Reviewerly/exHarmony huggingface dataset:
PRRGNVis: Multi-Level Visual Analysis of Comparison for Predicted Results of Recurrent Geometric Network

The structure of a protein determines its function, and the advancement of machine learning has led to the rapid development of protein structure prediction. Protein structure comparison is crucial for inferring the evolutionary relationship of proteins, drug discovery, and protein design. In this paper, we propose a multi-level visual analysis method to improve the protein structure comparison between predicted and actual structures. Our method takes the predicted results of the Recurrent Geometric Network (RGN) as the main research object and is mainly designed following three levels of protein structure visualization on RGN. Firstly, at the prediction accuracy level of the RGN, we use the Global Distance Test—Total Score (GDT_TS) as the evaluation standard, then compare it with distance-based root mean square deviation (dRMSD) and Template Modeling Score (TM-Score) to analyze the prediction characteristics of the RGN. Secondly, the distance deviation, torsion angle, and other attributes are used to analyze the difference between the predicted structure and the actual structure at the structural similarity level. Next, at the structural stability level, the Ramachandran Plot and PictorialBar combine to be improved to detect the quality of the predicted structure and analyze whether the amino acid residues conform to the theoretical configuration. Finally, we interactively analyze the characteristics of the RGN with the above visualization effects and give reasons and reasonable suggestions. By case studies, we demonstrate that our method is effective and can also be used to analyze other predictive network results.
Compiler-Aided Development of Trusted Enclaves with Rust

To optimally utilize Intel SGX, programs must be partitioned into trusted and untrusted parts. Writing the trusted part of a program with Intel’s SDK, however, requires manual effort that often becomes an obstacle for programmers. In this work, we investigate how compiler-level tooling can assist with the semi-automatic separation of code into a trusted and an untrusted partition. We present Cadote, a solution that generates SGX enclaves from programs written in Rust. Application developers are expected to mark functions as trusted, for which enclaves are then generated automatically. All other functions remain untrusted and are executed outside Intel SGX in the normal world. We implemented this concept using compiler optimization passes of the LLVM framework. Targeting Rust as input language allows us to benefit from high-level concepts, such as memory safety, which enable us to safely copy function parameters between the normal and trusted world in practice.
Secured Health Monitoring System Using AES

Wireless medical sensor network is used in healthcare applications that have the collections of biosensors connected to a human body or emergency care unit to monitor the patient's physiological vital status. The real-time medical data collected using wearable medical sensors are transmitted to a diagnostic centre. The data generated from the sensors are aggregated at this centre and transmitted further to the doctor's personal digital assistant for diagnosis. The unauthorised access of one's health data may lead to misuse and legal complications while unreliable data transmission or storage may lead to life threatening risk to patients. So, this system uses Advanced Encryption Standard (AES) algorithm to encrypt the data to make it secured transmission and access control system for medical sensor network. Further the data is sent to a centralised server through a wireless network. In this case this server can be a Personal Computer (PC) connected to the same network, for this transmission User Datagram Protocol (UDP) can be used. This data can be accessed at the receiver side only.
Motion Transfer &amp; Person Image Synthesis

Human motion transfer from a video to an image is one of the applications of GAN. This paper includes the techniques and GAN Models which transfers motion from a video to an image and produces digitally synthesized video of the person present in the image. This paper provides the insight of the Nested GAN model. It generates the human images along with the human features, pose, garments, etc., that is present in the wide source of images. The proposed method consists of two stages. First, the motion patterns are extracted from driving video sequences. Then image-to-image transformations are learned by maximizing the mutual information between the source image and the target image while simultaneously minimizing the distortion associated with the image-to-image transformations. Simulation results show that using our method reduces work factor values, and generates higher quality videos. Method discussed in this paper enables the generation of more realistic output images as well as provides the separated un-annotated attributes of the images. The suggested method along with some innovation outperforms the latest pose transfer and image synthesis models according to experimental results.
Across Cyberspace and Time Zones: How <i>Ways of Being</i> Explores Performances of Self in Material and Digital Spaces

After watching the same show over the course of three separate performances, Stephanie Fung and Jeff McGilton come to their own conclusions about Ways of Being, the final instalment in the Kingston-based Kick and Push Festival 2021. Presented live by Toronto-based Clayton Lee and Kraków-based Michael Rubenfeld, the performance was an investigation of connectivity across geographical space and time zones, using a participatory structure to subtly investigate our ways of being. The participatory nature of the piece informed its undeniable feeling of liveness, changing and developing with whoever was in attendance. Fung and McGilton consider how this work-in-progress invokes an understanding of presence, performances of self, and (re)connection through creative implementations of emerging technologies.
An Assessment of Game-Based Cyber Defense Strategy for SDN

