Reviewerly/exHarmony huggingface dataset:
An RNN Based Approach to Predict Next Word in Bangla Language

The communication paradigm has changed with time, and text messaging has incredibly increased among people of all aspects. In addition, people use email and social media sites for interactive communication. As a result, people spend a significant amount of time typing the entire content, which is repetitious and time-killing. Therefore, word prediction technologies have been introduced to make message exchange faster and easier for predicting the next most probable word, allowing participants to choose it from a list of recommended words rather than entering it. This paper suggests a technique that predicts the next most relevant and acceptable word in Bangla, the eighth most speaking language in the world. We applied two Recurrent Neural Network models named Bi-directional LSTM (Long Short-Term Memory) and Bi-directional GRU (Gated Recurrent Unit) as language development models to our data set, which is compiled from different Bangla newspapers, storybooks, and Bangla Wikipedia. We also applied different n-grams with different LSTMs, and our approach found a maximum accuracy of 99.43% for a 5-gram dataset using the Bi-LSTM model.
THE VALIDITY OF EXISTING SPEAKING MATERIALS BASED ON TASK BASED LEARNING

Course book is one of medium that is needed so much by both teacher and learners in facilitating teaching and learning process. However, the available course books do not always match with the learners and institution needs. Fortunately, the purpose of this study was to investigate the validity of students’ existing speaking material for elementary school students grade V based on Task Based Learning at MI Swasta Mardliatul Islamiyah. The data were collected through questionnaires, interview and documentary sheet. The validity of the device was assessed by students using a validation sheet. The validation result data were analyzed descriptively quantitative and descriptive qualitative. The results of data analysis show that the students’ speaking material have not reached a valid category with the mode of score ranging from 1 to 2.
SISTEM INFORMASI PEMESANAN PAKET KUNJUNGAN WISATA BERBASIS WEB MENGGUNAKAN METODE WATERFALL DI DESA WISATA LEREP

Due to the negative impact of Covid-19 on various sectors, including the tourism sector, the government supports the implementation of regional travel forÂ a tourismÂ recovery, namely regional travel as an alternative form of tourism during the Covid-19 pandemic. And theÂ LerepÂ Tourism Village as a rural tourism object needs to prepare several things to welcome the recovery of tourism, one of which is by providing information onÂ onlineÂ tour package options. In the development method, the researcher uses the waterfall method in building an information system for ordering tour packages in theÂ LerepÂ Tourism Village. The waterfall method is a systematic or sequential method of developing applications and system software. Methods of data analysis using quantitative methods and testing methods using quantitative methods to obtain the final data. The results of this study, when viewed from the aspect ofÂ a systemÂ functionality, the black box method involved 1 examiner with a total of 50 test scenarios with the final results valid.Â andÂ the usabilityÂ test the percentage value is 81.6% from the visitor side, the percentage value is 86.85% from the manager side.Keywords: Tour Packages, Tourism Village, Waterfall, Covid-19
Security Model for a Central Bank in Latin America using Blockchain

Banking institutions in Latin America are the target of increasingly sophisticated and advanced cyber-attacks and threats, which increase every year and leave substantial economic losses, due to the high level of global interconnection and digitization of their operations. The objective of this work is to design a model to guarantee information security in a Central Bank in Latin America using Blockchain technology. Exploratory research, observation and inductive and deductive methods are used to propose Blockchain solutions in a Central Bank. The results are a model for secure transactions in Blockchain, Smart Contract functions and a data management process. It was concluded that the security model for a central bank provides high level of information management and storage of transactions in a secure and immutable way.
Sistem Informasi Penjualan Pakaian Berbasis Web Pada Toko Kolor Murah Ungaran Menggunakan Metode Waterfall

Kolor Murah Shop is a pleace that sells clothes used in everyday life, such as negligee, baby doll, suits and shorts. Because Kolor Murah Shop is still in the development stage, this store does not yet have an adequate system to fulfill the process of selling goods to consumers. Therefore, the author makes web-based sales information system at the Kolor Murah Shop.This web-based sales information system aims to help Kolor Murah Shop consumers make purchases online without having to come to the store directly. In addition, this sales information system also makes it easier for the owner to check the goods purchased by the customers.This sales information system will be developed using the Waterfall method, the programming language used is PHP, MySQL database, and using the Notepad++ application as server-side scripting.This research will produce a web-based clothing sales information system at a Kolor Murah Shop and have an appropriate interface design so that it can provide broad information. Based on the results of the black box sytem testing, it produces decent features and usability testing on the questionnaire data produces 84,0%, which means the test results are very feasible and meet the usability aspect.The conclusion that can be drawn from the explanation above is to be able to implement a web-based clothing sales information system at the Kolor Murah Shop Ungaran using the waterfall method.Â Keywords : Waterfall, Notepad++, PHP, MySQL, Toko Kolor Murah.
SISTEM INFORMASI PENILAIAN KINERJA PEGAWAI PADA LEMBAGA PEMASYARAKATAN NARKOTIKA MUARA SABAK

