Reviewerly/exHarmony huggingface dataset:
Analysis of ARX encryption schemes resistance to the integral attack and impracticable differentials attack

Common ARX (Addition-Rotation-XOR) encryption algorithms are analyzed. These algorithms are Chacha, Speckey, Simon, Chaskey, Sparkle. These algorithms use three basic operations: modular addition, XOR addition, and rotation. 16-bit reduced models of these algorithms are developed, methods of analysis are selected and developed, and the analysis of the resistance of these algorithms to the most effective attacks (integral attack and attack of impossible differentials) for this class of algorithms is performed. According to the selected indicator – the number of elementary operations that is necessary to obtain parameters of random substitution and the absence of impossible differentials and integrals – the most effective ARX algorithms are determined. These are Speckey, which operates on two 8-bit subblocks and requires 36 elementary operations, and Chaskey, which operates on four 4-bit subblocks and requires 72 elementary operations. If we assume that one 8-bit operation is equivalent to two 4-bit operations, then these schemes are equal in terms of the chosen indicator. The worst performers were the 8-bit Simon scheme and the 4-bit ChaCha scheme, which require almost twice as many operations as the best schemes. A conclusion was also made about the importance of using not one, but several XOR operations of key addition for the overall cryptographic strength of ARX algorithms.
Face Recognition using Principal Component Analysis and Clustered Self-Organizing Map

Face recognition is one of the cornerstones of the face processing schemes that composed the contemporary intelligent vision-based interactive systems between computers and humans.Instead of using neurons of the Self-Organized Map (SOM) neural network to cluster the facial data, in this work, we applied an agglomerative hierarchical clustering to cluster the neurons of the SOM network, which in turns, used to cluster the facial dataset.In prior, Principal Component Analysis (PCA) is employed to reduce the dimension of the facial data as well as to establish the initial state of SOM neurons.The design of the clustered-SOM recognition engine involves post-training steps that labeled the clustered SOM neurons resulting in a supervised SOM network.The effectiveness of the proposed model is demonstrated using the well-known ORL database.Using five images per person for SOM training, the proposed recognizer results in a recognition rate of 94.7%, whereas using nine images raise the recognition rate up to 99.33%.The facial recognizer has attained a notable reliability and robustness against the additive white Gaussian noise, where increasing the level of noise variance from 0 to 0.09, the recognition rate decreased only by 8%.Furthermore, time cost is analyzed, where using 200 images for training takes less than 4 seconds to be performed, whereas testing using a new set of 200 images takes less than 0.013 seconds which is competitive to many artificial intelligence and machine learning based schemes.
Implementation of Convolutional Neural Networks for Batik Image Dataset

One method of image recognition that can be used is a convolutional neural network (CNN). However, the training model of CNN is not an easy thing; it takes tuning parameters that take a long time in the training process. This research will do Batik pattern recognition by using CNN. From the experiment that we conducted, the result shows that the feature extraction, selection, and reduction give the accuracy more significant than raw image dataset. The feature selection and reduction also can improve the execution time. Parameters value that gave best accuracy are: epoch = 200, batch_size = 20, optimizer = adam, learning_rate = 0.01, network weight initialization = lecun_uniform, neuron activation function = linear.
Development and Implementation of Blended Learning in the Practice of Bank Financial Statement Analysis Course

Blended learning is used to describe a learning solution that combines several different delivery methods.It provides several conveniences in teaching and learning, both with the process and materials.It is hoped that it can enhance student performance.The observation and survey methods are used in this research on the Practice of Bank Financial Statement Analysis course, which class consists of 29 students.We find the impact of using blended learning.The result shows that by implementing blended learning and the improvisation of it, the performance of students is better than that of the previous year.However, the result also shows that the students prefer conventional learning (studying in a classroom) to online-based learning.Based on these results, it would be a challenge for lecturers to enhance the students' attention.
Unsupervised Learning Based Brand Sentiment Mining using Lexicon Approaches A Study on Amazon Alexa

Consumer sentiment analysis has gained immense attention in the recent past. The abundance of data in today’s world, especially those generated from the social media platforms, has triggered sentiment exploration like never before. The analysis of consumer sentiments have indeed helped organizations in effective decision making worldwide. In the communication technology domain, voice activated virtual assistants (VAVAs) are one of the latest entrants and they are gaining immense popularity by the time. Brand sentiment studies on VAVAs being limited in number creates an opportunity to explore further. This study fits into the domain of sentiment mining and the purpose of the paper is to review the consumer sentiment towards the global leader brand in the voice activated virtual assistant product segment, Amazon Alexa. Of the various approaches available, the researchers chose unsupervised learning based lexicon approach to estimate the brand sentiment. Three popular lexicon based sentiment classifiers, TextBlob, VADER and AFINN, have been used in the present context for exploration purpose. To the best of the knowledge of the researchers, this research effort includes, for the first time, multiple lexicon based approaches in exploring the sentiment towards the brand Alexa. This study shows consumers to have a significantly positive sentiment towards the chosen brand. The output from the three comparative classifiers reveal similar results which also validates the robustness of the outcomes and that of the chosen methods. The study anticipates a bright sales potential of the brand. Also, the use of alternative lexicon approaches is expected to enrich the existing literature in the sentiment mining domain.
Machine Teaching-Based Efficient Labelling for Cross-unit Healthcare Data Modelling

