Reviewerly/exHarmony huggingface dataset:
NO DATA NO PROBLEM GIVING NUCLEAR INSPECTORS BETTER TOOLS WITHOUT REVEALING STATE SECRETS.

to details that until recently were left to common sense and common practice. In this role, he is as concerned with the avoidance as with the resolution of disputes. An important part of his task is to move participants from an adversarial to a collaborative attitude and behavior. Environmental-energy disputes are too large and complex for currently available dispute settlement procedures. New tools for resolving these disputes are being developed. Environmental professionals must learn to be process managers so they can match these tools to the many different kinds of disputes that arise during the decision-making process. An environmental-energy model developed by the US Fish and Wildlife Service (FWS) is described, and its potential for generating tradeoffs among conflicting power-plant-siting objectives is explained. Computer-Assisted negotiations (CAN) developed by the AAA are introduced as a method for reaching compromise. A research team of the American Arbitration Association and the FWS is now experimenting with ways to use CAN and the multi-objective model as tools for mediation and collaborative problem solving. The early experience of this research is described and analyzed.
Possible variant of the confidential data leak detection model

Abstract The paper discusses a model for warning about leaks of electronic documents, describes a possible structure of a system for monitoring document leaks. Within the framework of the paper, the model is verified by evidence and formal methods.
Arabic text summarization approaches: A comparison study

Text summarization is considered one of the essential parts of the NLP area, as it gets attention since the ’50s of the last century. Although it has evolved rapidly in the last decades for Latin languages, Arabic text summarization is still a green area for researchers. Many algorithms can be used to generate Arabic text summarization. The analysis shows that the best algorithm for single document summarization in the Arabic language is Arabic summarization using the clustering technique with word rooting capability. The unique algorithm for multi-document summarization is Text Summarization using the Centrality Concept. A detailed literature review covers Text summarization in general and Arabic text summarization in specific and its challenges.
Context attention network for occluded pedestrian detection

Pedestrian detection in occluded scenes has always been a thorny problem in computer vision.In this case, due to the large difference in scale of occluded pedestrians and low visibility, it usually brings great challenges to detection.To solve this problem, this paper proposes a model structure for pedestrian occlusion detection, which improves the pedestrian detection method based on anchor-free.Specifically, we introduce a structure for extracting multi-scale context information to learn a better feature representation, and a channel attention module on each decoder layer to provide global context as a guidance of low-level features to select category localization details.Experimental results show that this method achieves 41.93% of MR -2 on the occlusion subset of Caltech pedestrian dataset, which is better than other contrast detectors.
A small series of serendipity Balancing serendipity in the algorithmic recommendation design of video-on-demand layouts based on consumer characteristics

Recommendation systems are a common tool to guide consumers through information andproduct overload in online environments. The evolving market of video-on-demand (VOD) embracedthe prospect of recommendation systems and is continuously searching to enhance its performance.The implementation of serendipity in recommendation systems is increasingly linked as a solution forthe issue of filter bubbles and to assess and evaluate user satisfaction within VOD platforms.However, the concept of serendipity introduces complexity in analyzing its implementation, due to itssubjective essence and absence of an academic definition and measurement. This thesis provides aliterary foundation on recommendation systems, consideration of particular consumer characteristicsin user profiles, and a definition and measurement of serendipity. Therefore, this thesis’s objective isto establish the role and level of serendipity in VOD environments for consumers that presentparticular characteristics by following the research question: To what extent do users perceive and areaffected by serendipity in VOD layouts?.Similar to correlated literature, a quantitative approach is employed with data collectingthrough the distribution of a survey that includes a quasi-experiment. The data is gathered amongstVOD consumers that possess a user profile without the interference of others. Statistical analysis isperformed with the help of SPSS and found that two serendipity items, instead of three, are applicablein the research design. The serendipity elements of novelty and unexpectedness are combined, whilerelevance is separately considered.Concluded from the insignificant paths between consumer characteristics and both serendipityelements, the findings of this thesis indicate the inability of consumers to perceive serendipity in theirpersonalized VOD environment and a 100% serendipity stimulus. The main results indicate that theserendipity component of relevance records the most substantial mediating effect on the performanceof the recommendation systems, measured by means of user satisfaction. To known knowledge, thisthesis is the second attempt that considers the need for serendipity for specific consumercharacteristics in VOD environments. By including the presented consumer characteristics in userprofiles, the coping ability and need for serendipity are reflected in the algorithmic design and,therefore, the personalized VOD interface. The implementation of serendipity based on consumercharacteristics helps consumers to broaden their preferences and VOD companies to increasingly setfoot in the evolving market.
Interpretable Option Discovery Using Deep Q-Learning and Variational Autoencoders

Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free RL algorithms suffer from low sample efficiency and inadequate generalization for sparse state spaces. The options framework with temporal abstractions [18] is perhaps the most promising method to solve these problems, but it still has noticeable shortcomings. It only guarantees local convergence, and it is challenging to automate initiation and termination conditions, which in practice are commonly hand-crafted. Our proposal, the Deep Variational Q-Network (DVQN), combines deep generative- and reinforcement learning. The algorithm finds good policies from a Gaussian distributed latent-space, which is especially useful for defining options. The DVQN algorithm uses MSE with KL-divergence as regularization, combined with traditional Q-Learning updates. The algorithm learns a latent-space that represents good policies with state clusters for options. We show that the DVQN algorithm is a promising approach for identifying initiation and termination conditions for option-based reinforcement learning. Experiments show that the DVQN algorithm, with automatic initiation and termination, has comparable performance to Rainbow and can maintain stability when trained for extended periods after convergence.
A Survey Report on the Improvement of Pragmatic Competence Under the Circumstances of Smart Learning Environment

This study adopts the method of questionnaire to research the improvement of college students' English pragmatic competence on the effect of online course learning platforms and APPs and so on.The results show that college students are more inclined to use APPs for English learning, and their English pragmatic competence has been significantly improved after using them, but the roles of online course platforms has been ignored in the learning process to some extent.Therefore, this study puts forward the suggestions from the aspects of the combination of course learning online platforms and APPs, highlighting the usage of hardware resources and English learning strategies.
Evolution of robotic technology in the medical care system and corona virus risk administration

This paper depicts the importance and growth of advance robotic technology in the medical care system, rise of medical robotic market with the administration and control of the spread of the novel coronavirus disease in global. In the midst of extraordinary strain on the medical care framework, for example, during the COVID pandemic, usage of mechanical frameworks can essentially decrease the danger of irresistible ailment transmission to cutting edge medical care labourers by causing it conceivable to emergency, to assess, screen, and treat patients from a sheltered separation. The expectation of the current exploration is to feature the significance of clinical robotic technology as a rule and afterward to associate its use with the viewpoint of COVID-19 administration so the medical clinic the executives can guide themselves to amplify the utilization of clinical robots for different clinical methods. This is regardless of the ubiquity of telemedicine, which is additionally eﬀective in comparable circumstances.
LibGuides: Diversity, Equity, and Inclusion: STEM

Diversity and Inclusion resources available via University of South Florida Libraries and beyond
COOPERATIVE LEARNING STRATEGY IN CRITICAL READING ENGLISH TEXT THROUGH TEAM GAME TOURNAMENT (TGT) AND JENGA

The rapid development of information has the potential to spread negative content such as hoax news, hate speech, or issues of radicalism and intolerance. In this case, critical reading skills are needed. By reading critically, readers can become more objective, able to sort and not easily swayed. However, the learning implementation of critical reading is not easy, especially coupled with the students' low reading interest and ability, especially towards English reading texts. This narrative review aims to examine cooperative learning strategies in critical reading of English texts through Team Game Tournament (TGT) and Jenga. The method used is literature study by examining various sources from journal articles in various databases, proceedings, and official websites on critical reading, cooperative learning, TGT, and Jenga games. The selected data is then reduced, classified, analyzed, and concluded. The results of literature search show that in critical reading students not only understand the content of the text but also analyze and evaluate the text they read. In this case, cooperative learning facilitates students to critically exchange ideas about the insights, ideas, and points of view they get from the text. TGT, as a form of cooperative technique, has advantages of making students work together to try to improve critical reading scores quickly and accurately in groups. In practice, Jenga which is believed to have the superiority of cooperative, communicative, and effective aspects can be used by modifying and adding questions related to critical reading so that students try to understand and analyze the text accurately. Seeing the advantages of TGT as a cooperative learning strategy and the benefits of the Jenga in critical reading, the integration between the two can facilitate three important aspects, namely critical, fast and accurate so that it has the potential to develop students' critical reading skill in reading English text.Keywords: critical reading, cooperative learning, team game tournament (TGT), jenga
LibGuides: Test Preparation Materials: Science

