Miracle-dz/UniFAR-FTUs huggingface dataset:
A Taxonomy of Challenges for Self-Sovereign Identity Systems

Creating and utilizing digital identities are fundamental steps towards accessing online services. In order to facilitate the management of user identities, the concept of identity management has been introduced. Various systems and protocols have been developed to manage online identities. However, these systems are provider-centric, focusing on aiding providers in managing their user bases. As a result, users often have limited control over their identity data and remain unaware of how centralized identity providers use or potentially misuse their data. Self-Sovereign Identity (SSI) has emerged as a new paradigm in the digital identity management landscape, aiming to empower users by allowing them greater control over their identity data. Although SSI is a relatively new domain, there have been numerous efforts, primarily from the industry, to introduce SSI standards, protocols, and systems, with multiple options in each category. Researchers eager to contribute to the SSI domain might find it challenging to understand the interconnections among these components. Notably, the SSI domain faces several challenges, as highlighted in various research works. These challenges must be addressed before SSI can achieve widespread adoption. This article presents a comprehensive systematic literature review of SSI, offers a detailed taxonomy, and identifies and analyzes the open challenges in SSI.
A conceptual hydrogeological model of ophiolitic aquifers (serpentinised peridotite): The test example of Mt. Prinzera (Northern Italy)

The main aim of this study is the experimental analysis of the hydrogeological behaviour of the Mt. Prinzera ultramafic massif in the northern Apennines, Italy. The analysed multidisciplinary database has been acquired through (a) geologic and structural survey; (b) geomorphologic survey; (c) hydrogeological monitoring; (d) physico‐chemical analyses; and (e) isotopic analyses.
THE WEST FERMANAGH AND ERNE LAKELAND ENVIRONMENTALLY SENSITIVE AREA: EVALUATION OF FACTORS INFLUENCING PARTICIPATION AND PROSPECTS FOR REFORM

This dissertation considers the delivery design of the ‘flagship’ of agri-environmental policy, the Environmentally Sensitive Area (ESA) scheme. The ESA scheme in Northern Ireland continues to offer eligible farmers standard payments for following fixed management prescriptions. ESAs have been subjected to Cost Benefit Analysis (CBA) on a number of occasions, satisfying Pareto efficiency stipulations in each case (Hanley et al., 1999). Although conjectured, no paper has endeavoured to show that an alternative delivery design could decrease the cost of management agreements within an ESA, while maintaining (or enhancing) the production of environmental goods. Participation was taken as a proxy for environmental success, as a revision of management prescriptions was not a part of the alternative design. Previous studies have shown that several socio-economic, demographic and scheme design factors affect farmers’ participation decision in ESA (Wilson, 1997a). Brotherton’s behavioural approach was used to analyse data collected using a questionnaire survey of 93 farmers eligible to participate in the West Fermanagh and Erne Lakeland ESA. A dichotomous choice, contingent valuation method was used to elicit the minimum payment (MAP) that farmers would accept to remain in the scheme. It has been suggested that a contract competition (auction) offers an alternative to the current ESA delivery method (Latacz-Lohmann, 2001). It was assumed that the MAP value is the bid that farmers would enter in a ‘conservation auction’. The auction was found to deliver an 18.6% reduction in management agreement costs for the current participation group. Alternatively, participation was increased by 24% for a 12% increase in management agreement budget. Farm size, payment rate and attitude to the control of farm decision making, were found to be important factors in the participation decision. The results show that further development of the conservation auction design could increase the efficiency and viability of ESAs, and other standard payment schemes across Europe. However, a thorough investigation of this designs transaction costs will be necessary, as will the development of procedures to prevent strategic bidding.
Molecular Evaluation of the Effects of FLC Homologs and Coordinating Regulators on the Flowering Responses to Vernalization in Cabbage (Brassica oleracea var. capitata) Genotypes