A data custodian of a big organization (such as a Commonwealth Data Integrating Authority), namely teacher, can easily build an intelligent model which is well trained by comprehensive data collected from multiple sources. However, due to information security and privacy-related regulation requirements, full access to the well-trained intelligent model and the comprehensive training data is usually limited to the teacher only and not available to any unit (or branch) of that organization. Therefore, if a unit, namely student, needs an intelligent function similar to the trained intelligent model, the student has to train a similar model from scratch using the student’s own dataset. Such a dataset is usually unlabelled, requiring a big workload on labelling. Inspired by the Iterative Machine Teaching, we propose a novel collaboration pipeline. It enables the teacher to iteratively guide the student to select samples that are most worth labelling from the student’s own dataset, which significantly reduces the requirement for human labelling and, at the same time, prevents regulation and information security breaches. The effectiveness and efficiency of the proposed pipeline is empirically demonstrated on two publicly available healthcare datasets in comparison with baseline methods. This work has broad implications for the healthcare sector to facilitate data modelling in instances where the large labelled datasets are not accessible to each unit.
Analysis of Fire Risk Assessment in South Jakarta 2021

Jakarta is the capital city of Indonesia, with a dense population of more than 10 million population. DKI Jakarta Province is divided into five city areas, including South Jakarta. South Jakarta has an area of 141,17 km2, with more than three million population. The scope for this study focused on two sub-districts in South Jakarta, which are Pasar Minggu and Jagakarsa. The objective of this study was to provide fire risk situations in two sub-districts of South Jakarta. The study used a descriptive method with non-probabilistic sampling. Data collection was performed from June 2021 to July 2021 through focus group discussions (FGDs) and filling out questionnaires. FGDs were performed to the Local Government Fire Brigade, and questionnaires were filled out by the head of the RW. The FGD guideline and questionnaire were developed based on the fire risk assessment checklist. The checklist instrument was developed by utilizing a disaster fire risk approach, including fire hazard, vulnerability, and fire protection, with twenty-two questions. The data was then analyzed using univariate analysis, bar, and spider chart. The results showed three RWs with severe-risk classification. The factors that increase the risk of fire are the use of combustible building materials and the high density of population and buildings. There were several recommendations contributed such as firefighters must have a list of the high-risk areas and develop a rescue evacuation plan, doing a routine inspection in each target area, and raising public awareness for the community to increase participation in fire risk management.
ENCODING RASTER IMAGES BASED ON FRAGMENT SIMILARITY

This paper is devoted to image encoding based on determining the similarity of fragments by using neural networks to extract the features of fragments and machine learning algorithms to find similar fragments. In the modern world, the problem of image storage is quite relevant. Graphic data takes up quite a lot of disk space, while Internet users upload more and more pictures. Also, every year there is a development of photography and image quality is improving, respectively, and the size of graphic data is growing. Data warehouses of social networks, messengers, file sharers and other Internet resources are filled with tens of thousands of new pictures every day. Therefore, the question arises about reducing the size of graphic data. In general, it should be noted that one of the most important and defining aspects of both storage and transmission of information is its compression. The problem described above is solved by encoding and compressing images. With the help of coding, the size of graphic information is reduced, which saves storage space and, accordingly, the money spent. In view of this, it is important to develop a method and means of image coding. Many methods exist for compressing graphic information. For example, jpeg, webp, png and others. These methods usually use the removal of redundant information in the photo and work purely with the image itself, but none of the methods uses fragments of similar images. The article uses convolutional neural networks and KNN (k-Nearest Neighbors) classifier for image encoding. and compares the size of the encoded image with the input. In order to encode the image, you first need to fill the data warehouse with features of fragments of similar images, then for each fragment of the obtained images you need to select the features and write to the data warehouse. Once the snippet feature database is formed, you can encode new images using saved snippets.
Geochemical stream sediment signatures from Precambrian terrains of Uruguay

