Reviewerly/exHarmony huggingface dataset:
Case–Study as One of the Innovative Educational Technologies and Its Use in Biology Classes

Today, the Ukrainian education system is undergoing important large-scale transformations. Students of modern schools are required, as the main result of education, to master a set of universal learning activities that allow them to set and solve the most important life tasks that they may face directly in adult life. So, for example, in relation to different sections of biology, students must understand: what the subject teaches them, why they need biology, how the studied concepts are related to practical tasks and everyday life.&#x0D; In this regard, a new approach to the construction of the content of education, as well as to the use of modern educational technologies of learning, is envisaged. As you know, one of such effective technologies is life-situational learning using the case-study method. Active use of case-study in education began relatively recently, and now this approach has become one of the most effective learning technologies. This method confirms the didactic significance and relevance of the use of case technology in the process of teaching biology as an effective means of increasing students' biological knowledge. Teachers highlight a number of advantages of the case-study compared to traditional teaching methods: practical orientation, interactive format, development of soft skills of students. Scientists have developed several classifications of cases. But, despite the variety of cases, they all have a typical structure, which includes: a plot part, a methodological part, and an informational part.&#x0D; The article examines the features of the case-study and the possibilities of its application at biology lessons. Samples of cases from the "Human Anatomy" course are also offered.
A Study on National Defense Policy during the Chun Doo-hwan administration

This research aims to analyze the national defense policy during the Chun Doo-hwan administration in the 1980s. In particular, it deals with the decision-making process with significant elements to affect the policy. One of the main characteristics of the Chun administration's national defense policy was the ally-centered character. The Chun administration focused on the ally, the United States of America, to promote the regime legitimacy with support from the US. In the early 1980s, the end of the detent period causes other conflicts between the US and the Soviets. America needed to strengthen its relationships with its allies in Northeast Asia, South Korea, and Japan in such a situation. In addition, the Chun administration's national defense policy contains individual beliefs. Then-President Chun believed that importing cutting-edge weapons from America has more effective ways than self-development in Korea to narrow the military gap between North and South Korea. Such factors worked in complicated ways to lead the national defense policy of the Chun administration to turn to the ally-centered one.
High Utility Periodic Frequent Pattern Mining in Multiple Sequences

Periodic pattern mining has become a popular research subject in recent years; this approach involves the discovery of frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic pattern mining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodic patterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequences is more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences is important. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To address existing problems, three new measures are defined: the utility, high support, and high-utility period sequence ratios. Further, a new upper bound, , and two new pruning properties were proposed. MHUPFPS uses a newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improve the overall performance of the algorithm. Furthermore, the proposed algorithm is evaluated using several datasets. The experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utility periodic frequent patterns.
The role of the central bank in mitigating COVID-19 effects in Sierra Leone

The Covid-19 pandemic impacted the economy of Sierra Leone both directly and indirectly. The direct impacts were especially drawn from reduction and cancellation of flights and imposition of travel bans to and from Sierra Leone for an economy that was heavily nested in global trade and in the middle of managing stagflation. Informed by its experience in the fight against the Ebola Virus Disease of 2013-16 in the Mano River Union in West Africa, the Government of Sierra Leone swiftly took steps to mitigate the effects of COVID-19 before the country could record its first Index Case on 30th March 2020. The Central Bank of Sierra Leone was at the centre of the state’s proactive preparedness, given its epidemiological history of recent episodes. To this end, this chapter is aimed at bringing out key policy measures the Bank instituted to mitigate the effects of the pandemic on the country’s economy. The approach to the chapter is based on review of national and sectoral response studies, policies, and strategies, including the Bank’s strategic documents. Comparisons of the Sierra Leone’s monetary and financial policy response measures with other countries within the Economic Community of West African States (ECOWAS) were extensively undertaken in the context of addressing COVID challenges, on the one hand. While comparisons were also made between COVID response measures in Sierra Leone (and West Africa in general) and those undertaken in Western countries during economic crises dating since the 1930s Great Depression till the current pandemic. In Sierra Leone, during the COVID-19-episode, the Central Bank’s response was conducted within the government’s comprehensive Quick Action Economic Response Programme and its Health Preparedness and Response Plan. In order to support the buoying of the economy and protect household welfare during these trying times, the Bank reduced the monetary policy rate; created Special Credit Facility to finance the production, procurement and distribution of essential goods and services; provided foreign currency support to facilitate the importation of essential good; provided liquidity support to the banking sector; purchased government securities in the secondary market; supported Micro Finance Institutions; and created an Agriculture Credit Facility. These measures compare very well with those instituted in other states in West Africa and those implemented across the globe in economic crises preceding COVID-19. While the chapter concludes that the Central Bank of Sierra Leone’s measures have been laudable in cushioning the pandemic effects on the economy, building on the experience of the country in the fight against Ebola, the extent of effectiveness of these measures would require in-depth future studies for building better resilience and to inform a more robust response to future emergencies.
Time-Synchronized Projector–Camera System Design and Underwater Sharpening Image Acquisition Experiment