The flowering loci of cabbage must be understood to boost their productivity. In this study, to clarify the flowering mechanisms of cabbage, we examined the three flowering repressors BoFLC1, 2 and 3, and the flowering regulators BoGI, BoCOOLAIR, and BoVIN3 of early (CAB1), middle (CAB3), and late (CAB5) flowering cabbage genotypes. Analysis of allele-specifically amplified genomic DNA and various sequence alignments demonstrated that maximal insertions and deletions influenced cabbage flowering behavior, notably in CAB3 and CAB5. Phylogenetic studies showed that BoFLC1, 2, and 3 in the CAB1, 3, and 5 genotypes had the highest homologies to other Brassica species, with CAB3 and 5 the most similar. Although CAB3 and CAB5 have comparable genetic patterns, flowering repressors and flowering regulators were investigated individually with and without vernalization to determine their minor flowering differences. The expression investigation revealed that vernalized CAB5 downregulated all BoFLC genes compared to CAB3 and, in contrast, CAB3 exhibited upregulated BoCOOLAIR. We hypothesized that the CAB3 BoFLC locus’ additional insertions may have led to BoCOOLAIR overexpression and BoFLC downregulation. This study sheds light on cabbage genotypes—particularly those of CAB1 and CAB5—and suggests that structural variations in BoFLC2 and 3 bind flowering regulators, such as COOLAIR, which may affect cabbage flowering time.
Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances

This study emphasises the critical role of quality sleep in physical and mental well-being, exploring its impact on bodily recovery and cognitive function. Investigating poor sleep quality in approximately 40% of individuals with insomnia symptoms, the research delves into its potential diagnostic relevance for depression and anxiety, with a focus on intervention in mental health by understanding sleep patterns, especially in young individuals. This study includes an exploration of phone usage habits among young adults during PPI sessions, providing insights for developing the SleepTracker app. This pivotal tool utilises phone usage and movement data from mobile device sensors to identify indicators of anxiety or depression, with participant information organised comprehensively in a table categorising condition related to phone usage and movement data. The analysis compares this data with survey results, incorporating scores from the Sleep Condition Indicator (SCI), Patient Health Questionnaire-9 (PHQ-9), and Generalised Anxiety Disorder-7 (GAD-7). Generated confusion matrices offer a detailed overview of the relationship between sleep metrics, phone usage, and movement data. In summary, this study reveals the accurate detection of negative sleep disruption instances by the classifier. However, improvements are needed in identifying positive instances, reflected in the F1-score of 0.5 and a precision result of 0.33. While early intervention potential is significant, this study emphasises the need for a larger participant pool to enhance the model’s performance.
Comparison of Remimazolam versus Sevoflurane on the Postoperative Quality of Recovery in Cervical Spine Surgery: A Prospective Randomized Controlled Double-Blind Trial

Purpose Remimazolam is a newly developed ultra-short-acting benzodiazepine. We compared overall functional recovery, including the postoperative Quality of Recovery-15 (QoR-15) questionnaire scores, between balanced inhalational anesthesia using sevoflurane and total intravenous anesthesia (TIVA) with remimazolam in patients undergoing anterior cervical discectomy and fusion (ACDF). Patients and Methods Seventy-two patients were randomized to the remimazolam (group R) or sevoflurane (group S) group. The primary outcome was the total QoR-15 score on postoperative day (POD) 1. We also assessed the total QoR-15 score on POD2, sub-scores of the QoR-15, perioperative parameters, and postoperative recovery profiles. Group-time interaction effects on the QoR-15 and its sub-scores were analyzed using a linear mixed model. Results The total QoR-15 score on POD1 (120.2 in group R vs 114.3 in group S, P=0.189) was not statistically different between the groups. There were no significant group-time interaction effects on total QoR-15 scores. Instead, patients in group R showed significantly better sub-scores in psychological and postoperative nausea and vomiting (PONV) items on POD1, as well as a lower degree of PONV, than those in group S. Among the five dimensions of the QoR-15, a significant group-time interaction effect was observed for psychological support. Group R showed significantly less changeability in blood pressure and heart rate with a lower dose of intraoperatively administered vasopressor than group S. Conclusion Considering QoR-15, including PONV reduction, and intraoperative hemodynamic stability, remimazolam can be used as the novel and safe anesthetic agent for maintaining general anesthesia instead of sevoflurane in patients undergoing ACDF.
mDS-PCGR: A Bimodal Gait Recognition Framework With the Fusion of 4-D Radar Point Cloud Sequences and Micro-Doppler Signatures

