Reviewerly/exHarmony huggingface dataset:
Subjective Assessment of the Quality of Video Sequences by the Young Viewers

The paper presents the results of subjective tests of the general evaluation of the quality of video signals subjected to the encoding -H.264 / A VC (Audio Video Coding), H.265 / HEVC- (High-Efficiency Video Coding) and developed by Google VP09. The assessment was made at home, that is, when young people potentially watch movies on their laptops. The video signal was assessed by young viewers who were not experts in the field of quality assessment. The tests were performed taking into account different image resolutions and different bitrates. The results obtained show that the best quality for the H.265 standard was obtained for a minimum bitrate of 3000 kbps, while the value of 2000 kbps already provides acceptable image quality. In the remaining tested coding standards, the bitrates are higher and amount to 4000 and 2500 kbps, respectively.
WhatsApp Profile Photo Investigation in Relation with Personality Traits Prediction

Personality is closely related to performance, but the implementation of personality tests is not easy to carry out, with a short time and minimal bias. As the most popular private messaging application, WhatsApp is not only used in social but also in professional communication. Its intimate nature makes the profile photo more personal than other social media. This study aims to predict a person's personality based on a WhatsApp profile photo by extracting content features. In this study, 132 respondents have filled out the Big Five Personality Test online and have allowed us to access their WhatsApp profile photos. The results of this study indicate, except for openness, that there is a significant relationship between personality traits and pictures used as profile photos. The content features of WhatsApp profile photos can also be used to predict personality traits. These findings prove that someone's personality affects the selection of their WhatsApp profile photos and vice versa.
Blind Additive Gaussian White Noise Level Estimation using Chi-square Distribution

It is important for image denoising methods with accurate noise level on real-world noisy images. Traditional noise level estimation methods either overestimate or underestimate the noise level. The former will make denoising methods smooth rich structures while the latter will make them reduce noise incompletely. To accurately estimate AGWN level, this paper proposes a novel blind additive Gaussian white noise level estimation method using Chi-square distribution, including the following key points: First, we select an initial flat patch set from the base image, which is decomposed from the noisy image by the relative total variation. And the initial noise level is estimated by mapping the patch set to the original noisy image. Then, we get the detail images by the usage of the directional gradient operations on the noisy image. Next, the initial flat patches are refined by a patch selection method with initial noise level and Chi-square distribution on the detail images. Finally, an iterative criterion is reemployed to generate a stable noise level. Experimental results validate that the proposed noise level estimation method is effective and is even superior to the state-of-the-arts.
Overfitting multiplier parameters for content-adaptive post-filtering in video coding

Previous works have shown that artefacts generated by lossy video compression can be reduced by content-adaptive filtering via overfitting Neural Network (NN) filters on test content, and signalling a compressed weight update. This approach is applied to post-processing filtering, and three main contributions are proposed. Firstly, a new set of learnable parameters, named multipliers, are incorporated into the NNs, and only those are overfitted. Secondly, a new training scheme is proposed for jointly training multiple NNs, and used to adapt the weights of an efficient NN architecture (originally designed for in-loop filters) to function as post-filters. Thirdly, the weight update is signalled via a newly-designed Supplemental Enhancement Information (SEI) message. The proposed post-filter saved about 5.01% (Y), 18.95% (Cb), 17.33% (Cr) Bjøntegaard Delta rate (BD-rate) on top of the Versatile Video Coding (VVC) Test Model (VTM) 11.0 NN-based Video Coding (NNVC) 1.0, Random Access (RA) Common Test Conditions (CTC). Additional experiments provide comparisons of overfitting different parameters and trade-offs between overfitting time and coding gains.
An Empirical Evaluation of Internet of Things (IoT) Implementations in the Libraries

Internet of Things (IoT) innovation has significantly influenced the role and responsibilities of libraries in educational institutions, societies, and business organizations. In libraries, IoT will be used to evaluate user activities, feedback, and service efficiency, among other things. It is also beneficial in information management and evaluation. The present study aims to investigate the role of the IoT as a new technology in libraries. Library professionals should always be familiar with the various elements of IoT in library resources and services, even as technological advances have improved users' expectations of library resources and services. They also need to be educated on IoT, cyber security in libraries, and user lifestyle. The Internet of Things will continue to impact libraries and their services through creation, archive management, instruction, data protection, and information literacy. The study also is a novel attempt to propose readers to new-age IoT-related technologies that can help libraries improve external services and increase user satisfaction.
Efficient Deblurring Via High-Frequency and Low-Frequency Information Fusion

