Reviewerly/exHarmony huggingface dataset:
Analisa Kerentanan Sistem Dengan Menerapkan Open Vulnerability Assessment System Menggunakan Greenbone Vulnerability Management (GVM)

A computer network is a network telecommunication that connects one or more computers to exchange data and information with each other. Such huge benefit will certainly be reduced by the presence of interference that arises in the network, when the network only involves local devices or in other words, is not connected to the internet network then interference may be less calculated. However, when the local network is connected to the internet network, a security system will be something that must be considered. Every system and network will undoubtedly have vulnerabilities and can cause damage to the system and even data to cause losses. Tracing activities and identifying system vulnerabilities are effective ways to minimize the risk of continuous vulnerability, therefore this study aims to conduct network analysis to determine vulnerabilities system by applying the open vulnerability assessment system method using Greenbone Vulnerability Management (GVM) as a platform of network scanning and vulnerability management system. The results are achieved by doing the vulnerability analysis to evaluate the results of vulnerability findings and then categorized according to the level of risk, namely high, medium and low. Conducting a vulnerability assessment will improve information security and avoid bad risks that can cause losses. The results achieved doing the vulnerability assessment activity are to provide an evaluation of the risk of vulnerability found and the impact that can be generated and as a preventive measure to increase security system awareness.
Mineralogy and Geochemistry of some Phosphate Deposits for Possible Rare Earth Elements Mineralization Potentials within Sokoto Basin, Northwestern Nigeria

Objective: Phosphate rocks found in some parts of Sokoto Sedimentary basins in Nigeria were studied to understand their mineralogy and geochemistry specifically to determine their abundance in Rare Earth Elements hosting minerals notably Apatite, Monazite and Xenotime as there is an increasing demand for these elements globally in the production of green technology.Methods: Field observations were carried out in places with reported phosphate occurrences and seven (7) representative phosphate samples were collected, crushed, pulverized, and analyzed using X-Ray Diffraction (XRD) analysis for their mineralogical composition while samples with significant fluorapatite concentrations were further analyzed with Lithium Borate fusion Inductively Coupled Plasma Mass Spectrometry (ICPMS) for their elemental composition.Findings: The phosphates typically occur as nodules intercalated with Shale occurring at depth of about 0.25 -7m with a mineralogical composition including Fluoroapatite, Calcite, Smectite, Quartz, Kaolinite, Goethite, and Palygorskite.A high concentration of Fluorapatite was observed at Miyal'yako (79.28%) and Dillingo (36.60%) while interpretation of the relationship between P 2 O 5 with other major oxides and trace elements revealed that the phosphates were primary type and typically formed in an oxidizing environment with fluctuating pH in the presence of calcite or gypsum.Novelty: The study pinpoints that, of the seven locations, only two showed high concentrations of fluoroapatites enough to suggest that they are areas of interest for further investigation in the search for REEs mineralization in Nigeria.
Transfer and Ensemble Learning in Real-time Accurate Age and Age-group Estimation

Aging is considered to be a complex process in almost every species' life, which can be studied at a variety of levels of abstraction as well as in different organs.Not surprisingly, biometric characteristics from facial images play a significant role in predicting human's age.Specifically, automatic age estimation in real-time situation has begun to affirm its position as an essential process in a vast variety of applications.In this paper, two approaches are addressed as solutions for such application: prediction of accurate age and age group by using the two most fundamental techniques in the domain of deep learningconvolutional neural networks (CNNs) and deep neural networks (DNNs).In summary, this work can be split into two main key contributions.By applying a novel hierarchical aggregation built on the base of neural network developed from the training dataset, in the first stage, features extraction, the convolutional activation features are extracted from the captured facial image.As soon as this part is done, the features classification step is performed, in which Softmax Regression (SR) and majority vote classifiers are applied to predict accurate age and age group respectively.The effectiveness of the designed model was showed satisfactorily in the experimental results, which emphasizes the promising of the solution and indicates another direction for future development of algorithms and models in the field of machine learning.
The Effect of Service Robots and Other Technology on Job Profiles in The Hospitality Industry

The adoption of service robots has reshaped the hotel industry.Fueled by the prevailing manpower crunch and the recent COVID-19 pandemic, the industry was propelled to integrate technology into their operations and service robots were introduced as a complement to the human workforce.Even though the perceptions and impacts of service robots have been widely discussed, research pertaining to the impact of service robots from a human resource perspective is currently lacking in the literature.This qualitative study explores service robots in relation to the effect that this technology has on job profiles within the hotel industry, through semi-structured interviews with industry experts and academics.The key findings highlight that the acceptance of this technology is dependent on the tier of the hotel and the resistance arising from the cost and the technological disruption to the hotel.Moreover, job redesign was also found to be an effective strategy to complement the adoption of service robots.Finally, it was found that there is an impact on the level of service expectations from the guests as well as the productivity and efficiency of the staff upon the integration of service robots.
Minimizing Information Leakage under Padding Constraints

