Reviewerly/exHarmony huggingface dataset:
The Syllabus as a Student Privacy Document in an Age of Learning Analytics

Purpose: This study aims to reveal how instructors discuss student data and information privacy in their syllabi.Design/methodology/approach: The authors collected a mixture of publicly accessible and privately disclosed syllabi from 8,302 library and information science (LIS) courses to extract privacy language. Using privacy concepts from the literature and emergent themes, the authors analyzed the corpus.Findings: Most syllabi did not mention privacy (98%). Privacy tended to be mentioned in the context of digital tools, course communication, policies, and assignments. Research limitations/implications (if applicable): The codebook developed during the analysis provides a structure for future research on privacy issues in the higher education context. The transferability of the findings is limited because they address only one field and professional discipline, library and information science, and address syllabi for only online and hybrid courses.Practical implications (if applicable): The findings suggest a need for professional development for instructors related to student data privacy. The discussion provides recommendations for creating educational experiences that support syllabi development and constructive norming opportunities.Social implications (if applicable): Instructors may be making assumptions about the degree of privacy literacy among their students or not value student privacy. Each raises significant concerns if privacy is instrumental to intellectual freedom and processes critical to the educational experience. Originality/value: In an age of educational data mining and analytics, this is one of the first studies to consider if and how instructors are addressing student data privacy in their courses, and the study initiates an important conversation for reflecting on privacy values and practices.
Quantum Pricing with a Smile: Implementation of Local Volatility Model on Quantum Computer

Applications of the quantum algorithm for Monte Carlo simulation to pricing of financial derivatives have been discussed in previous papers. However, up to now, the pricing model discussed in such papers is Black-Scholes model, which is important but simple. Therefore, it is motivating to consider how to implement more complex models used in practice in financial institutions. In this paper, we then consider the local volatility (LV) model, in which the volatility of the underlying asset price depends on the price and time. We present two types of implementation. One is the register-per-RN way, which is adopted in most of previous papers. In this way, each of random numbers (RNs) required to generate a path of the asset price is generated on a separated register, so the required qubit number increases in proportion to the number of RNs. The other is the PRN-on-a-register way, which is proposed in the author's previous work. In this way, a sequence of pseudo-random numbers (PRNs) generated on a register is used to generate paths of the asset price, so the required qubit number is reduced with a trade-off against circuit depth. We present circuit diagrams for these two implementations in detail and estimate required resources: qubit number and T-count.
Robust measurement of wave function topology on NISQ quantum computers

Topological quantum phases of quantum materials are defined through their topological invariants. These topological invariants are quantities that characterize the global geometrical properties of the quantum wave functions and thus are immune to local noise. Here, we present a strategy to measure topological invariants on quantum computers. We show that our strategy can be easily integrated with the variational quantum eigensolver (VQE) so that the topological properties of generic quantum many-body states can be characterized on current quantum hardware. We demonstrate the robust nature of the method by measuring topological invariants for both non-interacting and interacting models, and map out interacting quantum phase diagrams on quantum simulators and IBM quantum hardware.
A Study on Cloud Computing and Hybrid Cloud Systems

Cloud computing is a recent evolving innovation. Each connection must interact with cloud computing conditions. We discussed an investigation into the characteristic hybrid cloud organizational model and cloud benefit model accessible in the field of cloud computing. We focused on industrial designs that could support future industry challenges by leveraging a hybrid cloud project to provide an effective way to store information in the cloud by leveraging a mix of public and private clouds. Bring a challenge to the upcoming industry. This allows offices to store sensitive information in a private cloud and a public cloud where basic information can be stored in bulk. Hybrid clouds are especially useful for dynamic or highly scalable workloads. This article presents an overview, service model, characteristics, vendor storage and cloud computing issues.
The relationship between brand experience and brand trust: the moderating role of brand awareness in Saudi mobile banking services

