Reviewerly/exHarmony huggingface dataset:
Handwritten to Text Document Converter

Handwriting recognition also recognized as handwriting Optical Character Recognition (OCR) is an OCR applied science that translates handwritten letters to analog digital text. In this paper, a software solution is provided to automatically convert the handwritten image into text. Almost every sector and all the organizations require information. Organizations like educational institutions, medical and healthcare sectors, IT organizations, banks, small-scale and large-scale industries and businesses require the customer or the client to fill out a handwritten form. This information is uploaded into the database manually which is tedious and time-consuming. In this project, CNN and bidirectional RNN by using open-source libraries like OpenCV, tensor flow and Matlibplot in Python language to perform segmentation, normalization and feature extraction are implemented. An attempt is made to use deep learning to convert the handwritten text in an offline format (e.g., on a piece of paper) into a text document. Many handwriting recognition tools have been in the market, but still, there are not many tools that give maximum accuracy as each person owns a unique style of writing, pressure and tilt. There are two ways to recognize characters: Markov model and artificial neural networks (ANN). Deep learning enables to train and test the model with huge datasets, thus leading to maximum accuracy. In this model, neural networks consisting of five convolutional neural network (CNN) layers and two recurrent neural networks (RNN) layers and connectionist temporal classification (CTC) model to decode are used. There are many methods for handwriting detection. Each method is different from the other, but the method used in this paper is CNN which uses neural networks to mimic the function of the human brain resulting in a higher accuracy rate when trained with large datasets.
DC-Gossip: An Enhanced Broadcast Protocol in Hyperledger Fabric Based on Density Clustering

Low transaction efficiency remains one of the primary constraints to the development of permission blockchain. To enhance the communication performance of blockchain, the majority of research focuses on optimizing the local architecture of blockchain and improving consensus. In practice, increasing the block dissemination capability at the network layer can significantly improve transaction efficiency. We find that the redundancy and instability of the gossip protocol as a broadcast method in Hyperledger Fabric have a significant impact on communication performance. In this work, we introduce the idea of density clustering to propose the DC-Gossip broadcast protocol, constructing a stable network architecture with highly dense connectivity for the blockchain network layer. This architecture can effectively reduce the propagation latency and ensure the integrity of the distributed ledger. In our experiments with Fabric, DC-Gossip reduces latency by more than 19% after 40 blocks are propagated in a stable network environment with more than 100 nodes. Moreover, the latency decreases by 14% in a dynamic network under the identical circumstances.
A Deep Convolutional Neural Network for Character Recognition in Ancient Syriac Manuscripts

The digitization of ancient manuscripts plays a crucial role in preserving cultural heritage by transforming handwritten documents into machine-encoded text, and making them publicly available. Over the past few decades, the digitization and restoration of handwritten ancient manuscripts have been an active research area. Unfortunately, research on Syriac text recognition significantly lags behind other languages, making the Syriac cultural heritage being in danger. In this context, this paper proposes a deep character recognition network for handwritten Syriac documents. The proposed network out-performs existing popular architectures in Syriac handwritten character recognition, reaching a classification accuracy of 97%.
Design of Enterprise Economic Dynamic Management System Based on Spark Technology

Aiming at the problem that the currently used dynamic management system based on Hadoop and B/S architecture is affected by the slow data mining rate, resulting in low efficiency of data dynamic management, a design of enterprise economic dynamic management system based on Spark technology is proposed. Deploy the physical architecture of the Spark-based economic dynamic management system, and build a data warehouse in this architecture to facilitate users to quickly view data in real time. The B/S (browser/server) model is adopted to design data collection modules, business service modules and performance modules to meet the needs of big data analysis and decision-making. When using Spark technology to dynamically adjust the difference data in the database, the rule base needs to be updated in time to convert the automatic conversion system to a detection system. Use the optimization algorithm of Spark Join operator to optimize the entire connection operation, filter out the project data without specific categories in the bank flow data, reduce the data entering the shuffle stage, and design a dynamic management process. It can be seen from the test results that the system has a maximum management efficiency of 92% in a safe environment. In a non-interference environment, the highest management efficiency is 0.95, which has an efficient management effect.
A Portrait for Operating Condition of Power Transformers from The Perspective of Multi-Dimensional Capabilities

