Reviewerly/exHarmony huggingface dataset:
Topic Modelling and Sentimental Analysis of Students’ Reviews

Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic.The fundamental concern has been the continuance of education.As a result, several novel solutions have been developed to address technical and pedagogical issues.However, these were not the only difficulties that students faced.The implemented solutions involved the operation of the educational process with less regard for students' changing circumstances, which obliged them to study from home.Students should be asked to provide a full list of their concerns.As a result, student reflections, including those from Saudi Arabia, have been analysed to identify obstacles encountered during the COVID-19 pandemic.However, most of the analyses relied on closed-ended questions, which limited student involvement.To delve into students' responses, this study used open-ended questions, a qualitative method (content analysis), a quantitative method (topic modelling), and a sentimental analysis.This study also looked at students' emotional states during and after the COVID-19 pandemic.In terms of determining trends in students' input, the results showed that quantitative and qualitative methods produced similar outcomes.Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study.Furthermore, topic modelling has revealed that the majority of difficulties are more related to the environment (home) and social life.Students were less accepting of online learning.As a result, it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot, such as social interaction and effective eye-to-eye communication.
Anomaly Detection of Consumption in Hotel Units: A Case Study Comparing Isolation Forest and Variational Autoencoder Algorithms

Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.
A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

Machine learning (ML) practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field, the pre-processing of medical data requires careful handling to make quality clinical decisions.Generally, medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features.These factors indirectly upset the disease prediction and classification accuracy of any ML model.To address this issue, various data pre-processing methods called Feature Selection (FS) techniques have been presented in the literature.However, the majority of such techniques frequently suffer from local minima issues due to large solution space.Thus, this study has proposed a novel wrapper-based Sand Cat Swarm Optimization (SCSO) technique as an FS approach to find optimum features from ten benchmark medical datasets.The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables.Moreover, K-Nearest Neighbor (KNN) classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm.The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy, optimum feature size, and computational cost in seconds.The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96% by selecting 14.2 features within 1.91 s.Additionally, the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSO-KNN method and six other algorithms for a p-value less than 5.00E-02.The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02.Moreover, potential future directions are also suggested as a result of the study's promising findings.5800
Telepresence Robots and Controlling Techniques in Healthcare System

In this era of post-COVID-19, humans are psychologically restricted to interact less with other humans.According to the world health organization (WHO), there are many scenarios where human interactions cause severe multiplication of viruses from human to human and spread worldwide.Most healthcare systems shifted to isolation during the pandemic and a very restricted work environment.Investigations were done to overcome the remedy, and the researcher developed different techniques and recommended solutions.Telepresence robot was the solution achieved by all industries to continue their operations but with almost zero physical interaction with other humans.It played a vital role in this perspective to help humans to perform daily routine tasks.Healthcare workers can use telepresence robots to interact with patients who visit the healthcare center for initial diagnosis for better healthcare system performance without direct interaction.The presented paper aims to compare different telepresence robots and their different controlling techniques to perform the needful in the respective scenario of healthcare environments.This paper comprehensively analyzes and reviews the applications of presented techniques to control different telepresence robots.However, our feature-wise analysis also points to specific technical, appropriate, and ethical challenges that remain to be solved.The proposed investigation summarizes the need for further multifaceted research on the design and impact of a telepresence robot for healthcare centers, building on new perceptions during the COVID-19 pandemic.
DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain

Vehicular Ad Hoc Network (VANET) is an important part of the modern intelligent transportation system, which can provide vehicle communication at a certain distance. More importantly, VANET can provide route planning and autonomous driving for drivers by analyzing data. However, VANET’s data privacy and security are a huge challenge when serving drivers. In this paper, we propose a VANET data-sharing model (DSVN) that combines ciphertext-based attribute encryption (CP-ABE), blockchain, and InterPlanetary File System (IPFS). DSVN uses an outsourced and revocable ciphertext policy attribute-based encryption (ORCP-ABE) scheme, which is improved based on CP-ABE. ORCP-ABE uses key encryption key (KEK) trees to manage user attribute groups and revoke user-level attributes. It eliminates redundant attributes in the access policy by attribute-weighted access trees. Moreover, DSVN has no single point of failure. We demonstrate the indistinguishability under the chosen-plaintext attack (IND-CPA) security of DSVN by a game based on the computational Diffie–Hellman (CDH) assumption. Experimental results show that DSVN can store and share data with low overhead. Additionally, it can revoke attributes of users safely.
Data Security Storage Mechanism Based on Blockchain Network