An attacker can gain information of a user by analyzing its network traffic. The size of transferred data leaks information about the file being transferred or the service being used, and this is particularly revealing when the attacker has background knowledge about the files or services available for transfer. To prevent this, servers may pad their files using a padding scheme, changing the file sizes and preventing anyone from guessing their identity uniquely. This work focuses on finding optimal padding schemes that keep a balance between privacy and the costs of bandwidth increase. We consider R\'enyi-min leakage as our main measure for privacy, since it is directly related with the success of a simple attacker, and compare our algorithms with an existing solution that minimizes Shannon leakage. We provide improvements to our algorithms in order to optimize average total padding and Shannon leakage while minimizing R\'enyi-min leakage. Moreover, our algorithms are designed to handle a more general and important scenario in which multiple servers wish to compute padding schemes in a way that protects the servers' identity in addition to the identity of the files.
Learning Policies from Self-Play with Policy Gradients and MCTS Value Estimates

In recent years, state-of-the-art game-playing agents often involve policies that are trained in self-playing processes where Monte Carlo tree search (MCTS) algorithms and trained policies iteratively improve each other. The strongest results have been obtained when policies are trained to mimic the search behaviour of MCTS by minimising a cross-entropy loss. Because MCTS, by design, includes an element of exploration, policies trained in this manner are also likely to exhibit a similar extent of exploration. In this paper, we are interested in learning policies for a project with future goals including the extraction of interpretable strategies, rather than state-of-the-art game-playing performance. For these goals, we argue that such an extent of exploration is undesirable, and we propose a novel objective function for training policies that are not exploratory. We derive a policy gradient expression for maximising this objective function, which can be estimated using MCTS value estimates, rather than MCTS visit counts. We empirically evaluate various properties of resulting policies, in a variety of board games.
Student Records: Functions and Roles for Students and Alums

This study aims to describe students&rsquo; understanding of the function of the student record and PDDikti. Student records and PDDikti are instruments used to record student activities during their study and after graduation. Forlap Dikti is a reference for scholarships, research, and student level competitions in Indonesia. This research uses questionnaires distributed to more than 300 students from the records and archives management program to collect data. The results of this study provide an overview of students&rsquo; understanding of the function of PDDikti, including student records as vital evidence and awareness regarding the importance of student record management in higher education.
SimpleStyle: An Adaptable Style Transfer Approach

Attribute-controlled text rewriting, also known as text style-transfer, has a crucial role in regulating attributes and biases of textual training data and a machine generated text. In this work we present SimpleStyle, a minimalist yet effective approach for style-transfer composed of two simple ingredients: controlled denoising and output filtering. Despite the simplicity of our approach, which can be succinctly described with a few lines of code, it is competitive with previous state-of-the-art methods both in automatic and in human evaluation. To demonstrate the adaptability and practical value of our system beyond academic data, we apply SimpleStyle to transfer a wide range of text attributes appearing in real-world textual data from social networks. Additionally, we introduce a novel "soft noising" technique that further improves the performance of our system. We also show that teaching a student model to generate the output of SimpleStyle can result in a system that performs style transfer of equivalent quality with only a single greedy-decoded sample. Finally, we suggest our method as a remedy for the fundamental incompatible baseline issue that holds progress in the field. We offer our protocol as a simple yet strong baseline for works that wish to make incremental advancements in the field of attribute controlled text rewriting.
Analysis of digital contact tracing technologies employed during COVID-19 Pandemic

Corona virus (COVID-19) is a worldwide pandemic that was first reported in Wuhan, China 2019.The virus claimed lives and negatively changed the mobility patterns of humans. As a measure to control the spread of the virus, Digital Contact Tracing Technologies (DCTTs) were employed to contain the virus. The (DCTTs) are used to track and notify users digitally about their interactions with infected individuals and help strengthen health service systems. The most widely used Digital Contact Tracing Technologies include those that use Global Positioning, Bluetooth, Quick Response (QR) Codes and Wireless Fidelity (Wi-Fi). These technologies depend on monitoring symptoms, mobility of a person, location and proximity of individuals. This paper reviews literature by analyzing the Digital Contact Tracing Technologies (DCTTs) employed for COVID-19 through a desk study. The review highlights the advantages and disadvantages of using these technologies, with an aim of recommending ways in which the technologies can be used to attain maximum benefit.&#x0D;
SwaRegex: a lexical transducer for the morphological segmentation of swahili verbs

