Reviewerly/exHarmony huggingface dataset:
The effects of the superstar learning system on learning interest, attitudes, and academic achievements

The COVID-19 pandemic has contributed to the production of a mobile learning app--the Superstar Learning System. Designed by Superstar Company, the Superstar Learning System is a mobile application for curriculum learning assisted with PPTs, knowledge dissemination, and management sharing based on micro-service architecture. To identify the effects of the Superstar Learning System, we randomly selected 71 tertiary students to participate in the study. The data were obtained through three research instruments: a scale to measure learning interest, a scale to measure learning attitudes, and a scale to measure academic achievements. Based on the analysis of a Mann-Whitney U test, it was concluded that the Superstar Learning System could: (1) enhance significantly more learning interest than the multimedia projecting system, (2) cultivate significantly more positive attitudes of students than the multimedia projecting system, and (3) lead to significantly better academic achievements than the multimedia projecting system. In the future, interdisciplinary cooperation, involving statistics, computer sciences, education, linguistics, and related disciplines, is necessary to arrive at solid conclusions.
Sentiment Analysis of Text Memes: A Comparison Among Supervised Machine Learning Methods

Meme is a new form of content in social media. A meme contains sentiment towards a particular issue, product, person, or entity. Memes can be in the form of text, images, or images that contain text. Memes are entertaining, critical, sarcastic, and may even be political. Traditional sentiment analysis methods deal with text. This study compares the performance of four sentiment analysis methods when used on Indonesian meme in the form of text and images that contain text. Firstly, the extraction of text memes was carried out, followed by the classification of the extracted text memes using supervised machine learning methods, namely Naïve Bayes, Support Vector Machines, Decision Tree, and Convolutional Neural Networks. Based on the experimental results, sentiment analysis on meme text using the Naïve Bayes method produced the best results, with an accuracy of 65.4%.
Improved Evaluation and Generation Of Grid Layouts Using Distance Preservation Quality and Linear Assignment Sorting

Abstract Images sorted by similarity enables more images to be viewed simultaneously, and can be very useful for stock photo agencies or e‐commerce applications. Visually sorted grid layouts attempt to arrange images so that their proximity on the grid corresponds as closely as possible to their similarity. Various metrics exist for evaluating such arrangements, but there is low experimental evidence on correlation between human perceived quality and metric value. We propose distance preservation quality (DPQ) as a new metric to evaluate the quality of an arrangement. Extensive user testing revealed stronger correlation of DPQ with user‐perceived quality and performance in image retrieval tasks compared to other metrics. In addition, we introduce Fast linear assignment sorting (FLAS) as a new algorithm for creating visually sorted grid layouts. FLAS achieves very good sorting qualities while improving run time and computational resources.
Target association rule mining to explore novel paediatric illness patterns in emergency settings

To assess the hospitalized sick children admitted to the pediatric emergency department (ED) and to find new patterns of clinical and laboratory attributes using association rule mining (ARM).In this observational study, 158 children with median (IQR) age 11 months and a PRISM III score of 5 (2-9) were enrolled. Hotspot data mining method was applied to assess clinical attributes, lab investigations and pre-defined outcome parameters of children and their association in sick hospitalized children aged 1 month to 12 years.We obtained 30 rules with value for outcome as discharge is given attributes as follows: duration of hospitalization > 4 days, lactate > 1.2 mmol/L, platelet = 3.67/μL, dur_ventil = 0 h, serum K = 5.2 mmol/L, SBP = 120 mmHg, pCO2 = 41.9 mmHg, PaO2 = 163 mmHg, age = 92 months, heart rate > 114-159 per minute, temperature > 98 °F, GCS (Glasgow Coma Scale) > 7-14, gas K = 4.14 mmol/L, gas Na = 138.1 mmol/L, BUN (Blood Urea Nitrogen) = 18.69 mg/dL, Diagnosis > 1-718, Creatinine = 1.2 mg/dL, serum Na = 148 mmol/L, shock = 2, Glucose = 144 mg/dL, Mg(i) > 0.23 meq/L, BUN > 6.54 mg/dL.ARM is an effective data analysis technique to find meaningful patterns using clinical features with actual numbers in pediatric critical illness. It can prove to be important while analysing the association of clinical attributes with disease pattern, its features, and therapeutic or intervention success patterns.
Knowledge Navigated Quantum-inspired Tabu Search Algorithm for Reversible Circuit Synthesis