Abstract : The Class IIb Muara Sabak Penitentiary fosters and provides a Rehabilitation Program for prisoners, especially users, drug addicts in collaboration with the National Narcotics Agency (BNNP) of Jambi Province and the National Narcotics Agency of East Tanjung Jabung Regency (BNNK). Currently, the capacity is 362 inmates, which is occupied by 792 inmates, which means there is an excess of occupants. This condition causes employees to work harder. Individual employee performance is very important in achieving organizational goals. Therefore we need a performance appraisal system that is reliable and will be used as a basis for performance appraisal and organizational development, employee performance follows a goal so that each employee can assess the extent to which his performance has resulted in an expected achievement following organizational goals. This information system is built by modeling the system using UML. So that the system that was built can help the Class IIb Muara Sabak Penitentiary in determining the performance appraisal.
Model Sistem Informasi Pemesanan dan Produksi Berbasis Web Menggunakan Metode Agile

This research discusses the ordering and production application model developed with the php programming language. The application model is developed with an agile method approach that suits the circumstances that suit the needs of application development quickly but still meets the needs of application owners and users. Data collection was carried out using observation methods, interviews, and literature studies. Data collection is carried out to find out the problems that occur, while observation activities on the ongoing process and PIECES-based analysis aim to confirm the problems to be solved through application development. Application development is carried out through the stages of analysis, design, development, and testing. The design stages are carried out using the Unified Modeling Language (UML) approach. The development stage is done using the php programming language with the codeigniter 3 framework and the mysql database with visual studio code as the text editor. In the final part, testing activities are carried out with a black box testing approach. The test results show that the developed application is functioning properly and in accordance with the development goals. The application of ordering and production applications produced in this study plays a positive role in improving business process performance, and increasing the satisfaction of application users and enterprise customers.
THE OVERSEAS DISSEMINATION AND RECEPTION OF THE TRANSLATION OF THE QUOTATIONS OF MAO ZEDONG: BASED ON AMAZON READER REVIEWS

For a long time, as the main object of foreign translation and dissemination of Chinese books, The Quotations of Mao Zedong has had a remarkable impact on China and the world. The comments and evaluations of foreign readers are an important aspect to study and measure the effectiveness of the work’s foreign translation and distribution. In this paper, with the help of data crawler tool and text mining tool ROST CM6, a quantitative and qualitative study on the reader reviews of the translation of The Quotations of Mao Zedong on Amazon platform is conducted. The trends of star ratings, emotional attitudes and number of reviews show that most readers have positive attitudes toward it, and give it positive evaluations. Readers’ motivation for reading is based not only on circulation but also on personal interests such as historical, philosophical or study needs; at the same time, its political ideology is another important reason. Although there are flaws in the quality of book design and the language quality of the translation, the overseas dissemination of the translation of The Quotations of Mao Zedong is on the rise and the overall acceptance is good.
Fraud Detection in Supply Chain 4.0: A Machine Learning Model

The fourth industrial revolution has begun with the introduction of artificial intelligence into the manufacturing environment. As a result, flow management have accelerated and worldwide Supply chain have become more automated than ever. In this context, Supply Chain financial risks have increased which requires companies to be more vigilant about fraud detection in their interactions with the different stakeholders. In this paper, we establish a machine learning model to predict fraudulent operations between the different links of Supply Chain. Three models are deployed to that purpose: Random forest, K-Nearest Neighbor and logistic regression. The most optimal model is then upgraded by using grid search cross validation. Results shows that the forecasting model is more efficient when cross validation is employed with a score of 97,7%.
A Study on the relationship between SSCM, SCRES and Corporate Performance : Focusing on Automotive Industry