The morphological syntax of the Swahili verb comprises 10 slots. In this work, we present SwaRegex, a novel rule-based model for the morphological segmentation of Swahili verbs. This model is designed as a lexical transducer, which accepts a verb as an input string and outputs the morphological slot occupied by the morphemes in the input string. SwaRegex is based on regular expressions developed using the C# programming language. To test the model, we designed a web scraper that obtained verbs from an online Swahili dictionary. The scrapper separated the corpus into two datasets: dataset A, comprising 163 verbs Bantu origin; and dataset B, containing the entire set of 715 non-Arabic verb entries obtained by the web scrapper. The performance of the model was tested against a similar model designed using the Xerox Finite State Tools (XFST). The regular expressions used in both models were the same. SwaRegex outperformed the XFST model on both datasets, achieving a 98.77% accuracy on dataset A, better than the XFST model by 41.1%, and a 68.67% accuracy on dataset B, better than the XFST model by 38.46%. This work is beneficial to prospective learners of Swahili, by helping them understand the syntax of Swahili verbs, and is an integral teaching aid for Swahili. Search engines will benefit from the lexical transducer by leveraging its finite state network when lemmatizing search terms. This work will also create more opportunities for more research to be done on Swahili.
Admission Requirements and Academic Performance of Board vs Non-Board Course in Higher Education Institution

Higher Education Institutions require specific admission criteria to select the best candidates for a particular program. This study aims to assess the admission requirements and academic performance of the board and non-board course learners. Specifically, this quantitative study utilized a descriptive-correlational research design. The data came from randomly selected third-year college students in the different Board and Non-Board programs enrolled in the academic year 2020-2021. There were 286 respondents who took part in the study, 153 from the board course program, Bachelor of Science in Elementary Education, and 133 from the non-board course program, Bachelor of Science in Information Technology. The data revealed that the most number of enrollees in the non-board course, BSIT is from the Technical-Vocational-Livelihood (TVL) Strand, while most learners from the Board course, BSEE belong to the General Academic Strand (GAS). Further, findings showed that the High-school Grade Point Average and the College Academic Performance of students from the non-board course, BSIT, were moderately correlated, and a similar relationship is observed for the variables College Admission Test and GPA. Meanwhile, for the Board Course, BSEE, admission tests, and high-school GPA are predictors of college academic performance. On the other hand, high-school strands are not predictors of the BSEE student's GPA. In conclusion, the College admission tests and high-school GPA are essential admission requirements for the board and non-board programs to predict academic performance. However, the high-school strand can be a determining factor for the academic performance for the non-board course, but not for the board course.
Data Protection for Users in Cloud Computing

Information technology might have simplified the way we do business, but it has also brought challenges that never existed.Accessing data from public repositories leads to multiple difficulties.It is also necessary for the data to be available in an accurate, complete, and timely manner.This paper will discuss the challenges and breaches of data protection for users in cloud computing and explore various techniques used to address these issues.
A Deep Learning based Approach for the Recognition of Facial Identity and Expression

This work aims to recognize human facial expressions and along with that it tries to incorporate the recognition of the person from the old data which were fed into the system earlier.The developed system uses the bounding box regression technique to perform the object localization task.Various instances from seven types of expressions can be detected using the developed system.From the real-time pictures/videos, the module can identify any of the instances of seven expressions and the person"s facial identity simultaneously.The proposed system has been tested across various natural environments and it did perform well in most of the scenarios..
Impact of ICT-Usage on SME Innovations in Western Province, Sri Lanka: Effect of Entrepreneurial Orientation

Small and medium-sized businesses are important sources of employment and value generation; hence they are essential to Sri Lankans' way of life.It shows that SME sector is not providing the expected results and lack in innovation.Thus, there is a burning need to identify drivers to galvanize sector's innovation capacity and business performance.Through a literature survey researcher defined the concepts ICT-usage, Innovation, EO and possible relationships.Questionnaire was devised based on literature and expert advice to find the relationship between ICT-usage and Innovations moderated by EO.Further research measures the level of ICT-usage among SMEs in Western province, Sri Lanka.This study used quantitative techniques.The population of the study was the SMEs with less than 500 employees in the Western Province.Sample frame was derived from the three main listings of organizations: Ceylon Chamber of Commerce and Ministry of Industries and Industrial Development Board.Accordingly, 350 respondents were selected by using simple random sampling method.Data was analyzed using SPSS and SMART-PLS using SEM modeling.Empirical findings proved the relationships between ICT-usage to SMEs innovations and EOs moderation effect.This study identified four main innovations namely: product, process, market and organizational and found that there is a significant effect of ICT-usage on SMEs innovations positively moderated by Entrepreneurial Orientation.Future research could be undertaken to examine the mediating effect of ICT-based innovations on ICT-usage to business performance.
Breast Cancer Detection using Machine Learning Techniques

