Reviewerly/exHarmony huggingface dataset:
Classification of Scientific Documents in the Kazakh Language Using Deep Neural Networks and a Fusion of Images and Text

The rapid development of natural language processing and deep learning techniques has boosted the performance of related algorithms in several linguistic and text mining tasks. Consequently, applications such as opinion mining, fake news detection or document classification that assign documents to predefined categories have significantly benefited from pre-trained language models, word or sentence embeddings, linguistic corpora, knowledge graphs and other resources that are in abundance for the more popular languages (e.g., English, Chinese, etc.). Less represented languages, such as the Kazakh language, balkan languages, etc., still lack the necessary linguistic resources and thus the performance of the respective methods is still low. In this work, we develop a model that classifies scientific papers written in the Kazakh language using both text and image information and demonstrate that this fusion of information can be beneficial for cases of languages that have limited resources for machine learning models’ training. With this fusion, we improve the classification accuracy by 4.4499% compared to the models that use only text or only image information. The successful use of the proposed method in scientific documents’ classification paves the way for more complex classification models and more application in other domains such as news classification, sentiment analysis, etc., in the Kazakh language.
An Interval AHP Technique for Classroom Teaching Quality Evaluation

Classroom teaching evaluation is one of the most important ways to improve the teaching quality of mathematics education in higher education, and it is also a group decision making problems. Meanwhile, there is some uncertain information in the process of evaluation. In order to deal with this uncertainty in classroom teaching quality evaluation and obtain a reliable and accurate evaluation result, an interval analytic hierarchy process (I-AHP) is employed. To begin with, the modern evaluation tool named RTOP is adapted to make it more consistent with the characteristics of the discipline. In addition, the evaluation approach is built by using the I-AHP method, and some details of weights of the criteria and assessors are developed, respectively. Thirdly, a case study has been made to verify the feasibility of the assessment approach for classroom teaching quality evaluation on mathematics. Finally, a comprehensive evaluation of classroom quality under an interval number environment is conducted, and some results analyses and comparisons are also discussed to show that the proposed approach is sound and has a stronger ability to deal with uncertainty.
Denoise ultra-low-field 3D magnetic resonance images using a joint signal-image domain filter

Ultra-low-field magnetic resonance imaging (MRI) could suffer from heavy uncorrelated noise, and its removal could be a critical post-processing task. As a primary source of interference, Gaussian noise could corrupt the sampled MR signal (k-space data), especially at lower B0 field strength. For this reason, we consider both signal and image domains by proposing a new joint filter characterized by a Kalman filter with linear prediction and a nonlocal mean filter with higher-order singular value decomposition (HOSVD) for denoising 3D MR data. The Kalman filter first attenuates the noise in k-space, and then its reconstruction images are used to guide HOSVD denoising process with exploring self-similarity among 3D structures. The clearer prefiltered images could also generate improved HOSVD learned bases used to transform the noise corrupted patch groups in the original MR data. The flexibility of proposed method is also demonstrated by integrating other k-space filters into the algorithm scheme. Experimental data includes simulated MR images with the varying noise level and real MR images obtained from our 50 mT MRI scanner. The results reveal that our method has a better noise-removal ability and introduces lesser unexpected artifacts than other related MRI denoising approaches.
THE MEME AS A FORM OF COMMUNICATION ON THE INTERNET

The present article focuses on the functioning of specific elements - memes in the Internet communication. They have taken over the internet. Memes are all over Instagram, Facebook and Twitter and it is nearly impossible to log onto these accounts without memes being everywhere. The origin of meme is the biological makeup of an organism gene, which correlates with the thought the ideas could spread like a gene. The internet meme has gained a new usage, it has become a piece of culture which is constantly created and shared by people through its rapid spreading. The internet meme is collectively produced and the meme can evolve and change over time. The current popularity of the meme can be explained by the ease of reading information and the convenience of replicating it. The theoretical thrust of this analysis indicates the usefulness of the notions of multimodal voicing and intertextuality. The article examines the prevalence of Internet memes in a virtual environment, and the role and functions of memes in organizing social interaction and human communication. This article analyzes randomly selected memes. The interest lies in the relationship between image and text, which contributes to concrete interpretive results, and to semiotic interrelation of modes. Memes are a way in our society to share thoughts and ideas. Memes are relatable and the audience thrives for things that they can relate to. Memes are a way of communication in our society, and they will continue to grow and become more popular. The article also discusses the fact that the internet memes are a manifestation of communication of visual society.
Computational Search of Long Skew-symmetric Binary Sequences with High Merit Factors