This research paper attempted to explore the relationships among brand experience and trust in Saudi mobile banking services by using brand awareness as a moderator variable Nonlinear partial least squares - structural equation modeling (PLS-SEM) was used for data analyses Data collected from 320 m-banking users by an online survey indicate that behavioral, affective, and intellectual experiences have positive and significant direct relationship with brand trust Whether sensory experience can motivate brand trust only through increasing the brand awareness This study provides different theoretical and philosophical contributions to brand trust in the online context Furthermore, this study reviewed practical implications in M-banking services, in the critical time of the COVID-19 pandemic, and the encouragement of electronic transactions in various fields © 2020 Journal of Advanced Pharmacy Education & Research
CLiMP: A Benchmark for Chinese Language Model Evaluation

Linguistically informed analyses of language models (LMs) contribute to the understanding and improvement of these models. Here, we introduce the corpus of Chinese linguistic minimal pairs (CLiMP), which can be used to investigate what knowledge Chinese LMs acquire. CLiMP consists of sets of 1,000 minimal pairs (MPs) for 16 syntactic contrasts in Mandarin, covering 9 major Mandarin linguistic phenomena. The MPs are semi-automatically generated, and human agreement with the labels in CLiMP is 95.8%. We evaluated 11 different LMs on CLiMP, covering n-grams, LSTMs, and Chinese BERT. We find that classifier-noun agreement and verb complement selection are the phenomena that models generally perform best at. However, models struggle the most with the ba construction, binding, and filler-gap dependencies. Overall, Chinese BERT achieves an 81.8% average accuracy, while the performances of LSTMs and 5-grams are only moderately above chance level.
Design Fast Algorithms For Hodgkin-Huxley Neuronal Networks

The stiffness of the Hodgkin-Huxley (HH) equations during an action potential (spike) limits the use of large time steps. We observe that the neurons can be evolved independently between spikes, $i.e.,$ different neurons can be evolved with different methods and different time steps. This observation motivates us to design fast algorithms to raise efficiency. We present an adaptive method, an exponential time differencing (ETD) method and a library-based method to deal with the stiff period. All the methods can use time steps one order of magnitude larger than the regular Runge-Kutta methods to raise efficiency while achieving precise statistical properties of the original HH neurons like the largest Lyapunov exponent and mean firing rate. We point out that the ETD and library methods can stably achieve maximum 8 and 10 times of speedup, respectively.
Weight Distribution of Some Codewords of 3-ary Linear Code over GF(27)‎

This paper is devoted to introduce the structure of the p-ary linear codes C(n,q) of points and lines of PG(n,q),q=p^h prime. When p=3, the linear code C(2,27) is given with its generator matrix and also, some of weight distributions are calculated.
PENERAPAN METODE PEMBERIAN TUGAS UNTUK MENINGKATKAN HASIL BELAJAR IPS SISWA KELAS V SD NEGERI 125 KOTA PEKANBARU TAHUN AJARAN 2016/2017