Geochemical data of Uruguayan crystalline terrains collected in the 80’s were recovered by the National Mining and Geology Directorate of Uruguay (DINAMIGE) in 2020 involving 32,000 soil and stream sediment samples analyzed for 22 elements (Ag, As, B, Ba, Be, Cd, Co, Cr, Cu, Fe, Mn, Mo, Nb, Ni, P, Pb, Sb, Sn, V, W, Y and Zn) by direct current plasma spectrometry. A set of 6755 samples of these data, more or less 21% of the total, were processed by multivariate statistical methods and geographic information system (GIS) procedures aiming characterization of geochemical signatures derived from environmental and geological processes. Samples were classified as background or anomalies/outliers at the beginning. The general data and the different sets were processed by statistical methods and GIS individually. The results considered the original units or sometimes, the transformed data for uniformity of variability. Nine geochemical signatures related to the geological domains were defined; three of them involved low contrast units with moderate enrichment in Ba, Co, Cu or Mn. The remaining six geochemical signatures were related to specific geological units and presented multivariate signatures. Seven signatures related to anomalies were defined, three of them of lithological origin, two superficial and the others mixed, including the possibility of sulphide deposits.
Homogenisasi Sekunder terhadap Kadar Hemoglobin

This study aims to determine the results of hemoglobin examination with secondary homogenization techniques 3, 5, 7, and 8 times, after being left for 60 minutes after primary homogenization. The research method used is pre-experimental research. The results showed that the average hemoglobin measured by an automatic hematology analyzer in the secondary homogenized blood group was 3 times 13.3 g/dl, homogenized 5 times 13.1 g/dl, homogenized 7 times 13.2 g/dl, and homogenization 8 times 13.1g/dl. In conclusion, there is no difference in hemoglobin levels in the secondary homogenization process 3, 5, 7, and 8 times.&#x0D; &#x0D; Keywords: EDTA Blood, Hemoglobin, Homogenization
Implementasi Sistem Informasi Akuntansi Pada Sekolah Terpadu Gema Nurani

Integrated Islamic Schools as a resource for science and technological advancements play an important role in concept development and implementation in IT units, especially those required to quickly solve system and data problems. Gemanurani Integrated Islamic School is a private school in Bekasi that focuses on Islamic education and has 25 school programs. The annual accounting reporting process at the Gemanurani School demands a good and useful information system as a monitoring of the foundation's wealth to determine management decisions in all teaching and learning activities. Along the way, it encountered obstacles such as undocumented cash flows, income returns, and annual tax reporting which had an impact on the slow completion of requests for household needs for each school unit. With an accounting information system, it will produce an overview of school household budget planning that focuses on data, applications and technology in the orientation of business needs.
IMPLEMENTASI METODE ADDIE PADA SISTEM INFORMASI PEMBUATAN RPP 1 LEMBAR DI SMK PGRI 4 PASURUAN

&lt;span class="fontstyle0"&gt;Abstract: &lt;/span&gt;&lt;span class="fontstyle2"&gt;The form of planning is RPP, which is a plan that describes learning procedures and organizations to achieve the basic competencies set out in the content standards and described in the syllabus. In this case, almost all teachers have difficulty in making RPP and in 2019 the Ministry of Education and Culture issued a new rule regarding changing the RPP into 1 sheet so that in this study it can assist teachers in making RPP in the form of RPP. digitizing and assisting teachers in the transition from the new RPP format to 1 sheet.SMK PGRI 4 Pasuruan is one of the private schools that is developing in order to improve the quality of schools. In carrying out daily operational activities, the flow of information systems at SMK PGRI 4 Pasuruan is still not optimal because there is no database that functions as a repository and making Learning Implementation Plans (RPP). ledger in the preparation of RPP reports.The research method used is the ADDIE model, in this system development method the application has experienced considerable development because each stage has experienced a good system evaluation. The results of this study were that teachers of SMK PGRI 4 Pasuruan were helped by archiving data and making lesson plans effectively and efficiently and the curriculum development team was assisted in filing curriculum documents better able to monitor the progress of the&lt;br /&gt;teacher's lesson plans used.&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;
STRATEGI KESANTUNAN BERBAHASA DALAM TALK SHOW Q&amp;A DAN RELEVANSINYA TERHADAP PEMBELAJARAN BAHASA INDONESIA TINGKAT SMP