This study investigates the impact of sustainable supply chain management on supply chain resilience and corporate performance in the automotive industry. Data was collected through a survey of primary industry suppliers and analyzed using structural equation models. The findings suggest that sustainable supply chain management has a positive effect on supply chain resilience, which, in turn, positively affects both of SCM and ESG performance. This study contributes to the literature by providing empirical evidence of the relationship between sustainable supply chain management, supply chain resilience, and corporate performance in the context. The study also has practical implications for decision-makers in the automotive industry, suggesting effective ways to allocate resources and capabilities to enhance sustainability practices and supply chain resilience. The study highlights the importance of sustainable supply chain management in achieving corporate competitiveness and mitigating supply chain disruptions caused by unpredictable events.
A Critical Evaluation of Central Bank of Nigeria (CBN) Cash-less Policy, Currency Redesign, Financial Performance and Macroeconomy: The Socio- Economic Ramifications perspectives

The reform of the financial sector is a critical component for the process of economic growth and development.These views are abundantly emphasised in empirical literatures that relate financial sector expansion to economic growth.Recognising this fact, Nigerian economic managers have focussed on the banking sector as a route to impact economic policy changes and policy shifts.The most recent of these financial sectors economic policy was the formal introduction of cashless financial policy in January 2012.The advantages and disadvantages of the cashless policy with the Naira redesign strategy on financial performance and the Nigerian macroeconomy were analysed in this research.This research study employed a systematic literature review (SLR) to examine this.A SLR was performed using a sample of 100 research articles chosen from a pool of 400 papers obtained from Scopus, Web of Science, Google Scholar, ABS journals and, among other sources.As a result, the cashless policy provides enormous advantages to the financial industry.It was suggested that sufficient infrastructural and legal framework should be enhanced to assist in facilitating the effective implementation of the policy.
Study on Students' Learning Ability from the Design and Analysis of Primary English Test Paper

This paper mainly focuses on the design and analysis of English test papers which can better understand primary students' level of learning ability and test quality to promote students’ English learning and teachers' teaching quality. TOS (Table of Specifications) and the theory of Bloom's Taxonomy of Educational Objectives are the guidance to the design of the test paper. For the test analysis part, Winsteps Rasch Model is applied to analyze the reliability of items and students, difficulty factor, and level of discrimination. As a result, it can be employed to precisely identify the questions and students based on their level of achievement, revealing the actual level of the learners' ability regardless of the small number of samples.
Research on Network-based College English Teaching Modes

The network information technology has exerted a great influence on the traditional college English teaching mode.With powerful functions of information storage, search, clarification, communication and interaction, Interne enriches the teaching contents and methods, optimizing the students' knowledge structure system, improving the cross-language and cross-cultural competence of students which greatly expands and deepens the connotation of college English teaching.
Code Generation from Natural Language Using Two-Way Pre-Training

Code generation aims to generate code from natural language (NL) descriptions. A known bottleneck is the lack of parallel data since manual annotation is costly. Previous researchers have compensated for this by designing special model structures to introduce prior knowledge. But this approach complicates the model, making it more difficult to use and maintain. In fact, we can improve the model performance by exploiting the available data without the complicated design of the model structure. In this paper, we adopt the idea of continual pre-training, using a two-way pre-training approach to improve the model performance. This approach does not require any task-related model modifications. On the CoNaLa dataset, our method achieves a BLEU score of 33.30, which is a 2.59 improvement compared to the baseline. Our result exceeds SOTA 0.73 for the same period of doing the experiment. Finally, we conduct case studies to demonstrate the code generation capability of our two-way pre-training method.
Smart Mail Access SMA-01

Nowadays security issues concern also mail correspondence, therefore we consider mandatory before open or even get closer to a letter or a postal collet to have information about the type of correspondence, information about the sender (name, address) and the contents of the envelope or postal parcel in order to minimize the danger of sending explosive or toxic materials. This paper describes the structure of such automat system we designed, built at the prototype level and we brevetted it, i.e. Smart Mail Access=01, (SMA-01) which can detect unwanted and possible dangerous correspondence. Experiments we made on different type of letter of boxes proved to be a success. Further developments of SMA-01 system are mentioned in the last chapter of this article.
Face Recognition Based Attendance System

Face recognition, fingerprint recognition, and other biometric authentication methods are gradually becoming the most promising means for identifying a person's face. We can use them in our phones, laptops, offices, and other devices. However, pupils' attendance is still taken manually, which takes a long time and is extremely inefficient. To preserve that valuable time, we needed an automated operating attendance system that eliminated all of the drawbacks of collecting attendance on paper. We're putting together a system that's both efficient and dependable. Using OpenCV, we created a face recognition-based attendance system. We can apply our attendance system anywhere, not just for students. For face detecting and recognizing, the Haar Cascade algorithm is utilized.
Collision Avoidance and Give Way of Heterogeneous and Variable-Sized Multiple Mobile Robots Based on Glued Nodes

