bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pay As You Earn (PAYE) Tax as Panacea to Backlog of Gratuities and Pensions of Civil Servants in Nigeria
Authors: Mohammed Sani Damamisau , Garba Sunusi , Sani Armaya’u Alhaji , Ibrahim Rukayya Tijjani , Muhammad Yusra Musa , Kassim Ibrahim Ahmad 
Published: May 2024

Abstract

Gratuities and pensions are payments due to retired civil servants; however, payments of these have not being timely and consistent to the extent that 23 state governments are owing 836.40 billion as gratuities and pension liabilities as at end of 2022. Non-payment of these retirement benefits is often accompanied with social and health problems leading to premature death of retirees. Consequently, the aim of this study is to examine the potentials of monthly Pay As You Earn (PAYE) tax to eliminate the backlog of retirement benefits owed retired civil servants. To achieve this aim, quantitative secondary data of PAYE tax collections by state governments is collected from the annual Internally Generated Revenue reports of states published by National Bureau of Statistics (NBS) 2010–2022. Descriptive statistical tools of analysis are employed to analyze collected data while Burgess’s activity theory and public policy analytical framework guided the conduct of the study. On the overall, results from the study revealed that while the 23 state governments owe 836.40 billion as liabilities of retirement as at 2022, the same states have collected the sum of 5,672 billion as PAYE tax 2010–2022. On individual states, 20 states have collected PAYE tax 2010–2022 more than their liabilities of retirement benefits while only three states collected PAYE tax less than their accumulated liabilities of retirement benefit. The short run policy implication is for states to set aside collected PAYE tax to settle the backlog of benefits and adopt the Contributory Pension Scheme (CPS) to avoid future recurrence of the backlog.
Data-Driven Optimisation Based Machine Learning for Thermal Comfort in Building Environment
Authors: Alarab Ismail , Efstratiou Christos , Prakoonwit Simant , Otero Fernando , Nikolopoulou Marialena , Barker Matthew , Baldwin Andrew , Clarke Malcolm 
Published: May 2024

Abstract

Achieving thermal comfort under minimum energy considerations has become a hot topic in the field of energy building management. The existing studies have suggested several methods to predict thermal comfort and accordingly adjust the temperature setpoints to reduce the energy. The two drawbacks of the existing studies involve the increased complexity in optimisation methods using multiple models and the lack of these methods in the optimisation approach. Our novel work proposes a data-driven optimisation solution based on machine learning (ML) to maintain thermal comfort under energy efficiency considerations. We simply infer the input from a desired output using supervised learning models as an optimisation solution. We discuss the efficiency and cost-effectiveness of the proposed solution using a public UK-schools dataset from the ASHRAE database.
Virtual Reality Rehabilitation using Hand Tracking
Authors: Coox Aaron , McKittrick Andrea , Tornatore Giovanna , Desselle Mathilde , Laracy Sue , Bo Antonio Padilha Lanari 
Published: May 2024

Abstract

Virtual reality rehabilitation (VR) can complement traditional rehabilitation therapy to increase motivation and thus the quality of exercise, while transferring effectively to real-world tasks. Hand tracking can significantly improve usability and immersion. A VR hand-tracking-based interaction system has been developed that supports various hand interactions, including grasping, pinching, and gesturing to complete virtual exercises. Interactions support modifiable difficulty and requirements, metrics, and visual error augmentation (EA). Audio-visual signifiers and feedback to guide interactions are also applied. A proof-of-concept rehabilitation game was tested with non-expert (N=7) and expert occupational therapy (N=5) participants. Participants completed tasks including grabbing objects, pinching, pulling handles, tracing paths, and making hand gestures. The system's usability was the primary outcome of the study. The results have shown this system is highly usable, with a modified System Usability Scale (SUS) of 88.93 (non-expert) and 74.5 (expert). A preliminary performance analysis was also conducted, indicating a limited number of unsuccessful hand-object interactions, although the accuracy in some interactions was impacted by tracking occlusion.
Utilizing Association Rule Mining algorithms to Identify Dependencies between Software Defects
Authors: Eldesouky Mohamed Tamer , Raslan Atef Tayh Nour El-Din 
Published: May 2024

Abstract

Focusing on identifying hidden patterns and dependencies between software defects that are difficult to detect using traditional analysis methods, this study employs Association Rule Mining (ARM) to analyze over 140,000 open-source GitHub issues. By leveraging ARM, we have been able to extract explicit association rules that illustrate the interrelations among various issue attributes such as labels and release versions. Our findings indicate strong, meaningful associations that equip developers and quality assurance teams with the information necessary to strategically prioritize issues. This prioritization significantly influences bug fixing and system enhancements. Offering a marked improvement over conventional analysis techniques, ARM provides a clearer and more precise understanding of defect dynamics, thereby enhancing the efficiency and effectiveness of software development practices.
QuantForecast - Navigating the Financial Future
Authors: Aggrawal Niyati , Rathi Megha , Kansal Riya , Jamwal Aditya , Agarwal Swati 
Published: May 2024

