Reviewerly/exHarmony huggingface dataset:
A statistical model for computing global spectral solar irradiance under all-sky conditions at four stations situated in main regions of Thailand

This paper presents a statistical model for computing global spectral solar irradiance under all-sky conditions at four stations situated in main regions of Thailand. The model expresses the global spectral irradiance under all-sky conditions as a multiplication of two functions, namely, a function of global spectral irradiance under clear-sky conditions and a cloud modification function. To develop the model, global spectral solar irradiance was measured at four stations in the main regions of Thailand, namely Chiang Mai (18.77˚ N, 98.97˚ E) in the northern region of Thailand, Ubon Ratchathani (15.25˚ N, 104.87˚ E) in the north-eastern region of the country, Nakhon Pathom (13.82˚ N, 100.04˚ E) in the central region of this country, and Songkhla (7.18˚ N, 100.60˚ E) in the southern region of Thailand. The spectral data from these stations were gathered and divided into two groups. The first group (January 2017 to December 2020) was used for modeling and the second group (January 2021 to December 2021) for validating the model. The first function was developed using the first group of data. Clear-sky conditions were detected using sky images obtained from a sky camera installed at each station. To develop the second function, a satellite-derived cloud index derived from Himawari-8 data at the stations was utilized. To validate the model of the global spectral solar irradiance under all-sky conditions, the model was employed to compute the global solar irradiance at the four stations for the year 2021, and the findings were compared to the global solar irradiance determined from the measurements taken at the four stations. It was discovered that the spectral irradiance calculated from the model and that acquired from the measurements was in good agreement. There was a discrepancy in the form of root mean square difference relative to the mean measured value (RMSD) of 9.48%. We concluded that the model performed well in computing the global spectral solar irradiance.
Generalized Rank Dirichlet distributions

We study a new parametric family of distributions on the ordered simplex ∇d−1={y∈Rd:y1≥⋯≥yd≥0,∑k=1dyk=1}, which we call Generalized Rank Dirichlet (GRD) distributions. Their density is proportional to ∏k=1dykak−1 for a parameter a=(a1,…,ad)∈Rd satisfying ak+ak+1+⋯+ad>0 for k=2,…,d. The density is similar to the Dirichlet distribution, but is defined on ∇d−1, leading to different properties. In particular, certain components ak can be negative. Random variables Y=(Y1,…,Yd) with GRD distributions have previously been used to model capital distribution in financial markets and more generally can be used to model ranked order statistics of weight vectors. We obtain for any dimension d explicit expressions for moments of order M∈N for the Yk’s and moments of all orders for the log gaps Zk=logYk−1−logYk when a1+⋯+ad=−M. Additionally, we propose an algorithm to exactly simulate random variates in this case. In the general case a1+⋯+ad∈R we obtain series representations for these quantities and provide an approximate simulation algorithm.
Dual mode information fusion with pre-trained CNN models and transformer for video-based non-invasive anaemia detection

Anaemia, caused by low haemoglobin levels, affects billions of people worldwide. India has one of the highest anaemia rates among developing nations, according to WHO data. Non-invasive, low-cost, and accurate anaemia screening technologies are in high demand because invasive methods are expensive and difficult to administer globally. The current effort aims to construct a reliable anaemia detection system by combining cutting-edge computational methods with the age-old practise of imprecise assessment of haemoglobin levels from pallor in palm. The proposed method utilises time-domain analysis to establish the connection between blood haemoglobin concentration and colour changes in the palm caused by pressure application and release. A smartphone camera sensor which records the full event of palm colour changes generated by a customised device is processed and analysed. Some key frames are selected from extracted frames and passed to the pre-trained deep learning models for feature extractions. These features are then passed through vision transformers for classification and regression tasks. This approach have the power of pre-trained models for feature extraction and utilises the flexibility of transformer encoders and MLP networks for efficient and effective representation learning, classification, and regression. Two different approaches are proposed i.e., decision level and feature level fusion. The suggested approach accurately estimates blood haemoglobin levels with RMSE and accuracy of 0.483 and 96.296%, respectively based on 531 video samples of palm evidence.
Improvement of Validated Manufacturing Processes with Fuzzy Logic