Multiple mobile robot systems (MMRSs) play an important role in workshops, storage and other scenarios. A complex MMRS can contain heterogeneous robots, and even the robot sizes are not fixed, making it more difficult to avoid collisions. As a widely studied method, the zone-controlled method is not accurate enough for a system with heterogeneous and variable-sized robots, and it increases the difficulty of applying MMRS. Based on the concept of glued nodes, this paper proposes a more accurate calculation method than the zone-controlled method to avoid collisions among heterogeneous and variable-sized robots. In order to improve the efficiency of calculating glued nodes, this paper proposes a time-to-space method, which has a constant complexity after the system runs long enough. In addition to collision avoidance, this paper also studies the problem of giving way of idle robots. This paper proposes a method which can detect those idle robots blocking the working robots and plan paths for the idle robots to find avoidance nodes to give way to the working robots. Simulation experiments are carried out based on a real automated production line scenario, and the experimental results prove the effectiveness and efficiency of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In a complex MMRS, the size of different robots can be different, and even the size of a robot is different when loaded and unloaded. One of the motivations of this paper is to avoid collisions in such a complex MMRS. A collision avoidance method is proposed to avoid collisions among robots by allocating nodes in real time. At the same time, this paper also addresses the problem of idle robots blocking working robots. If an idle robot blocks some working robots, an avoidance node will be found for the idle robot to give way to the working robots. The methods proposed in this paper can be applied to the roadmap of any structure. This means that in a scenario, regardless of the structure of roadmap and the different sizes of robots, the methods can be applied directly. The methods have been applied to many practical industrial projects in warehousing, manufacturing and other scenarios, and greatly reduces the difficulty of applying MMRS. In future research, there is a need to improve the efficiency of the methods so that they can be applied to a large-scale system.
Autonomous cyber warfare agents: dynamic reinforcement learning for defensive cyber operations

In this work, we aim to develop novel cybersecurity playbooks by exploiting dynamic reinforcement learning (RL) methods to close holes in the attack surface left open by the traditional signature-based approach to Defensive Cyber Operations (DCO). A useful first proof-of-concept is provided by the problem of training a scanning defense agent using RL; as a first line of defense, it is important to protect sensitive networks from network mapping tools. To address this challenge, we developed a hierarchical, Monte Carlo-based RL framework for the training of an autonomous agent which detects and reports the presence of Nmap scans in near real-time, efficiently and with near-perfect accuracy. Our algorithm is powered by a reduction of the state space given by a transformer, CLAPBAC, an anomaly detection tool which applies natural language processing to cybersecurity in a manner consistent with state-of-the-art. In a realistic scenario emulated in CyberVAN, our approach generates optimized playbooks for effective defense against malicious insiders inappropriately probing sensitive networks.
Command and control with poisoned temporal batch data

Data manipulation could alter the performance of joint all domain command and-control decisions (JADC2). We present a Bayesian decision theoretic approach for adversarial forecasting when the underlying data collected over time is subject to attack from intelligent adversaries. Proposed adversarial risk analysis-based framework allows incomplete information and uncertainty. We solve the adversary’s poisoning decision problem where he manipulates batch data being inputted into the forecasting method of statistical autoregressive models. The findings show the vulnerability of forecasting models under adversarial activities. We discuss potential defender strategies.
Cognitive Computing with a Big Data System in a Secure Internet of Things

This editorial aims to summarize the contents of the ten papers included in the Special Issue entitled “Cognitive Computing with a Big Data System in a Secure Internet of Things” [...]
Convolutional Neural Networks for Multiclass Classification of Masks

Recently, the COVID-19 pandemic has caused many human infections and spread the virus around the world. Research has shown that wearing masks goes a long way toward preventing the spread of infections, so correct use is very important. Today, wearing masks in public places is a common practice, but the virus will continue to spread if not used correctly. This work aims to correctly detect and classify the use of masks, for which the use of cumulative neural networks has been proposed. Deep learning is most effective at detecting whether a person is wearing a mask correctly. The model is trained with the MaskedFace-Net dataset and evaluated with different images of it. We use the Caffe model for face detection and preprocess the images to extract features. These images are entered into a convolutional neural network, where they are classified into the use of masks, non-use of masks or incorrect use of masks. The proposed model achieved an accuracy of 99.6865% on the test data improving the percentage compared to other authors.