With the rapid development of information technology, the development of blockchain technology has also been deeply impacted.When performing block verification in the blockchain network, if all transactions are verified on the chain, this will cause the accumulation of data on the chain, resulting in data storage problems.At the same time, the security of data is also challenged, which will put enormous pressure on the block, resulting in extremely low communication efficiency of the block.The traditional blockchain system uses the Merkle Tree method to store data.While verifying the integrity and correctness of the data, the amount of proof is large, and it is impossible to verify the data in batches.A large amount of data proof will greatly impact the verification efficiency, which will cause end-to-end communication delays and seriously affect the blockchain system's stability, efficiency, and security.In order to solve this problem, this paper proposes to replace the Merkle tree with polynomial commitments, which take advantage of the properties of polynomials to reduce the proof size and communication consumption.By realizing the ingenious use of aggregated proof and smart contracts, the verification efficiency of blocks is improved, and the pressure of node communication is reduced.
A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III

The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs).The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past.Three different cases based on the delay differential system with the Holling 3 rd type of the functional response have been used to solve through the proposed LVMBPNNs solver.The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification.Thirteen numbers of neurons have been used based on the input, hidden, and output layers structure for solving the delay differential model with the Holling 3 rd type of functional response.The correctness of the proposed stochastic scheme is observed by using the comparison performances of the proposed and reference data-based Adam numerical results.The authentication and precision of the proposed solver are approved by analyzing the state transitions, regression performances, correlation actions, mean square error, and error histograms.
Comparative Analysis of Text Complexity in English Textbooks

The article presents the results of a comparative analysis of the complexity of texts on the material of the linguistic subcorpus of English textbooks “English”, “Starlight” and “Rainbow English” for the 11th grade. The total volume of the sub-corpus of texts acting as empirical material for the research amounted to 36 texts (3955 word uses). One of the characteristics of the complexity of the text is its readability. It depends on the degree of complexity of words and the structures used in the sentences, as well as on such quantitative parameters of the text as the average length of the sentence (in word usage) and the average length of the syllable in the word. The readability of the text is calculated with the help of the formulas of Flesh Reading Ease, Flesh-Kincaid Grade, and the Gunning Fog index known in linguistics. The results of the complexity assessment were obtained using the TextInspector automated text complexity analyzer program. It was revealed that the complexity of the texts in the textbooks under discussion corresponds to the age characteristics and intellectual level of high school students. At the same time, the complexity indicators of the texts in the textbook “Starlight” are higher than the indicators of the textbooks “Rainbow English” and “English”, which is explainable, since the textbook “Starlight” is intended for schools with in-depth study of the English language. The automatic analysis of the complexity of educational texts using information technologies based on methods of their
Synchronous multicast-based quantum teleportation scheme via a six-qubit entangled state

With the deepening of research on quantum information transmission, multicast-based quantum teleportation (QT) has attracted considerable attention. In this paper, utilizing the six-qubit cluster state entanglement feature, a synchronous multicast-based QT scheme is presented, which fulfills multicast-based QT among four participants. This means that a controller is not needed, the sender Alice can realize quantum multicast communication successfully and efficiently, which makes the receivers Bob, Charlie and David gain the desired states synchronously with only knowing the measurement outcomes of Alice. Our scheme realizes the synchronous transmission of different coded information. In addition, we analyze the impact of four distinct noises and calculate the fidelities of the output states.
Foregrounding Women's Safety in Mobile Social Matching and Dating Apps

The design of social matching and dating apps has changed continually through the years, marked notably by a shift to mobile devices, and yet user safety has not historically been a driver of design despite mounting evidence of sexual and other harms. This paper presents a participatory design study with women-a demographic at disproportionate risk of harm through app-use-about how mobile social matching apps could be designed to foreground their safety. Findings indicate that participants want social matching apps to augment women's abilities for self-protection, reflected in three new app roles: 1) the cloaking device, through which the social matching app helps women dynamically manage visibility to geographically nearby users, 2) the informant, through which the app helps women predict risk of harm associated with a recommended social opportunity, and 3) the guardian, through which the app monitors a user's safety during face-to-face meetings and augments their response to risk.
Motivation and Strategies for Effective Inclusion of Cloud Solution Provider Certifications in Computing Curricula

A series of Working Groups has met at previous ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas and those areas to student learning objectives. The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work.
Design Justice in Practice

While varying degrees of participatory methods are often explored by the HCI community to enable design with different user groups, this paper seeks to add weight to the burgeoning demand for community-led design when engaging with diverse groups at the intersections of marginalisation. This paper presents a 24-month-long qualitative study, where the authors observed a community-based organisation that empowers refugee and migrant women in Australia through making. We report how the organisation led its own process to pivot from face-to-face to online delivery during the COVID-19 pandemic, analyzing the design and delivery of an app and the intersectional challenges faced by the women as they learnt to navigate online making. This paper expands feminist intersectional praxis in HCI to new contexts and critiques the positionality of researchers in this work. It contributes to the literature on design justice, providing an exemplar of how community-led design more effectively dismantles the compounding constraints experienced by intersectional communities. This paper also argues that the ethos of care and safe spaces, which are central to black feminist thought, are vital to community-led design and underpin the 10 design justice principles when executed in practice.
Synergetic fusion of Reinforcement Learning, Grey Wolf, and Archimedes optimization algorithms for efficient health emergency response via unmanned aerial vehicle