The distortion of high-frequency information is the most fundamental problem of dynamic scene blur, which leads to the degradation of image quality. However, most deep-based methods fail to show satisfactory results because of ignoring the importance of image structural information (high-frequency and low-frequency preception) in deblurring. In this paper, we propose a high-frequency and low-frequency information fusion deblurring network (HLFNet) that uses edge perception as a guide. The proposed HLFNet consists of the high-frequency information network (HFNet) and the low-frequency information network (LFNet). Besides, we adopt the proposed multi-scale atrous convolution (MSA) block into LFNet, which can effectively reduce the number of model parameters while expanding the receptive fields. Extensive experiments show that the proposed model can achieve state-of-the-art results with smaller parameters and shorter inference time on the public datasets.
Open, Collaborative Commons: Web3, Blockchain, and Next Steps for the Canadian Humanities and Social Sciences Commons

This paper provides an update on the Canadian Humanities and Social Sciences (HSS) Commons, an in-development online hub for open social scholarship in Canada and beyond, and considers the next steps for the platform in an ever-evolving digital landscape. It outlines various recent outreach and engagement events intended to introduce the Canadian HSS Commons to the larger communities to which it belongs. Because the Canadian HSS Commons is committed to supporting the growth and evolving needs of these communities, this paper also considers how increasingly popular internet technologies such as Web3 and blockchain might play a part in the future of digital research infrastructure and the Canadian HSS Commons specifically. It concludes that while Web3 and blockchain currently raise important questions and concerns about governance, accountability, and commercialization, in the near future, these same technologies could also help engender new forms of functionality and participation on the Commons.
DISTANCE LEARNING IN SECONDARY VOCATIONAL EDUCATION

The article examines the disciplinary nature of the modern education system, mainly based on the symbolic representation of knowledge. The specificity of secondary vocational education is noted, which consists in its practical orientation, inherited from the institute of apprenticeship (old vocational school). It is pointed out that this property of secondary vocational education is an obstacle to the use of exclusively distance learning in it.
Forecasting the Number of Bugs and Vulnerabilities in Software Components Using Neural Network Models

The frequency of cyber attacks has been rising rapidly lately, which is a major concern. Because each attack exploits one or more vulnerabilities in the software components that make up the targeted system, the number of vulnerabilities is an indication of the level of security and trust that these components provide. In addition to vulnerabilities, the security of a component can also be affected by software bugs, as they can turn into weaknesses, which if exploited can become vulnerabilities. This paper presents a comparison of several types of neural networks for forecasting the number of software bugs and vulnerabilities that will be discovered for a software component in certain timeframe, in terms of accuracy, trainability and stability to configuration parameters.
Cancer Through the Lens of Social Media: An Examination of Cancer-Related Content on Reddit

Cancer incidence and mortality is increasing globally. The people affected constantly remain in distress the family is under enormous financial and psychological pressure. Social media provides an excellent outlet to vent out all the feelings that develop in one’s mind, which could prove critical for one’s health and survival in cases such as cancer. Reddit is one such social platform with its subreddit dedicated to folks affected by cancer. Subreddit is estimated to be sentiment heavy. We aimed to examine these sentiments and categorize the kind of discussions within the cancer subreddit community, with the intent of categorizing the topic of discussion. Using Python, push shift application programming interface (API) was used nearly ten thousand posts were extracted. After proper data cleaning, we validated the authenticity of data extracted via visualization, then performed Latent Dirichlet Allocation (LDA) to categorize these posts using libraries to separate the posts based on semantics. The topics were categorized in to 3, most topics belonged to category 1 which included emotions, immediate response to diagnosis.
HUMAN DEVELOPMENT AND GOVERNANCE IN AFRICA

This study examines the nexus between governance and human development in Africa. It uses data for the period 2010–2019 and takes into account the existence of spatial dependence and controls for the endogeneity problem through a Generalized Spatial Two Stage Least Squares (2SLS) technique. The exploratory spatial data analysis reveals the existence of spatial dependence on human development and governance quality. Our empirical findings support that in Africa, “good fences make good neighbors,” or proximity matters in the distribution of human development. Implications are discussed. This study complements the extant literature by using more updated data and employing an alternative and more robust estimation approach.
Interpretability and Explainability in Machine Learning

In machine learning, making a model interpretable for humans is becoming more relevant. Trust in and understanding of a model greatly increase its deployability. Interpretability and explainability are terms that refer to the understanding of a machine learning model. The relation between these two terms and the requirements for data mining tools are not always clearly defined. This chapter provides a framework for interpretability and provides a taxonomy of interpretability based on the literature. Properties of interpretability are related to the domain and to the methods involved. A distinction is made between inherently interpretable models and post-hoc interpretable models, which in the literature are also referred to as explainable models. This overview will argue that inherently interpretable models are more favorable for deployment than explainable models, which are not as reliable as inherently interpretable models.
Crypto-Economic Model for Data Security in IoT Network