In this paper, we present a computational search for best-known merit factors of longer binary sequences with an odd length. Finding low autocorrelation binary sequences with optimal or suboptimal merit factors is a very difficult optimization problem. An improved version of the heuristic algorithm is presented and tackled to search for aperiodic binary sequences with good autocorrelation properties. High-performance computations with the execution of our stochastic algorithm to search skew-symmetric binary sequences with high merit factors. After experimental work, as results, we present new binary sequences with odd lengths between 201 and 303 that are skew-symmetric and have the merit factor $F$ greater than 8.5. Moreover, an example of a binary sequence having $F > 8$ has been found for all odd lengths between 201 and 303. The longest binary sequence with $F > 9$ found to date is of length 255.
E-MODULE DEVELOPMENT OF WRITING THE SYAIR PERANG MENTENG VERSE-BASED DRAMA SCRIPT USING FLIP HTML 5 (PENGEMBANGAN E-MODUL MENULIS NASKAH DRAMA BERBASIS SYAIR PERANG MENTENG MENGGUNAKAN FLIP HTML 5)

This research is motivated by the unavailability of teaching materials for writing drama scripts that satisfy the instructional needs of the Palembang Malay Traditional Theater Course. Therefore, development research was conducted to obtain the teaching materials. The procedure starts from needs analysis, expert validation, small group evaluation, and field test. The data of the study were collected using questionnaires, interviews, assessment sheets, and tests. The study show that: (1) all students and lecturers needed an e-module to write a drama script based on the Menteng War Verse using Flip html 5; (2) the e-module design can be categorized as valid based on the material score of 34 (75.55%), language score of 41 (74.54%), and the graphic score of 32 (71.11%); (3) this e-module teaching material can be categorized as practical on the basis of the score obtained, i.e. 11 (73.33%); (4) The e-module teaching material can be categorized as very effective based on the average score increase (45.89). Thus, the e-module can be used as teaching materials to write a drama script of the Menteng War Verse.
An Efficient Estimation Method for Dynamic Systems in the Presence of Inaccurate Noise Statistics

The uncertainty of noise statistics in dynamic systems is one of the most important issues in engineering applications, and significantly affects the performance of state estimation. The optimal Bayesian Kalman filter (OBKF) is an important approach to solve this problem, as it is optimal over the posterior distribution of unknown noise parameters. However, it is not suitable for online estimation because the posterior distribution of unknown noise parameters at each time is derived from its prior distribution by incorporating the whole measurement sequence, which is computationally expensive. Additionally, when the system is subjected to large disturbances, its response is slow and the estimation accuracy deteriorates. To solve the problem, we improve the OBKF mainly in two aspects. The first is the calculation of the posterior distribution of unknown noise parameters. We derive it from the posterior distribution at a previous time rather than the prior distribution at the initial time. Instead of the whole measurement sequence, only the nearest fixed number of measurements are used to update the posterior distribution of unknown noise parameters. Using the sliding window technique reduces the computational complexity of the OBKF and enhances its robustness to jump noise. The second aspect is the estimation of unknown noise parameters. The posterior distribution of an unknown noise parameter is represented by a large number of samples by the Markov chain Monte Carlo approach. In the OBKF, all samples are equivalent and the noise parameter is estimated by averaging the samples. In our approach, the weights of samples, which are proportional to their likelihood function values, are taken into account to improve the estimation accuracy of the noise parameter. Finally, simulation results show the effectiveness of the proposed method.
A study on the asymmetry and non-linear effects of education quality attributes on student satisfaction

