteria for evaluating the Pareto fronts of the optimization methods, normalized hypervolume and relative coverage, are used to compare performance. Results show that NSBBO generally performs better than the other methods.
Enhanced optimization of high order concentrated matrix-exponential distributions

This paper presents numerical methods for finding high order concentrated matrix-exponential (ME) distributions, whose squared coefficient of variation (SCV) is very low.Due to the absence of symbolic construction to obtain the most concentrated ME distributions, non-linear optimization problems are defined to obtain high order concentrated matrix-exponential (CME) distributions .The number of parameters to optimize increases with the order in the "full" version of the optimization problem.For orders, where "full" optimization is infeasible ( > 184), a "heuristic" optimization procedure, optimizing only 3 parameters independent of the order, was proposed in [6].In this work we present an enhanced version of this heuristic optimization procedure, optimizing only 6 parameters independent of the order, which results in CME distributions with lower SCV than the existing 3-parameter method.The SCV gain of the new procedure compared to the old one is *
Towards Sneaking as a Playful Input Modality for Virtual Environments

Using virtual reality setups, users can fade out of their surroundings and dive fully into a thrilling and appealing virtual environment. The success of such immersive experiences depends heavily on natural and engaging interactions with the virtual world. As developers tend to focus on intuitive hand controls, other aspects of the broad range of full-body capabilities are easily left vacant. One repeatedly overlooked input modality is the user&#x0027;s gait. Even though users may walk physically to explore the environment, it usually does not matter how they move. However, gait-based interactions, using the variety of information contained in human gait, could offer interesting benefits for immersive experiences. For instance, stealth VR-games could profit from this additional range of interaction fidelity in the form of a sneaking-based input modality. In our work, we explore the potential of sneaking as a playful input modality for virtual environments. Therefore, we discuss possible sneaking-based gameplay mechanisms and develop three technical approaches, including precise foot-tracking and two abstraction levels. Our evaluation reveals the potential of sneaking-based inter-actions in IVEs, offering unique challenges and thrilling gameplay. For these interactions, precise tracking of individual footsteps is unnecessary, as a more abstract approach focusing on the players&#x0027; intention offers the same experience while providing better comprehensible feedback. Based on these findings, we discuss the broader potential and individual strengths of our gait-centered interactions.
Kualitas Air Tanah di Sekitar TPA Km 14 Kota Palangka Raya

The lack of clean water distribution by local water service (PDAM) of Palangka Raya city is the major reason for people around the final processing site/landfill (Tempat Pemrosesan Akhir) at Km 14 to use groundwater for their daily life. The landfill may cause pollution over groundwater known as leachate, which commonly unnoticed by its users. This study aims at assessing the quality of groundwater in that area. The groundwater was taken at different distances from the site in three rainless-days. The physical, chemical and biology parameters of groundwater are observed in the field (in situ) and analyzed in the laboratory, refers to water quality standard of Class I, according to Government Regulation No. 82 of 2001 and Minister of Health Regulation No. 32 of 2017. Public opinion on that issue is cached through interview. The results showed that the groundwater around the landfill in three rainless-days was harmless to be consumed as drinking water, and did not exceed the water quality standards stipulated by government in term of odorless, tasteless, and colorless. Some indicators also show that the groundwater meets the specified quality standards, i.e. TDS (17-68.14 mg/L), DHL (17,15-69.39 ?s), turbidity (0.11-2.50 mg/L), iron (0.227-0.71 mg/L), manganese (&lt;0.0123-0.02 mg/L), coliform (&lt;1.8-280 MPN/100 ml), while Pb and H2S were not detected. From community side, there are no any health complaints arise as long as they use the water.
2D Image Reconstruction using Differentiable Plasticity

The greatest boon on mankind is the adaptive nature of the brain. Apparently this roots in the exceptional flexibility possessed by the brain while learning. This trait of human learning bids an inspiration to Artificial Intelligence researchers to incorporate the human brain as a new design mechanism. The research opens a new perspective to embrace Artificial Intelligence (AI) with neural networks adding a key to mimic the functioning of the human brain. In contrast, recurrent neural networks (RNNs) and LSTMs can learn from ongoing experiences which will require additional neurons to store previous experiences. Here, the paper describes a simple methodology by taking the inventiveness from biological brains and recreating it as differentiable plasticity to adapt the features into learnings of neural network connections. Thereby, showing that plasticity participating with neural connection along with the Hebbian plastic connection rule can be optimized in recurrent neural networks. On the basis of a plastic neural network, a unique varied solution to image reconstruction tasks is experimented with and also affirmed that the traditionally used recurrent neural networks fall short to resolve these tasks. In this paper, an approach to novel problems to solve reconstructing and memorizing tasks is described on the sets of neoteric colored images by tracing plastic recurrent networks. In addition, this method is evaluated against existing models of RNN's to endorse the results. Hence, concluding that the plasticity method provides a firmer way to solve meta-learning situations in the new era of technology.
PERANCANGAN SISTEM INFORMASI PERPUSTAKAAN BERBASIS WEB DI SMK PLUS PRATAMA ADI BANDUNG

The rapid development of the computer world that brings every individual or group of people will use information and communication technology in every activity. Likewise in the world of education, including libraries that utilize information and communication technology in managing the process of borrowing and returning library books. The benefits of making a web-based library information system include being able to save time where there is no need for manual recording, making it easier to find book data or library members, making it easier in making book loan reports and other reports and other benefits. The research method used is Research and Development (R&amp;D). The making of software is done by a waterfall model consisting of: (1) needs analysis, (2) design, (3) implementation, and (4) testing. Then UML visual modeling is used which is the standardization of modeling language for object-oriented software development. The expected results of this study produce a web-based library information system using the YII Framework with MVC (Model View Controller) method in SMK Plus Pratama Adi Bandung that can solve existing problems. The formulation of the problem of this research is how to build a library information system in SMK Plus Pratama Adi Bandung so that it can present accurate and efficient information. The purpose of this research is to produce a library information system.