SMEs are essential entities for the economy of countries, so they need to implement strategies to maintain and achieve economic stability. Technology is a great support tool to achieve this. Still, entrepreneurs, generally acting empirically, need to determine which technology to select, how to do it, and its scope of implementation. Fuzzy logic is a technology adapted to human linguistic thinking, which served as a methodology in this case study to measure the degree of behavior given by the implementation of software and hardware in a company. The result of the research validated the benefits that the gradual implementation of the technology presented to the company in terms of utility, time, and quantity of production, which were related in degrees of uncertainty with variables that were labeled as “high,” “medium,” and “low.” The established membership was validated through fuzzy graphs, showing the company’s status, and adjusted with the appropriateness of the technology until profits were placed within the “high” range. Thus, fuzzy logic is a tool that helps measure variables in degrees of belonging, with words used by business people to make it more understandable. Furthermore, the data coding in fuzzy terms showed the prediction of the behavior of the variables adjusted with technological processes.
Performance evaluation considering academic misconduct of China’s higher education institutions

Performance evaluation is essential for managing and allocating resources in higher education institutions (HEIs), guiding their development. However, in the current evaluation system, the growing issue of academic misconduct (AM) as an undesirable output in research assessments is highly overlooked. To fill this gap, we propose the creative inclusion of AM as an undesirable output in the evaluation system. To validate the potentially questionable nature of the evaluation system without AM, we took China’s 32 HEIs within the period of 2016–2018 as an example, separately calculating efficiencies without and with AM through DEA approach. Then we use Wilcoxon test of paired samples and Pearson correlation coefficient confirming that there are increasingly significant differences between the two efficiencies and the two rankings in the sample period, respectively. Therefore, we believe that other performance evaluations for HEIs that do not incorporate AM are likely to be questionable, such as the influencing factors of research efficiency and returns to scale (RTS). Incorporating AM into the evaluation index system, we further explore the influencing factors of research efficiency in sample HEIs through Tobit regression model. Then we propose innovative models tailored specifically for analyzing RTS of HEIs in which undesirable outputs are under extended strong disposability and apply them to the empirical study of China’s HEIs. The results show that (1) AM is playing an increasingly prominent role in the research efficiency evaluation of HEIs; (2) local economic development, external exchanges and emphasis on AM in HEIs can improve research efficiency; however, government funding has a negative effect on it while the effect of human capital is not significant; (3) the investment scale in most China’s HEIs is optimize or excessive.
Why and how bug blocking relations are breakable: An empirical study on breakable blocking bugs

Blocking bugs prevents other bugs from being fixed, which is difficult to repair and negatively impacts software quality. During software maintenance, developers usually try to break the blocking relationship between blocking and blocked bugs, e.g., propose a temporary fix. However, to our knowledge, no studies have investigated why and how blocking relations between bugs are breakable. In this study, we aim to construct an empirical analysis to explore breakable blocking bugs (BBBs). Specifically, we employ quantitative and qualitative analysis to study these BBBs from two aspects. One is to investigate the characteristics of these bugs, and the other is to explore why and how developers break the blocking relationship between bugs during software maintenance. We build a dataset on five large-scale open-source projects and classify bugs into three types (BBBs, normal blocking bugs, and other bugs) to compare the differences between BBBs and other types of bugs. We observe that BBBs have higher levels of involvement, take longer to fix, and involve more complex source code than other bugs. Moreover, we summarize four reasons blocking relationships between bugs are broken, i.e., partial association (41.87%), serious influence (26.40%), time pressure (19.73%), and flawed blocking (12.21%), and three measures developers adopt to break these blocking relationships, i.e., quick patch for blocking bugs (41.33%), quick patch for blocked bugs (38.67%), and ignore the blocking relation and fix blocked bugs directly (20.00%). Through these analyses, it is meaningful for software maintainers to have a deeper understanding of the characteristics and repair practices of BBBs, which will help solve these BBBs effectively in the future.
Joint task semi-supervised semantic segmentation for TRUS image

