Reviewerly/exHarmony huggingface dataset:
AN IMPROVED PORT KNOCKING AUTHENTICATION FRAMEWORK FOR MOBILE CLOUD COMPUTING

The latest developments in mobile cloud computing (MCC) have changed user's priorities for computing. However, the change towards MCC brings new challenges to cloud service providers and administrators. Authentication is one among the challenges categorized in the classification of security issues for MCC. Port knocking authentication method eliminates user's collaboration during the authentication process. Thus, such technique has the potential to be applied on the MCC environment which can ensure reliable communication. However, current port knocking authentication techniques lack of addressing the issue of knock-sequence length. It is challenging to deploy appropriate length sequence for port knocking authentication for the reason that shorter length knock sequence degrades security, whereas, deploying longer length sequence involves performance issues in terms of time and buffer management. This paper proposes a dynamic length port knocking authentication framework which addresses the issue of security degradation and optimizes performance in terms of time up and buffer managment. We employ MikroTik RouterOS for the evaluation of the proposed technique. Analysis of the results shows that dynamic length port knocking authentication technique improves performance in terms of time up to 23% and buffer management up to 28% by reducing the imposed load. Furthermore, by deploying dynamic length (DL) and pool of length (PoL), the proposed method reveals high security, which decreases the probability of hacking knock-sequence near to zero for a number of parallel authentication requests. Hence, dynamic length port knocking authentication technique providesan optimal solution for reliable communication in MCC.
The prototype of the Greenhouse Smart Control and Monitoring System in Hydroponic Plants

This research helps the farmers to do the humidification control and monitor the condition of hydroponic plants in greenhouses in real-time. When it comes to watching the hydroponic plants in greenhouses, the farmers usually experience difficulties because they still do it manually. Activities such as checking the temperature, air humidity, and also water quality in hydroponic plants by coming directly to the greenhouse are still ineffective. Therefore this research aims to make a smart greenhouse prototype for hydroponic plants. Smart greenhouse hardware was built based on the Arduino microcontroller, DHT11 sensor, pH sensor, TDS, DS18b20 temperature, ultrasonic, and esp8266 wifi module. The monitoring system features information on water quality from hydroponic plants and the ability to record farming activities from planting preparation to web-based harvesting. The test results of smart greenhouse monitoring system can display the hydroponic plant conditions and able to do the humidification control with an upper limit of 35 degrees celsius because plant can survive with disease under 35 degrees celsius, with small average offset for the sensor, and an average offset of 1.49 from TDS sensors, with temperature of 0.50 and pH of 0.34. &#x0D; Keywords: Greenhouse, NFT hydroponics, Humidification, Monitoring System, Arduino
AN OVERVIEW ON DIGITAL LIBRARY

Today, libraries are much more than storehouses for books, journals, and newspapers, in print form. Present day libraries apart from print resources, acquire e-resources, audio/visual material, multimedia material and other resources depending upon the demands from the users. This change is evident in all kinds of libraries. However, one thing that has not changed is the universe of information or knowledge; it is forever expanding and is continuing to do so at ever increasing speeds. One of the best examples of a digital library is, Digital Library of India (DLI).
Exploiting the Web of Data to bridge formal and informal learning experiences

Smart Education promises personalized learning experiences that bridge formal and informal learning. Our proposal is to exploit the Web of Data to automatically create learning resources that can be, later on, recommended to a learner based on her learning interests and context. For example, a student enrolled in an arts course can get recommendations of learning resources (e.g., a quiz related to a monument she passes by) by exploiting existing geolocalized descriptions of historical buildings in the Web of Data. This paper describes a scenario to illustrate this idea and proposes a software architecture to support it. It also provides some examples of learning resources automatically created with a first prototype of a resource-generator module.
Penerapan Algoritma Apriori Dalam Memprediksi Hasil Penjualan Sparepart PC (Studi Kasus : Toko Sentra Computer)

Technological advances are increasing and becoming an important need for everyday life both in the fields of education, economics, social, culture and politics. One of the technologies needed by society is computers. Sentra Computer is a store engaged in the sale of computers. The researcher has conducted research at the Computer Center Store and found a problem when the stock of PC spare parts that cost more than Rp. 450,000 is often insufficient because the stock is bought with a small amount so that capital can be used on a cheaper PC spare part. The purpose of this thesis research is to find out the sales data of PC spare parts at the Sentra Computer Shop which are most purchased by consumers. The methodology used is a priori algorithm by calculating the value of support and the value of confidence carried out manually and computerized using rapidminer. The result is that the Sentra Computer Store owner can provide sufficient stock.
Clothing Images Attributes Classification Based on Deep Neural Network