Tujuan penelitian untuk meningkatkan hasil belajar siswa pada mata pelajaran IPS. Apakah melalui Penerapan Metode Pemberian Tugas dapat meningkatkan hasil belajar siswa. Penelitian dilaksanakan pada siswa kelas V SDN 125 Pekanbaru jumlah siswa 22 orang. Proses pengumpulan data menggunakan teknik observasi. Teknik observasi dilakukan dengan menggunakan lembar pengamatan proses kegiatan pembelajaran guru, kegiatan siswa, kegiatan KBM dan tes belajar pada materi. Setelah dilaksanakannya Penerapan Metode Pemberian Tugas terjadi kenaikan hasil belajar siswa pada siklus I (77,27%) 17 siswa berhasil tuntas dan pada siklus II mengalami kenaikan hasil belajar siswa (100%) 22 siswa berhasil tuntas. Dapat disimpulkan bahwa hasil belajar siswa mengalami kenaikan dari siklus I ke siklus II sebesar (22,73%) Hasil postest, untuk mengetahui kemampuan siswa menguasai pelajaran pada siklus I memperoleh rata-rata kelas 78,36 sedangkan pada siklus II memperoleh rata-rata kelas sebesar 82,22. Penulis menyimpulkan hasil belajar siswa dari siklus I ke siklus II mengalami kenaikkan yang sangat signifikan. The purpose of research is to improve student learning outcomes in social studies subjects. Is through the Application of Assignment Methods can improve student learning outcomes. The study was conducted on fifth grade students of SDN 125 Pekanbaru with 22 students. The process of collecting data uses observation techniques. Observation techniques were carried out using observation sheets of the process of teacher learning activities, student activities, teaching and learning activities and learning tests on the material. After the implementation of the Application of Assignment Methods there was an increase in student learning outcomes in the first cycle (77.27%) 17 students successfully completed and in the second cycle experienced an increase in student learning outcomes (100%) 22 students successfully completed. It can be concluded that student learning outcomes have increased from cycle I to cycle II by (22.73%) Posttest results, to determine the ability of students to master the lessons in the first cycle to obtain a class average of 78.36 while in the second cycle obtain a class average amounting to 82.22. The author concludes that student learning outcomes from cycle I to cycle II experience a very significant increase.
Application of Dijkstra’s Algorithm in Searching the Shortest Path of Coal Production Locations

To exploit coal the company needs to hold discussions and it takes time to reach the location, each coordinate point traveled has a different distance, the shorter the distance, the faster the time it will take. In this problem the company does not yet have hauling distance data at each coordinate point, the solution to this problem is how to build an application for determining coal production points using the dijkstra algorithm. There are three stages used to collect data, namely, literature study, observation, and interviews. The development of the system used to build this application uses the prototype method whose stages consist of communication, rapid planning, rapid design modeling using flowcharts, prototype formation, and submission of the customer system in the form of an application that analyzes the determination of PIT location distances using algorithms dijkstra, the output of this application is a description of the track map display, the destination location and its node points with detailed routes with a distance of kilometers. At this stage, the application is tested using the blackbox test, by checking whether the buttons can function properly and the expected result from this test is a success.
Inspected by systems for control of minimal efforts with reductions of ecological characteristics

Relevance of work. The work is devoted to the improvement of the quality control system of detergents with improved ecological characteristics and the presentation of innovative formulations with its compliance with modern environmental standards. This meets the needs of the modern market of eco-products and the scientific need to develop and evaluate a new generation of detergents. The purpose of the work is to test a number of methods for validity for diagnosing washing powders for their environmental and functional acceptability. Research methods - theoretical and modeling (development of a scheme of complex analysis of washing powders), representative (presentation of innovative recipes) and diagnostic. Within the framework of the latter, the possibilities of correlation-regression and imitation methods in the assessment of detergents with improved environmental characteristics, as well as the method of component-functional analysis of detergents were tested. Results. The validity of the method of ecological labeling for research and quality control of detergents on the basis of the maximum allowable ecological concentration, which limits or prohibits the use of harmful and dangerous substances, is proved. The innovative recipe of detergents on the example of washing powders of the DeLaMark trademark is presented and on the basis of the developed standards its check is carried out. Its ecological and functional acceptability is proved. Perspectives. Detergents' evaluation indicators and methods of their determination established on the basis of experimental researches allow to improve the procedure of control of products of this type, which is important for satisfaction of consumer needs. This makes it possible to develop recommendations for improving the domestic regulatory framework for the production of detergents with improved environmental performance through the use of OSU OEM 08.002.12.065:2016 "Detergents and cleaners. Environmental criteria for life cycle assessment ". The alternative methods used in the work to assess the quality of detergents allow you to effectively choose the most correct method of determining them.
The Drivers of Cyber Risk