The essence of attacks in a software defined network (SDN) always starts in a collusive manner and often targets OpenFlow due to fundamental issues prevailing in its architecture and due to cyber-criminal advancement. Attackers send client requests to a set of server port addresses on the host, a known weakness of OpenFlow infrastructure that is misused by exploiting an active port. In such instances, most of the conventional strategies that protect against such threats become ineffective because they function separately. There is a need for a constructive cyber security mechanism for leveraging SDN to avoid this; by using cyber deception, an attacker can be engaged strategically, and it is possible to manipulate his behaviors by establishing and improving his network view. Because game theory uses a quantitative model, the cyber decoy can be used to examine the impact that deception can have on the attacker’s beliefs. In this way, the sequence of an attack can be investigated, and how the values of the attacker evolve and impact his behavior can be determined.
Pengembangan Media Pembelajaran Berbasis Aplikasi Canva dengan Model Microblogging sebagai Pembelajaran Teks Prosedur Tingkat SMP

This research was conducted to give teachers a more interactive view of learning media to students, namely making a learning media product in the form of Canva with the concept of Microblogging, as well as an effort to develop Canva-based learning media with a renewable model with the Microblogging model used as language learning. procedure text at the junior high school level. The use of the Canva application in language learning is expected to increase students' understanding and enthusiasm in capturing the learning material presented. The development of Canva-based learning media with the Microblogging model is carried out in 4 stages, namely (1) the define stage; (2) the design stage (design); (3) the development stage (development); (4) and the disseminate stage. This research was conducted by taking samples from educators and students of classes VII A and VII B of SMP Negeri 1 Wlingi Blitar Regency by asking questions to one of the language educator teachers through the media zoom meeting and students via google form to find out what kind of learning media has been used. by the teacher at the school. The hope is that with the development of the Canva application with the Microblogging model, it can provide a solution to the conventional teaching and learning process of teachers to turn it into learning that is in harmony with current technological and information developments. So that students can be more interactive and active in paying attention to and receiving learning material delivered by the teacher.
Human-UAV Collaborative Task Scheduling for 360° Video Generating in Intelligent Transportation

With the rapid development of virtual reality technology, 360&#x00B0; video has been widely adopted in intelligent transportation, e.g., traffic monitoring, virtual driving, etc. Therefore, it is necessary to recruit workers and unmanned aerial vehicles (UAVs) to complete the task of video recording. However, we can not guarantee the quality of video recording by only employing workers or UAVs alone. Due to the real-time nature of traffic, task scheduling for 360 video generating is a major challenge in intelligent transportation. In this paper, we propose a human-UAV collaborative 360&#x00B0; video recording task scheduling framework from the perspective of crowdsensing. We build a point of interest (POI) model according to the characteristics of urban intelligent transportation to determine the locations of the video recording tasks and the number of camera stands. We introduce the relay UAV as the video streaming data transmission node to ensure the quality of video transmission and design the route planning algorithm of the relay UAV to guide the route planning of the camera UAV to ensure recording task synchronization. Meanwhile, workers are introduced into the route planning algorithm of camera UAVs to compete for tasks. The simulation experiments of the urban environment show that our proposed framework outperforms worker-mode and UAV-mode under the same environmental settings.
The <scp>AES</scp> prize essay award

Journal of Agricultural EconomicsVolume 73, Issue 2 p. 629-629 PRIZE ESSAY COMPETITION The AES prize essay award First published: 09 June 2022 https://doi.org/10.1111/1477-9552.12493Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume73, Issue2June 2022Pages 629-629 RelatedInformation
Using the Web of Science to Populate Faculty Articles in an Institutional Repository