Faced with the massive amount of online shopping clothing images, how to recognize the attributes of clothing images quickly and accurately is a challenging task of image classification. In this paper, a new attribute classification method for clothing images is proposed based on the position relevant features acquired on the region of interest in the image. To extract the relevant regional features, one deep neural network model is designed with the pose estimation. Then the final attributes of clothing images are generated by fusing classification features. The performance of classification by fusion feature is compared with the method by single global feature of the image. The experiment results show that it is effective to combine with pose estimation in attribute classification. And the fusion feature shows good performance regardless of the neural network model.
Strategies of social partnership in the spheres of education, employment and employment of young people with restricted health and disability

The article discusses the current effective social partnership strategies in the fields of education, employment and employment of young people with handicappedness and disabilities. The author's approaches to the formation of regional experience in social support of young people with disabilities, their training, vocational guidance and employment are analysed. In modern socio-economic conditions, the search for new approaches to the quality of training for specialists with disabilities is of great importance. Therefore, it is necessary to consider new learning technologies, including holding competitive venues in various competences at regional and national championships in professional skills among the disabled and handicapped people"Abilympics." In order to ensure effective vocational guidance and motivation of young people with disabilities to receive vocational education, promoting their employment and socio-cultural inclusion in society.
Breaking the hardness assumption and IND‐CPA security of HQC submitted to NIST PQC project

Hamming quasi-cyclic (HQC) cryptosystem, proposed by Aguilar Melchor et al., is a code-based key encapsulation mechanism (KEM) submitted for the NIST standardisation process of post-quantum cryptography (PQC). Under the assumption that the s -decision quasi-cyclic syndrome decoding (s -DQCSD) problem is hard for s = 2 and 3, HQC, viewed as a public-key encryption scheme, is proven to be indistinguishability under chosen plaintext attack (IND-CPA) secure, and can be transformed into an IND-Adaptive chosen ciphertext attack secure KEM. However, the authors will show that the s -DQCSD problem is actually not intractable and HQC cannot attain IND-CPA security with all the proposed parameter sets. As HQC was selected as one of the second-round candidates by NIST, it was also updated to resist attack. The underlying s -DQCSD problem was replaced by the s -DQCSD with a parity problem and they claimed that the updated HQC could attain IND-CPA security under the hardness of the new problem. However, they find that there is some flaw in their security proof and the updated HQC is still vulnerable to attack. To fix it, they define a new problem called s -DQCSD with variable weight and present revised scheme HQC-β, which finally attains the IND-CPA security under the hardness assumption of the new problem.
Study on the Multivariant Interactive Teaching Modes of College English under the Information Technology Environment

Teaching modes can achieve twice the result with half the effort for teaching effect.The multivariant interactive teaching modes based on constructivism theory are a new kind of teaching modes supported by information technology.The multivariant interactive College English teaching modes under the information technology environment has changed the relationship between teachers and students in the classroom teaching process and the relationship between teachers and students and the teaching content.It can improve the quality of English teaching to achieve the goal of effectively improving the teaching effect of the course and improving students' ability of using English language.Based on the background of the information technology environment, this research takes the college students as the object and the multivariant interactive teaching modes as the research method and analyzes the effectiveness of the multivariant interactive teaching modes through an example.Povzetek: Opisana in testirana je multivariantna analiza učenja angleščine.
Fall Detection with Support Vector Machine for Elderly Care using Pressure Sensor Grid

Generally, falling is prevalent in elderly of age 65 and above due to age-related biological changes.Falls can be life-threatening if noticed late.Hence, a fall detection and surveillance device was developed to monitor elderly living alone.The primary objective is to build a pressure sensitive mat capable of detecting fall through image processing.The subjects tested resembled the stature of elderly.The accuracy of the system in detecting fall is 93% while the accuracies for the other postures such as standing and sitting yield 93.5 and 81.5%, respectively.
STRATEGIC SESSIONS ORGANIZATION AND CONDUCTION

The paper analyzes the case of organizing and conducting strategic sessions, their practice being realized over the past few years at the Moscow School of Management Skolkovo and in other projects on developing university strategies with the author’s participation. This case is important, as the strategic sessions today have a mass format, and their practice analysis often shows insufficient theoretical and methodological validity. The case presents the main elements of the methodology for conducting strategic sessions. There are shown a number of methods for providing the basic processes of such sessions – thinking and self-determination. The main element of such sessions is described in detail – that is, the formation of content. This case study is addressed to university leaders who are responsible for strategic planning, as well as to session leaders, to experts, and to consultants in this field.
Pentingnya Kecerdasan Emosional Pendidik Dalam Meningkatkan Pendidikan Karakter

