Reviewerly/exHarmony huggingface dataset:
PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.
Reannealing of Decaying Exploration Based On Heuristic Measure in Deep Q-Network

Existing exploration strategies in reinforcement learning (RL) often either ignore the history or feedback of search, or are complicated to implement. There is also a very limited literature showing their effectiveness over diverse domains. We propose an algorithm based on the idea of reannealing, that aims at encouraging exploration only when it is needed, for example, when the algorithm detects that the agent is stuck in a local optimum. The approach is simple to implement. We perform an illustrative case study showing that it has potential to both accelerate training and obtain a better policy.
Bigeminal Priors Variational auto-encoder

Variational auto-encoders (VAEs) are an influential and generally-used class of likelihood-based generative models in unsupervised learning. The likelihood-based generative models have been reported to be highly robust to the out-of-distribution (OOD) inputs and can be a detector by assuming that the model assigns higher likelihoods to the samples from the in-distribution (ID) dataset than an OOD dataset. However, recent works reported a phenomenon that VAE recognizes some OOD samples as ID by assigning a higher likelihood to the OOD inputs compared to the one from ID. In this work, we introduce a new model, namely Bigeminal Priors Variational auto-encoder (BPVAE), to address this phenomenon. The BPVAE aims to enhance the robustness of the VAEs by combing the power of VAE with the two independent priors that belong to the training dataset and simple dataset, which complexity is lower than the training dataset, respectively. BPVAE learns two datasets'features, assigning a higher likelihood for the training dataset than the simple dataset. In this way, we can use BPVAE's density estimate for detecting the OOD samples. Quantitative experimental results suggest that our model has better generalization capability and stronger robustness than the standard VAEs, proving the effectiveness of the proposed approach of hybrid learning by collaborative priors. Overall, this work paves a new avenue to potentially overcome the OOD problem via multiple latent priors modeling.
A framework for developing a smart and adaptive environment for aging population

This work presents a framework that takes a holistic approach toward fostering active aging of elderly people in the future of smart homes and empowering them to perform ADLs in an independent manner. Two specific functionalities presented are (1) a personalized task recommendation system that takes into account the various micro- and macro -actions that a user performs in the context of any given activity and recommends them actions and tasks to complete the same and (2) a fall prediction model that can analyze several dynamics of human motion and predict whether the person is going to fall, even before the fall occurs. These functionalities of the framework have been tested on several datasets and the results are presented and discussed. Comparison studies were also performed for each of these two functionalities where several learning models were implemented. Based on the performance characteristics of these learning models, a learning model that achieves the highest performance accuracy for each of the respective functionalities of this framework was adopted for our study. To the best knowledge of the authors, no similar approach has been done in this field yet. The results presented uphold the relevance and demonstrate the feasibility for practical implementation of this framework in the future of smart homes. This framework is envisioned to lead toward the development of the future of adaptive behavior intervention technologies in health care and social welfare that will be able to analyze, assess, model, profile and provide efficient personalized behavior interventions to enhance user experiences and foster elderly care while addressing the varying diversities of the elderly population. Future work would involve setting up an IoT-based environment through a host of both wireless and wearable sensors to implement this framework in a real-time context.
Design on College Japanese Mobile Learning Resources Platform based on SSi Framework

Mobile learning has the characteristics of portability, personalization, real-time and interaction, which brings new opportunities for the development of modern education. Traditional college Japanese teaching has the characteristics of exam-oriented education, and cannot meet the needs of higher education that keeps pace with the times and changes. Develop a university Japanese learning resource platform to provide rich resources for Japanese university Japanese learning, and improve the individuality and autonomy of students. This article designs the basic SSI framework structure, taking advantage of the combined advantages of Struts2, Spring, and iBatis. Based on previous research results, it designed a university Japanese learning resource platform structure composed of a user layer, a function layer, and a data layer. Based on a collaborative filtering model, it designed a personalized learning resource recommendation algorithm and process. The research results of this paper provide a complete solution for the development of a university Japanese mobile learning resource platform, which can reduce the risk of system requirements and improve development efficiency
Design and development of 3D Face Mask Clips for Hijab