Reversible circuits are the essential building blocks of quantum computers and have zero energy dissipation. However, there is no general rule on how to synthesize an effective circuit with minimal cost. Many researchers have placed a high value on the design of algorithms for reversible circuit synthesis as it is the fundamental component to implement in many paradigms, such as Shor’s algorithm. In this paper, the knowledge navigated quantum-inspired tabu search algorithm (KNQTS) is proposed to synthesize several benchmark functions. KNQTS is a quantum-inspired algorithm with the concept of getting closer to the best solution and keeping away from the worst solution, and it has a great search ability. Furthermore, the proposed algorithm uses self-adaptive, global-best guided and Quantum-Not gate mechanisms to make all procedures more efficient and avoid sticking to the local optimum. This paper also compares the KNQTS approach’s experimental results with those obtained via other metaheuristics and previous algorithms. Finally, the result shows that the proposed KNQTS method outperforms other state-of-the-art methods and achieves the same functionality at a lower cost. The solutions are optimal or near-optimal.
What is category theory to cognitive science? Compositional representation and comparison

Category theorists and cognitive scientists study the structural (analogical) relations between domains of interest albeit in different contexts, that is, formal and psychological systems, respectively. Despite this basic commonality, very few cognitive scientists take a category theory approach toward understanding the structure of cognition which raises the question, What is category theory to cognitive science? An answer is given as the slogan "Category theory is to cognitive science as functor is to representation; as natural transformation is to comparison" to make category theory more accessible and informative for cognitive scientists.
Chinese Fine‐Grained Geological Named Entity Recognition With Rules and FLAT

Abstract Geological named entity recognition (NER) is an essential prerequisite to realizing geological information extraction and information retrieval and is an actual means for accomplishing structured reconstruction of unstructured geological data. Existing geological NER methods mainly focus on coarse‐grained geological entity recognition, but geological entities are fine‐grained. To solve this problem, a Chinese fine‐grained geological entity corpus encompassing 21 types of fine‐grained labels is constructed. In addition, in this article, a fine‐grained geological entity recognition model based on Bidirectional Encoder Representations from Transformer (BERT)‐Flat‐Lattice Transformer is designed. This paper names this method FGNER ( F ine‐grained G eological N amed E ntity R ecognition) which adds geological naming rules to revise the model results to improve the recognition of complex geological entities. The fine‐grained geological entity recognition method is evaluated using regional geological literature reports as experimental data. The experimental results show that the precision, recall, and F1‐score of the FGNER model are 95.73%, 89.26%, and 92.05%, respectively, thus achieving better performance than baseline models, such as BERT‐Conditional Random Field.
Dicke-like superradiance of distant noninteracting atoms

Fully excited two-level atoms separated by less than the transition wavelength cooperatively emit light in a short burst, a phenomenon called superradiance by R. Dicke in 1954 [Phys. Rev. 93, 99 (1954)]. The burst is characterized by a maximum intensity scaling with the square of the number of atoms $N$ and a temporal width reduced by $N$ compared to the single atom spontaneous decay time. Both effects are usually attributed to a synchronization of the electric dipole moments of the atoms occurring during the process of light emission. Contrary to this explanation, it was recently shown by use of a quantum path description that the peak intensity results from the quantum correlations among the atoms when occupying symmetric Dicke states. Here we investigate from this perspective the temporal evolution of the ensemble, starting in the small sample limit, i.e., when the atoms have mutual separations much smaller than the transition wavelength $\ensuremath{\lambda}$ and pass down the ladder of symmetric Dicke states. In addition, we explore the temporal evolution for the case of distant noninteracting atoms with mutual separations much larger than $\ensuremath{\lambda}$. We show that in this case a similar superradiant burst of the emitted radiation is observed if the quantum correlations of the atoms are generated by conditional photon measurements retaining the atomic ensemble within or close to the symmetric subspace.
Faculty perceptions, use, and needs of library resource and services in a public research university

This research aims to examine whether faculty's perceptions of the importance of the library's resources and services for their teaching, clinical practice or research were different based on disciplines, and to explore the degree that faculty's perceptions of the library's importance and its role have changed over time. The findings revealed that online journals, databases, and Interlibrary loan (ILL) were considered by the majority of faculty as the most important for faculty's research. Another key finding indicated that faculty from social sciences perceived assistance from a subject specialist librarian as more important than faculty members from physical sciences. The top three strategies faculty use to obtain articles not available through the university include searching for a freely available online version, using ILL, and obtaining it from Google Scholar. These findings assist the library to better understand our user community's needs. As this survey is regularly deployed, it also allows us to observe potential changes over time, and as necessary, make strategic changes to library resources and services.
Random area pixel variation and random area transform for visible-infrared cross-modal pedestrian re-identification