A guide to the test preparation materials available in the OHS Library, Frederick County Public Libraries, and online.
Human being emotion in cognitive intelligent robotic control. Pt.I: Quantum/ soft computing approach

The article consists of two parts. Part I shows the possibility of quantum / soft computing optimizers of knowledge bases (QSCOptKB™) as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface. In particular case, the aim of this part is to demonstrate the possibility of classifying the mental states in on line of a human being operator with knowledge extraction from electroencephalograms based on SCOptKB™ and QCOptKB™ sophisticated toolkit. Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described. The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown. Developed information technology examined with special (difficult in diagnostic practice) examples emotion state estimation of autism children (ASD) and dementia and background of the knowledge bases design for intelligent robot of service use is it. Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.
Research Guides: Learning Tools - Humanities: Learning Commons Chat

Learning Tools for student study help in HUM courses. Instructors that are interested in improving this page should consider applying for the Faculty Fellowship.
Towards Predicting Software Defects with Clustering Techniques

The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analyzed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) self-organizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.
COMMUNITY POLICING IN NIGERIA: IMPLICATIONS FOR NATIONAL PEACE AND SECURITY

The provision of security is viewed as the most fundamental obligation of any nation. States that enjoy the legitimate use of force in the protection of its citizenry has its people and the nation at heart. But this scenario is yet to be pictured in Nigeria as presently the nation is grappling with insecurity such that it has become a big headed monster that the government is finding too difficult to defeat. Ranging from insurgency, banditry, farmer-herder clashes to the current spade of kidnap, Nigeria is on a daily basis confronted with security threats that the security apparatus established for the main purpose of internal security is unable to cope with the situation. Though huge resources have been allocated and spent towards ensuring national peace and security, relative gains in this regard is yet to be recorded. As part of government’s efforts to ensuring the security of lives and properties of Nigerians, the Police Force was reformed and community policing was adopted to allow for a symbiotic relationship between the police and members of the society so as to enhance peace and security. However, this strategy is yet to produce results. Using a systematic review of literature it was observed the community policing in Nigeria has only been adopted in principle as full implementation of its tenets is yet to be practiced. Secondly, the challenges that confront the formal security framework of the country are yet to be address such that the required level of trust and partnership between members of the society and the police is to be achieved. The study, therefore recommend that if peace and security is to be guaranteed in Nigeria, a community policing devoid of politics and corrupt practices must be in place. This will help build the confidence level required for information sharing and crime fighting.
Embedded image coding using laplace transform for Turkish letters.

In this paper, a different cryptographic method is introduced by using a Power series transform. A new algorithm for cryptography is produced. The extended Laplace transform of the exponential function is used to encode an explicit text. The key is generated by applying the modular arithmetic rules to the coefficients obtained in the transformation. Here, ASCII codes used to hide the mathematically generated keys to strengthen the encryption. Text steganography is used to make it difficult to break the password. The made encryption is reinforced by image steganography. To hide the presence of the cipher text, it is embedded in another open text with a stenography method. Later, this text is buried in an image. For decryption, it is seen that the inverse of the Power series transform can be used for decryption easily. Experimental results are obtained by making a simulation of the proposed method. As a result, it is stated that the proposed method can be used in crypto machines.
A Study on the Farmers’ Perception on the Sugarcane Expert System based Mobile Application