The Internet of things (IoT) is the future research area in the networking domain. IoT is the Internet of things that connects everyday objects. It is the Internetwork of objects that enables these objects to collect the information from the environment and forward the information to the central server for further processing using several communication technologies. IoT is a new emerging technology that is used in variety of applications such as smart-building, smart-city, artificial intelligence, tracking, remote sensing, online emergency healthcare services. Routing plays a significant role in IoT network. This paper proposes a crypto-economic model for improving the security of data and determining the economic value of records in IoT Network.
Dynamic Key Extraction Technique Using Pulse Signal and Lightweight Cryptographic Authentication Scheme for WBAN

As a key component of ubiquitous computing, the wireless body area network (WBAN) can be used in a variety of disciplines, including health monitoring. Our everyday routines have been transformed by wearable technology, which has changed the medical industry and made our lives more convenient. However, the openness of the wireless network has raised concerns about the privacy and security of patient’s data because of the latent threat imposed by attackers. Patients’ sensitive data are safeguarded with authentication schemes against a variety of cyberattacks. Using pulse signals and a lightweight cryptographic approach, we propose a hybrid, anonymous, authentication scheme by extracting the binarized stream (bio-key) from pulse signal. We acquired 20 different sample signals to verify the unpredictability and randomness of keys, which were further utilized in an authentication algorithm. Formal proof of mutual authentication and key agreement was provided by the widely known BAN logic, and informal verification was provided by the Automated Validation of Internet Security Protocol and Applications (AVISPA) tool. The performance results depicted that storage cost on the sensor side was only 640 b, whereas communication cost was 512 b. Similarly, the computation time and energy consumption requirements were 0.005 ms and 0.55 µJ, respectively. Hence, it could be asserted that the proposed authentication scheme provided sustainable communication cost along with efficient computation, energy, and storage overheads as compared to peer work.
Research on Generalized Hybrid Probability Convolutional Neural Network

This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probability convolutional neural network (GHP-CNN) is proposed to solve abstract feature classification with an unknown distribution form. To measure the generalization ability of the CFM, a new index is defined and the positive correlation between it and the CFM is researched. Generally, a fully trained CFM can extract features that are beneficial to classification, regardless of whether the data participate in training the CFM. In the CNN, the fully connected layer in the MLP is not always optimal, and the extracted abstract feature has an unknown distribution. Thus, an improved classifier called the structure-optimized probabilistic neural network (SOPNN) is used for abstract feature classification in the GHP-CNN. In the SOPNN, the separability information is not lost in the normalization process, and the final classification surface is close to the optimal classification surface under the Bayesian criterion. The proposed GHP-CNN utilizes the generalization ability of the CFM and the classification ability of the SOPNN. Experiments show that the proposed network has better classification ability than the existing hybrid neural networks.
Reaching New Levels in Groundwater Monitoring

As regions around the world face record-breaking droughts, researchers are using seismology to track groundwater levels and show that sustainable policies reduce strain on aquifers.
Wavelet Packet Sub-band Cepstral Coefficient for Speaker Verification

Existing speaker verification systems perform well under clean condition, however their performance degrades significantly when subjected to noise interference. Current solutions usually include noisy data in the model training process to improve its noise immunity and generalization performance, but in fact, they do not reduce the noise impact from the feature extraction process. In the paper, Wavelet Packet Sub-band Cepstral Coefficient is proposed to solve this problem. It replaces the Discrete Fourier Transform in Mel-frequency Cepstral Coefficient with Wavelet Packet Decomposition and adding wavelet threshold denoising to reduce the noise data in wavelet sub-band coefficient. Updated the original threshold calculation method to increase the number of decomposition layers based on the Shrink threshold, and calculate the threshold independently on each frame of the signal. Experimental results on the publicly available dataset TIMIT show that the performance of Wavelet Packet Sub-band Cepstral Coefficient is improved in different degrees compared to both Mel-frequency Cepstral Coefficient and Filter Bank.
The tabular Strathbogie batholith in central Victoria: a comment on emplacement

"The tabular Strathbogie batholith in central Victoria: a comment on emplacement." Australian Journal of Earth Sciences, 70(1), pp. 145–146 AcknowledgementsI thank the Bureau of Mineral Resources (now Geoscience Australia) for the opportunity to carry out regional field mapping in North Queensland from 1957 to 1961 (Branch, 2007 Branch, C. D. (2007). Two strong legs and a winch rope. Self published, copies in Geoscience Australia and the National Libraries. [Google Scholar]), and the encouragement of Dave White as party leader [he wrote a paper about the Strathbogie Igneous Complex in 1954 and is referenced by Phillips et al. (2022 Phillips, G. N., Kisters, A. F. M., & Clemens, J. D. (2022). The tabular Strathbogie batholith in central Victoria. Australian Journal of Earth Sciences, 69(6), 776–800. https://doi.org/10.1080/08120099.2022.2032340[Taylor & Francis Online], [Web of Science ®] , [Google Scholar])]. Dr Norm Fisher as my Chief Geologist at BMR and Dr Tom Vallance at The University of Sydney are thanked as joint supervisors of my PhD studies.Disclosure statementNo potential conflict of interest was reported by the author(s).
The need for measurement science in digital pathology