In this paper, we propose the Random Area Pixel Variation (RAPV) which is a straightforward and enormously effective data enhancement method for visual-infrared cross-modal pedestrian re-identification (VI-REID) and extend this method as the Random Area Transform (RAT). The difficulty of the VI-REID task is to correlate features of heterogeneous data from both modalities. RGB images and IR images contain three channels and one channel, respectively. We add or subtract a random value to the pixel values for each channel of the two types of images, which enables them to retain the image structure information even with random color changes. Also, this allows more correspondence to be established between the two modal images. In this way, the models can find correlative features with better robustness. In addition, to control the noise generated by random variations, we apply the method only to a random rectangular area of arbitrary size in the image, which is RAPV. Simultaneously, we found that global data augmentation methods provide better results when used locally, so we make these methods applicable to a random rectangular region of arbitrary size in the image, which is RAT. Both RAPV and RAT use very few computational resources and can be flexibly integrated into existing models, outperforming many existing methods when applied only to baseline networks.
Security analysis and improvement of a public auditing scheme for secure data storage in fog-to-cloud computing

Human society has drastically changed as a result of the widespread application of Internet of Things (IoT) technology, particularly in various industrial fields. In spite of the advantages brought by IoT services, the underlying security challenges cannot be underestimated. Cloud-based IoT data collection poses a number of challenges, including how to ensure its integrity. In order to address this issue, Tian et al. have proposed a fog-to-cloud computing-based public auditing scheme in IoT scenarios, which provides the data-privacy preserving mechanism and tag transformation strategy. However, in this paper, we show that Tian et al.'s scheme fails to achieve soundness, an essential security property, by giving two attacks. In the first attack, the malicious cloud server can delete all the data and then deceive the Third Party Auditor (TPA) into believing that the data is well-preserved. In the second attack, the malicious cloud server can modify the outsourced data and then deceive TPA into believing that all the data is kept intact. We further provide a simple but effective solution for Tian et al.'s scheme to resist the above-mentioned attacks. Security analysis and performance evaluation are also given to demonstrate the robustness and high efficiency of the improved scheme.
Virtual Reality images’ impact on cognition and affect in hotel promotions: Application of self-reported and psycho-physiological measures

The current study examines how two types of Virtual Reality (VR) hotel images impact cognition and affect when used for promotional purposes. The study employs psycho-physiological measurement tools along with a self-reported survey method to evaluate: (1) affective responses, (2) Cognitive Load, and (3) and attitudinal and behavioral intention responses that two types of VR hotel images, static and 360°, produce in an experimental lab setting. A boutique hotel lobby and a guest room were captured as static and 360° VR images. Participants were randomly assigned to one of the two types of VR images. The sample was comprised of 60 university students from the South-Central United States. Parametric and non-parametric tests were used to compare survey, fNIR, Biopac MP-160 skin conductance and heart rate, and eye tracking responses. No statistically significant differences between two experimental conditions were found by means of self-reported measures, except temporal dimension of Cognitive Load. However, psycho-physiological measures detected statistically significant differences between the two experimental groups in arousal (one of the main affective dimensions), thus, supporting usefulness of adding psycho-physiological tools in consumer behavior studies and usage of more immersive and engaging 360° VR images. The findings also help to explain inconsistencies in previous VR imaging studies that relied on self-reported measures only.
Vbswp-CeaH: Vigorous Buyer-Seller Watermarking Protocol without Trusted Certificate Authority for Copyright Protection in Cloud Environment through Additive Homomorphism

Cloud-based storage ensures the secure dissemination of media. Authentication and integrity are important aspects in the distribution of digital media. Encryption-based techniques shelter this media between the communicating parties which are involved in a transaction. The challenge is how to restrict the digital media which is illegally redistributed by the authorized users. However, the digital watermarking technique and encryption-based methods are also not sufficient enough to provide copyright protection. The watermarking protocol is used to provide intellectual property for the customer and the service provider. This research paper provides a vigorous buyer-seller watermarking protocol without trusted certificate authority for copyright protection in the cloud environment. This research work uses the cloud environment which enables the cloud as a service infrastructural provider for storing credentials such as public and private secret keys and the digital certificates of interacting parties. The scheme uses additive homomorphism encryption with an effective key exchange algorithm for exchanging digital media. This proposed approach addresses the problems of anonymity and copy deterrence and protects the digital rights of the buyer and seller; these most up-to-date issues are related to information security. Furthermore, the experiment results conclude that the proposed protocol is flexible and secure even in a non-secure communication channel. We have used performance measures such as PSNR, NCC and cost in time methods for checking the integrity of the proposed protocol. The conducted experiments show a stronger robustness and high imperceptibility for the watermark and watermarked images.
Software Risk Prediction: Systematic Literature Review on Machine Learning Techniques

