Reviewerly/exHarmony huggingface dataset:
Forensic Analysis of Crimes in the Sphere of Information Technologies

The article deals with the issues of foren-sic support for preventing commission of crimes in the field of information tech-nology. It is called to attention that alt-hough introduction of modern infor-mation processes in various spheres of government activities, production and public relations has brought positive re-sults, it has also led to an increase in the cases of committing cyber crimes. Cyber crimes are a new phenomenon in general crime, therefore, the development of ad-vanced techniques to combat them is re-quired. In this process, the forensic sci-ence plays an important role, first of all, for the preparation of recommendations on methods for investigating cyber crimes. Forensic analysis of information technology crimes which are called cyber crimes indicates that their investigation is different from the investigation of other crimes. This is largely due to the fact that computer objects can be both means of committing such crimes and objects of criminal encroachment. Information stored on computer media can be per-ceived and evaluated solely by using computer equipment and digital technol-ogy. Without this, it is impossible to iden-tify persons involved in committing a crime and obtain the evidence necessary to solve the crime. To study and evaluate them as sources of evidence, one needs to resort to the help of specialists, com-puter equipment and sophisticated soft-ware. It distinguishes computer objects from other material objects that is why it is necessary to put computer objects into a separate category of sources of evi-dence in forensic science and criminal procedure.
The Rattles from Tell El-Farcha

Vessel rattles were one of the first sound-producing tools made from clay. Throughout history, they were developed in many ancient cultures, convergently in many places around the world. To obtain a complete picture of the sounds produced by clay rattles, the short-time Fourier transform analysis is used. On top of that, to determine the full spectrum of their acoustical possibilities, numerical reconstruction of sound is done. The results provide us an opportunity to explore the soundscape of the past.
ANALISIS ANGKA KEMATIAN BAYI KABUPATEN HALMAHERA UTARA DENGAN METODE REGRESI NONPARAMETRIK SPLINE TRUNCATED

Infant Mortality Rate is a reflection of infant survival which will determine the quality of life of human resources. North Maluku Province is an archipelago province with a relatively high infant mortality rate. The still high infant mortality rate in North Maluku is influenced by several factors, apart from the low equity and affordability of health services, socio-economic factors also greatly affect infant and under-five mortality rates. North Halmahera District is one of the highest IMR contributors in North Maluku Province. This study aims to mathematically analyze the infant mortality rate in North Halmahera. The results showed that all predictor variables had an effect on infant mortality. The analysis results found the best model with three point knots with a determination coefficient of 93% and a minimum GCV value of 1.52.
On the historical and philosophical origins of digital reality

The purpose of the study is to reveal the genesis of the concept of digital reality and its connection with the millennial Pyphagorean tradition in European philosophy and culture, based on the logic and dialectic of the number as a metaphysical entity. The article implements an attempt to historically and philosophically consider the phenomenon of digital reality: the conclusions of modern researchers are confirmed about the key importance of this philosophical tradition as a special system of thought, which defined modern ideological and scientific paradigms and contributed to the formation of the social model of post-industrial, information-digital society. Scientific novelty is to reveal a modern scientific understanding of the notion that numbers can materialize the world by reflecting reality and returning it to man through knowledge derived through the programming or construction of digital reality on the basis of symbolic-applied logic. As a result of the analysis of philosophical sources, it has been proved that information in our world can be considered as another kind of movement of matter, and the Internet is not only an "information technology" but a "social form", a "universal social space" that appeared not overnight – with the invention of "breakthrough" technology, but formed by centuries of cultivated scientific knowledge.
Sufficient Statistic Based Suboptimal Strategies in Infinite Horizon Two-Player Zero-Sum Stochastic Bayesian Games

This paper considers two-player zero-sum stochastic Bayesian games with infinite horizon time frame where each player’s dynamic state is unknown to the others. Based on our previous work in finite horizon games [1], we provide a sufficient statistic based suboptimal strategy in the infinite horizon games using the receding horizon idea. In this sub-optimal strategy, both the strategy and the sufficient statistic can be updated using linear programming (LP) formulations. The performance analysis of the suboptimal strategy provides an upper bound on the performance difference between the suboptimal strategy and the optimal one. It is shown that the upper bound decreases exponentially with respect to the window size. The performance of the suboptimal strategies is demonstrated in an underwater jamming problem.
Learners’ Intrigue and Attitude Towards Presenting Self-Documentaries Technique in Speaking Skill