Emotional intelligence is a skill on how to manage emotions to be better for themselves and others, emotional intelligence is divided into two namely intrapersonal intelligence (ability to understand intentions, motivations, moods, temperament and desires of others) and interpersonal (ability to form a a careful and self-referring model of oneself and the ability to use capital as a tool to lead an effective life)
Penerapan Algoritma C4.5 pada Penentuan Tingkat Pemahaman Mahasiswa Terhadap Matakuliah

This study aims to classify the level of understanding of students at STIKOM Tunas Bangsa Pematangsiantar. STIKOM Tunas Bangsa is one of the private universities in North Sumatra that is engaged in the field of computer science. In carrying out lecture activities, students are required to understand each lecture material provided by the lecturer. There are several things that can affect the level of understanding of students in receiving lecture material. The data source was obtained from the results of the fifth semester and seven student questionnaires in the STIKOM Tunas Bangsa Information System department. Attributes used are as many as five, namely communication, learning atmosphere, learning media, appearance and how to teach. The method used in the research is C4.5 Algorithm and assisted by RapidMiner software to make decision trees. From the results of the study, there were 14 rules for classification in determining the level of understanding with 9 rules, the best status and 5 rules did not understand. C4.5 algorithm can be used in the case of determining the level of understanding of students at STIKOM Tunas Bangsa with an accuracy rate of 87.10%. With this analysis it is expected to be a motivation for students to be able to understand the course well.
An Association Prediction Model: GECOL as a Case Study

Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making.In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL).This technique has been applied for the first time on the data of production, transportation and distribution departments.These data are in the form of purchase and work orders of operational material strategic equipment spare parts.This technique would extract prediction rules in order to assist the decisionmakers of the company to make appropriate future decisions more easily and in less time.A supervised machine learning technique has been adopted and applied for the mining data warehouse.A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work.The WEKA tool is applied to the collected data from GECOL.The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time.The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system.A set of experiments has been conducted on those data to obtain prediction models.These models are in the form of decision rules.The produced models were evaluated in terms of accuracy and production time.It can be concluded that the obtained results are very promising and encouraging.
Sarah’s Participation in WAT 2019

This paper describes our MT systems' participation in the WAT 2019.We participated in the (i) Patent, (ii) Timely Disclosure, (iii) Newswire and (iv) Mixed-domain tasks.Our main focus is to explore how similar Transformer models perform on various tasks.We observed that for tasks with smaller datasets, our best model setup are shallower models with lesser number of attention heads.We investigated practical issues in NMT that often appear in production settings, such as coping with multilinguality and simplifying pre-and post-processing pipeline in deployment.
A combined algorithm of K-means and MTRL for multi-class classification

The basic idea of multi-class classification is a disassembly method, which is to decompose a multi-class classification task into several binary classification tasks. In order to improve the accuracy of multi-class classification in the case of insufficient samples, this paper proposes a multi-class classification method combining K-means and multi-task relationship learning (MTRL). The method first uses the split method of One vs. Rest to disassemble the multi-class classification task into binary classification tasks. K-means is used to down sample the dataset of each task, which can prevent over-fitting of the model while reducing training costs. Finally, the sampled dataset is applied to the MTRL, and multiple binary classifiers are trained together. With the help of MTRL, this method can utilize the inter-task association to train the model, and achieve the purpose of improving the classification accuracy of each binary classifier. The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset, Wine dataset, Multiple Features dataset, Wireless Indoor Localization dataset and Avila dataset.
Implementasi Pengembangan Materi Ajar Interferensi Leksikon Bahasa Inggris ke dalam Bahasa Indonesia Berbasis Media Komunikasi Elektronik Youtube

This study has two objectives: (1) to identify interference of the English lexicon into Indonesian used on Youtube and (2) to identify the imple-mentation of the development of Youtube-based English lexicon interfer-ence teaching material into Indonesian. This research is a qualitative description which involve data in the form of video from Youtube. The collection techniques are using listening methods and collecting record-ings, taking notes, observation and focus group discussions which in-volve studens. The data analysis technique uses translational equivalent method and the aggregate method. The results of this study show that there are six forms of interference of the English lexicon into the Indone-sian language. (a) 39 nouns, (b) 6 verbs, (c) 3 pronouns, (d) 1 conjunc-tion, (e) 3 adverbs, and (f) 8 adjectives. This finding is useful in the im-plementation of sociolinguistic learning with lexicon interference mate-rial from English into Indonesian based on the standard set by the Minis-try of Education.
CodeeGAN: Code Generation via Adversarial Training

