Reviewerly/exHarmony huggingface dataset:
CTNRL

Network representation learning is one of the important works of analyzing network information. Its purpose is to learn a vector for each node in the network and map it into the vector space, and the resulting number of node dimensions is much smaller than the number of nodes in the network. Most of the current work only considers local features and ignores other features in the network, such as attribute features. Aiming at such problems, this paper proposes novel mechanisms of combining network topology, which models node text information and node clustering information on the basis of network structure and then constrains the learning process of network representation to obtain the optimal network node vector. The method is experimentally verified on three datasets: Citeseer (M10), DBLP (V4), and SDBLP. Experimental results show that the proposed method is better than the algorithm based on network topology and text feature. Good experimental results are obtained, which verifies the feasibility of the algorithm and achieves the expected experimental results.
An Improved Bat Algorithm With Time-Varying Wavelet Perturbations for Cloud Computing Resources Scheduling

Resources scheduling is a major challenge in cloud computing because of its ability to provide many on-demand information technology services according to needs of customers. In order to acquire the best balance between speed of operation, average response time, and integrated system utilization in the resource allocation process in cloud computing, an improved bat algorithm with time-varying wavelet perturbations was proposed. The algorithm provided a perturbation strategy of time-varying Morlet wavelet with the waving property to prevent from local optimum greatly and improve the converging speed and accuracy through the guide of individual distribution to control diversity and time-varying coefficient of wavelets. The experiments showed the proposed could significantly upgrade the overall performance and the capability of resource scheduling in cloud service compared to similar algorithms.
The Conceptual Framing, Design, and Development of Mobile-Mediated Professional Development for Primary Mathematics Teachers

This paper examines the design and development of a mobile-mediated professional development program for primary school teachers. It explores learning design frameworks and strategies to offer effective, 24/7, anywhere-anytime PD using mobile technology. The study highlights the significance of design requirements and best practices in mobile-mediated TPD, and presents a conceptual framework incorporating various pedagogical/andragogic approaches and instructional design possibilities enabled by mobile technology. The framework is based on the integrative learning design framework (ILDF), CSAM, RASE, Keller's ARCS, and TPACK framework. The resulting framework enacts evidence-informed and theory-led models, and best practices to cater to instructional design requirements for creating meaningful mobile-based learning experiences for teachers.
Cyber Security in English Secondary Education Curricula

Human error continues to be a contributing factor within the majority of cyber security incidents. Despite this, the education system is not providing the skills individuals need to protect themselves against cyber threats leading to poor cyber hygiene and a lack of cyber security professionals. Knowledge is primarily only available to those who partake in more advanced qualifications such as computer science. This paper utilises CyBOK, a cyber security content framework methodology alongside qualitative data collected from experienced computing educators in order to evaluate the current landscape of cyber security in English secondary education curricula. The content analysis of secondary education computing qualifications with regards to CyBOK, and the thematic analysis of interview data led to the discovery of how cyber security is an imperative educational topic that is largely absent in secondary education curricula. Interviews also revealed a discrepancy in pedagogical methods used to deliver computing education and assessed the issues associated with further inclusion of cyber security education.
Discovering and Quantifying Misconceptions in Formal Methods Using Intelligent Tutoring Systems

In this paper we advocate the study of misconceptions in the formal methods domain by integrating quantitative and qualitative methods. In this domain, so far, misconceptions have mostly been studied with qualitative methods, typically via interviews with less than 20 subjects. We discuss workflows for (1) determining the commonness of qualitatively established misconceptions by quantitative means; and for (2) the initial discovery of misconceptions by quantitative methods followed by qualitative assessments.
Desired Qualifications Sought in Entry Level Software Engineers

Global demand for software engineers continues to strain the technology sector with unfilled software engineering positions and stiff competition for hiring developers who are on the market. To attract more candidates, technology firms have increasingly been working closely with universities to recruit new graduates to fill their jobs. Universities hoping to minimize mismatches in job placement for new software developers should teach the skills and attributes that enable entry-level software developers to succeed. This paper presents a case study of hiring demands at one large, well-established technology company that reveals the most sought-after attributes in new hires. We discuss results of our interviews with five software development hiring managers and the results from a wide survey of engineers and architects from various levels and experiences. The interviews and surveys reveal that some qualifications are widely desired, and others have varying demand based on functional area, technology, or type of development. Ultimately, professional skills (e.g., teamwork) and personality traits (e.g., strong initiative) top the list of desired attributes, along with a few fundamentally broad technical skills. One key takeaway is that candidates who learn professional skills from university programs may be more readily hired into their first software engineering job than those whose education focused mostly on technical areas.
Generation of Code Tracing Problems from Open-Source Code