The purpose of this study is to identify which attributes to strengthen and which attributes to improve to improve student satisfaction through asymmetric and non-linear analysis of educational quality attributes. In order to achieve the purpose of this study, an asymmetry analysis was performed on the attributes that affect student satisfaction. For this, PRCA analysis and AIPA(asymmetric impact-performance analysis) were used, and a matrix between IA and attributes was constructed and presented to further suggest strategic directions. Through this analysis, the influence of educational attributes affecting student satisfaction was identified. The analysis results of this study are as follows. First, as a result of PRCA analysis, the major curriculum was classified as a one-dimensional factor, the liberal arts curriculum, non-curricular programs and administrative services were classified as attractive factors, and professor counseling, information system service, and this class were classified as must-be factors. Second, IA and attribute matrix analysis results show that non-curricular programs, liberal arts curriculum, and administrative services are areas for improvement through aggressive investment, and information system, professor counseling, and this class are classified as areas that must be improved first. and the major area was analyzed as an important area as a factor that should further strengthen investment. Lastly, the implication of this study is to provide the priority of allocation of educational resources through the application of AIPA to educational attributes and to classify the attributes that need to be improved first within the educational attribute factors. This will help to efficiently utilize resources and improve student satisfaction.
Geology, Geochemistry and Geophysical Studies in Exploration of Copper and Iron Reserves: A Case Study

The large Miocene stock work-vein porphyry copper mineralization of Sarcheshmeh are already known, but there are still few studies regarding mineral prospection in smaller deposits. This paper proposes exploration techniques for Cu prospecting in Hefdah Chenar area, Kerman Province, Iran, as case study. This study investigate the Hefdah Chenar area's mineralization in light of its natural context, focusing on geology, geochemistry, and geophysics. Hefdah Chenar district consists of an Eocene volcanic andesite succession with a gently northeast dipping, which was intruded by at least two phases of copper-bearing granodiorite porphyries. Several documents and data including topographic, geological, land use, soils maps, Hefdah Chenar area's map, field visit, the geochemical study's results, and stream sediment samples proved the presence of some points with a grade higher than the threshold limit. The results of the geophysical IP&amp;RS method indicated that the sulfur mineralization trend was northeast and consistent with the effects of surface mineralization. The result of this investigation indicates that based on the results of geophysical models, there is an increase in the amount of induced polarization at the location of possible faults. Furthermore, among these, magnetic data presents that there is no significant changes except in areas with magnetic veins.
Deployment of Honeypot and SIEM Tools for Cyber Security Education Model In UITM

Nowadays the threat of cyber-attacks is increasing as more organizations undergo digital transformation. Therefore, organizations need to take proactive measures to mitigate the cyber threat to avoid further loss to their business. To mitigate cyber risk effectively, organizations need to employ competent people in the IT security team to implement effective security controls. But there is a shortage of cyber security talent or professionals in the job market and to produce talents in the cyber security field requires extensive effort in education and training. A good cyber security education program should have to date curriculum and provide practical experience. To achieve this, the program must be supported by a cyber security lab equipped with various software, equipment, and tools used by a real professional in the industry. Therefore, this paper proposed a model of a cyber security lab equipped with honeypot and SIEM systems to enhance the quality of cyber security education. The cyber security lab based on the model was deployed at Universiti Teknologi MARA (UiTM) and used for teaching and learning activities. The honeypot will provide student experience analyzing the behaviour of hackers while the SIEM system will aggregate the logs data of the Campus Network Firewall in real-time. To evaluate the effectiveness of the proposed lab model, a functional test and a survey was conducted. The survey result shows that majority of the respondent agreed that the cyber security lab improve their teaching and learning experience while taking the cyber security subject.
Sign language recognition by means of common spatial patterns: An analysis

Currently there are around 466 million hard of hearing people and this amount is expected to grow in the coming years. Despite the efforts that have been made, there is a communication barrier between deaf and hard of hearing signers and non-signers in environments without an interpreter. Different approaches have been developed lately to try to deal with this issue. In this work, we present an Argentinian Sign Language (LSA) recognition system which uses hand landmarks extracted from videos of the LSA64 dataset in order to distinguish between different signs. Different features are extracted from the signals created with the hand landmarks values, which are first transformed by the Common Spatial Patterns (CSP) algorithm. CSP is a dimensionality reduction algorithm and it has been widely used for EEG systems. The features extracted from the transformed signals have been then used to feed different classifiers, such as Random Forest (RF), K-Nearest Neighbors (KNN) or Multilayer Perceptron (MLP). Several experiments have been performed from which promising results have been obtained, achieving accuracy values between 0.90 and 0.95 on a set of 42 signs.
Highly illogical, Kirk: spotting type mismatches in the large despite broken contracts, unsound types, and too many linters