Cyber incidents are becoming more sophisticated and their costs difficult to quantify. Using a unique database of more than 100,000 cyber events across sectors, we document the characteristics of cyber incidents. Cyber costs are higher for larger firms and for incidents that impact several organisations simultaneously. The financial sector is exposed to a larger number of cyber attacks but suffers lower costs, on average, thanks to proportionately greater investment in information technology (IT) security. The use of cloud services is associated with lower costs, especially when cyber incidents are relatively small. As cloud providers become systemically important, cloud dependence is likely to increase tail risks. Crypto-related activities, which are largely unregulated, are particularly vulnerable to cyber attacks.
Blockchain Enabled Smart Contract Based Applications: Deficiencies with the Software Development Life Cycle Models

With the recent popularity of Blockchain and other Distributed Ledger Technologies (DLT), blockchain enabled smart contract applications has attracted increased research focus. However, the immutability of the blocks, where the smart contracts are stored, causes conflicts with the traditional Software Development Life Cycle (SDLC) models usually followed by software engineers. This clearly shows the unsuitability of the application of SDLC in designing blockchain enabled smart contract based applications. This research article addresses this current problem by first exploring the six traditional SDLC models, clearly identifying the conflicts in a table with the application of smart contracts and advocates that there is an urgent need to develop new standard model(s) to address the arising issues. The concept of both block immutability and contract is introduced. This is further set in a historical context from legacy smart contracts and blockchain enabled smart contracts extending to the difference between “shallow smart contracts” and “deep smart contracts”. To conclude, the traditional SDLC models are unsuitable for blockchain enabled smart contract-based applications.
Counter Digit Recognition in Power Meter Image Using YOLO and CRNN

This study proposes a method for automatically recognizing digits in power meter images. Our targets are analog power meters that display usage with four counters. We employed object detection to find the numeric domain in the meter image. And optical character recognition was performed to recognize digits in the detected numeric area. Two types of deep neural network models are used for object detection and optical character recognition. To train the model and evaluate its performance, we generated datasets from meter images. In this paper, we propose a model in which two types of deep neural networks are connected, and this model has more than 98% recognition performance.
A preliminary study for designing and developing augmented reality-based module for teaching chemical bonding in Nigerian secondary schools

The conventional strategies for teaching chemical bonding in Nigerian secondary schools, mostlyutilizes two-dimensional images and structures either on paper sketches or boards drawing. As a result ofthese adverse situations, chemistry students in Nigeria have no background knowledge and skills in 3Dchemical structures and could influence their visualization skills in learning abstract concepts. In this regard,it is considered imperative to adopt digital technologies such as augmented reality to convert the twodimensional structure to three-dimensional. This study investigated the chemistry teacher’s opinion on theneed for designing and developing an augmented reality module for teaching and learning chemical bondingNigerian secondary school. It involved a survey research design with a questionnaire validated by threeexperts in chemistry and technology education with a reliability of 0.74 using Cronbach Alpha. Thirty (30)chemistry teachers served as respondents selected using a purposive sampling technique. Their responses onthe instrument (i.e. questionnaire) were analysed using descriptive statistics. Most of the respondentsindicated low spatial visualization skills, spatial reasoning skills and achievement among the students due tolow clarity of dimensions of images utilize. They further agreed that it is suitable to incorporate threedimensional and digital graphics into the real-world learning environment for chemical bonding and use of amobile phone to present the chemical bonding mechanisms, shapes and structures even though 90% of themare not aware of the augmented reality and its principle guidelines in teaching
A Study on the anti-competition contract for trade secret protection and freedom of occupation choice

