Reviewerly/exHarmony huggingface dataset:
Literasi Mahasiswa: Studi Kebiasaan dan Sikap Membaca Mahasiswa Program Studi Ilmu Perpustakaan

Literacy is the ability of reading and also writing. In a broader sense, literacy deals with reading ability accompanied by positive habits and attitudes towards reading activity. This study aimed at eliciting information about students’ reading habits and attitudes.This literacy information was obtained from the fifth semester students of Library Science Study Program, Faculty of Humanities, State Islamic University Raden Fatah Palembang. The selection of 39 students who participated in this study used opportunity sampling technique. Easy accessibility, students’ willingness and availability were the considerations of using this sample selection technique in Covid-19 pandemic situation. The participants were also the students who joined in Intensive Reading Course in which the researcher taught them in the ongoing semester.Data were collected by using questionnaire survey sent via WhatsApp facility. The participants’ responses were sent back to Google Classroom platform. The results of the study showed that the majority of the students (95%) have reading likeness since they were in school ages, particularly in elementary school. Meantime, they also have positive attitudes (view and feeling) towards reading activity and its impact on increasing their literacy ability and academic performance.
Implementasi Kebijakan Pengembangan Koleksi Perpustakaan Perguruan Tinggi (Tinjauan Literature Review)

Collection development activities require a collection development policy.Without a collection development policy, like a business without a business plan.This study aims to see the implementation of collection development policies in college libraries.This research is a qualitative study with a literature review.The criteria are articles according to journal research objectives regarding collection development, displayed in full text and publication for the last 5 years.The search for articles was carried out on the Indonesia One Search (IOS) database via https://onesearch.id/, then the articles were checked according to the criteria.The examination process resulted in 5 articles.Data analysis with data extraction, analyzing data based on the author's name, title, objectives, research methods and grouping important data in articles.The results showed that the implementation of collection development encountered obstacles, including relevance, multiple information or overpublishing, bibliographic supervision, budget and gaps between selection, lack of communication, user analysis, absence of written policies, lack of role of authorized parties to select and lack of tools.help selection, and less trust in librarian.Libraries have not fully held full autonomy in library management, so the contextualization of collection development policies does not run optimally.Another finding is that user involvement in the formulation of collection development policies is very small.
Systematics for the individual assessment of augmented reality potentials to support product validation

By integrating multiple disciplines like mechatronics, electronics and software, products become ever complex.The number of product variants increases while the product life cycles are getting shorter.At the same time, individualization and customer orientation are intensified.Often products are specifically designed and produced for customers according to their individual needs and requirements.For multidisciplinary products, a broad spectrum of product features can be of importance to customers in this context.To be successful in the highly competitive markets, companies need to further reduce their development times while fulfilling the individual customer requirements.This demands a continuous and efficient validation during the development.But classic validation approaches like physical prototypes or digital simulations are either not cost-and time-efficient or do not support a subjective validation by the customer.Additionally, an increasing geographical separation and flexible working hours hamper the necessary collaboration of the developers and customers.The existing challenges can be addressed through the innovative visualization and communication technology Augmented Reality (AR).AR provides enormous potentials to support product validation.Products or product features can be validated with AR even across locations based on virtual prototypes.However, the basic complexity of AR in combination with a lack of technology expertise and experience prevents companies from identifying and exploiting the potentials.This work therefore presents a systematic approach for the individual assessment of AR potentials to support product validation.Based on individual requirements, the systematics empowers companies to analyze the potentials on their own and plan AR-based validation accordingly.Thus, the presented work enables the utilization of so far largely unexploited innovative technological potentials.
Mathematical Model Simulation of Detailed Classification of Telemedicine Sensing Data

Abstract Medical and health field is a hot application field of wireless sensor networks. How to correctly refine and classify telemedicine sensor data is the research focus in related fields. Therefore, a detailed classification mathematical model simulation of telemedicine sensor data based on multi feature fusion is proposed. On the basis of telemedicine sensor data acquisition, it is preprocessed to reduce the computational overhead of detailed classification. The reliability features of the preprocessed telemedicine sensing data are extracted, the extracted features are fused by the principal component analysis method, and the refined classification model of telemedicine sensing data is constructed based on the principle of machine learning. The fused features are input into the model to complete the refined classification of telemedicine sensing data. The experimental results show that the correct refinement classification rate of the proposed method is more than 90%, the refinement classification accuracy is higher than 98.5%, the convergence speed is good, and the refinement classification time is 4 ~ 12 s, which proves that the correct refinement classification rate and accuracy of the proposed method are high, the classification time is short, and has good application performance.
Improving the Understanding of Low Frequency Magnetic Field Exposure with Augmented Reality