When developing automatic quizzing systems and intelligent tutoring systems, significant effort has to be spent on developing the problem bank. Question and problem generation is a field of study concerning automating this routine work. The two most common methods of problem generation are constrained random generation and template-based generation, but each of them has disadvantages. In this work, we study a possibility of generating code-tracing learning problems from existing open-source code. We generated a set of learning problems and evaluated their distinctness from human-authored problems and readiness for usage in the learning process. Both teachers and students showed the rate of determining machine-generated problems only slightly above random guessing. Teachers strongly agreed with the problems' relevance and agreed with their suitability for the learning process. Automatic labeling to filter desired problems for the assignment includes used concepts, possible errors during solving, and difficulty estimates. The studied type of learning problem required little additional data to add to the code; our further work will concern problem types with more dynamic data to overcome this limitation.
Qupcakery

Quantum computing (QC) is an emerging field at the intersection of computer science and physics. Harnessing the power of quantum mechanics, QC is expected to solve otherwise intractable problems significantly faster, including in encryption, drug development, and optimization. High-quality and accessible QC resources are needed to help students develop the critical skills and confidence to contribute to the field. However, existing programs are often aimed at college students with an advanced mathematics or physics background, shutting out potential innovators.
Fast Timeline Based Multi Object Online Tracking

Abstract Fast state-of-the-art multi-object-tracking (MOT) schemes, such as reported in challenges MOT16 and Mot20, perform tracking on a single sensor, often couple tracking and detection, support only one kind of object representation or don’t take varying latencies and update rates into account. We propose a fast generic MOT system for use in real world applications which is capable of tracking objects from different sensor / detector types with their respective latencies and update rates. An SORT inspired online tracking scheme is extended by time awareness using timelines as unifying concept. The system supports different object, sensor and filter and tracking types by modularizing and generalizing the online tracking scheme, while ensuring high performance using an efficient data-oriented C++-template-based implementation. Using the proposed system we achieve, with comparable evaluation metrics, framerates up to ten times higher than the fastest MOT schemes publicly listed for the axis-aligned bounding-box tracking challenges MOT17 and MOT20.
University Teachers’ Experiences of Teaching Hands-On Components in Science and Technology in Primary Teacher Education during COVID-19

Teacher education programs have the dual task of teaching specific subject content while also providing examples of how this content can be taught in schools. This task is especially important, and also problematic, when it comes to technology and science education, where hands-on components such as design/construction exercises, laboratory exercises, and excursions are central epistemic practices. When COVID-19 hit, Swedish universities were forced to change from campus-based teaching to online distance education, termed “emergency remote teaching (ERT).” The present study aims to investigate university teachers’ experiences of how hands-on components in science and technology education worked in the ERT mode that arose during the COVID-19 pandemic. The analysis was performed with a social semiotics and community of inquiry framework, and shows that both the type of instruction and the subject content were impacted. In particular, the reduced opportunities for students to apply scientific and technical methods and the reduced ability of teachers to determine whether the students had understood their instruction generated new ways of communicating and supporting the students’ learning. Therefore, analysis of meaning making in science and technology online-learning contexts needs to address the topics of the nature of science (NOS) and the nature of technology (NOT). An extended, three-dimensional model of meaning making is suggested.
The nexus between non-fungible tokens (NFTS) and cryptocurrencies

BACKGROUND: Like Bitcoin or any other cryptocurrencies, non-fungible tokens (NFTs) count on blockchain technology, and NFTs are the latest and the most popular in a series of blockchain solutions. Traders in this ecosystem need to pay a dynamic fee, called a gas fee, for making any transactions on the Ethereum blockchain. The gas fee is measured by gwei, and traders must consider this as an additional cost. So, the current price of this fee may affect the decision of NFT creators or traders. OBJECTIVE: This study investigates the interrelationships between NTFs, cryptocurrencies (Ethereum and BTC), and gas fees using daily market data from January 2019 to November 2021. METHOD: Fourier Shin’s (2016) cointegration test, Fully Modified Ordinary Least Squares, and Group Dynamic Least Squares tests were employed to analyze the data. Then, the variance Decomposition method was applied to determine what other variables explain the percentage of the total variance on NFTs— it also used Impulse-response functions for measuring the response of the NFTs variable for one standard deviation shock. RESULTS: Results show that an increase in gas fees, the daily volume of Bitcoin, and the daily volume of Ethereum decrease NFTs sales. There is a unidirectional relationship between lnSales and lnGasFee variables. Also, there is a determined unidirectional relationship between lnBTC and lnSales variables. Lastly, there is a one-way causality relationship between lnSales and lnETH variables. CONCLUSIONS: The primary causation of the relationship between NFTs, gas fees and Ethereum fees is most likely related to the use of Ethereum as the primary means of payment in the NFTs market and gas fees being a significant cost element in NFTs trading. Another point of view is that the dominance of Bitcoin in the market is very effective in pricing of other cryptocurrencies and in the sales and pricing of NFTs indirectly. It is supported by empirical findings that the main elements in the blockchain ecosystem are interrelated.
Dynamic model for implicit aspect detection in sentiment analysis using novel aspect pointer compendium