The agglomeration of expert system into mobile app paves the way for new era in transfer of technology in agriculture. The study is focused on farmer’s perception on the Android App based Expert system in Sugarcane. Data were collected using a well-structured interview schedule and analyzed through percentage analysis. The results revealed that farmers had very little awareness about the android mobile application in agriculture. The perception of the farmers about the functioning of TNAU android expert system application in sugarcane was highly satisfied. Adequate extension efforts are required to be taken to popularize this App. among sugarcane growers.
Pengidentifikasian Citra Ikan Berformalin Dengan Menggunakan Metode Multilayer Perceptron

The richness of Indonesia's natural resources in the marine area, makes the sea an ecosystem of the existing diversity of fish. Fish is one of the types of animal protein that can be consumed by humans. Fish also contains essential vitamins and amino acids needed by the body with a biological value of up to 90% with binding tissue that makes it easier for the body to digest them. With the large number of fish that fishermen get per day, fish traders also have to make the fish they sell durable, one of which is by preserving fish with formaldehyde. Formlain is also a dangerous substance if used for food, this is because this substance can cause death if consumed long term. So that the existing problems encourage the author to identify formalin fish images using the MLP (Multilayer Perceptron) method which is a fairly reliable method in the image detection process because the search process is very directional (paying attention to backpropagation) where the feature extraction used is GLCM ( Gray Level Co-Occurrence Matrix). From this study, it was found that the Accuracy value was 62%. Where the error rate is 50%. Recall is 85%, application is 39%, precisson is 58% and F1 score is 71%.
Syllabification of Balinese Words Using the Syllabification Algorithm

Abstract&#x0D; This research discusses the syllabification of Balinese words. The syllabification of Balinese words is necessary in determining padalingsa in pupuh. In order to be able to develop the application of technology in the field of pupuh literary, a system that can automatically find the correct division of a Balinese word into syllabels is required. Several studies have been conducted on the syllabification Spanish and Sinhala language using rule-based approach. Both of the studies achieved good accuracy score. Thus, in this research we apply the rule-based syllabification approach on Balinese words. The data used in this study were 257 Balinese words in which the system managed to correctly divide the word as many as 244 and obtained an accuracy score of 94.94%.&#x0D; Keywords: Syllabification Algorithm, Word, Balinese, Word Syllabification, Rule-based
MEKANISME DIGITALISASI TERHADAP KOLEKSI LANGKA DI UPT PERPUSTAKAAN PROKLAMATOR BUNG KARNO BLITAR

The proclamator Bung Karno Library has many important collection of the history of  Soekarno. In order to maintain and preserve collections of historical value. The purpose of this research to provide an overview of the process of digitizing and distributing the results of digitization. This research is a qualitative study with a descriptive approach. Data collection was carried out in three ways, namely participant observation, interviews, and documentation. The results showed that the digitization process consisted of taking photos of rare collections, editing photos using the Photoshop, entering photos into the flipbook maker, and entering the digitizing files into a CD. The distribution process is done by uploading it to the library website and serving in audio visual services. With the digitization of rare collections, the Bung Karno Library has contributed to preserving rare collections that cannot be served conventionally.
Knowledge, Attitude, and Preventive Behaviors Regarding Emerging Respiratory Infectious Disease (H1N1, MERS, COVID-19) among Field-Grade Officers in the Republic of Korea Army

Purpose: The purpose of this study was to identify the relationships between knowledge, attitude, and preventive behaviors on emerging respiratory infectious disease among field-grade officers in Republic of Korea Army. Methods: This cross-sectional descriptive study included 194 participants who currently serving as field-grade officers. Data were collected by structured questionnaires and were analyzed through descriptive statistics, independent t-test, analysis of variance, and Pearson's correlation coefficient. Results: Knowledge, attitude, and preventive behaviors score related to emerging respiratory infectious disease were 8.42±1.25 points out of 11 points, 60.78±4.63 points out of 65 points, and 54.25±5.29 points out of 60points, respectively. There were strong positive correlations between knowledge, attitude, and preventive behaviors, despite no correlation between knowledge and attitude. Conclusion: Results of this study provided basic data for establishing health policy for the prevention and management of military infectious disease. To promote emerging respiratory infectious disease preventive behaviors, various education and methods should be considered to improve the knowledge of field-grade officers and change their attitude.