Reviewerly/exHarmony huggingface dataset:
Incorporating information literacy in the Croatian school environment

Despite the relevance, potential and impact of IL activities on the wider social and pedagogical context, libraries in Croatia are lagging far behind the emerging global information literacy movement. Due to the general lack of written and widely published educational programs, mission statements or policies of Information Literacy on a national level, the majority of school libraries in Croatia are forced to provide only simple and most fundamental forms oriented more towards library and not information literacy. Diagnosing the emerging problem and trying to find a solution to the problem presented an idea for a pilot project. Educating the educators i.e. school librarians was set as a starting point in implementing information literacy in the educational environment.&#x0D; Results of the pilot project carried out with the Education and Teacher Training Agency in advocating information literacy in the school environment are presented in this paper. Research on the theory of current education problems together with research on best case scenarios was used as a foundation for creating learning materials and organizing a series of workshops for educating school librarians. Future plans for expanding the project with all participants of the educational process (principals, teachers and school librarians) are discussed and key elements of possible information literacy program are defined.
Self Separation and Misseparation Impact Minimization for Open-Set Domain Adaptation

Most of the existing domain adaptation algorithms assume the label space in the source domain and the target domain are exactly the same. However, such a strict assumption is difficult to satisfy. In this paper, we focus on Open Set Domain adaptation (OSDA), where the target data contains unknown classes which do not exist in the source domain. We concluded two main challenges in OSDA: (i) Separation: Accurately separating the target domain into a known domain and an unknown domain. (ii) Distribution Matching: deploying appropriate domain adaptation between the source domain and the target known domain. However, existing separation methods highly rely on the similarity of the source domain and the target domain and have ignored that the distribution information of the target domain could help up with better separation. In this paper, we propose a algorithm which explores the distribution information of the target domain to improve separation accuracy. Further, we also consider the possible misseparated samples in the distribution matching step. By maximizing the discrepancy between the target known domain and the target unknown domain, we could further reduce the impact of misseparation in distribution matching. Experiments on several benchmark datasets show our algorithm outperforms state-of-the-art methods.
Software Fault Prediction Using Machine Learning Models and Comparative Analysis

Software Testing is an important phase of Software Development Life cycle. Effective software testing helps in identifying faulty modules, but this process becomes very time consuming, especially for large /complex software. Moreover early identification of error prone modules can be useful in producing better quality software. Hence software fault prediction has gained significant attention of the researchers in the recent years. Mainly two types of techniques are being used for it: statistical methods and Machine learning models, out of which recent trend is more inclined towards machine Learning based techniques. Identification of faulty modules is a binary classification problem and machine learning models such as Decision Tree and its variants, Random Forest and Support Vector Machine, are best suited for the fault prediction. This paper attempts to predict software faults using four different classification models. Further, the performance evaluation of these models is also carried out in this paper using accuracy, Precision, Recall, F1-Score and execution time of over 12 datasets, extracted from one of the most commonly used PROMISE repository. Based on these performance metrics comparison of applied four models is done and our comparative analysis indicates that in most of the cases Support Vector Machine model is able to predict software faults more efficiently than the rest in terms of accuracy as well as execution time
Developing E-module based on mobile learning as a preparation media

Abstract The purpose of this study was to: (1) Developing an electronic module based on mobile learning; (2) Describe the validity of mobile learning application as an alternative media competency exam preparation (UKK); (3) Determine students’ response to the application of mobile learning. This software development process uses the Borg and Gall. Subject trial involving two media experts, one subject matter expert and students on vocational education. The instrument used to collect data was a questionnaire product validation. Data collection was carried out from February 2020 to March 2020 at SMKN 3 Sorong, West Papua. The Results showed (1) E-module based mobile learning was obtained that could be used by student in preparation for UKK; (2) The Functionality test of the media expert obtained a percentage score of 80,55%, and the material expert obtained a score of 95% which indicated the e-module was feasible to use; (3) Testing by student obtains a score of 90% which shows that e-module based on mobile learning are effectively used by students.
SCIENTIFIC APPROACHES TO THE PROCESS OF FORMATION OF PROFESSIONAL COMPETENCE OF FUTURE JOURNALISTS