This study aims to probe the students’ intrigue towards presenting self-documentaries technique in learning speaking skills and to probe the students’ attitudes towards presenting self-documentaries techniques in improving students’ speaking skill. This research was a survey research design using observation and questionnaires to get data. To find out the students’ responses regarding intrigues, researcher gave seven questionnaires; interesting technique, exciting topic, enthusiastic, relax, self-confidence, actively in the class, and students’ impression to presenting self-documentaries technique, and to find out the students’ attitudes responses, there were ten questionnaires given to the students. They were a trend technique that can improve students speaking skill, easy technique to improve students’ spoken, improve students fluency, improve students accuracy, improve students vocabulary, feeling confidence, getting many ideas, easy to express ideas in speaking, improve students’ curiosity about the topic of speaking, giving students ideas to increase their speaking through using social media. The data showed that 83.79 % agreed, and 16.20% disagreed. It mean that the positive response of intrigues percentage was higher. Students were immensely intrigued by the topic, and students was very interested in learning English in the class through Presenting Self-Documentaries Technique. Response to the students’ attitudes was that 78.32 % agreed, and 21.68 disagreed. It means that students positively react to the Presenting Self-Documentaries Technique in increasing their speaking skills. Based on the data above, students have more significant positive responses. The Presenting Self-Documentaries Technique is interesting in learning the speaking skill, and it is an effective technique to increase students’ speaking skills. Presenting self-documentaries designs can motivate students to learn English autonomously to improve students’ speaking skills significantly.
Accurate Multicarrier Waveform Classification Using Convolutional Neural Networks

To improve spectrum utilization efficiency and flexibility in diverse application scenarios, several new multicarrier modulation techniques have been proposed in recent years, and thus accurate recognition of various multicarrier waveforms is critical in heterogeneous wireless networks. After analyzing their different characteristics of popular multicarrier waveforms, this paper designs a new classification scheme based on 4-layer convolutional neural networks. In particular, the Fourier synchrosqueezing transform and Haar wavelet transform are exploited to discriminate different multicarrier waveforms. The proposed approach does not require any priori knowledge of the received signals, unlike the traditional methods. Simulation results demonstrate that the proposed classification scheme outperforms the benchmark schemes in the literature, in terms of the classification accuracy even at low signal-to-noise ratio.
Fair Personalized Recommendation through Improved Matrix Factorization by Neural Networks

Existing recommendation systems mainly focus on the improvement of accuracy but neglect to involve non-popular items, which leads to the unfairness problem of recommendation results. More specifically, the low exposure rate of non-popular items impairs the diversity of recommendation results and thus weakens the customer experience for online e-commerce platforms. To address this problem, we propose a Fair Personalized Recommendation Algorithm(FPRA) combining improved Matrix Factorization and Back-Propagation Neural Network, which can simultaneously acquire both the accuracy and the fairness of recommendation results. In the FPRA, popular items and non-popular items are separated according to a pre-defined proportion. On the one hand, we consider that the influence of scenario factors (i.e., browsing location, clicking number, etc.) will generate users’ assessment bias for popular items, and we leverage BP Neural Networks to train the bias when recommending popular items. On the other hand, we design a new function, namely Fairness & Accuracy function, to achieve the goal that non-popular items will be presented to customers more frequently. Using this function, the goal can be achieved by searching for the optimal solution under the condition constraints. Experimental results show that the effectiveness of our proposed that FPRA greatly outperforms other baseline methods.
DeTable: Table data extraction model based on deep

The rapid development of the information age leads to the mass production and frequent transmission of data, which is difficult to deal with by human alone. With the rise and development of artificial intelligence, the use of data is becoming more efficient. Table, as a special data form, has attracted wide attention gradually. However, extracting data from table subimages presents a number of challenges, including accurately detecting table regions in the image, and then detecting and extracting information from detected table rows and columns. While some progress has been made in table detection, extracting table contents remains a challenge because it involves more fine-grained table structure identification. In this paper, DeTable: table data extraction model based on deep learning is proposed. The model uses the interdependence between the twin tasks of table detection and table structure recognition to divide the text region and the box-line region of the table. Then, rows based on semantic rules are extracted from the identified table regions. The proposed models and extraction methods were evaluated on publicly available ICDAR 2019 and Marmot table datasets and the most advanced results were obtained.
Handwritten Arabic Touching and overlapping Characters Separation Methods

Text-line segmentation is one of the key factors which affect the performance of handwriting recognition system. One of the problems making this task crucial, is the presence of touching letters representing a connection between the characters of successive text-lines. This paper explains the concepts of touching characters and presents a survey of various approaches for their segmentation once detected, to join each component piece to its natural and real text-line.
Investigating 5<sup>th</sup>-grade students' opinions and attitudes towards using an Arabic pedagogical agent