Background: Faculty at Central Washington University (CWU) were not depositing and preserving their research articles in the University Institutional Repository (IR), so an alternative method to identify and include faculty scholarship in the IR was developed. Librarians used the Web of Science to discover articles published by the CWU faculty and then deposited them in the IR. Analysis: Thousands of articles written by CWU faculty were located and deposited. This project increased interaction with the IR from outside the library and the university beyond any expectations. Conclusion and implications: This was a successful project, but it required a useful interface to locate the metadata and librarians with highly technical skills.RésuméContexte: Les facultés à Central Washington University (CWU) ne déposaient ni ne preservaient leurs articles de recherche au dépôt institutionnel, alors une méthode alternative d’identifier et inclure la bourse de la faculté dans ce dernier a été développé. Les documentalistes utilisaient le Web of Science pour découvrir les articles publiés pa la faculté de CWU et ensuite les déposer au dépôt institutionnel. Analyse: Des milliers d’articles écrits par la faculté de CWU on été retrouvé et déposé. Ce projet a augmenté considérablement les interactions externes avec le dépôt institutionnel. Conclusion et implications: Ce projet fut un succès mais nécessiterait d’utiliser une interface permettant de localiser les metadata et les documentalistes grâce à de grandes compétences techniques.
Relationship Between Purpose, Types and Elements of Instruction and Assessment

Instruction and assessment are commonly used terms in teaching and learning processes. The meaning of these critical concepts in education have been studied and documented by various scholars overtime. Curriculum is a continuous reconstruction, moving from the learner’s present experience out into that represented by the organized bodies of truth that disciplines. Curriculum is also all the learning experiences planned and directed by the school to attain its education goals ,aims and objectives. All scholars point to the fact that curriculum consists of all that the school or college may select and consciously organize for the purpose of developing the personality of learners and for making behavior change in them. A curriculum purpose is achieved through instruction. Instruction is defined as a process of facilitating a learner to acquire knowledge, skills, attitudes and values through organized schedules. The purpose of instruction is evaluated through assessment. Assessment refers to a process by which information is obtained relative to some known objectives or goals. Assessments is a abroad term that includes testing. Assessments measure progress at the end of a programme. Assessment determine the extent to which a programme goal/goals have been achieved. The definitions of these educational terminologies denote a strong relationship instruction and assessment. The purpose of this paper is to analyze the relationship among these concepts with a view of enabling an effective alignment in teaching and learning process. The major relationship between instruction and assessment lie in purpose, types and elements.
Fraud likelihood determinants in the road toll collection system in Cameroon

The study set out to investigate fraud likelihood determinants in the road toll collection system in Cameroon.The specific objectives were to examine fraud likelihood determinants in the management of road toll ticket production, inventory of vehicles accessing tollgates, sale of road toll tickets, physical security of money collected at the tollgate, and physical security of money conveyed to the public treasury.The poor physical state of toll roads in Cameroon points to the degree to which toll collections are used in road maintenance.The roads are usually plagued with potholes and poor or no road signs leading to deadly road accidents.The researcher collected data through a questionnaire, and regressed using a summation model.The findings revealed that toll ticket production, ticket sales, and physical security of money at tollgates have crucial internal control weaknesses that create fraud opportunities.It was therefore recommended that manual tollgate systems be converted to electronic systems so as to cut down on tollgate traffic congestions and secure more road toll cash inflow for road maintenance.The second recommendation was that toll fee collections be visibly seen by tollgate actors to be used for road maintenance when they use good roads.In this way, their road ethics increases while road toll evasion reduces.The third recommendation was that government fight against corruption and fraud should moves from rhetoric to action.Lastly, monitoring and impromptu audits at tollgates be instituted and activated.
Video storytelling based on gated video memorability filtering

Video storytelling aims to generate a successive and succinct paragraph composed of multiple sentences to describe the visual contents in a long video. The major difficulty with video storytelling is the conflict between complex visual semantics and concise textual description. Existing methods address this problem by applying different segmentation strategies to long videos. However, these methods ignore that some story-irrelevant visual contents could be considered as noises, so visual information in a long video is not always conducive to story generation. Therefore, we propose a novel video storytelling model based on gated video memorability filtering (GVMF), which generates high-quality textual descriptions by filtering out clips with lower memorability scores, where objects and characters appear less frequently. The reason for introducing video memorability prediction is that video clips with higher video memorability scores tend to have more characters and objects so tend to be noticed by annotators for writing sentences. Experimental results demonstrate the effectiveness and competitiveness of our method, which achieves the new state-of-the-art results on Video Story dataset.
A knowledge base of public acts based on the grammar of the Official Gazette

Many sources and the growing volume of data lead to a need for new models of information management. One of the most important sources fonts are the records published in the Official Gazettes and their extraction, as well as their corresponding interpretation, represents an interesting opportunity to provide govern transparency. Usually, these Official Gazettes are available in a closed format, which makes them difficult to process and inspect. This paper presents a proposal for extracting these public data by regular expression techniques. As a result, it promotes public transparency and possibly aids government decision-making process by building a public knowledge base driven by the grammar and entities of the Official Gazettes. The scope was limited to publications involving personnel movements in the last 9 years. The result was an RDF knowledge base of public acts available for SPARQL queries.
Data literacy and technology impacting libraries