The DefinitelyTyped repository hosts type declarations for thousands of JavaScript libraries. Given the lack of formal connection between the types and the corresponding code, a natural question is are the types right? An equally important question, as DefinitelyTyped and the libraries it supports change over time, is how can we keep the types from becoming wrong? In this paper we offer Scotty, a tool that detects mismatches between the types and code in the Definitely-Typed repository. More specifically, Scotty checks each package by converting its types into contracts and installing the contracts on the boundary between the library and its test suite. Running the test suite in this environment can reveal mismatches between the types and the JavaScript code. As automation and generality are both essential if such a tool is going to remain useful in the long term, we focus on techniques that sacrifice completeness, instead preferring to avoid false positives. Scotty currently handles about 26% of the 8006 packages on DefinitelyTyped (61% of the packages whose code is available and whose test suite passes). Perhaps unsurprisingly, running the tests with these contracts in place revealed many errors in Definitely-Typed. More surprisingly, despite the inherent limitations of the techniques we use, this exercise led to one hundred accepted pull requests that fix errors in DefinitelyTyped, demonstrating the value of this approach for the long-term maintenance of DefinitelyTyped. It also revealed a number of lessons about working in the JavaScript ecosystem and how details beyond the semantics of the language can be surprisingly important. Best of all, it also revealed a few places where programmers preferred incorrect types, suggesting some avenues of research to improve TypeScript.
Comprehension of Computer Programs Through Reverse Engineering Approaches and Techniques: A Systematic Mapping Study

The maintenance phase is an activity carried out by software engineers that requires an understanding how computer programs work. However, most legacy systems lack associated documentation and have poorly designed artifacts. As a result, technical debt is generated, which causes a significant increase in maintenance costs. Reverse engineering is applied to help software engineers to understand how the program was designed. Within reverse engineering, different approaches reduce the effort required for comprehension. In addition, static, dynamic, and hybrid analysis techniques are used to generate artifacts where the program's behavior can be easily visualized. This paper presents the results of a Systematic Mapping Study (SMS) conducted to identify reverse engineering approaches to help software engineers understand computer programs. Forty-eight studies were selected. Ten different approaches were identified in these studies, the main ones being Model-Driven Reverse Engineering (MDRE) and visualization graphics; static, dynamic, and hybrid analysis techniques were found; and fifteen artifacts featuring visualization graphs, class diagrams, and sequence diagrams.
Improving Heuristic Functions in A* Algorithm Path Planning

To address the problems that the traditional A* algorithm often fails to obtain the optimal solution when performing path planning leading to increased search cost, more nodes to be searched, more inflection points of the planned path, and easy access to local optimal solutions, two methods of improving the heuristic function of the A* algorithm are used.The first one is to dynamically weight the heuristic function, and the second one is to use different heuristic functions in segments.The simulation results show that these two methods can effectively solve the problems of reducing the cost, reducing the inflection point, and avoiding entering the local optimal solution as much as possible.Also in the deepening of the learning of the A* algorithm, improvements are made in parts other than the heuristic function.
Identifying Practical Applications of Blockchain Technology in Agriculture

Blockchain, also known as distributed ledger technology, is considered to have the potential to cause significant economic, political, and social transformations in India. Blockchain technology has been most helpful in understanding the source and journey of produce in agriculture. This is vital for farmers and consumers: it authorizes farmers to bargain for better prices throughout the supply chain while giving consumers confidence in knowing exactly where the produce comes from. This is crucial considering the growing lack of trust in sourcing produce sold in markets. While still challenged with fundamental limitations, Blockchain technology is a transformative Information and Communications Technology (ICT) that has changed our notion of trust. Improved efficiencies for sustainable agricultural development have been demonstrated when ICT-enabled farms have access to knowledge banks and other digital resources. The visible effects of this technology are already being noted there. This paper presents early evidence linking the use of blockchain in overcoming agricultural challenges facing India. The paper examines blockchain technology's impact and critical applications in agriculture and the food supply chain. It demonstrates how blockchain can help promote transparency, build trust and reputation, and enhance transaction efficiency. This paper looks at opportunities and key triggers for blockchain diffusion in agricultural practices in India. It also delves into challenges and obstacles developing economies will likely encounter in using blockchain.
Few-Shot Object Detection Based on the Transformer and High-Resolution Network

Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However, the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR).High-resolution feature extraction maintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition, the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.