Radar-based gait recognition has emerged as a promising solution for noninvasive human identification. However, relying solely on single-modal radar gait representations, such as micro-Doppler signature and radar point cloud, proves inadequate for robust gait recognition in the presence of complex perceptual conditions. Additionally, achieving a high level of generalization, particularly when dealing with new subjects having limited training samples, is crucial for practical gait recognition. To address these challenges, we present a novel joint micro-Doppler and radar point clouds gait recognition (mDS-PCGR) framework in this study. This framework fuses gait features derived from both micro-Doppler signatures and 4-D radar point cloud sequences. First, a tracking-based preprocessing method is proposed to acquire high-quality micro-Doppler signatures and 4-D radar point cloud sequences, while suppressing the multipath interference in complex perceptual conditions. Second, a dual-flow fusion network is designed to extract discriminative gait features based on complementation of the two modalities to each other. Finally, a metric-based few-shot learning mechanism is used to instruct the optimization of dual-flow fusion network, combining triplet loss with center loss to achieve the identification of new subjects with few training samples. Extensive evaluation on real 4-D millimeter-wave radar measurement under multipath interfered and cross-view conditions is provided. Experimental results show the superior performance of the proposed mDS-PCGR, leveraging effective gait information from two modalities. It outperforms single-modal gait recognition methods and achieves the highest identification accuracy for new subjects with limited gallery samples.
Understanding the effects of socialness and color complexity in listing images on crowdfunding behavior

PurposeColor psychology theory reveals that complex images with very varied palettes and many different colors are likely to be considered unattractive by individuals. Notwithstanding, web content containing social signals may be more attractive via the initiation of a social connection. This research investigates a predictive model blending variables from these theoretical perspectives to determine crowdfunding success.Design/methodology/approachThe research is based on data from 176,614 Kickstarter projects. A number of machine learning and artificial intelligence techniques were employed to analyze the listing images for color complexity and the presence of people, while specific language features, including socialness, were measured in the listing text. Logistic regression was applied, controlling for several additional variables and predictive model was developed.FindingsThe findings supported the color complexity and socialness effects on crowdfunding success. The model achieves notable predictive value explaining 56.4% of variance. Listing images containing fewer colors and that have more similar colors are more likely to be crowdfunded successfully. Listings that convey greater socialness have a greater likelihood of being funded.Originality/valueThis investigation contributes a unique understanding of the effect of features of both socialness and color complexity on the success of crowdfunding ventures. A second contribution comes from the process and methods employed in the study, which provides a clear blueprint for the processing of large-scale analysis of soft information (images and text) in order to use them as variables in the scientific testing of theory.
Optimisasi Hiperparameter Tuning pada Metode Support Vector Machine untuk Klasifikasi Tingkat Kematangan Buah Kopi