Abstract Owing to the recent technological innovations, unmanned aerial vehicles (UAVs) are progressively employed in various civil and military applications, including healthcare. This requires estimating an optimum route under various real‐world complexities, such as non‐uniform obstacles. However, most of the reported work considers only uniform obstacles as an object, which limit their practical applicability. Hence, Archimedes optimization algorithm (AOA) is examined to overcome this limitation. Further, it is observed that many a time, AOA over‐exploits the search space, resulting in higher computational time. Therefore, the present work fuses AOA with grey wolf optimizer (GWO) to improve the convergence capability. Also, reinforcement learning (RL) is employed to intelligently switch between the exploration and exploitation phases. The efficacy of the developed algorithm is statistically analysed and validated against various metaheuristics on several benchmark functions. The simulated results verified that the developed RLGA provides optimal or near‐optimal solutions more efficiently relative to other metaheuristics. Moreover, it also affirms the hypothesis that the proposed modifications significantly improve the convergence speed of AOA. Finally, the appropriateness of RLGA is tested and validated by rigorous experimentation on real‐world 3D‐route estimation problems for UAVs. The simulated results reveal that RLGA produces a flyable path with 51.46%, 62.06%, and 70.42% lesser cost than RLGWO, AOA, and GWO, respectively. This ensures the employability of RLGA for efficient medical assistance in minimum time‐, energy‐, and transportation‐cost with safe and smooth UAV auto‐navigation for developing drone doctors.
Bertalign: Improved word embedding-based sentence alignment for Chinese–English parallel corpora of literary texts

Abstract Bertalign is designed to improve sentence alignment accuracy for Chinese–English parallel corpora of literary texts. Aligning bilingual literary texts is not trivial, since most of the translation is interpretative and not based on 1-to-1 mappings between source and target sentences. Existing alignment methods highlight 1-to-1 links while having difficulty coping with 1-to-many and many-to-many alignments that are common in literary texts. To overcome the weaknesses of current approaches, we propose a novel two-step algorithm for bilingual sentence alignment. The first step finds the optimal paths for 1-to-1 alignments based on the top-k most semantically similar target sentences for each source sentence using the bidirectional encoder representations from transformer-based cross-lingual word embeddings. The second step relies on search paths found in the previous step to recover all valid alignments with more than one sentence on each side of the bilingual text. A comprehensive experiment was conducted on a newly built Chinese–English literary parallel corpus and a large-scale publicly available bilingual corpus of the Bible to compare the performance of Bertalign with five baseline systems: Gale-Church, Hunalign, Bleualign, Bleurtalign, and Vecalign. The results show that Bertalign achieves the highest accuracy in terms of F1 score on the two evaluation datasets than previous methods.
Multi‐feature fusion attention network for single image super‐resolution

Abstract Single Image Super-Resolution algorithms have made enormous progress in recent years. However, many previous Convolution Neural Network (CNN) based Super-Resolution algorithms only stack uniform convolution layers of fixed kernel size, and frequently ignore inherent multi-scale properties of the images, resulting in unsatisfactory reconstruction results. Here, a multi-feature fusion attention network (MFFAN) is proposed for capturing information at diverse scales. MFFAN is composed of multiple efficient sparse residual group (ESRG) modules. Several multi-scale feature fusion blocks (MSFFB) are constructed using a cascade manner in each ESRG module and it is capable of exploiting various cross scales information. Subsequently, a local-global spatial attention block (LGSAB) is inserted at the tail of the ESRG module for further improving the interaction of inter-pixel, which strengths essential features and suppresses irrelevant information. Additionally, owing to the fact that only feeding final output into the reconstruction layer has exacerbated the long-range dependency problems, an enhanced hierarchy feature fusion block (EHFFB) is designed to fuse low-level information and high-level semantic information. Experiment results indicate that the proposed MFFAN is competitive in comparison to several state-of-the-art algorithms.
Fault detection for discrete-time fuzzy systems with measurement errors using interval observers

This paper presents an interval observer-based fault detection (FD) strategy for discrete-time T–S fuzzy systems with measurement errors. The system and measurement outputs are selected as the premise variables of plant and observer respectively. The bounds of mismatch items caused by the measurement errors are established by covering matched region, mismatched left adjacent region and right adjacent region. Piecewise Lyapunov function, taking full account of possible transitions, is employed to drive observer design condition. FD is turned into optimization problem with disturbance attenuation, fault sensitivity and nonnegativity constraints. The decision is implemented by judging whether zero is excluded from the residual interval. Finally, simulation is explored to verify the scheme.