The article substantiates the basics of professional competence of future journalists in the process of studying at universities. The importance of the professional competence and the life of modern human is determined. The controversial points in perception of professional competence in academic discourse are highlighted. The main approaches to the process of professional competence formation among students are highlighted: systemic, synergetic, competence. The professional competence of the future journalist is an integral quality, a complex and multifaceted concept, which determines the application of a systematic approach to the study of its development. The systems approach makes it possible to study an object as an integral set of elements in a set of relations and connections between them, to consider an object as a system. The synergetic approach provides for the construction of a holistic system of professional training of future journalists, the creation of a creative educational environment, a system of spiritual interaction of teachers and students as subjects of pedagogical cooperation. With a synergetic approach to the training of specialists, future journalists are involved in the creative search, in the disclosure of the author's personality.The essence of the competence approach is explained by the set of knowledge and skills necessary for effective professional activity: the ability to analyze, predict the consequences of professional activity, use information. From the standpoint of the competence approach, competence as a complex personal category means the practical readiness and ability of a person to act in a particular field; it includes knowledge, ideas, skills, motives, values, implemented in life and professional contexts, provides experience. The discussion on the formation of professional competence of future journalists in the process of professional training is demonstrated. The prospects for further research are outlined. The future of the state and society largely depends on the professional competence and creative approach of journalists to fulfill their purpose, functions and social roles.
Simulation of underwater vehicle control based on code generation technology

Model-based design is an effective means for rapid development of embedded software, and automatic code generation is an important technology for model-based development. Combining the automatic code generation method of Matlab and STM32 with the operation and control of the autonomous underwater robot makes the design of the system more convenient. Use tools such as the Simulink library STM32 MAT/Target and STM32 CubeMX of the STM32 microcontroller to realize the automatic generation of readable and portable C code project files. At the same time, based on the design of the model, the control code of the autonomous underwater robot(AUV) is automatically generated, and the control code is added to the automatically generated C code project file. With Matlab/Simulink as the basic software platform, the motion controller of AUV is mounted on the STM32F407, and a real-time simulation system for the closed-loop control of AUV manipulation motion is constructed. The results of the semiphysical real-time simulation test show that the AUV motion controller has good heading depth control performance, realizes the manipulation and control of AUV, and verifies the practicability of the automatically generated code.
The Consideration of Job Satisfaction in the Design of Assistance Systems in Production

Abstract Assistance systems designed to help workers in their jobs are increasingly used in industry. Technological progress makes these systems more powerful and extensive, but often nobody questions the extent to which they actually support the users and do not patronize them. For the development of such systems, we found the requirement analysis to be rather complex because human factors and social constraints are more difficult to determine than technical requirements. To counteract these difficulties, we pursue in our approach the involvement of people as knowledge carriers in the development of new technologies. In this paper we outline our framework how human factors aspects of acceptance and job satisfaction can be taken into account in the conception and design of assistance systems.
Seismic Intensity Distribution of the 1914 Sakurajima Earthquake in Kagoshima City, Japan

A large earthquake (M7.1) occurred during the 1914 eruption of Sakurajima volcano in Kagoshima prefecture, Japan. I estimated seismic intensities in the present Japan Meteorological Agency (JMA) scale for the 1914 Sakurajima earthquake in Kagoshima city. Previous studies on seismic intensities in Kagoshima city are used their own unique methods to estimated seismic intensities from data of damaged houses and stonewalls. I used a method of estimating seismic intensities from data of damaged houses, which has been commonly used. Previous studies using this method have assumed that each household owns one house, but the numbers of damaged houses in some towns are significantly larger than those of households in the case of the 1914 Sakurajima earthquake. I, therefore, made two assumptions A and B. The assumption A is that each household owns one house. The assumption B is that the ratio of houses to households in each town equal to the maximum ratio of total damaged houses to households. As a result, the maximum seismic intensities for the assumptions A and B are 6 upper and 6 lower, respectively. The areas with higher seismic intensity than 6 lower are consistent with the previous studies on the 1914 Sakurajima earthquake, but the areas with lower seismic intensities than 5 upper are inconsistent because I did not take stonewall damages into consideration, which the previous studies did. I compared our results with distributions of seismic intensities predicted by an attenuation relation between intensity and distance with two different sets of source parameters. One set of source parameters showed seismic intensities of 6 lower and 6 upper in Kagoshima city, and the other set showed those of 5 lower and 5 upper. The observed seismic intensities are on average lower than the predicted ones, probably suggesting that large coseismic slip area is distributed further than epicenter, that Kagoshima city is located in the opposite direction of the rupture propagation and its directivity effect make the amplitudes smaller, or that amplitudes at frequencies which affect seismic intensities are smaller than those expected from the magnitude. The observed seismic intensity distributions are also rougher than the predicted ones and could be affected by local soil conditions.
Detection of Armed Assailants in Hostage Situations- A Machine Learning based approach