With the continuous expansion of the scale of the smart grid system, the transformer monitoring data presents the characteristics of multiple, huge and rapid growth. Carrying out portrait research on operating condition of power transformers can fully mine the effective information of the data, avoid the subjectivity in the current transformer state evaluation methods, and better carry out equipment health management. By sorting out the relationship between state quantity and equipment operation, a portrait method of power transformer based on multi-dimensional capabilities and components is proposed. Firstly, an equipment evaluation system with five capabilities and equipment state quantities is constructed. Secondly, the weight of each state quantity is determined by the entropy weight method, and the state of each capability of the equipment is scored by the TOPSIS method. Thirdly, using the membership function of fuzzy mathematics method to integrate various capabilities in order to obtain the overall state of the equipment. Finally, the effectiveness of the proposed method is verified by the actual situation of 521 transformers, and the diagnostic accuracy rate is 83.11%.
Deep Learning Fusion for Attack Detection in Internet of Things Communications

The increasing deep learning techniques used in multimedia and networkIoT solve many problems and increase performance. Securing the deep learning models, multimedia, and networkIoT has become a major area of research in the past few years which is considered to be a challenge during generative adversarial attacks over the multimedia or networkIoT. Many efforts and studies try to provide intelligent forensics techniques to solve security issues. This paper introduces a holistic organization of intelligent multimedia forensics that involve deep learning fusion, multimedia, and networkIoT forensics to attack detection. We highlight the importance of using deep learning fusion techniques to obtain intelligent forensics and security over multimedia or NetworkIoT. Finally, we discuss the key challenges and future directions in the area of intelligent multimedia forensics using deep learning fusion techniques.
CUMULATIVE GRADING SYSTEM AS A FACTOR OF THE EDUCATIONAL PROCESS

Introduction: Enhancement of the efficiency of the educational process is one of the priority objectives of educational institutions and new approaches are developed to accomplish it. In the chase for innovations, however, the internal environment of an educational organization, in which they are supposed to be implemented, is often overlooked. The study aims to identify the factors of the educational process and their influence on the effectiveness of mastering subjects and developing competencies. Materials and methods: The participants in the experiment were the first- and second-year students and the lecturers of the Department of Foreign Languages of Peter the Great Saint Petersburg Polytechnic University. The survey of the participants in the experiment was carried out remotely by means of Google Forms. The collected data were processed using analytical methods and tools (MO Excel, Google Forms). The results of the current examinations were compared with similar indicators of the 2018–2019 and 2019–2020 academic years, which were taken from the annual reports of the Department of Foreign Languages. Results: The majority of students (72.5%) and lecturers (63.6%) noted the positive influence of the experimental grading system in terms of psychological factor. However, the study revealed a contradiction in the opinions about changing attitudes towards examination. Discussion and Conclusions: Changes made to the algorithms of the assessment of students’ performance contributed to the identification of internal trends related to the distribution of efforts made to master the subject and revise for examinations, as well as to the general approach to work, and psychological comfort during examinations.
Human-Computer Interaction Challenges and Opportunities in the Arab World Design Education

The Arab world is constantly evolving in the field of human-computer interaction. However, HCI’s involvement in design education in Arab institutions faces many different challenges than in the West. A single disciplinary study, industry-academia separation, Arabic language, and limited HCI research resources are seen as major barriers to HCI integration, backed by examples of the Egyptian education system. This poster proposes five solutions to be implemented in the Arab world to help introduce HCI concepts and principles to students, academia, and industry researchers. These solutions encourage improving interdisciplinary collaboration, increasing awareness of the importance of HCI education, creation of research databases for students and researchers, policies and activities organization, improving teaching systems, and discovery of language opportunities. The adoption of such solutions creates an environment in which the corporate sector, philanthropy, and the general public can all contribute to general academic education and research, especially HCI-related projects.
APIs and Endpoints

In this chapter, we will begin looking at coding web applications, starting with designing our endpoints: URLs and APIs. These are the building blocks of a web application. HTTP leaves a number of choices to us: what request method to use, what response code to return, what format to use for the request and response body.
MODELING OF STUDENTS ACADEMIC ACHIEVEMENT

Assessment of student learning outcomes is the most important part of the learning process. The progress of students can be determined on the basis of obtaining nal grades in certain subjects. The nal marks of students can be used to predict their future achievements.The issues of assessing the formed competencies of students within the studied disciplines and educational program of the specialty are considered.A conditional model for determining the achievements of student learning outcomes is proposed. The process of assessing students› competencies within the discipline and educational program is presented.
A Probabilistically-Oriented Analysis of the Performance of ASR Systems for Brazilian Radios and TVs