The automatic generation of code is an important research problem in the field of Machine Learning. Generative Adversarial Network (GAN) exhibits a powerful ability in image generation. However, generating code via GAN is so far an unexplored research area, the reason of which is the discrete output of language model hinders the application of gradient-based GANs. In this paper, we propose a model called CodeeGAN to generate code via adversarial training. First, we adopt Policy Gradient method in Reinforcement Learning (RL) to solve the problem of discrete data. Data generated by the generative model is discrete data which makes the generative model cannot be adjusted by gradient descent. Second, we use Monte Carlo Tree Search (MCTS) to create our rollout network for evaluating the loss of generated tokens. Based on the two mechanisms above, we create CodeeGAN model to generate code via adversarial training. We evaluate the model with datasets from four different platforms. Our model shows a better performance than other existing works and proves that code generation via adversarial training is an advanced efficient method.
PENGEMBANGAN MEDIA KOMIK DENGAN PENDEKATAN SAINTIFIK UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH MATEMATIKA SD

This research and development aims to develop and test the validity, practicality, and effectiveness of comic-based modules in mathematics in elementary schools. The research method used is R&amp;D with the ADDIE development model which consists of five stages, namely 1) analysis, 2) design, 3) development, 4) implementation and 5) evaluation ). Product validity was obtained from expert testing with expert validation instruments. The practicality of the product in the form of comic media was obtained through limited trials with a questionnaire instrument. The effectiveness is obtained through one class trial with one group pretest-posttest design then the results are tested using Paired SamplesvT Test. The result of this research is that the comic-based media module generally improves the ability to solve mathematical problems in the classroom material of five elementary school students. Learning media are declared valid based on the test of media experts, material experts and learning experts. Learning media are declared practical based on the results of the questionnaire responses of teachers and students. Learning media are declared effective based on the results of students' Paired SamplesvT Test pretest-posttest.
Some Considerations and a Benchmark Related to the CNF Property of the Koczy-Hirota Fuzzy Rule Interpolation

The goal of this paper is twofold. Once to highlight some basic problematic properties of the KH Fuzzy Rule Interpolation through examples, secondly to set up a brief Benchmark set of Examples, which is suitable for testing other Fuzzy Rule Interpolation (FRI) methods against these ill conditions. Fuzzy Rule Interpolation methods were originally proposed to handle the situation of missing fuzzy rules (sparse rule-bases) and to reduce the decision complexity. Fuzzy Rule Interpolation is an important technique for implementing inference with sparse fuzzy rule-bases. Even if a given observation has no overlap with the antecedent of any rule from the rule-base, FRI may still conclude a conclusion. The first FRI method was the Koczy and Hirota proposed "Linear Interpolation", which was later renamed to "KH Fuzzy Interpolation" by the followers. There are several conditions and criteria have been suggested for unifying the common requirements an FRI methods have to satisfy. One of the most common one is the demand for a convex and normal fuzzy (CNF) conclusion, if all the rule antecedents and consequents are CNF sets. The KH FRI is the one, which cannot fulfill this condition. This paper is focusing on the conditions, where the KH FRI fails the demand for the CNF conclusion. By setting up some CNF rule examples, the paper also defines a Benchmark, in which other FRI methods can be tested if they can produce CNF conclusion where the KH FRI fails.
The Use of Circle Games to improve the Mastery of English Vocabulary of the Indonesian Maritime Academy Students

The research's aim was to find out if the use of circle games substantially enhances the vocabulary competence of the learners and to find out whether or not the learners are interested in using circle games. The study used a quasi-experimental technique. The study population was AMI AIPI Makassar's first year students in the academic year 2018/2019. The sample consisted of 50 students in Technical Class A and B. The research used cluster random sampling. In AMI AIPI Makassar, the investigator took two classes of the first year Technical Class students. The classes were split into the experimental class and class control. The results showed an important distinction between the score of the learners taught using circle games and without Circle Games. It was demonstrated by the experimental class ' mean score, which in the posttest was greater than the control class. The test outcome showed that the use of Circle Games substantially increased the mastery of the students. Analysis using the Likert Scale showed that learners wanted to learn English through Circle Games.