Image measurement technology for structures on the seafloor and underwater is one of the most important underwater survey methods. The time-synchronized projector–camera (SyncPro-Cam) technology is employed to obtain clear images in turbid water environments in contrast to hazy images caused by light scattering and captured by standard cameras. In this study, we designed and built a SyncProCam prototype and verified its feasibility in acquiring clear images in turbid water environments. In the measurement principle of SyncProCam, the measurement range is determined by the trigger delay signal, camera scanning speed, and baseline length between the camera and the projector. Therefore, the prototype was designed such that these parameters can be controlled by external signals. The imaging experiments were conducted in turbid water environments with two different turbidity levels. Under these, conditions the output images from the prototype underwater SyncProCam system showed a very high signal-to-noise ratio compared to those captured by standard cameras.
An Adaptive ABC Variant For Numerical Optimization

It is known that the ABC algorithm has a slow convergence rate, this could be attributed to the fact that new solutions are generated by updating only one optimization parameter. Therefore, updating more optimization parameters could enhance the optimization performances. However, increasing this number arbitrary could decrease the optimization performance, as updating only one optimization parameter could be more efficient for some cases. To this end, we are proposing a new ABC variant that can adaptively control the number of optimization parameters to update during the run.The performance of the proposed approach has been evaluated against five other ABC variants using 16 numerical benchmark functions of varying characteristics. The obtained results have demonstrated the efficiency of the proposed algorithm.
Capturing notable architectural decisions

The inherent complexity of a software system of any size requires intentional design of the architecture. Numerous tools have been developed so support recording architecture decisions, but generally architecture decisions remain inadequately documented. Architecture decisions are generally recorded as text, but the ideas do not lend themselves to textual representation. One obstacle to documenting architecture decisions is the inherent difficulty of capturing the decisions themselves. It can be difficult to remember to document a decision when it is made. We propose focusing on recording "Notable Decisions", those that require explicit documentation. We identified characteristics of such decisions. We conducted a limited controlled experiment to investigate the nature of notable decisions. Preliminary results indicate that most of the decisions made were obvious, but some were notable and needed explicit documentation, suggesting that focusing on documenting notable decisions may be useful.
Modernized Crowd Counting Strategies for Mass Gatherings-A Review.

Estimation of crowd size for large gatherings is an indispensable metric for event planners, local authorities, and emergency management. Currently, most crowd counting relies on dated methods such as people counters, entrance sensors, and ticket sales. Over the past decade, there has been rapid development in crowd counting techniques and related technology. Despite progress, theoretical advances in crowd counting technology have outpaced practical applications. The emergence of the vast array of crowd counting techniques has added to the challenge of determining those advances that can be most readily implemented. This article aims to provide an overview of promising crowd counting strategies and recent developments applied within the disaster medicine context along with the best use cases and limitations.
Robust weighted fusion Kalman estimators for systems with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and two‐step random measurement delays