Sentiment analysis is the contextual analysis of words to retrieve the social opinion of a brand which aids the business firms/institutions to know the impact of their products/services. It is habitual that users may express different opinions regarding various aspects of the same entity. Therefore, there is a strong demand to extract all the opinion targets may those be explicitly mentioned aspects or implicit aspects which are not directly specified in the reviews. In this context, comparatively less amount of work has been carried out concerning implicit aspect detection. The proposed work has been dedicated solely to extracting the implicit aspects using a dynamic approach based on the type of sentence containing the clues for implicit aspect. A novel aspect pointer compendium (APC) has been developed that catalyzes the task of finding implicit aspects to the maximum extent possible. The APC incorporates the usage of different types of clues such as synonym clues, context clues, phrase clues, and partially implicit aspects that aid in the detection of hidden aspects. Based on this idea, the proposed work classifies the implicit aspect sentences into six types and proceeds with the task in an efficient manner. To strengthen the task of implicit aspect detection, the proposed work utilizes a hybrid technique encompassing APC, domain-specific adjective-noun collocation list (DSANCL), and the explicit aspect-opinion word pairs extracted from the reviews. The experimentation and results reveal that the proposed hybrid approach shows a good improvement in terms of the efficacy of extracting the implicit aspects as compared to the existing baseline models.
Muti-channel graph attention networks for POI recommendation

This paper focuses on the task of Point-of-interest (POI) recommendation whose goal is to generate a list of POIs for a target user based on his or her history check-in records. Different from the traditional recommendation tasks (e.g., movie recommendation), there are many factors, like temporal factor and geographical factor, which make a great influence on user preference. Though existing POI recommendation methods tend to model the user preference from temporal factor, geographical factor or social factor, they fail to model these factors into a jointly model, leading to learn the suboptimal user preference. To tackle this issue, we propose a Muti-channel Graph Attention Network (MGAN) for POI recommendation which learns the user preference from multiple aspects in a unify model. Specifically, MGAN first constructs several graphs with corresponding contextual features to capture the user preference from temporal, geographical, semantic and social aspects. Then MGAN leverages the graph attention networks to learn the representations of POIs from these graphs. Finally, MGAN estimates the user preference from the history check-in records and other similar users via the learned POI representations. We conduct extensive experiments on real-world datasets. And the results indicate that our proposed MGAN outperforms mainstream POI recommendation methods.
Quantum Machine Learning Applications to Address Climate Change

In the previous three to four decades, numerical weather and climate modelling have advanced significantly, yet many challenges still exist. Appropriate adaptation techniques to reduce loss of life and property require geographically and temporally targeted predictions. Artificial Intelligence and machine learning (AI and ML) based technologies are improving predictions. However, they are bound by the absence of a hardware's or a software's—or both—capable of handling the enormous data volumes created on a global basis. The burgeoning paradigm of quantum computing (QC) has potential applications across many industries. This review shows that the current progress in quantum ML for quantum computers may lead to technological advancements in climate change research. The subsequent climate forecasting improvements are expected to have several socioeconomic benefits. The authors have also provided three or four examples showing how quantum technology might be used with ML systems to study climate change.
A Secure Quantum Technology for Smart Cities Using Travelling Salesman Problem (TSP)

Modern computing techniques like quantum computing are based on the remarkable phenomena of quantum mechanics. With an increasing number of instances of quantum supremacy displaying that a programmable quantum device can overcome the challenge that no computer algorithm can solve in any reasonable amount of time being shown by Google, IBM, Honeywell, and other distinct positions where quantum computing research is being conducted, quantum computing has grown in significance in recent days. Ultimately, travelling salesman problem (TSP) method is used and gives additional constraints on the beginning and ending points of the routing path. Hence, the outcome collections demonstrate that it is possible to obtain a strong quality result that has significant real-world applications even though the samples show an increase in problem complexity.
New Trends of Edge Computing Techniques for Trusting Analysis of Networks