Innocent persons may be captivated by a criminal abductors and may threaten the person's employer, government, relatives, etc. To exploit unlawfully for their advantage after expiration of an ultimatum. Abducting people and threatening is a criminal act or act of terrorism. It is important to distinguish the assailants from the hostages. The rescuer has milliseconds to distinguish and any slight mistake in identifying a person might cost the lives of all the innocent hostages in that situation. To help the rescuers with this a model is developed in this research work that can identify if a person is an assailant or the hostage in a hostage situation. The proposed model takes the real time video as input and detects the assailant by making a rectangular bounding box on his/her face. YOLO (You only look once) and OpenPose algorithms are used in the proposed system and both algorithms are object detection systems targeted for real-time processing for both classification and localizing the object using bounding boxes and Heat maps.
Transductive Mutual Information Encoder Network for Few Shot Learning

Abstract Deep learning has made breakthroughs in recent decades and has been widely used in many domains. However, most of those methods heavily rely on large labeled datasets, which results in poor performance when provided with limited labeled data. Few-shot learning (FSL), which aims at learning a novel task with limited samples, has attracted a lot of research recently. The previous metric-based methods ignore the internal bias between the training and testing datasets since the categories of the testing dataset are completely different from the training set. Transfer learning methods also suffer from few labeled data and tends to be overfitting in this situation. This paper proposes Transductive Mutual Information Encoder Network (TMIN) for few-shot learning problems. TMIN typically trains a convolutional neural network with a mutual information maximization module in an unsupervised manner. The trained network maps images to a high dimensional embedding space. Then the embeddings are exploited to measure the similarity between samples by a distance metric. Experiments indicate that the proposed model achieves competitive performance compared with the counterparts.
Significant role of mathematical metamodels in analyzing snaking ladders and laddering snakes in real world data

Appearances of data need not always demonstrate the real facts in many real-world situations. Immediate outcomes need not exhibit the real nature of ultimate outcomes. Initial assumptions about data models may get proved to be illogical in the long run. In effect, certain rewarding elements can ultimately perform like punishments and even punishing ones may perform the role of rewards. Certain ladders silently perform the role of snakes and vice versa in the context of gaming and gamification models of Snakes and Ladders. The machine learning techniques like Q-Learning are helpful to analyze the ultimate underlying nature of complex data models by collaborating mathematical and statistical techniques. With the help of meta-models from gaming, gamification and real world instances, this study demonstrates the scope for intelligent software system models with self-learning and self-improving capabilities, in discovering the real and ultimate nature of data through repeated explorative learning epochs, which is highly relevant in the context of analyzing complex data models in many real world situations, as in an epidemic situation. Also, this paper points out the significant role of experts and expertise in Mathematics and Statistics to associate with domain experts and technologists in modeling intelligent software solutions.
Object Detection in Image Using Components of Convolutional Neural Network and YOLO

Extracting features has always been a fascinating subject for users. Though our human brain is known as fast computation machine to detect not only multiple features but also provide relationship between them. Deep learning has always been known as subfield of artificial intelligence. It gives us the clear image of another portal like image recognition, vector quantization, pattern matching, and data clustering. With the help of neural network, it became much easy to compute, extract, and process. One such variant is CNN also known as convolutional neural network that works in area of detecting image. With input image passing through, multiple layer objects get extracted. Here, in this paper, brief discussion regarding CNN and how objects is been detected solve? With YOLO framework new trend is discussed that provide knowledge about with each passing year how ANN gets more advanced?
Layout Optimization of Landscape Architecture based on Intelligent Visual Scene Recognition and Saliency Analysis

Layout optimization of the landscape architecture based on intelligent visual scene recognition and saliency analysis is studied in this article. Among the many scenaiios, the core behavior analysis of crowd-intensive scenarios in the public areas is particularly important. This article considers the mentioned scenario for general testing. The multi-level parallel processor receives the core image signal from the retina and processes the image information. Through the multi-level visual image feature extraction and image target detection, the learning, memory and recognition functions of the visual information are completed. Hence, consider this technique novelty to apply into the core layout optimization of the landscape. The simulation results have proven the effectiveness.
Language as a Complex System