The world has suffered a critical shortage of Personal Protective Equipment (PPE) during the pandemic of COVID19 for medical staffs, the front liners. Like the whole world, Malaysia also imposed the stay-at-home and Movement Control Order (MCO) to break the chain of infections and flatten the curve of cases. The supply of PPE became a challenge during the lock down. There have been joined efforts from various parties stepping up, with different ways to help the production process of these key equipment but mostly focus on PPTs for male. Another challenge was face mask for Muslims leady health workers who wear hijab. This paper is about how to overcome these challenges and designed a novel face mask clip for hijab, using 3D printing. The clips were tested in a local hospital. The results show that the clips are very effective and easy to use. &#x0D; Keywords: Covid-19; Personal protective equipment; Pandemic; Movement Control Order; 3D Face mask
Option of Control of Access to Information Resources Based on Implicit Authentication

Purpose of research is to build a model of a system for effective authentication of mobile users based on public data of the user and his behavioral factors as well as to study algorithms for calculating the threshold value at which the authentication of a mobile device user is considered successful. Methods . When analyzing the behavioral factors of a user who needs to be authenticated when interacting with mobile devices, application of the following methods for calculating the threshold value are proposed: dynamic methods for determining the threshold value of user authentication based on the standard deviation and the calculation of the aggregate mean score; the method based on the standard when the system divides the aggregate flow of estimates into several blocks of the same length, where the first block is used for training, and the calculated threshold is used in the second block (this sequence of actions is repeated continuously, that is, the previous block provides training results for calculating the threshold for the current block); a method for calculating the aggregate mean score, where instead of using a single total score as input, the system uses the mean value of the current block, and the new calculated threshold is used as the threshold for decision making for the next block. A mathematical model that balances the speed and reliability of mobile users authentication is proposed. Results . The result of the research is the development of an effective system for calculating the threshold value of successful authentication of a mobile device user based on behavioural features which adapts to changes of the user's behavioural factors. Experimental studies and comparisons with analogs confirming the completeness and correctness were carried out as well as various variants of the proposed solutions. Conclusion . The proposed method of implicit authentication for mobile access control is easy to implement, easy to use, and adaptive to changes in input data. Options for calculating the threshold value at which implicit authentication is considered successful are also proposed.
Strengthening Pix CAPTCHA Using Trainlm Function in Backpropagation

CAPTCHA (Completely Automated Public Turing Test) security mechanism is a new and innovative technology in the world of the Internet and IOT. It is becoming a regular feature of the majority website’s login system. Pix CAPTCHA’s are more secure as compared to text-based CAPTCHA as it saves the websites from bots attacks. In this paper, we introduce another mechanism for the storage of Pix CAPTCHA using the techniques of artificial neural networks. The experiment results prove that the new model has a good recognition effect of Pix CAPTCHA when trained using backpropagation algorithm.
Analyzing of Usage Effect of the Distribution Functions for SMDO Algorithm via Benchmark Function with Matlab Toolbox

This paper presents solution comparisons of benchmark functions by using stochastic multi-parameters divergence (SMDO) method with different distribution functions. Using benchmark functions is an important method in measuring the effectiveness of algorithms. Because benchmark functions are used by all algorithm producers while trying their algorithms and this provides a good tool for the others to compare their algorithms with similar procedures. Benchmark functions are used in this paper for the main purpose of analyzing randomization process. It is known that distribution functions take place a vital role in getting random numbers. These random numbers are used in stochastic methods through specifying step size. It is believed that a suitable random number acquisition process can support the search processes of algorithms. In this study the effects of distribution functions on benchmark functions are analyzed. For this purpose, a program is developed with MATLAB. The comparisons via the help of this program is shown in tabular form. The results are analyzed from the viewpoint of whether developing the randomization process makes contribution to problem solving power of algorithms. In this study SMDO algorithm is analyzed with different distribution functions by using different benchmark functions. In addition, in the study, a useful friend-friendly Matlab toolbox is proposed in which SMDO algorithm can be tested over different benchmark functions according to different distribution functions. (https://www.mathworks.com/matlabcentral/fileexchange/75044-smdo-with-distribution-function-forbenchmarking)
The Rising of Blockchain Technology and Its Adoption in India