Specification, design, and development principles of the future internet are enmeshed in the communication system. This term encompasses the emergence of future large multi-dimensional high-speed heterogeneous and interconnected networked ICT infrastructures to facilitate new groundbreaking trends like the internet of things (IoT) together with the communication system. In this research, an analysis of NdR structures has been carried out, and a comparison to chordal rings of 4th degree made. It was found that there do not exist optimal NdR structures with the number of nodes in one ring greater than 5. It was discovered that there is a close correlation between NdR structures and 4th-degree chordal rings that make it possible to choose NdR structure with the minimum diameter and average path length based on the analysis of correlated chordal ring graphs. It simplifies and shortens the process of choosing the best length of the inner ring chord.
Providing Security in Internet of Things Using Quantum Cryptography

Quantum computing has gained an advantage in recent years due to its compatibility and usage in various fields. The IoT environment is huge, and almost the whole world relies on it to enable efficient data transmissions. Security of the internet of things is a major concern. This is because it deals with personal data, has to be reliable, and can direct and manipulate device decisions in a harmful way. When quantum computing is combined with IoT, the system's performance is drastically improved. Shor's algorithm is used to secure this proposed quantum computing system for IoT. Shor's algorithm is more secure than other existing algorithms.
AcTrak: Controlling a Steerable Surveillance Camera using Reinforcement Learning

Steerable cameras that can be controlled via a network, to retrieve telemetries of interest have become popular. In this paper, we develop a framework called AcTrak , to automate a camera’s motion to appropriately switch between (a) zoom ins on existing targets in a scene to track their activities, and (b) zoom out to search for new targets arriving to the area of interest. Specifically, we seek to achieve a good trade-off between the two tasks, i.e., we want to ensure that new targets are observed by the camera before they leave the scene, while also zooming in on existing targets frequently enough to monitor their activities. There exist prior control algorithms for steering cameras to optimize certain objectives; however, to the best of our knowledge, none have considered this problem, and do not perform well when target activity tracking is required. AcTrak automatically controls the camera’s PTZ configurations using reinforcement learning (RL ), to select the best camera position given the current state. Via simulations using real datasets, we show that AcTrak detects newly arriving targets 30% faster than a non-adaptive baseline and rarely misses targets, unlike the baseline which can miss up to 5% of the targets. We also implement AcTrak to control a real camera and demonstrate that in comparison with the baseline, it acquires about 2× more high resolution images of targets.
Face Recognition Based Biometric Identification System with COVID-19 Detection

Abstract: In this paper face recognition system for the purpose of security is explored whose design is based on Raspberry Pi system. An add on temperature detection system also gives detection of COVID affected people. With the COVID-19 pandemic, temperature checking before you enter a premise is a necessary step to ensure you are well and help keep others safe. One of the most common ways is to use a handheld digital thermometer to find out your own temperature and recording it on some kind of logbook placed near the front door. For a workplace, employees are encouraged to record their temperature twice a day
Measurement-device-independent quantum protocol for Private Set Intersection/ Union Cardinality

In this paper, we present a novel measurement-device-independent quantum protocol for Private Set Intersection/ Union Cardinality. This protocol takes weak coherent pulses in the BB84 polarization states as quantum resources, instead of ideal single photons, and only needs to perform Bell state identifications, instead of perfect Bell state measurements. With the current technology, our protocol has better feasibility and higher security than the related quantum protocol. Finally, we verify the correctness and feasibility of the proposed quantum protocol by circuit simulations in IBM Qiskit.
Docker Vectorization, a Cloud-Native Privacy Agent—The Analysis of Demand and Feasibility for Era of Developing Complexity of Privacy Management

Currently, a large amount of biological information is accumulated, such as the area of genome sequencing as well as high-precision biometric information stored in wearable terminals and a growing database of health, medication, and medical information. The development of AI (artificial intelligence) and machine learning has increased its analytical power overwhelmingly. It is becoming more difficult to take measures against the leakage of personal information, and it is becoming difficult to determine privacy risks in advance. In this paper, we review those problems and propose a new method of managing private data. To solve such problems, we look at concepts of dynamic consent and privacy agents, which are drawing growing interest. In particular, efficient and broadly applicable technical means to support such concepts have been proposed. We considered using the current cloud platforms as an effective solution to this problem. We designed an architecture named Docker Vectorization and carried out a comprehensive analysis of the demand and feasibility of such a system in an era of increasing privacy management complexity. We believe we provided sufficient explanations for why Docker Vectorization of privacy agents in the cloud will be a powerful tool for providing sustainable and scalable privacy controls for data subjects.