오늘날 이른바 4차산업혁명을 비롯하여 근래 정보통신기술의 급속한 발전이 사회전반의 변혁을 유발시키고 있는 가운데 기업들은 경쟁에서 뒤처지지 않기 위하여 경쟁기업들이 보유한 영업비밀에 대한 수집활동에 적법, 위법을 가리지 않고 나서는 풍토가 만연되어 있다. 근로자들도 자신에게 유리한 최고의 근무환경과 최고의 급여를 누리기 위하여 직장에서 습득한 영업비밀이나 기술을 바탕으로 근무하던 기업과 경쟁관계에 있는 기업으로 전직하거나 영업비밀을 누설하는 사례가 증가하고 있다. 세계적인 수준의 기술을 보유한 우리나라 기업들은 국내외로부터 산업기술 및 영업비밀 수집의 대상이 되고 있어 기업의 생존이 위협당하는 경우가 증가하고 있다. 기업이 보유한 영업비밀 및 산업기술을 보호하기 위하여 퇴직 후 경쟁업체로 이직하는 근로자에 대한 전직금지 가처분 신청이나 소송 등의 법적 조치가 최근 대폭 증가하고 있다. 현행 법체계는 직접적으로 근로자의 전직금지를 제한하는 규정을 두고 있지 않으나 기업의 영업비밀보호 및 산업기술보호를 위하여 근로자에 대하여 전직제한, 즉 경업행위에 제한을 가할 수 있도록 사용자와 근로자간에 ‘경업금지계약’, 즉 ‘전직금지약정’을 맺고 이를 적용하여 근로자의 전직을 규제할 수 있도록 하고 있다. 그러나 근로자에 대한 전직금지약정은 헌법에 근로자의 기본권으로 보장된 직업선택의 자유와 상충되는 점이 있어 근로자와 사용자간에 전직금지약정을 맺었다 하더라도 근로자의 기본권과 기업의 이익을 조정하는 측면에서 일정한 제한을 가하고 있다.Today, the rapid development of information and communication technology including the so-called 4th industrial revolution has caused the transformation of society as a whole, and companies are in a widespread climate that they are legally and illegally collecting business secrets held by competitors in order not to lag behind in competition. In order to enjoy the best working environment and the best salary in favor of the workers, there are increasing cases of transferring to a company that is in competition with a company that worked based on trade secrets or skills acquired at work or leaking trade secrets. Korean companies with world-class technology are becoming the target of collecting industrial technology and trade secrets from domestic and foreign countries, and the survival of companies is increasing. In order to protect the business secrets and industrial technology owned by the company, legal measures such as application for injunction against employees who are leaving as competitors after retirement or lawsuits have been increasing recently. The current legal system does not have a regulation to restrict the prohibition of employees job transfer directly, but it makes it possible to regulate the workers job transfer by applying the currency prohibition contract , that is, pre-contract between users and workers so that they can limit the workers job transfer to protect the business secrets and industrial technology. However, the prohibition of the workers transfer is in conflict with the freedom of occupation choice guaranteed by the basic rights of the workers in the Constitutional Law, so even if the prohibition of the transfer is made between the workers and the users, it gives a certain limit in terms of adjusting the basic rights of the workers and the interests of the company.
Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions

We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ESP)model, which learns said properties in terms of human provided features. Action preferences can then be explained by contrasting the future properties predicted for each action. To address cases where there are a large number of features, we develop a novel method for computing minimal sufficient explanations from anESP. Our case studies in three domains, including a complex strategy game, show that ESP models can be effectively learned and support insightful explanations.
Serverless GEO Labels for the Semantic Sensor Web.

With the increasing amount of sensor data available online, it is becoming more difficult for users to identify useful datasets. Semantic Web technologies can improve such discovery via meaningful ontologies, but the decision of whether a dataset is suitable remains with the users. Users can be aided in this process through the GEO label, which provides a visual summary of the standardised metadata. However, the GEO label is not yet available for the Semantic Sensor Web. This work presents novel rules for deriving the information for the GEO label’s multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. Thereby, this work enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. Further, the prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. Nonetheless, more real-world semantic sensor descriptions, an analysis of requirements for GEO label facets specific to the Semantic Sensor Web, and an integration into large-scale discovery platforms are needed.