Pathology services experienced a surge in demand during the COVID-19 pandemic. Digitalisation of pathology workflows can help to increase throughput, yet many existing digitalisation solutions use non-standardised workflows captured in proprietary data formats and processed by black-box software, yielding data of varying quality. This study presents the views of a UK-led expert group on the barriers to adoption and the required input of measurement science to improve current practices in digital pathology.With an aim to support the UK's efforts in digitalisation of pathology services, this study comprised: (1) a review of existing evidence, (2) an online survey of domain experts, and (3) a workshop with 42 representatives from healthcare, regulatory bodies, pharmaceutical industry, academia, equipment, and software manufacturers. The discussion topics included sample processing, data interoperability, image analysis, equipment calibration, and use of novel imaging modalities.The lack of data interoperability within the digital pathology workflows hinders data lookup and navigation, according to 80% of attendees. All participants stressed the importance of integrating imaging and non-imaging data for diagnosis, while 80% saw data integration as a priority challenge. 90% identified the benefits of artificial intelligence and machine learning, but identified the need for training and sound performance metrics.Methods for calibration and providing traceability were seen as essential to establish harmonised, reproducible sample processing, and image acquisition pipelines. Vendor-neutral data standards were seen as a "must-have" for providing meaningful data for downstream analysis. Users and vendors need good practice guidance on evaluation of uncertainty, fitness-for-purpose, and reproducibility of artificial intelligence/machine learning tools. All of the above needs to be accompanied by an upskilling of the pathology workforce.Digital pathology requires interoperable data formats, reproducible and comparable laboratory workflows, and trustworthy computer analysis software. Despite high interest in the use of novel imaging techniques and artificial intelligence tools, their adoption is slowed down by the lack of guidance and evaluation tools to assess the suitability of these techniques for specific clinical question. Measurement science expertise in uncertainty estimation, standardisation, reference materials, and calibration can help establishing reproducibility and comparability between laboratory procedures, yielding high quality data and providing higher confidence in diagnosis.
Breast Cancer Detection using Convolutional Neural Networks

Breast cancer is one of the most common cancers, especially among women. If diagnosed early, the treatment process can be easier and more successful. For this reason, it is of great importance to reach the right result quickly in the scans performed. Deep learning algorithms are one of the methods that can be used in the diagnosis of breast cancer because they provide fast and successful analysis opportunities. Convolutional Neural Networks (CNN), which is one of the deep learning methods, provides high success in image-based analysis. This article includes a research and application study on breast cancer diagnosis with CNNs. With the created CNN model, successful predictions with an recall of 84% were made.
Geometric property of energetic cost in transitionless quantum driving

Transitionless quantum driving is a useful approach to mimic the adiabatic evolution of a quantum system so that the system can rapidly evolve from its initial state to the target state without transitions between instantaneous eigenstates of the reference Hamiltonian. To quantify the energetic cost in the transitionless quantum driving process, the instantaneous cost is defined as the Frobenius norm of the driving Hamiltonian and the integral of instantaneous cost is used to describe the total cost in the entire evolution. In this paper, we find that the minimal integral of instantaneous cost has a geometric property being equal to the length of the evolution path on the Riemannian manifold spanned by the control parameters with a positive definite metric, but independent of the evolution details such as the changing rate of the parameters. Based on this property, we further show that the optimal transitionless quantum driving with the minimum total cost corresponds to the geodesic path on the Riemannian manifold, which provides a method for optimizing transitionless quantum driving.
An Approach for Securing JSON Objects through Chaotic Synchronization

Abstract Nowadays the interoperability of web applications is carried out by the use of data exchange formats such as XML and JavaScript Object Notation (JSON). Due to its simplicity, JSON objects are the most common way for sending information over the HTTP protocol. With the aim of adding a security mechanism to JSON objects, in this work we propose an encryption approach for cipher JSON objects through the use of chaotic synchronization. Synchronization ability between two chaotic systems offers the possibility of securing information between two points. Our approach includes mechanisms for diffusing and confusing JSON objects (plaintext), which yields a proper ciphertext. Our approach can be applied as an alternative to the existing securing JSON approaches such as JSON Web Encryption (JWE).