Due to covid-19, educational institutions all around the world resorted to e-learning regardless of the readiness of e-learning tools used or the students&#x0027; attitude and readiness towards using them. In this paper, we developed a pedagogical conversational agent, which is an e-learning tool and considered as a solution for the lack of social interaction that e-learning suffers from, to know the attitude of 5th-grade students in an elementary school in Tunisia towards learning &#x00AB; Fractions&#x00BB; with the help of a pedagogical conversational agent as learning support. The attitude towards using the conversational agent was favorable with the suggestions of providing a more natural interaction that focuses on making the conversational agent have more personality and emotionally intelligent and making the conversations longer with including multi-media elements.
Detection of Nutrient Deficiency in Rice Plants Based on Leaf Image

In 2019 the yield of rice production experienced a considerable decline, one of which was caused by weather factors that resulted in drought on the land, so that the absorption of nutrients given to rice plants was not optimal. Seeing developments in the field of image technology, researchers used image processing and computer vision to determine the availability of nutrients contained in the image of rice leaves, by applying the feature extraction method. Feature extraction produces six feature values which are then used for detection using the Learning Vector Quantization (LVQ) method. The detection in this final project can recognize the image of rice plants lacking in nutrients N, P, and K which has an accuracy of 87.5
Design and Implementation of Rencana Pembelajaran Semester (RPS) Document Generation System

One of the problems that are often faced by teachers is the many administrative tasks. This additional task has caused teachers, especially lecturers, ignoring lecturing to their students. Making Semester Learning Plan or called Rencana Pembelajaran Semester (RPS) documents is one of the additional tasks that must be done by lecturers. In Addition, lecturers also make this RPS document from scratch and manually. So, this research aims and focuses on making a RPS generation system to help lecturer. This system also applies several methods such bloom&#x0027;s taxonomy framework for guidelines and UX Design for process activity to make better result. This research is tested through several scenarios such as functional testing and user testing. The functional testing results show that the system has successfully run all scenarios and can run smoothly. Furthermore, the user testing result show that the user gives good feedback and give average score 83&#x0025;. Because of this result, the conclusion of this research is the RPS generation system was built correctly. In addition, this system is expected to help lecturers in making RPS documents more effective, efficient, and easily.
Covid-19 Awareness Game for Deaf Children

Serious games are gaining increasing interest as they have been found successful in learning. The use of serious games has been vastly accelerated in light of the current crisis of Covid-19 pandemic. In order to reduce the spread of the virus, Covid-19 information and preventive measures should be accessible for all. However, serious games implemented for delivering covid-19 awareness are mainly dedicated for person with normal development. A lack of interest to implement games for people with special needs is noted. For that, this paper presents a new serious game for deaf children aiming to raise their awareness about the precautions needed during the outbreak in funny way. The game is based on delivering covid-19 preventive measures based on sign language assistance. While playing, the player has to collect prevention objects and avoid collision with viruses to keep healthy and win the game.
PENGARUH MODEL PEMBELAJARAN READING QUESTIONING AND ANSWERING (RQA) MELALUI VIRTUAL CLASS TERHADAP KETERAMPILAN METAKOGNITIF DAN HASIL BELAJAR KOGNITIF MAHASISWA PROGRAM STUDI PENDIDIKAN BIOLOGI PADA MATA KULIAH KEMAMPUAN DASAR MENGAJAR

This study aims to determine the effect of the Reading Questioning And Answering (RQA) learning model through Virtual Class on metacognitive skills and cognitive learning outcomes of students in the biology education study program in the basic teaching ability course. There are two variables in this study, namely the independent variable is the Reading Questioning And Answering (RQA) Learning Model and the dependent variable is Metacognitive Skills and Cognitive Learning Outcomes. The method used in this research is a quantitative method. The form of research used is a quasi-experimental research design with pretest posttest control group design. Data collection techniques using the Metacognitive Awareness Inventory (MCAI) Questionnaire, Cognitive Learning Outcomes Test and Documentation. The results of this study indicate that there is an effect of the Reading Questioning And Answering (RQA) learning model through the Virtual Class on metacognitive skills and cognitive learning outcomes of students of the biology education study program in the subject of basic teaching abilities.
A Machine Learning Framework for Data Filtering: A Case Study on Chandrayaan-1 SIR-2 Data