Penelitian ini bertujuan untuk mengembangkan model klasifikasi yang optimal untuk mengidentifikasi tingkat kematangan buah kopi berdasarkan fitur-fitur warna dari citra buah kopi. Fitur warna red, green dan blue pada ruang warna RGB dan hue, saturation dan value pada ruang warna HSV diekstraksi dari citra buah kopi dan digunakan sebagai input untuk model klasifikasi support vector machine (SVM). Agar kinerja SVM lebih optimal, digunakan hyperparameter tuning dengan metode grid search untuk penentuan parameter terbaik pada model klasifikasi yang dibangun. Dengan menggunakan 180 citra latih pada penentuan parameter optimum, diperoleh hasil hyperparameter tuning metode grid search yaitu pada cross validation (cv) = 6, cost (C) = 1000, gamma (γ) = 0,001, dan kernel = linear. Kemudian parameter optimum tersebut digunakan sebagai model SVM untuk mengklasifikasikan 45 citra uji ke dalam tiga kelas berbeda yakni matang, matang setengah dan mentah. Berdasarkan evaluasi dengan confusion matrix dapat disimpulkan bahwa model yang dibangun memiliki kinerja yang baik dengan tingkat akurasi 98% dengan. Hal ini mengindikasikan bahwa model yang dibangun mampu membedakan ketiga kelas dengan tingkat kesalahan yang rendah. Dengan kemampuan ini, model yang dibangun memiliki potensi besar dalam industri pertanian guna mendukung penggunaan teknologi pada bidang pertanian khususnya pertanian kopi seperti pengolahan buah pasca panen. Model ini dapat dikembangkan pada mesin sortasi tingkat kematangan buah kopi secara otomatis agar dapat meningkatkan kualitas dan efisiensi produksi serta membantu dalam pengambilan keputusan yang lebih baik.
Program Of Formative Assessment Instruments In The Management Of Learning Evidence Of Teachers Of The Arena – Piura, 2023

The purpose of this study was to determine the influence of the formative assessment instrument program on the management of learning evidence of teachers in La Arena, Piura. It is of the applied type because its specific purpose is to improve a problematic situation that affects teachers, who through the application of the Design of Evaluation Instruments Program improved the management of learning evidence. The study population consisted of 40 teachers at both educational levels. The sample consisted of 26 teachers who met the inclusion and exclusion criteria. To achieve this purpose, 15 work sessions were held at the teacher level, and all the sessions contributed to strengthening professional competencies in each of the dimensions of the dependent variable. We worked with a single group of teachers to whom a pre-test was applied, then the program was applied and then a post-test was applied. Comparing the pre-test and the post-test, it is confirmed that there are significant differences, allowing us to demonstrate that the application of the program has had significant effects.
Fish, wildlife and recreational values of Michigan's coastal wetlands /

A survey of the existing literature pertinent to Michigan's coastal wetlands reveals that these environments provide important ecological functions and economic values to the general public. Compared to the state's total, approximately 21 percent of the waterfowl harvest, 14 percent of the duck production, 11 percent of the muskrat take, 15 percent (by value) of the commercial fish landings, and a large proportion of the sport fishing occurs in the coastal wetlands or adjacent shallow waters. This is significant because a 1972 inventory revealed that Michigan has 105,855 acres of coastal wetlands which represents only 3.5 percent of the state's to total wetland acreage. In terms of average economic value for fish, wildlife and recreation, Michigan's coastal wetlands contribute an estimated $489.69 per wetland acre/year, for a total of $51.8 million. Depending on site conditions and recreational use, the annual economic return from individual wetland parcels may vary considerably about this average value. Specifically, these economic values, in decreasing order of value, are: sport fishing ($286), nonconsumptive recreation ($1 38 . 24 ) ,waterfowl hunting ($31. 2 3) , trapping of furbearers ($30 . 44) , and commercial fishing($3. 78 ) per wetland acre/year. Inclusion of other values, e.g., for waste assimilation and filtering of suspended solids, as well as ecological values, e.g., use of wetlands by endangered species, could increase the average annual economic return per acre to over $3,000. Techniques more comprehensive than those employed in thi-s study
Fertilizer concentration and irrigation method affects growth and flowering of two bedding plants