Transrectal ultrasound (TRUS) is an important tool for prostate observation, playing a crucial role in medical processes such as multimodal image registration and surgical planning. However, TRUS images are often plagued by blurry boundary information and artifacts, making manual annotation a time-consuming and labor-intensive task, which results in insufficient pixel-labeled data for training neural networks. Therefore, this paper introduces a semi-supervised neural network based on joint tasks for TRUS image segmentation, which combines confidence information from target localization and semantic segmentation to generate pseudo-labels for collectively training unlabeled samples. Additionally, this method supports the generation of pseudo-labels using only location information for weakly supervised training of neural networks. Compared to other state-of-the-art models, our results show that this approach outperforms in segmentation performance with a smaller dataset, achieving an mean Dice similarity coefficient and mean Jaccard similarity coefficient of 92.8 % and 88.4 %, respectively. Furthermore, the experiments reveal that reducing the coupling of two decoders' parameters between the segmentation and detection tasks improves segmentation accuracy as dataset size decreases.
Microdisk modulator-assisted optical nonlinear activation functions for photonic neural networks

On-chip implementation of optical nonlinear activation functions (NAFs) is essential for realizing large-scale photonic neural chips. To improve the reconfigurability and enrich the functions of photonic neural network (PNN), an NAF unit with a unique capability of performing multiple types of NAFs is highly preferred. In this work, we propose and experimentally demonstrate a microdisk modulator (MDM)-assisted NAF unit, in which multiple types of electro-optic NAFs with a high response speed and multiple types of all-optical NAFs with a low latency and reduced power consumption are performed in a single device. The fabricated MDM has an add-drop configuration, in which a lateral PN junction is incorporated to achieve high-speed resonance wavelength tuning. With the use of the high-speed nonlinear electro-optic effect, three different kinds of electro-optic NAFs including sigmoid function, radial basis function (RBF), and negative rectified linear unit (ReLU) function are realized by exploiting the free-carrier dispersion effect on silicon. Moreover, thanks to the strong optical confinement in the disk cavity, all-optical NAFs can be performed by exploiting the thermo-optic effect on silicon. In the experiment, four different kinds of all-optical NAFs including softplus function, RBF, clamped ReLU function, and leaky ReLU function are demonstrated. With the use of the realized clamped ReLU function, a convolutional neural network is simulated to perform a handwritten digit classification benchmark task, and an accuracy as high as 98 % is demonstrated. Thanks to its strong electro-optic and thermo-optic effects, the proposed MDM provides a unique capability of performing multiple types of electro-optic and all-optical NAFs, which is potential to be served as a flexible nonlinear unit in large-scale PNN chips.
Comparative Study of HMD-based Virtual and Augmented Realities for Immersive Museums: User Acceptance, Medium, and Learning

Technologies like Head Mounted Display (HMD)-based Virtual Reality (VR) and Augmented Reality (AR) have made HMD-based immersive museums possible. To investigate the user acceptance, medium, and interaction experience for HMD-based immersive museums, an app entitled The Extended Journey has been designed, developed, and deployed on both VR headsets and AR headsets. Subsequently, a between-subjects design experiment with 62 participants was conducted to measure the user experience and learning outcome in HMD VR and HMD AR conditions. Quantitative results revealed that HMD VR museums had statistically significantly better immersion and empathy compared to HMD AR museums. Qualitative data indicated HMD-based immersive museums were embraced by most young participants while HMD VR had better user acceptance than HMD AR for immersive museums. The interview also demonstrated that the advantage of the HMD-based immersive museum over the traditional online museum is not only the sensory immersion from the medium itself but also the interactive narrative experience that the HMD medium facilitates, especially the natural interaction with the CG characters and the environment in the story.
Cross-modal Multiple Granularity Interactive Fusion Network for Long Document Classification