Low frequency magnetic fields are often present in our everyday life due to the multitude of electronic devices. High magnetic fields can occur in the workplace from a wide variety of machines and systems, which must be measured and evaluated from the point of view of occupational safety. To facilitate the understanding of magnetic fields by supervisors and employees in the workplace, an augmented reality (AR) application was developed to visualize the measured flux densities and the resulting safety distances. The application was deployed on two smartphones, allowing for the simultaneous viewing of the same scene without the use of additional markers. Whether the application creates a better understanding of the exposure situation was evaluated with the help of an online survey. In this survey, participants received either a classic measurement report or a report enhanced by augmented images. The evaluation shows that it subjectively felt less difficult for participants with the augmented report to answer questions about the exposure situation. Furthermore, they also objectively performed better in answering the questions than did the group with the classic report. Therefore, this work shows that AR enhanced images can improve the understanding of an exposure situation, and it describes how such images and videos can be created.
Glimpse

Success of Deep Neural Networks (DNNs) and their computational intensity has heralded Cambrian explosion of DNN hardware. While hardware design has advanced significantly, optimizing the code for them is still an open challenge. Recent research has moved past traditional compilation techniques and taken a stochastic search algorithmic path that blindly generates rather stochastic samples of the binaries for real hardware measurements to guide the search. This paper opens a new dimension by incorporating the mathematical embedding of the hardware specification of the GPU accelerators dubbed Blueprint to better guide the search algorithm and focus on sub-spaces that have higher potential for yielding higher performance binaries. While various sample efficient yet blind hardware-agnostic techniques have been proposed, none of the state-of-the-art compilers have considered hardware specification as hints to improve the sample efficiency and the search. To mathematically embed the hardware specifications into the search, we devise a Bayesian optimization framework called Glimpse with multiple exclusively unique components. We first use the Blueprint as an input to generate prior distributions of different dimensions in the search space. Then, we devise a light-weight neural acquisition function that takes into account the Blueprint to conform to the hardware specification while balancing the exploration-exploitation trade-off. Finally, we generate an ensemble of predictors from the Blueprint that collectively vote to reject invalid binary samples. We compare Glimpse with hardware-agnostic compilers. Comparison to AutoTVM [3], Chameleon [2], and DGP [16] with multiple generations of GPUs shows that Glimpse provides 6.73×, 1.51×, and 1.92× faster compilation time, respectively, while also achieving the best inference latency.
Innovation and Entrepreneurship of College Students Based on Random Matrix Big Data Analysis Model Educational Ecological Model Optimization

In this paper, the random matrix big data analysis model is thoroughly studied and constructed, the ecological model of college students’ innovation and entrepreneurship education is analyzed, and the optimization model of college students’ Innovation and entrepreneurship education environmental model based on the random matrix big data analysis model is designed. This paper briefly explains the random matrix and its M-P rate theory deduces the idea of feature extraction by the difference of eigenvalue limit spectrum distribution between different nonrandom matrices and random matrices, gives the data matrix representation method of FEMPL and the specific feature composition basis, and describes the steps of FEMPL feature extraction. A performance model for predicting the running time of Hadoop jobs is constructed using a random matrix. In this paper, innovation and entrepreneurship education has been carried out gradually, and the innovation and entrepreneurship education curriculum, platform, and mechanism have been progressively established. However, there is still a gap between the proper level of innovation and entrepreneurship education development. This study takes education ecology as the research perspective, analyzes the ecosystem of typical schools of innovation and entrepreneurship education, summarizes the dimensions and parameters of the invention and entrepreneurship education ecosystem, constructs an ecological model of innovation and entrepreneurship education for college students, and analyzes the problems and causes of the current innovation and entrepreneurship education ecology for college students based on the model, to propose specific strategies to promote the ecological development of innovation and entrepreneurship education for college students.
Challenging Channels: Encrypted Covert Channels within Challenge-Response Authentication