Machine learning is emerging as a promising technology with immense applications in various stages of planetary missions. Our focus is to use reflectance data obtained remotely from the Moon surface that can be used for detailed compositional mapping. These datasets need to be adequately preprocessed by removing noise and artifacts before performing an analysis. A traditional way of data preprocessing is to define a set of rules to remove unwanted effects but these methods totally rely on specific information available to the user. Such an approach can significantly improve the quality of the data but unidentified effects may still persist and influence any further analysis. To mitigate this problem, here, we propose to use a new approach which uses Machine Learning algorithms (MLAs) for the preprocessing of all kind of remote sensing data which identified sand filter out unsuitable spectra in the data. We applied MLAs to hyperspectral datasets obtained by the Infrared Spectrometer SIR-2 onboard Chandrayaan-1. In this work, data preprocessing is implemented as a supervised classification problem which aims to classify an input spectrum into desirable/undesirable ones labeled as "GOOD&#x2019; and "BAD". The approach was used to filter good spectra from the set of 173 SIR-2 orbits comprising of a total of ~2 million spectra. The methodology followed was a two-stage methodology. In the first stage, subset (~1 million) of data was sampled using systematic sampling. The spectra in the sub-set was manually annotated and different supervised learning approaches namely Logistic regression, Decision tree, KNN, ensemble were applied on this labeled data. On comparing their efficiency using performance metrics, we found five-algorithm voting based ensemble method to be best in terms of accuracy (99.8%) as well as reliability. In the second stage this approach was applied on the remaining unlabeled data to predict the class of spectrum and decide on the usability of that spectrum. As a final outcome, the five-algorithm ensemble approach, classified a total of ~1.35 million spectra as &#x201E;Good spectra&#x2019; and remaining ~1.04 million spectra as &#x2018;Bad spectra&#x2019;. One of the strength of this approach is that we can obtain comparative performance using very less number of labeled samples. This work demonstrates that our proposed method is suitable for the preprocessing of huge datasets obtained from different missions and of varied nature.
Topographic and Meteorological Challenges in Dissemination of Solar Technologies: An Overview of Leh, India

Leh was identified as a solar city by the Government of India in 2014. The location, altitude, and environmental condition of the city make it favorable for harnessing solar energy efficiently under ideal conditions. However, its topography and economic growth act as a hindrance to its successful transformation into a solar city. This study assesses the technical potential of rooftop areas and concentrated solar plants in Leh, under ideal conditions, using remotely sensed data and the ability of Geographic Information System. The technical potential of the solar systems was calculated considering the geographical potential area, meteorological indicators, and the efficiency of the plant modules. The findings suggest that the technical potential to generate electricity annually from SPVs in rooftops is 60 MWh and CSPs in the open wastelands is 16 MWh. This study also provides an insight into how topography and an uneven distribution of insolation should not hinder the Government and other stakeholders to invest in solar infrastructure in these cities.
Integration Of Desis With Multispectral Data For Geological Analysis Of Cuprite Hills From Nevada

This work integrates the recently available Visible to Near-Infrared hyperspectral data, DESIS, and ASTER multispectral data for geological analysis of a famous testing site at Cuprite hills in Nevada, United States. End member extraction techniques like Automatic target generation procedure and Vertex Component analysis, Image-based Kernel-Principal Component analysis and Minimum Noise Fraction, and Band ratios were estimated to segregate and map the absorption features responsible for phyllite, quartzite, limestone, dolomite, and kaolinite. The results show that the high spectral resolution of DESIS is responsible for accurate mineral features identification in the Visible to Near-Infrared wavelength range.
Integration of SAR (Sentinel -1a) and Optical (Sentinel -2a) Data for Lithology Discrimination in Arid Tracts of the Thar Desert (Nagaur, Rajasthan)

This study investigates the efficiency of integrated dual polarimetric SAR (Sentinel-1A) and optical (Sentinel-2A) data for discriminating lithological variations in the arid tract of the Thar Desert (Nagaur, Rajasthan). Textural analysis and Principal Component Analysis (PCA) was performed on both SAR and optical data, respectively. Different RGB colour composite images were created using various textural features computed from SAR and principal components (PC) which were obtained from Sentinel-2A data as well as after integration of textures and principal components. Optimum band combinations were then chosen after evaluating different RGB colour composites in order to discriminate different lithological units. Finally, results obtained after analysis have been verified with a published lithology map of the study area which shows good similarity with results presented in this work.
VTPC: Variable Trapezoidal Parity Cycle Learning Rate for Deep Neural Network

The Stochastic Gradient Descent(SGD) optimization algorithm is an important method for training deep learning models, which relies on the fine adjustment of hyper-parameters to provide a suitable optimization for model training. Learning rate is one of the most influential hyper-parameters. This paper proposes a novel learning rate scheduling method called Variable Trapezoidal Parity Cycle Learning Rate(VTPC). The method improves the existing cyclic learning rate methods and makes learning rate circulate between three reasonable boundary values. It can better overcome a large number of different saddle points in the high-dimensional non-convex loss functions, and effectively improve the convergence speed and accuracy of the deep neural network. This paper also describes a specific method for determining the boundary value of the cyclic learning rate to reduce uncertainty of the learning rate parameter tuning. Finally, the empirical results show that VTPC has a good effect in practical application and compares favorably to other learning rate methods mentioned in this paper.