Long Document Classification (LDC) has attracted great attention in Natural Language Processing and achieved considerable progress owing to the large-scale pre-trained language models. In spite of this, as a different problem from the traditional text classification, LDC is far from being settled. Long documents, such as news and articles, generally have more than thousands of words with complex structures. Moreover, compared with flat text, long documents usually contain multi-modal content of images, which provide rich information but not yet being utilized for classification. In this article, we propose a novel cross-modal method for long document classification, in which multiple granularity feature shifting networks are proposed to integrate the multi-scale text and visual features of long documents adaptively. Additionally, a multi-modal collaborative pooling block is proposed to eliminate redundant fine-grained text features and simultaneously reduce the computational complexity. To verify the effectiveness of the proposed model, we conduct experiments on the Food101 dataset and two constructed multi-modal long document datasets. The experimental results show that the proposed cross-modal method outperforms the single-modal text methods and defeats the state-of-the-art related multi-modal baselines.
A Hypersphere Information Granule-Based Fuzzy Classifier Embedded With Fuzzy Cognitive Maps for Classification of Imbalanced Data

In this article, a hypersphere information granule-based fuzzy classifier integrated with Fuzzy Cognitive Maps (FCM), named FCM-IGFC, is proposed for the classification of imbalanced data. The proposed FCM-IGFC is structured by sequentially linking a Takagi-Sugeno-Kang (TSK) fuzzy system (FCM-TSK) – which is embedded with FCMs – and a rule-based fuzzy system (IGFS) based on hypersphere information granules. The FCM-TSK leverages the inference capabilities of the FCM to allow for the movement of data within the original space, while the IG-FS creates a mapping between samples and majority and minority classes using information granules. In this study, we introduced an innovative bottom-up granulation method and an overlap elimination technique for constructing hypersphere information granules. These methods facilitate the creation of information granules that accurately represent the structure of classes, even when dealing with im- balanced data. Moreover, the stacked structure of the FCM-IGFC offers data transfer capabilities. This helps reduce the complexity of distributions such as small, disjointed clusters and irregular class boundaries, with the support of the FCM, thereby making it easier to use information granules to describe the class structure. A series of experiments conducted on 12 publicly available datasets demonstrated that the performance of FCM-IGFC significantly surpasses that of existing granule-based fuzzy classifiers. Additionally, it is competitive with top-tier classifiers that incorporate sampling methods.
Semi-supervised, Neural Network based approaches to face mask and anomaly detection in surveillance networks

In the post-pandemic world, surveillance cameras play a key aspect when it comes to detecting various kinds of security risks. These can range from burglars entering a premises to an individual wearing or not wearing a mask where convention dictates one way versus the other. We are proposing a system that would allow the autonomous detection of these security risks with minimal human intervention. We propose using Multi-task Cascaded Convolutional Neural Networks as the face detector, followed by a Gabor image feature extractor, and the Kernel-based Online Anomaly Detection algorithm for detecting and identifying potential risks in real-time. We have tested our proposed framework on five different datasets comprising two datasets from public online repositories, one publicly available dataset from the Georgia Institute of Technology, and two from real-life settings. We compared our results to the Viola Jones legacy face detector, and with two other anomaly detection algorithms based on Principal Component Analysis (PCA) and the Autoencoder scheme. Our proposed framework has yielded high detection rates with low false alarm rates, in addition to being adaptive, portable, and requiring minimal infrastructure.
Cyber offending predictors and pathways in middle adolescence: Evidence from the UK Millennium Cohort Study