Challenge-response authentication is an essential and omnipresent network service. Thus, it is a lucrative target for attackers to transport covert information. We present two covert channels in nonce-based network authentication that allow the encrypted transfer of covert information. Both channels exploit fundamental problems, not contained to the specific implementation or cryptographic mechanisms. We provide implementations and evaluations for hash- and key-based challenge-response authentication. Our implementation achieves hard detectability and acceptable throughput rates. Further, we analyze how the throughput can be maximized by applying compression and codebook techniques. We also describe how the presented approach is suitable for the extraction of sensitive information and performing command-and-control communication, showcased by the exfiltration of three different malware code snippets. Further, we discuss potential countermeasures, that can detect, limit and eliminate the proposed covert channels.
The basis of design tools for quantum computing

Quantum computers promise to efficiently solve important problems classical computers never will. However, in order to capitalize on these prospects, a fully automated quantum software stack needs to be developed. This involves a multitude of complex tasks from the classical simulation of quantum circuits, over their compilation to specific devices, to the verification of the circuits to be executed as well as the obtained results. All of these tasks are highly non-trivial and necessitate efficient data structures to tackle the inherent complexity. Starting from rather straight-forward arrays over decision diagrams (inspired by the design automation community) to tensor networks and the ZX-calculus, various complementary approaches have been proposed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
REACT

On-chip training improves model accuracy on personalised user data and preserves privacy. This work proposes REACT, an AI accelerator for wearables that has heterogeneous cores supporting both training and inference. REACT's architecture is NoC-centric, with weights, features and gradients distributed across cores, accessed and computed efficiently through software-configurable NoCs. Unlike conventional dynamic NoCs, REACT's NoCs have no buffer queues, flow control or routing, as they are entirely configured by software for each neural network. REACT's online learning realises upto 75% accuracy improvement, and is upto 25× faster and 520× more energy-efficient than state-of-the-art accelerators with similar memory and computation footprint.
A Study on Issues Regarding Working Conditions of Educational Official and Measures for Labor Act Improvement

Educational officials are employees in principle and they are protected according to the Labor Relations Act. At actual schools, however the issues and controversies over the protection and treatment of the educational officials have been continuing for a long time. Such issues and controversies have to be considered as they cause huge confusions in the labor-management relations and provision of high-quality educational service at school.BR While there have been efforts to amend the Act or relevant statutes since the 19th National Assembly, amendments could not be made due to the oppositions from the parties of labor relations. Currently employment and working conditions are regulated in accordance with the municipal ordinance, collective bargaining, rules of employment, and labor contract.BR The treatment and protection of the educational officials have significant meaning in improving the quality of the educational services including educational welfare. However the present ordinance-based discipline system causes difference in the scope and main working conditions depending on duties and regions. Such difference results in exhausting and repeated conflicts.BR Still, offices of education and solidarity conference of non-regular school employees are carrying out collective bargaining to regulate the standardized salary and allowances. The working conditions are also gradually getting standardized by reducing the gaps between offices of education. Such issue is also spotlighted because it may become the basis for establishing the separate statutes related to the educational officials.BR There also should be further discussions on various measures for closing the gaps in the working conditions of the educational officials.
Metaheuristic based Task Scheduling for Load Balancing in the Cloud Computing Environment

Cloud computing has emerged as a leading technology that handles allocation of tasks in a dynamic manner. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the Service Level Agreement (SLA) requirements, which is a document offered by cloud service providers to the users. Load balancing serves as one of the key concepts of cloud computing that avoids the hectic situation where certain nodes are overloaded while the others are having a very smaller number of tasks and certain not at all loaded. The performance of QoS metrics such as makespan, throughput, performance, response time etc. are having some amount of impact to the degree of imbalance. Cat Swarm Optimization (CSO) and Spider Monkey Optimization (SMO) metaheuristic algorithms are integrated and CSO-SMO-LB has been proposed in Cloud Computing inorder to balance the scheduling of tasks to the available resources. This proposed metaheuristic algorithm aims to optimize resources and improve Load Balancing in terms of the Quality of Service (QoS) task parameters, the priority of Virtual Machine (VM), makespan and resource utilization. The performance of metaheuristic-based algorithms, are analysed by performing simulation and better results were produced.
Normalising the Digital Currency as an Innovation in Digital Banking Practice