Zonal geranium (Pelargonium × hortorum ‘Real Mintaka’) and petunias (Petunia × hybrid Hort. Vilm.-Andr. ‘Giove’) were grown in closed soilless systems to evaluate the effects of irrigation system (drip and subirrigation) and nutrient solution concentration (half and full) during spring growing season in terms of substrate electrical conductivity (EC(s)), growth, quality, crop evapotranspiration, and growth index water use efficiency (WUE(GI)). For both crops, at the end of the cultural cycle the highest EC(s) in the upper and lower layers were recorded in plants fertilized with the full strength solution, whereas the effect of irrigation system was less pronounced where the highest EC(s) was recorded in subirrigation but only in the upper layer. The highest EC(s) recorded with subirrigation under full strength nutrient solution on geranium had a negative effect on plant growth parameters (shoot dry weight, root-to-shoot ratio, final leaf area, and growth index) and on number of flowers, while no significant effects were observed for petunia, indicating that this bedding plant can be considered more tolerant to salinity. No significant differences among treatments were recorded for the petunia WUE(GI), whereas for geranium the lowest value of WUE(GI) was observed in the subirrigation treatment using half strength nutrient solution.
Uaffect: Um Modelo para Predição de Estados Afetivos em Ambientes Educacionais a partir de Históricos de Contextos

Estudos apontam o crescimento e a relevância dos sistemas de reconhecimento de estados afetivos nas mais diferentes áreas. Sabe-se ainda, que existe uma forte relação entre emoções e aprendizagem. Assim, qual a importância das informações de contexto na predição de estados afetivos em ambientes educacionais? Dessa forma, este artigo propõe o Uaffect, um modelo computacional que utiliza históricos de contextos formados em ambientes educacionais para classificação e predição de estados afetivos. O modelo foi avaliado a partir de um estudo quase experimental com 25 alunos. Os resultados de precisão encontrados no melhor cenário são de 89% para estados afetivos positivos e de 61% para estados afetivos negativos. Os testes realizados apresentaram evidências que indicam a possibilidade de uso de históricos de contextos na classificação e predição de estados afetivos em ambientes educacionais, podendo ser usados para prover serviços inteligentes que auxiliem na tomada de decisão no planejamento educacional e regulação afetiva do aluno.
Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks

In autonomous driving research area, vehicular positioning has become a critical issue. For vehicular positioning, global positioning system (GPS) and inertial measurement unit (IMU) are commonly used. However, when GPS signal is lost or vehicular networks are interrupted, the positioning accuracy degrades. In order to improve the positioning accuracy, this article proposes a multiple data fusion framework of GPS/IMU aided by vehicular networks, which can optimize vehicle localization systems for accuracy and robustness. Specifically, at the first phase, attention-based long short-term memory (ALSTM) network is utilized to fuse GPS and IMU information. Comparing with traditional GPS and IMU fusion methods, this model avoids requiring any prior information about state transfer model. It aims to enhance the exploitation of past state information by integrating an attention layer above the long short-time memory (LSTM) layer to operate more optimally and comprehensively. At the second phase, the vehicular-to-everything (V2X) network is developed to further improve localization accuracy. A collaborative graph model-based on graph convolutional network (GCN) for vehicular networks is proposed. Each communication node is modeled as an undirected graph node, and can exchange its GPS information with other nodes. At the final phase, a nonlinear regression layer is designed to fuse the information from the prior two phases. Numerical results show that the proposed ALSTM-GCN networks can fuse GPS, IMU, and V2X observation information better, which can achieve high localization accuracy in comparison with the conventional approach.
REVIEW ON SINGLE IMAGE HAZE REMOVAL USING DARK CHANNEL PRIOR

This review paper presents a study about various image dehazing methods to remove the haze in the captured hazy images to recover a better and improved quality of haze free images. One of the critical problems in the field of image processing is the restoration of the images those are corrupted due to various degradations. Images of natural outdoor scenes is degraded due to bad weather conditions such as fog, haze etc. Due to the presence of these atmospheric particles there is a resultant decay in the colour and contrast of the captured image in the bad weather conditions. This may cause difficulty in detecting the objects in the captured hazy images or scenes. Now -a- days due to the recent development of the computer vision area, it is possible to improve the outdoor hazy images and remove the haze from the images.