Despite the pervasiveness of cyber crime victimisation, knowledge is limited regarding the prevalence, characteristics and pathways of offenders. The present study examines predictors of self-reported engagement in cyber crime in middle adolescence in a large (N = 13,277) longitudinal dataset from the UK Millennium Cohort Study. We adopted an ecological systems approach to examine a range of multicausal, intersecting factors across individual, familial, psychosocial and environmental systems. The overall prevalence of self-reported cyber offending (account hacking or the deployment of viruses) was 5.6% at age 14 and 3.8% at age 17, although persistence over time by the same individuals was relatively low (1.1%). Significant predictors of cyber offending at age 17 were being male, domestic violence between parents, low parental monitoring, low wellbeing, self-harm, exclusion from school, spending more time online gaming, participating in offline leisure activities, and engaging in serious violence (weapon carrying or use), assault, and cyber crime at age 14. Findings indicate that young cyber offenders are often males and those who have experienced a range of risk factors that are connected to poorer wellbeing and engaging in multiple risky/offending behaviours. Implications for theory, policy and practice are discussed.
BERT based severity prediction of bug reports for the maintenance of mobile applications

Mobile application maintenance is crucial to ensuring the accurate operation and continuous improvement of mobile applications (mobile apps). To effectively address issues and enhance the user experience, developers utilize issue-tracking systems that gather bug reports to refine mobile apps. Users can submit bugs through these systems, allowing them to determine the severity of each reported issue. The severity level plays a pivotal role in prioritizing bug resolution, enabling developers to address critical bugs promptly. Nonetheless, manually assessing the severity of each issue can be laborious and prone to errors. To overcome this challenge, this paper presents Bidirectional Encoder Representations from Transformers (BERT) based severity prediction of bug reports (called BERT-SBR) that leverages a deep neural network for automatic bug severity classification for mobile app maintenance. We collect the publicly available mobile apps bug reports dataset from the Hugging Face. BERT-SBR first computes the sentiment of reporters of bug reports and preprocesses them by leveraging BertTokenizer input formatting techniques. Next, it passes the formatted text and computed sentiment of each bug report to generate word embeddings. Then, it introduces a fine-tuned BERT classifier for bug report severity prediction. After that, it passes the generated word embeddings to the fine-tuned BERT classifier for training and testing. Finally, the proposed classifier’s performance is evaluated. The BERT-SBR assessment results confirm that the fine-tuned BERT classifies bug reports significantly more effectively than other deep learning classifiers. On average, BERT-SBR achieves a remarkable improvement of 40.43%, 67.78%, 40.71%, and 58.14% in the accuracy, precision, recall, and f-measure. This indicates its superiority in accurately predicting the severity of bug reports for mobile application maintenance.
Dynamic-group-aware networks for multi-agent trajectory prediction with relational reasoning

Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works mainly consider static, pairwise interactions with limited relational reasoning. To more comprehensively model interactions and reason relations, we propose DynGroupNet, a dynamic-group-aware network, which (i) models time-varying interactions in highly dynamic scenes; (ii) captures both pairwise and group-wise interactions; and (iii) reasons both interaction strength and category without direct supervision. Based on DynGroupNet, we further design a prediction system to forecast socially plausible trajectories with dynamic relational reasoning. The proposed prediction system leverages the Gaussian mixture model, multiple sampling and prediction refinement to promote prediction diversity for multiple future possibilities capturing, training stability for efficient model learning and trajectory smoothness for more realistic predictions, respectively. The proposed complex interaction modeling of DynGroupNet, future diversity capturing, efficient model training and trajectory smoothing of prediction system together to promote more accurate and plausible future predictions. Extensive experiments show that: (1) DynGroupNet can capture time-varying group behaviors, infer time-varying interaction category and interaction strength during prediction; (2) DynGroupNet significantly outperforms the state-of-the-art trajectory prediction methods by 28.0%, 34.9%, 13.0% in FDE on the NBA, NFL and SDD datasets.