Purpose This paper aims to focus on data literacy and its use with technology in libraries. The trends in technology and data literacy emphasize making information accessible and easily usable for purposes of informed decision-making. Design/methodology/approach Systematic literature review is done, exploring blogs and wikis, to collect information on the ways in which data literacy is being and can be used in libraries. Findings It is found that the gap between the information user and the information provider is bridged by data literacy training. Also, that data literacy, technological savviness, people advocacy and business acumen are all essential in bringing about employee-centric workplace. Originality/value This study concludes that change management and data literacy in technology-based environments are necessary especially now, and in libraries of the future.
Blockchain-enabled Internet of Things (IoT) platforms for financial services

Blockchain technology is considered a breakthrough since the advent of Internet. This technology can be used financially for the Internet of Things (IoT)-based commerce platform building and marketing platform is a new business model based on the management of private transactions on IoT devices technologies that can manage a shared network using current blockchain solutions, most of which are digital currencies. The emerging popularity of digital currency makes them acceptable as one of the payment sources. The use of IoT-based blockchain technology such as social procurement, e-commerce blockchain applications and use cases describes various aspects of finance, including payments, security, supply chains, automation of work with smart contracts, and ethical practices for financial transaction transparency. With the rapid development of cryptography and the proliferation of computers systems, blockchain technology is widely expected to transform many industries with better clarity, higher security, and lower purchase costs. This chapter shall provide IoT-based blockchain technology use in ecommerce applications such as social shopping. The chapter shall provide the number of impacts of learning bodies, platform operators, and developers of blockchain technology in the finance field. The chapter discusses the use of IoT and blockchain technology in the finance industry. Blockchain applications and use cases are discussed for various aspects of finance like payment, security, supply chain, work automation with smart contract, ethical practices for transparency in financial transactions.
A comparison of distributed machine learning methods for the support of “many labs” collaborations in computational modeling of decision making

Deep learning models are powerful tools for representing the complex learning processes and decision-making strategies used by humans. Such neural network models make fewer assumptions about the underlying mechanisms thus providing experimental flexibility in terms of applicability. However, this comes at the cost of involving a larger number of parameters requiring significantly more data for effective learning. This presents practical challenges given that most cognitive experiments involve relatively small numbers of subjects. Laboratory collaborations are a natural way to increase overall dataset size. However, data sharing barriers between laboratories as necessitated by data protection regulations encourage the search for alternative methods to enable collaborative data science. Distributed learning, especially federated learning (FL), which supports the preservation of data privacy, is a promising method for addressing this issue. To verify the reliability and feasibility of applying FL to train neural networks models used in the characterization of decision making, we conducted experiments on a real-world, many-labs data pool including experiment data-sets from ten independent studies. The performance of single models trained on single laboratory data-sets was poor. This unsurprising finding supports the need for laboratory collaboration to train more reliable models. To that end we evaluated four collaborative approaches. The first approach represents conventional centralized learning (CL-based) and is the optimal approach but requires complete sharing of data which we wish to avoid. The results however establish a benchmark for the other three approaches, federated learning (FL-based), incremental learning (IL-based), and cyclic incremental learning (CIL-based). We evaluate these approaches in terms of prediction accuracy and capacity to characterize human decision-making strategies. The FL-based model achieves performance most comparable to that of the CL-based model. This indicates that FL has value in scaling data science methods to data collected in computational modeling contexts when data sharing is not convenient, practical or permissible.
Simulated trial and error experiments on productivity

Trial and error experiments in socioeconomics were proved to be beneficial by Nobel prize laureates. However, replication is challenging and costly in term of time and money. The approach required interventions on human society, and moral issues have to be carefully considered in research designs. This work tried to make the approach more feasible by developing virtual economic environment to allow simulated trial and error experiments to take place. This research demonstrated the framework using 19 macroeconomic indicators in 6 interested categories to study the effect on productivity if each indicator value grew by 5 percent for each of 65 countries. Seven predictive models including some machine learning (ML) models were compared. Neural network dominated in accurateness and was selected as the core of the simulator. Experimented results are in full of surprises, and the framework acted as expected to be a data-driven guide toward country-specific policy making.