With the use of neural network-based technologies, Automatic Speech Recognition (ASR) systems for Brazilian Portuguese (BP) have shown great progress in the last few years. Several state-of-art results were achieved by open-source end-to-end models, such as the Kaldi toolkit and the Wav2vec 2.0. Alternative commercial tools are also available, including the Google and Microsoft speech to text APIs and the Audimus System of VoiceInteraction. We analyse the relative performance of such tools – in terms of the so-called Word Error Rate (WER) – when transcribing audio recordings from Brazilian radio and TV channels. A generalized linear model (GLM) is designed to stochastically describe the relationship between some of the audio’s properties (e.g. file format and audio duration) and the resulting WER, for each method under consideration. Among other uses, such strategy enables the analysis of local performances, indicating not only which tool performs better, but when exactly it is expected to do so. This, in turn, could be used to design an optimized system composed of several transcribers. The data generated for conducting this experiment and the scripts used to produce the stochastic model are public available.
A Reinforcement Learning Decentralized Multi-Agent Control Approach exploiting Cognitive Cooperation on Continuous Environments

Multi-agent system control is a research topic that has broad applications ranging from multi-robot cooperation to distributed sensor networks. Reinforcement learning is shown to be promising as a control strategy in cases where the dynamics of the agents are non-linear, complex, and highly uncertain since it can learn policies from samples without using much model information. The presented manuscript proposes a multi-agent decentralized control approach based on a new multi-agent reinforcement learning setting in which two virtual agents, sharing the same environment, control a single avatar but have access to complementary details necessary to finish the task. Each of them is responsible for solving a portion of the problem, and in order to efficiently solve it, a collaboration should emerge among the virtual agents not to compete but to focus on the final goal. Each virtual agent, performing individually, is not fully autonomous since it does not have a complete vision of the scene and needs the other one to properly command the avatar. The proposed approach proved to be able to solve efficiently constrained navigation problems in two different simulated setups. An actor-critic architecture with a Proximal Policy Optimization (PPO) algorithm has been employed in continuous action and state spaces. The training and the testing have been done in a maze-like environment designed using the StarCraft II Learning Environment.
Existing and Potential Use Cases for Blockchain in Public Procurement

The purpose of this article is to assess the possibility of using blockchain technology in the realm of public procurement within the EU, particularly in connection with the award of public contracts. In this context, blockchain is used as an umbrella term covering IT technologies and cryptographic solutions used to generate consensus on a distributed ledger. The article will present three real use cases for public procurement in Spain, Colombia and Peru. It will also posit two specific areas of EU public procurement practice that might benefit from the use of blockchain technology – the area of data management and accessibility and in situations of clear lack of confidence in public powers. Keywords: blockchain; use case; public procurement
Method for Search and Ranking of Related Objects in Scientometric Systems

Modern information systems used in scientific sphere usually deal with large numbers of object types and relationships between them. For example, a “scientific publication” object is linked, on one side, with one or multiple “scientist” objects, and on the other side with “scientific journal” or “collection” object, which in turn are linked to “publishing house” or “collection series” objects. Having such relationships allows us to represent all objects in a system as a graph, and to use information about relationships in this graph to solve various problems, such as: determining the thematic similarity of two objects, assigning an object to a certain subject area, assessing the importance of an object. The paper describes the methods used in the “ISTINA” information analysis system, developed in Lomonosov Moscow State University, for thematic search of objects (scientific publications and experts) by keywords and classification elements, as well as ranking search results, taking into account the significance of each result.
Optimal Task Offloading Strategy in Vehicular Edge Computing Based on Game Theory

In vehicular edge computing, when there are many vehicles requesting offloading services at the same time, relying only on the resources of edge servers often cannot meet the needs of delay-sensitive tasks. Most existing task offloading studies tend to only consider pure offloading strategies for vehicles, which may not be the optimal strategy for some splittable tasks. In this paper, we jointly optimize the vehicle hybrid offloading strategy and the server resource pricing strategy. For a requesting task, it can be executed locally, be offloaded to the edge server, and be offloaded to the cloud center at the same time. We model the interaction between vehicles, the edge server and the cloud center as a game model. Based on the analysis of backward induction, we prove that the game has a unique Nash equilibrium. Meanwhile, an algorithm that can converge to the equilibrium point in polynomial time is proposed. Numerical experimental results show that the proposed algorithm has better performance in terms of delay and cost than existing algorithms.
Remote Sensing Image Detection Based on Attention Mechanism and YOLOv5