Modern electronic payment systems by banks and other sectors have introduced the significant innovations in recent days. This paper covers, what would be the impact if the ‘Digital Currency’ is normalised as existing physical currency in banking and other transactions. That is involving digital currency in day-to-day transactions in e-payment as digital banking practice. Effect of Digital currency in the general economy, is it a boon or bane for the society? how the general public will get affected by this technological change in money. In the study we managed to collect the primary data from the general public from various localities of Karnataka and organized their opinion about the normalizing the digital currency.
IMPROVING THE ECO-TOURISM EXPERIENCE WITH GUIDING SERVICES AND CHSE

ABSTRACTour guides are other people who most often interact with tourists while travelling in an ecotourism destination. Therefore, the service activities provided by the guide are considered entirely instrumental in shaping the tourist experience. In this Covid-19 pandemic condition, the implementation of the CHSE Health protocol is also considered very important in supporting the creation of a quality tourist experience. This article presents a discussion regarding the influence of CHSE guidance services and protocols on the tourist experience in the Nglanggeran Ancient Volcano Ecotourism Area. The method used is a quantitative inferential method using the Partial Least Square (PLS) approach. The results show that the guidance service has a positive effect on creating the tourist experience. However, the CHSE health protocol implementation has not been proven to affect the tourist experience.Keyword: Tourist experience, tour guide service, chse, new normal ABSTRAKPemandu wisata adalah orang lain yang paling sering berinteraksi dengan wisatawan selama berwisata di suatu destinasi ekowisata. Oleh karena itu, kegiatan pelayanan yang diberikan oleh pemandu dianggap sepenuhnya berperan dalam membentuk pengalaman wisatawan. Dalam kondisi pandemi Covid-19 penerapan protokol kesehatan CHSE juga dinilai sangat penting dalam mendukung terciptanya pengalaman wisata yang berkualitas. Artikel ini menyajikan pembahasan mengenai pengaruh layanan pemandu wisata dan protokol kesehatan CHSE terhadap pengalaman wisatawan di Kawasan Ekowisata Gunung Api Purba Nglanggeran. Metode yang digunakan adalah metode kuantitatif inferensial dengan menggunakan pendekatan Partial Least Square (PLS). Hasil penelitian menunjukkan bahwa layanan pemandu wisata berpengaruh positif dalam menciptakan pengalaman wisatawan. Namun, penerapan protokol kesehatan CHSE belum terbukti mempengaruhi pengalaman wisatawan.Kata Kunci: Pengalaman wisatawan, pelayanan pemandu wisata, CHSE, new normal
Information security economics: cyber security threats

Abstract This article takes into consideration cyber security economics to understand whether cyber security and economics is a partnership among equals or any of these two disciplines have the upper hand. Cyber security economics is more than just computer science, security studies and economics in a holistic way, it is a dynamic cross disciplinary matter involving psychology, forensic, sociology, etc. The argument is that there is no clear definition of cybersecurity, apart from a set of protocols that are highly technical, and when it comes to cyber security economics Gordon-Loeb model provides a good picture on the general image over the industry, yet, much more is ignored, as the costs of a cyber security attack to succeed is huge, save the hidden costs and other externalities. Apart from regulation establishing a common ground, information security economics is an under researched field of interest, especially as digital sovereignty act is about to be voted for, together with common rules Information security.
Cybersecurity challenges in the knowledge economy

Abstract The paper aims to study cybersecurity correlations with the knowledge economy, focusing on its challenges in this new age of economy. The relevance of the cybersecurity concept is sustained by the academic literature that shows its important role in the business environment, where disruptive changes will become the norm. In the digital age the disruptions are happening frequently which gives organizations less time to manage the change and the fact that they are almost constrained to develop new strategies to answer the challenges of the changing business environment. The knowledge economy has evolved and with that, new concepts began to appear out of necessity and the need to secure the dynamic environment of knowledge management systems. Considering the unprecedented access to information and advancements in conducting academic research, in the present landscape of the knowledge economy and cybersecurity domain, new methods are available to structure and examine a body of literature. The text mining and scientific mapping analysis conducted with VOSviewer software version 1.6.18 is allowing us to identify meaningful insights about the knowledge economy concept, such as the (1) existing research gaps, at least on cybersecurity challenges in the knowledge economy and the (2) the research interests seen for the time period between 2019 and 2021. To achieve this, a database derived from Web of Science’s core collection has been used, and the text mining based on term co-occurrence analysis contributed to a deeper understanding of current and future workspace dynamics.