The Software Development Life Cycle (SDLC) includes the phases used to develop software. During the phases of the SDLC, unexpected risks might arise due to a lack of knowledge, control, and time. The consequences are severe if the risks are not addressed in the early phases of SDLC. This study aims to conduct a Systematic Literature Review (SLR) and acquire concise knowledge of Software Risk Prediction (SRP) from the published scientific articles from the year 2007 to 2022. Furthermore, we conducted a qualitative analysis of published articles on SRP. Some of the key findings include: (1) 16 articles are examined in this SLR to represent the outline of SRP; (2) Machine Learning (ML)-based detection models were extremely efficient and significant in terms of performance; (3) Very few research got excellent scores from quality analysis. As part of this SLR, we summarized and consolidated previously published SRP studies to discover the practices from prior research. This SLR will pave the way for further research in SRP and guide both researchers and practitioners.
Choosing the Right Educational Technology Tool for Your Teaching: A Data-Privacy Review and Pedagogical Perspective into Teaching with Technology

Recently, there has been a demand for increased educational technology usage in the university classroom, despite expanded access and implementation there has been little attention paid to student data privacy concerns. Our work is one institution's response to addressing the data privacy gap by creating a comprehensive review process for educational technology discernment and adoption. Our review process provides a critical degree of reliability and validity for informed decision-making surrounding educational technology selection. We prioritize the frames of safety, pedagogy, consistency, and accessibility throughout the review. With the creation of this review process educational technology stakeholders can make empirically based decisions regarding educational technologies. While this work is only one institution's response, we believe that this process and approach can empower a broad set of stakeholders to consider their method behind adopting educational technologies.
Autonomous trajectory planning method for hypersonic vehicles in glide phase based on DDPG algorithm

An autonomous optimal trajectory planning method based on the deep deterministic policy gradient (DDPG) algorithm of reinforcement learning (RL) for hypersonic vehicles (HV) is proposed in this paper. First, the trajectory planning problem is converted into a Markov Decision Process (MDP), and the amplitude of the bank angle is designated as the control input. The reward function of the MDP is set to minimize the trajectory terminal position errors with satisfying hard constraints. The deep neural network (DNN) is used to approximate the policy function and action-value function in the DDPG framework. The Actor network then computes the control input directly according to flight states. Using a limited exploration strategy, the optimal policy network would be considered fully trained when the reward value reached maximum convergence. Simulation results show that the policy network trained using a DDPG algorithm accomplishes 3-dimensional (3D) trajectory planning during the HV glide phase with high terminal precision and stable convergence. Additionally, the single step calculation time of the policy network occurs in near real time, which suggests great potential as an autonomous online trajectory planner. Monte Carlo experiments prove the strong robustness of the implementation of an autonomous trajectory planner under aerodynamic disturbances.
An empirical evaluation of the “Cognitive Complexity” measure as a predictor of code understandability

Code that is difficult to understand is also difficult to inspect and maintain and ultimately causes increased costs. Therefore, it would be greatly beneficial to have source code measures that are related to code understandability. Many "traditional" source code measures, including for instance Lines of Code and McCabe's Cyclomatic Complexity, have been used to identify hard-to-understand code. In addition, the "Cognitive Complexity" measure was introduced in 2018 with the specific goal of improving the ability to evaluate code understandability. The goals of this paper are to assess whether (1) "Cognitive Complexity" is better correlated with code understandability than traditional measures, and (2) the availability of the "Cognitive Complexity" measure improves the performance (i.e., the accuracy) of code understandability prediction models. We carried out an empirical study, in which we reused code understandability measures used in several previous studies. We first built Support Vector Regression models of understandability vs. code measures, and we then compared the performance of models that use "Cognitive Complexity" against the performance of models that do not. "Cognitive Complexity" appears to be correlated to code understandability approximately as much as traditional measures, and the performance of models that use "Cognitive Complexity" is extremely close to the performance of models that use only traditional measures. The "Cognitive Complexity" measure does not appear to fulfill the promise of being a significant improvement over previously proposed measures, as far as code understandability prediction is concerned.