Abstract For multisensor networked systems with uncertain noise variances, multiplicative noises and multiple networked‐induced uncertainties including missing measurements, packets dropouts and two‐step random measurement delays, the robust weighted fusion estimation problem is addressed in this article. More precisely, the system noise variances are assumed to be uncertain but bounded, the other four uncertainties are compensated into fictitious white noise by the proposed model transformation method, which includes the augmented method and extended fictitious noise technique. Then local multi‐model system is obtained, for which robust local Kalman estimator is obtained based on the minimax robust estimation principle and unified estimation method. Based on this, the six robust weighted fusion time‐varying Kalman estimators are presented in a unified form, which include robust weighted fusers weighted by matrices, diagonal, scalars, and a robust covariance intersection (CI) fuser and two fast CI (FCI) fusers. The robustness proving method, including the extended Lyapunov equation approach with two kinds of generalized Lyapunov equations, non‐negative matrix factorization and elementary transformation of matrix, is presented to prove that the actual estimation error variances are guaranteed to have minimal upper bounds for all admissible uncertainties. The accuracy relations are proved. Further, the robust local and fused steady‐state Kalman estimators are presented. Finally, a simulation example applied to Internet‐based three tank water system is given to demonstrate effectiveness of the proposed results.
Determining the Security Standards of Different Crypto Algorithms

The protection of digital information has become increasingly important as technology advances. Testing each cryptosystem individually can be time-consuming. To address this problem, a novel technique has been developed for identifying the most appropriate encryption algorithm for images by using a support vector machine. This study has developed a novel approach to evaluate the security level of encrypted images using a dataset with standardized criteria to perform encryption cryptography. These parameters, for instance, entropy, contrast, homogeneity, peak signal-to-noise ratio, mean square error, energy, and correlation serve as features for the dataset, which is categorized into three security levels: tolerable(acceptable), poor(weak), and powerful(strong). The proposed results indicate the effectiveness of our approach in determining the security level of encrypted images.
Implication and Advantages of Machine Learning-based Chatbots in Diverse Disciplines

Artificial Intelligence (AI) and Machine Learning (ML) are the techniques, which helps in enacting system with the human beings. Now a days chatbots are playing a vital role in changing the mode of conversation between humans and computer systems. The implementation of these chatbots helps to improve the conversation between computers and humans. Chatbots are the software programmed virtual machines which are intended to make ready for building conversation with the humans and to deploy this system the Artificial Intelligence (AI) techniques are used in all the message plans, these virtual machines are not only bounded for only one area but can be used in many of the areas like education sector, medical, social media, banking and finance etc. Chatbots are essential in educational institutes for handling administrative activities. Chatbots acts as a platform for solving queries of individual with ease. Natural Language input and the deep neural networks are the major tools, which are used in the development of these chatbots. These tools use a LSTM mechanism to understand the related user query. By using these techniques, the chatbots are trained and that helps in giving instantaneous answers for the user queries. All the data from the user will be collected and stored in the specified database and that data will be used for further references. This study depicts the importance of chatbots in various fields with their working principle using Machine Learning(ML) and Artificial Intelligence (AI)-based algorithms.
LKH Model based Enhancing Security in E - Auction System for Cloud Communication

The evolving e-commerce platforms open the way for a wide range of applications to migrate from an offline to an online system. E-Auction is a strong e-commerce solution that uses online platforms to link bids and sellers. Building a completely secure e-auction system that meets the security criteria of all stakeholders in these systems, on the other hand, demands exceedingly complex work in traditional architecture. The concept of cloud security refers to the methods, standards, and practices of protecting data in the cloud. One of the most significant obstacles for e-auction is a dearth of faith between many sellers, buyers, and auctioneers. Loyalty among auction players and a secure transaction atmosphere have been identified as important triggers for online transactions and a significant worry in e-auctions. The existing C-Ream method was introduced to provide auction participants with a secure and trustworthy transact setting. Unfortunately, this technique does not adequately handle privacy protection. The suggested study’s objective was to decrease security concerns in the eauction system. By using a tree-based key generation methodology, the Logic Key Hierarchy (LKH) model is used in this research to create a secure data-sharing mechanism in an e-auction for dynamic members. Using one-way hashing and reverse hashing, this key was generated. Several studies have demonstrated that this method maintains forward and backward compromise assaults most effectively and that it can generate keys in less time than any other hash-based method paradigm.
G.C.E Advanced Level Students Usage of the Learning Resource Center - A Review (Based on G.C.E Advanced Level students in the selected schools underthe SandilipayDivisional Education Office)