Due to the lack of characteristic information of remote sensing small targets and the large interference of complex environments, the detection accuracy is not high and the detection performance is not good. Aiming at this problem, an improved Yolov5 algorithm based on attention mechanism is proposed, which firstly adds a prediction head suitable for small targets detection, and secondly uses mosaic data enhancement technology at the input to enrich the dataset, and at the end of the backbone network, the C3Transformer module is introduced to obtain global information; at the same time, the BiFPN feature fusion network is used in the neck network to better process the feature information, and some CBAM modules are integrated to focus on the acquisition of key feature information. Then it is compared with other network models on RSOD dataset and NWPU VHR-10 dataset. The results show that the size of the improved network model is only 17.8 MB, and compared with the original Yolov5, the detection accuracy on the two datasets is increased by 6.9% and 2.6% respectively.
Museum Virtual Reality with Design Thinking Methods

Keris Nusantara Museum provides equipment for traditional objects from all over Indonesia. Visitors include historical objects, activists, academics, researchers, and the general public. Several technological solutions can help the success of the museum's current vision and mission, one of which is using innovative products based on Virtual Reality technology. A practical method is needed to design and create a good VR application. The method is Design Thinking. Design thinking can be used as a method in completing various fields of work, including in making virtual reality applications. The design Thinking method has advantages in solving complex problems. It is a mindset method for empathizing with human-centered issues and concerns. Moreover, it is also closely related to product innovation. The presence of Design Thinking is helpful for startups when creating business models. Design thinking comprises five stages; the first stage is emphasized, which is the core of the human design process centered on understanding user characteristics related to the product that will be designed based on observations, interviews, literature studies, etc. The second stage is Define, which is the process of analyzing the results of the empathy activity to determine the formulation of the research problem. The third stage Ideate is the process of generating ideas which is a transition from problem formulation to problem-solving. The fourth stage of the prototype is the initial design of the product that will be made by applying trial and error to generate possibilities. The fifth stage of the test is product testing to generate user feedback.
CHIMERIZATION OF A TEACHER’S THINKING (HISTORICAL AND CULTURAL ASPECT)

The article deals with the problem of chimerization of the thinking of a Russian school teacher. It reveals the tra­ ditional from the value­epistemological side in the thinking of the domestic teacher and the replacement of his (traditional) with opposite realities. The chimerization of the teacher’s thinking is associated with such processes in society and the educational sphere as the decline in the social and socio­cultural status of the teacher, competi­ tion in the educational environment, the current system of teacher remuneration, standardization of the content of education, specialization of the education worker. Among the chimerical features of the modern teacher’s thinking are such as total pragmatism, standardness, uncreative nature, incompetence (to the student), lack of initiative and low responsibility for what is happening at school. The chimerization of the teacher’s thinking is associated with his inability to solve truly pedagogical tasks in the process of education. The methodology of the work is the idealistic­ substrate reflection of A.A. Gagaev.
An experimental analysis of quad wheel autonomous robot location and path planning using Borahsid algorithm with GPS and ZigBee

This paper presents a hardware system structure and wireless navigation system for both localisation and path navigation of a mobile robot implementing a 32-bit ARM processor (LPC2148 Board) into the design process of a mobile robot integrated with GPS and ZigBee wireless communication device. A novel path-navigation algorithm (Borahsid Algorithm) with less complexity is compared with the existing algorithms adopted for mobile robot realistic work. It utilises GPS localisation as well as ZigBee communication. Existing algorithms have higher accuracy in path following, but are a little slower in processing time. The proposed algorithm has faster processing speeds compared with the existing algorithms because of its lower cost function. MATLAB programming language has been used to simulate the mobile robot localisation and path navigation. The results show the effectiveness of the model and the feasibility of the Borahsid Algorithm (BsA). However, the entire control structure is executed and the experimental results were obtained in a real time system by which experimental results authenticates the performance and steadiness of the implemented control system process.
Multi-Transverse-Mode Optical Processors: Towards On-chip Programming and Calibration

We design a Multi-Transverse-Mode Optical Processor (MTMOP) exploiting the first two quasi-transverse electric modes (TE0 and TE1). This design enables the on-chip measurement of optical phase required for programming the optical processors.