The demonetization of India has become a major impetus for the country to switch to digitalization and become a cashless country. Although new and potentially disruptive innovations, such as Cryptocurrencies and the Blockchain, are in their development stages, they are able to convert paper money into digital currency, providing a decentralized and secure environment. Blockchain features such as distributed computing, privacy, authenticity, non-denial, data integrity, and data availability can help a country like India become a cash economy. Finally, people in India started comprehending the necessity of digital money and non-cash loom it offers. After the successful adoption of large-scale non-cash payments, India is currently fanatical to move toward blockchain adoption. Blockchain technology is budding as the prime innovations that will shape the potential of digital and online transactions in India.
The Sparse Vector Technique, Revisited

We revisit one of the most basic and widely applicable techniques in the literature of differential privacy - the sparse vector technique [Dwork et al., STOC 2009]. This simple algorithm privately tests whether the value of a given query on a database is close to what we expect it to be. It allows to ask an unbounded number of queries as long as the answer is close to what we expect, and halts following the first query for which this is not the case. We suggest an alternative, equally simple, algorithm that can continue testing queries as long as any single individual does not contribute to the answer of too many queries whose answer deviates substantially form what we expect. Our analysis is subtle and some of its ingredients may be more widely applicable. In some cases our new algorithm allows to privately extract much more information from the database than the original. We demonstrate this by applying our algorithm to the shifting heavy-hitters problem: On every time step, each of $n$ users gets a new input, and the task is to privately identify all the current heavy-hitters. That is, on time step $i$, the goal is to identify all data elements $x$ such that many of the users have $x$ as their current input. We present an algorithm for this problem with improved error guarantees over what can be obtained using existing techniques. Specifically, the error of our algorithm depends on the maximal number of times that a single user holds a heavy-hitter as input, rather than the total number of times in which a heavy-hitter exists.
Computational and Performance Aspects of Face Recognition Method (HOPFALRB)

Biometric face recognition is extensively explored during the last few decades. Face recognition performance measure evaluations provide the model and the implementation process. These performance models will identify, compare the characteristics of the subject and the number of opportunities will abound with this model. Although the design and evaluation of the proposed model will draw some unique features and challenges for the system. Face recognition blends the methods from engineering, computer science, statistics, etc. Due to the practical applications, its usability features, it is being attracted by the multiple users. To measure the performance, multiple evaluation methods and tools exist. In this research, initially, we evaluate the accuracy of the model and then after the multiple performance measures.
GERAKAN LITERASI DINI READHATON SEBAGAI UPAYA MEMBANGKITKAN MEMBACA SISWA DI SEKOLAH ALAM SMP INSAN LITERA - DESA CIHAMPELAS

This research is backed by a low interest condition of reading communities among children and adolescents in Kp. BBC Village of Cihampelas West Bandung Regency. Especially the students in the junior high School of Insan Litera and any strategy that can be used as an effort to inspire reading. The purpose of this research is (1) to know the strategy to build a community to learn. (2) To know the process of student Readathon activity in Sekolah Alam SMP Insan Litera. The theories used in this research include the concept of community fond of learning, and the concept of Readathon. The method used is a qualitative approach with a descriptive method. Data collection techniques using observation techniques, interviews, and documentation. The location of research in Sekolah Alam SMP Insan Litera which is under the auspices of Bening Saguling Foundation and which is the subject of research is all students of Sekolah Alam SMP Insan Litera, amounting to 11 people, principals, and also representatives of one teacher. Based on the research results that Readathon has carried out well. In this readathon there are several activities carried out consisting of 4 stages of 1) the implementation of Readathon routinely once a week, carried out on Thursday. With the target one student finished reading one book one week. 2) reading book read by PPL students who act as tutors. 3) Discussion of read books packed with fun games or quizzes. 4) Create a geulis tree (literacy Movement) where the student who has completed reading one book writes his name and the title of his book later in the Geulis tree. The condition of learning interest in students is also quite high, visible from students ' participation in following learning programs.
Refined Heuristic Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.
New Protocols and Negative Results for Textual Entailment Data Collection

Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding. However, the crowdsourcing protocol that was used to collect this data has known issues and was not explicitly optimized for either of these purposes, so it is likely far from ideal. We propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples. Using these alternatives and a fifth baseline protocol, we collect and compare five new 8.5k-example training sets. In evaluations focused on transfer learning applications, our results are solidly negative, with models trained on our baseline dataset yielding good transfer performance to downstream tasks, but none of our four new methods (nor the recent ANLI) showing any improvements over that baseline. In a small silver lining, we observe that all four new protocols, especially those where annotators edit pre-filled text boxes, reduce previously observed issues with annotation artifacts.
Design of Deep Convolutional Neural Network for Efficient Classification of Malaria Parasite

The standard method used for the testing of malaria is the blood stream examined under the parasite-infected red blood cells by qualified technicians. The test is carried out under a microscope. The inefficiency of this traditional method has been improved with advanced computer vision and deep learning techniques to automatically identify the object as infected or not infected through the use of microscope. The work proposed employs a deep convolutional neural network (DCNN) to identify the parasites of malaria from images of human bladder cells. The focus of the work is primarily on the comparison of validation and precision by setting the hyperparameters so that images are classified into parasitized and uninfected cells. The expertise in finding the parasitized and non-parasitized and quality of smear play an important role in system accuracy; the proposed deep learning technique provides better solution with end-to-end extraction and classification of feature. The datasets consist of 26,188 images which contain 13,105 parasitized images and 13,083 uninfected images. The kappa coefficient (KC) and Matthews correlation coefficient (MCC) are evaluated for the proposed network.
Training of patient handover in virtual reality

Abstract Patient handover is an important part for information transfer between medical professionals in a clinical setting. Yet, in current medical education, these conversations are only trained sparsely, since they are costly to perform as they take place in offsite courses and are led by experts over several days. Virtual reality (VR)-based training courses could increase the availability of training, by eliminating travel costs and reducing the time-commitment of the teaching experts. This work presents a VR prototype of a multi-user training and examination application for patient handover. To ensure a similar interaction quality to its current real world counterpart, this work used omni-directional video recordings to create a realistic setting and compared different projection methods. A pilot study highlighted distinct use-cases of sphere and mesh projections to visualize the recordings. The results suggest enhanced spatial presence relating to the usage of omni-directional videos in VR-applications.
Discovering Sequential Patterns by Neural Networks

Sequential pattern mining can discover many interesting phenomena such as bank transactions, web page requesting sequences, customer behavior, etc. There have been many frequent itemset mining algorithms proposed so far, yet it is still a challenging task. In this paper, we propose a deep learning architecture for discovering closed sequences. The U-Net network is trained with random, synthetic sequences and, afterward, is able to discover unknown (not seen during training) sequences. The proposed solution is faster than traditional sequential data mining methods for longer sequences.
Docker Security Analysis Mechanism with Linux Platform

Presently, the uses of virtualization technologies are increased very frequently. This increases the IT agility, flexibility, and scalability. There is a layer of virtualization between the guest OS and physical hardware. This paper analyze the performance of virtualization technology with comparitive analysis. Our experiment showed that KVM gives the best performance as compared to XEN. This paper suggests best-suited hypervisors for aimed application.
THE EFFECTIVENESS OF TWO STAY TWO STRAY IN TEACHING READING COMPREHENSION

This research was aimed: to find out significant effect of Two Stay Two Stray in Teaching Reading Comprehension at the second year Students of MTs Hidayatussibyan NW Sengkerang in academic year 2013/2014. The kind of this research was quantitative research. The population of this research was the second grade students that consist of 28 students. The instrument of collecting data used test which was 50 multiple choice items. In this research, the researcher gave a treatment and prepared a test in form of multiple choices to look for the effect of Two Stay Two Stray in Teaching Reading Comprehension. The result of the data analysis indicated that the grade students obtained high score on the post test. It means that, the Two Stay Two Stray gave a significant effect in students’ reading comprehension. It was proven by the lowest and highest score in pre-test and post-test, they were 20 and 58 and post-test were 32 and 80. Thus, the result of alternative hypothesis was accepted. It was provable that ttest was higher than ttable, which was the value of ttest was 4.875 and ttable 27 in significance level of 0.05% was 2.052.