The main objective of the study is to explore how the G.C.E Advanced Level Learning Resource Center is utilized by the students and to identify the challenges that the students face when utilizing the Learning Resource Center. This study is a field study with quantitative and qualitative data. 205 students were selected on a random sampling method from 1AB and 1C schools in Sandilipay Divisional Education Office and all the teacher-librarians working in these schools were selected as samples for this study. The questionnaire, focal group discussion, and observation tools were instrumental to collect data from students, and personal interviews were also held to collect data from teacher librarians. Data were analyzed with the adaption of the MS Excel software. The results were derived from the percentage system and presented in the form of tables, diagrams etc. It was revealed that the dissatisfaction with the availability of information resources by students during the use of the learning resource center was 56.7% (n=97), lack of updated books was 20.5% (n=35), lack of opportunity to use the internet in the learning resource center was 70.8% (n=121), learning resource center building was used for some other purposes 35.1% (n=97), lending facilities to access only during the subject period of the learning resource center was 63.7% (n=109), lack of internet access was 81.9% (n=140), disorganized books was 25.7% (n=44), and over control in the learning resource center was 83% (n=142). These are the challenges faced by the students. It is recommended that action has to be taken to send the students regularly to the Learning Resource Center to develop the use of the Learning Resource Center. Besides, awareness is to be created for the students about the benefits and importance of the Learning Resource Center. Availability of Books and non-books materials for the Learning Resource center should also be ensured through social assistance. Further, using the learning resource center for some other purposes should be prohibited and training programmes.
Academic Library service model for e-learning environment: a case study

During the crisis time, teaching and learning activities were carried out remotely using digital teaching and learning apps/devices. In online learning, it is essential to identify the need to respond on time. Libraries are the primary provider of information resources/services to the academic community. In Sri Lankan academia, the time to adopt technologies has become mandatory due to safety concerns and the COVID-19 situation. In this research, one state university was chosen as a case study, and academic staff, students, and library staff were considered as the population. A stratified random sampling method was used to conduct the study. The data collection is done through the questionnaire. The primary framework was modified based on suggestions identified through data analysis. This study aimed to seek the current situation of the e-learning support of the University libraries and to identify other service opportunities in an e-learning environment. Research gaps and factors were identified because of the review process and findings. The findings were identified through major areas, such as e-learning, resources, learning services, ICT infrastructure facilities, Information professionals' roles, and skills and management policies. Library services can proactively organize according to user requirements. For this, library pre-planning is greatly important. The conceptual model presented in this paper was developed based on gaps identified through reviewing existing literature, research results, and user suggestions. Libraries that are conducting user surveys could consider the factors presented in this model.
Multi-level feature disentanglement network for cross-dataset face forgery detection

Synthesizing videos with forged faces is a fundamental yet important safety-critical task that has caused severe security issues in recent years. Although many existing face forgery detection methods have achieved superior performance on such synthetic videos, they are severely limited by the domain-specific training data and generally perform unsatisfied when transferred to the cross-dataset scenario due to the domain gaps. Based on this observation, in this paper, we propose a multi-level feature disentanglement network to be robust to this domain bias induced by the different types of fake artifacts in different datasets. Specifically, we first detect the face image and transform it into both color-aware and frequency-aware inputs for multi-modal contextual representation learning. Then, we introduce a novel feature disentangling module that mainly utilizes a pair of complementary attention maps, to disentangle the synthetic features into separate realistic features and the features of fake artifacts. Since the features of fake artifacts are indirectly obtained from the latent features instead of the dataset-specific distribution, our forgery detection model is robust to the dataset-specific domain gaps. By applying the disentangling module to multi-levels of the feature extraction network with multi-modal inputs, we can obtain more robust feature representations. In addition, a realistic-aware adversary loss and a domain-aware adversary loss are adopted to facilitate the network for better feature disentanglement and extraction. Extensive experiments on four datasets verify the generalization of our method and present the state-of-the-art performance.