According to the World Health Organization (WHO), in 2020, around 2.3 million women diagnosed with breast cancer, and 685,000 of them died globally.Though, this calculation is terrible to think, there is always a hope for the patients who are able to be diagnosed at the very early stage.Keeping this helpfulness of early diagnosis in mind, there have been proposed a lot of research works in the recent years.And, most of these researches are computer aided.This is the reason, in the recent years, machine learning techniques are getting quite noticed because of their efficiency and reliability.In this paper,6 different machine learning techniques such as Logistic Regression, Decision Tree Classifier, KNN (K-Nearest Neighbors), Random Forest Classifier, SVM (Support Vector Machine), and Gradient Boosting Classifier have been proposed to detect breast cancer.The very popular Breast Cancer Wisconsin (Original) Dataset [1] collected from UCI machine learning repository has been used to apply the proposed machine learning techniques.In this research work, 20% of data has been used for testing and the rest 80% of data has been used for training.Decision Tree Classifier outperformed the other techniques giving the highest accuracy of 96.89%.The results of other techniques were quite competitive.
Improved Task Scheduling for Virtual Machines in the Cloud based on the Gravitational Search Algorithm

The rapid and convenient provision of the available computing resources is a crucial requirement in modern cloud computing environments.However, if only the execution time is taken into account when the resources are scheduled, it could lead to imbalanced workloads as well as to significant under-utilisation of the involved Virtual Machines (VMs).In the present work a novel task scheduling scheme is introduced, which is based on the proper adaptation of a modern and quite effective evolutionary optimization method, the Gravitational Search Algorithm (GSA).The proposed scheme aims at optimizing the entire scheduling procedure, in terms of both the tasks execution time and the system (VMs) resource utilisation.Moreover, the fitness function was properly selected considering both the above factors in an appropriately weighted function in order to obtain better results for large inputs.Sufficient simulation experiments show the efficiency of the proposed scheme, as well as its excellence over related approaches of the bibliography, with similar objectives.
Penerapan Metode Skimming Melalui Media Digital Glossary Dictionary (DGD) Untuk Meningkatkan Pemahaman Teks Naratif di MTsN 6 Bantul

The purpose of this article is to describe the extent of the application of the skimming method through the Digital Glossary Dictionary (DGD) in increasing the understanding of narrative texts in class IX B MTsN 6 Bantul odd semester of 2022/2023. The method used is Classroom Action Research (CAR). The results showed that the application of the skimming method through the use of the DGD digital dictionary proved a positive change. This is evidenced by the increase in several aspects, namely student activity, knowledge value, and positive responses from students. In the aspect of student activity, pre-cycle to cycle I activities increased by 11%. As for from cycle I to cycle II also experienced an increase of 3.53%. Likewise, the results of research on aspects of knowledge that are measured using multiple choice written tests. The results of cycle tests carried out at the end of the cycle showed an average increase in yield by 9%. The test results from cycle I to cycle II obtained an average value of 64.67 increasing to 80.6 or 11%. Aspects of student achievement in terms of English subject matter of narrative text material in pre-cycle activities and increased to 9 people in cycle I, so if it is percentaged, the increase from pre-cycle to cycle is 50%. This is quite satisfying for the teacher because the children who have achieved the specified minimum score are 76. Significant changes also occur in cycle I to cycle II where the number of students who have completed cycle I activities is 9 students and rises to 23 students. in cycle II so that when percentaged the figure becomes 44%.
Reversible Data Hiding in Encrypted Text Using Paillier Cryptosystem

Reversible Data Hiding in Encrypted Domain (RD-HED) is an innovative method that can keep cover information secret and allows the data hider to insert additional information into it. This article presents a novel data hiding technique in an encrypted text called Reversible Data Hiding in Encrypted Text (RDHET). Initially, the original text is converted into their ASCII values. After that, the Paillier cryptosystem is adopted to encrypt all ASCII values of the original text and send it to the data hider for further processing. At the data hiding phase, the secret data are embedded into homomorphically encrypted text using a technique that does not lose any information, i.e., the homomorphic properties of the Paillier cryptosystem. Finally, the embedded secret data and the original text are recovered at the receiving end without any loss. Experimental results show that the proposed scheme is vital in the context of encrypted text processing at cloud-based services. Moreover, the scheme works well, especially for the embedding phase, text recovery, and performance on different security key sizes.