The previous chapter was dedicated to considering the background of this book from the viewpoint of universals. It involved notions originating in the humanities, especially linguistics and philosophy. Because language is a system to represent meaning, the analysis to acquire those linguistic universals involved meaning. In contrast, statistical universals, as briefly introduced before, derive from statistical analysis methods. The means—statistics—of this book goes beyond counting and involves statistical mechanics. Hence, this chapter sets out the basis for applying such methods to language.
Classical (co)recursion: Mechanics

Abstract Recursion is a mature, well-understood topic in the theory and practice of programming. Yet its dual, corecursion is underappreciated and still seen as exotic. We aim to put them both on equal footing by giving a foundation for primitive corecursion based on computation, giving a terminating calculus analogous to the original computational foundation of recursion. We show how the implementation details in an abstract machine strengthens their connection, syntactically deriving corecursion from recursion via logical duality. We also observe the impact of evaluation strategy on the computational complexity of primitive (co)recursive combinators: call-by-name allows for more efficient recursion, but call-by-value allows for more efficient corecursion.
ACS: Auto-Tuning the Configurations of Structured Streaming Programs by Using SVM

Recently, Apache Structured Streaming (ASS) has been released with many benefits such as supporting incremental query models and end-to-end applications, integrating with Spark SQL engine, and etc. However, ASS has a huge amount of configuration parameters needing to be set properly, which is critical for performance. And it is extremely difficult, if feasible, to manually tune these configuration parameters for optimal performance. In this paper, we propose ACS to tune the configuration of Apache Structured Streaming programs automatically. ACS includes two key technologies: 1) Support Vector Machine (SVM) is leveraged to accurately build a performance model; 2) Genetic Algorithm (GA) is employed to realise the optimal configurations searching. We adopt four typical structured streaming programs for performance evaluation. The results show that compared to the default configurations recommended by official guidelines, ACS improves the throughput by an average of 2.29× and up to 2.52×, and reduces the latency by an average of 3.08× and up to 3.96×.
Perancangan Pondok Pesantren Modern di Kota Tangerang dengan Konsep Asitektur Islam

Education is an important part of life that also distinguishes humans from other livng things. Education is a systematic conscious effort in developing all the potential that exists in humans to become fully human. Indonesian citizens are citizens who have a religious life, so that to meet the needs of adequate education, every citizen wants to be met the need for religious education and education in general.The application of islamic architecture because of the similarities in the effort to restore and carry out islamic values in various aspects. The application of islamic architecture in pesantren buildings is more to the physical building which is expected to be able to produce a building as well as a place to gain knowledge in which islamic values can be applied.
ArSarcasm Shared Task: An Ensemble BERT Model for SarcasmDetection in Arabic Tweets

Detecting Sarcasm has never been easy for machines to process. In this work, we present our submission of the sub-task1 of the shared task on sarcasm and sentiment detection in Arabic organized by the 6th Workshop for Arabic Natural Language Processing. In this work, we explored different approaches based on BERT models. First, we fine-tuned the AraBERTv02 model for the sarcasm detection task. Then, we used the Sentence-BERT model trained with contrastive learning to extract representative tweet embeddings. Finally, inspired by how the human brain comprehends the surface and the implicit meanings of sarcastic tweets, we combined the sentence embedding with the fine-tuned AraBERTv02 to further boost the performance of the model. Through the ensemble of the two models, our team ranked 5th out of 27 teams on the shared task of sarcasm detection in Arabic, with an F1-score of %59.89 on the official test data. The obtained result is %2.36 lower than the 1st place which confirms the capabilities of the employed combined model in detecting sarcasm.
Analysis of an AR Technical Experiment Based on 3D Architectural Display

In recent years, with the accelerating pace of technological development and the growth of people’s living standards, customers with housing needs are gradually becoming younger. The emergence of new thoughts and technologies has also curbed the development of the traditional 3D architectural industry. Through the research experiment of AR technology, this paper integrates AR technology with 3D digital technology, and puts the experimental results AR APP into actual projects. Through the analysis of project results, the audience can use AR technology to become the active operator of the viewing.