Abstract&#x0D; The purpose of this study to describe the politeness strategies of Brown and Levinson in the Q&amp;A talk show and describe the relevance of politeness-language strategies towards learning Indonesian at the Middle School level. The theory used to solve the problem in this study is the theory of Brown and Levinson. While the approach used is descriptive qualitative approach with data collection techniques namely documentation techniques. The results of this study indicate that the forms of Brown and Levinson's politeness strategy that occurred in the Q &amp; A talk show in August 2018 consisted of Brown and Levinson's first politeness strategy, namely without strategies found that included speech without strategy. The second strategy of brown politeness and Levinson's positive politeness strategy is found to pay attention to the opponent's speech, exaggerate attention, agreement, and sympathy, intensify the opponent's attention by dramatizing facts, intensify using group identity markers, request approval with general topics or repeat part or all, avoiding disagreement by pretending to agree, using jokes, expressing understanding of the wishes of speakers, giving offers or promises, showing optimism, involving speakers in activities, giving questions asking for approval, giving gifts. Where negative politeness strategies are found to be pessimistic, minimize coercion, and apologize. Finally, the Off Record strategy was found which was included in the strategy. The relevance of the results of the study can be applied in learning Indonesian in junior high school class IX 3.9-4.10.
Pengaruh Penggunaan Aplikasi Google Classroom terhadap Hasil Belajar Siswa Kelas X di SMK LAB SCHOOL UNESA

The purpose of this study was to determine: the effect of using the google classroom application on student learning outcomes. In Indonesia, distance or online learning begins on March 16, 2020, where children start learning from their own homes without the need to go to school. Choosing the right application will affect students' interest in learning (Wulandari, 2016), which is usually done by giving assignments personally to students which tends to reduce student interest in learning. The learning process through Google Classroom is very easy to do.Various conveniences are indeed presented by today's technological advances that support the realization of more effective learning. In this study, the researchers focused on the use of google classroom learning media. The use of the google classroom application at this school is used as a medium in carrying out learning. The research method used is descriptive quantitative method. The sampling technique used was purposive sampling. The results of this study prove that there is an effect of google classroom on student learning outcomes, this is evidenced by the results of the paired sample test showing that H0 is rejected and Ha is accepted which shows that there are differences between pretest and posttest before and after using the google classroom application which shows that by using the google classroom application student learning outcomes can be increased.
The Effectiveness of Darask Platform as E-Learning Tool to Improve the Educational Process during COVID-19

The globe is grappling with the COVID-19 pandemic, which has enslaved the economy, including the educational sector. This epidemic has caused recent changes and disruptions in all aspects of life, this pandemic has compelled educational systems all across the world to find altarenate methods to keep the process of education going on. To cope with the current case, Darasak platform is authorized by Jordanian Ministry of Education to be an alternative choice for proceeding with the educational process. The goal of the current study is to know the impact of Darasak platform to be compensate tool to perform the educational process during Covid 19. The data were collected by questionnaires and distributed by webs side to students, teachers and parents. The result of the study shown that Darasak platform was a new application to students so it was found that facilities, flexibility appropriateness and effectiveness of Darasak platform were not up to expectation. Students were not satisfied about the performance of Darasak platform.
A continuous bitstream-based blind video quality assessment using multi-layer perceptron

We propose a continuous blind/no-reference video quality assessment (NR-VQA) algorithm based on features extracted from the bitstream, i.e., without decoding the video.The resulting algorithm requires minimal training and adopts a simple multi-layer perceptron for score prediction.The algorithm is computationally appealing.To assess the performance of the algorithm, both the Pearson Correlation Coefficient (PCC) and the Spearman Rank Ordered Correlation Coefficient (SROCC) are computed between the predicted values and the quality scores of two databases.The proposed approach is shown to have a high correlation with human visual perception of quality.
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning

To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate any order-optimal learning algorithm towards a targeted policy in episodic RL and examines the potential damage of two natural types of poisoning attacks, i.e., the manipulation of reward or action. We discover that the effect of attacks crucially depends on whether the rewards are bounded or unbounded. In bounded reward settings, we show that only reward manipulation or only action manipulation cannot guarantee a successful attack. However, by combining reward and action manipulation, the adversary can manipulate any order-optimal learning algorithm to follow any targeted policy with \Theta(\sqrt{T}) total attack cost, which is order-optimal, without any knowledge of the underlying MDP. In contrast, in unbounded reward settings, we show that reward manipulation attacks are sufficient for an adversary to successfully manipulate any order-optimal learning algorithm to follow any targeted policy using \tilde{O}(\sqrt{T}) amount of contamination. Our results reveal useful insights about what can or cannot be achieved by poisoning attacks, and are set to spur more work on the design of robust RL algorithms.
Present Situation and Development Trend of Information Construction of Educational Management in Colleges and Universities

In the era of educational informatization 2.0, colleges and universities introduce information technology into teaching management and establish a systematic and perfect teaching management system, which has become an inevitable choice for colleges and universities to implement talent training strategy and realize innovative transformation of teaching development. Based on this, starting from the current situation of the informatization construction of teaching management in Colleges and universities, this paper considers the problems existing in the current construction and development, analyzes the development trend of teaching informatization, and then explores the corresponding development path in combination with the reality, in order to improve the informatization construction level of colleges and universities and smoothly enter the era of educational informatization 2.0.