Abstract

In this visionary work, we embark on a journey to revolutionize stock price prediction. Beginning with an insightful immaterial, we emphasize the critical role of predictive modeling in deciphering the ever-evolving dynamics of financial markets. Our work meticulously curates a spectrum of technical indicators, including renowned ones like RSI (Relative Strength Index) and MACD (Moving Average Convergence Divergence), tailored to empower both astute investors and discerning traders. Delving deeper, we navigate the intricate landscape of trading algorithms, carefully weighing their merits and demerits. From this analytical exploration, we pinpoint the algorithm that showcases unparalleled promise, thus setting the stage for groundbreaking advancements. The work showcases not only the prowess of machine learning and deep learning but also the art of visualization. By elegantly presenting the model's accuracy scores and visually encapsulating its performance, we bring forth an intuitive understanding of its potential. Infused with sophistication, we seamlessly blend time-honored time series analysis techniques such as ARIMA, LSTM, and jump diffusion into our models. Every step is a masterpiece, meticulously executed to ensure optimal epochs and stellar forecasting. Our models are rigorously tested, their predictions meticulously juxtaposed with real-world stock prices, and quantified using industry-standard metrics like RMSE and MAPE. In essence, this work isn't merely a quest for the best model—it's a testament to our dedication to empower investors and traders, providing them with a cutting-edge tool to navigate the volatile world of financial markets with unmatched precision and confidence.
Exploring Sentence-level Revision Capabilities of LLMs in English for Academic Purposes Writing Assistance
Authors: Du Zhendong , Hashimoto Kenji 
Published: May 2024

Abstract

The English for Academic Purposes (EAP) is pivotal for scholarly communication; however, it poses significant challenges for non-native English speakers. Recently, Large Language Models (LLMs) have been extensively utilized in EAP to assist with writing tasks. EAP writing assistance typically encompasses several downstream tasks in natural language processing (NLP), such as Grammatical Error Correction (GEC). Non\-etheless, some studies have revealed that the performance of LLMs in GEC tasks is inferior to traditional GEC solutions. To explore the capabilities of LLMs more thoroughly in aspects like deep semantic and syntactic structures, this study aims to rigorously assess the performance of LLMs in the Sentence-level Revision (SentRev) task. We designed three sets of meticulous experiments to evaluate the efficacy of different LLMs. The first experiment assessed LLMs using prompts in ten different languages, finding that the SentRev performance of LLMs was heavily influenced by the language of the prompt and the quality of the input text. The second experiment investigated the performance of English LLMs with minimal prompting in the SentRev task, yet the results showed no significant changes, contradicting some prior studies. In the third experiment, we devised an innovative and straightforward method that significantly enhanced the performance of multiple LLMs by integrating academic phrases from the Formulaic Language (FL) Academic Phrasebank\footnote{\url{https://www.phrasebank.manchester.ac.uk/}}, thus overcoming the performance limitations imposed by different languages on LLMs. Additionally, our study highlights the deficiencies in existing evaluation benchmarks and suggests that higher-level, discourse-based EAP text evaluation benchmarks merit deeper exploration.
Enhanced Network Security against SQL Injection Attack Using Machine Learning
Authors: Joshi Seema , Oza Neel 
Published: May 2024

Abstract

SQL injection attacks pose a significant risk to the security of computer networks. These attacks have the potential to gain unauthorized access to sensitive data, modify or remove data, or even render entire websites and databases inoperable. Conventional techniques for identifying and stopping SQL injection attacks are frequently resource-intensive, rendering them unfeasible for devices that manage substantial amounts of traffic. The objective of the proposed research is to improve the identification and prevention of structured query language injection attacks (SQLIAs) in web applications, with a specific focus on attaining a high rate of detection while minimizing false alarms. We utilized flow data obtained from several SQL injection attack scenarios that specifically targeted widely-used database engines. We obtained a high level of accuracy in identifying and mitigating these assaults by utilizing machine learning methods, specifically logistic regression. The method we used showed a detection rate over 98% and a false alarm rate below 0.029%. The results demonstrate substantial enhancements in the prevention of unauthorized access and the protection of sensitive information within databases. The results of our research align with or surpass current techniques, offering enhanced security for web applications. The model’s exceptional accuracy and minimal false alarm rate provide a groundbreaking method for detecting SQL injection attacks.
Timed Automata Verification and Synthesis via Finite Automata Learning
Authors: Sankur Ocan 
Published: May 2024

Abstract

We present algorithms for model checking and controller synthesis of timed automata, seeing a timed automaton model as a parallel composition of a large finite-state machine and a relatively smaller timed automaton, and using compo-sitional reasoning on this composition. We use automata learning algorithms to learn finite automata approximations of the timed automaton component, in order to reduce the problem at hand to finite-state model checking or to finite-state controller synthesis. We present an experimental evaluation of our approach.
Quantifying the carbon footprint of academic clinical trials: building the evidence base and hotspot identification
Authors: Griffiths Jessica , Adshead Fiona , Salman Rustam Al-Shahi , Anderson Craig , Bedson Emma , Bliss Judith , Boshoff Ana , Chen Xiaoying , Cranley Denise , Doran Peter , Dunne Fidelma , Gamble Carrol , Gillies Katie , Hood Kerenza , Kavanagh Columb , Malone Julia , McGregor Naomi , McNamara Carolyn , Midha Elis , Moore Keith , Murphy Lucy , Newman Christine , O’Reilly Seamus , Perkins Alexis M , Pett Sarah , Sydes Matthew R , Whitty Laura , You Frank , Fox Lisa , Williamson Paula 
Published: May 2024

Abstract

Background
Clinical trials are fundamental to healthcare, however they also contribute to anthropogenic climate change. Following previous work to develop and test a method and guidance to calculate the carbon footprint of clinical trials, we have now applied the guidance to 10 further UK and international, academically-sponsored clinical trials to begin identification of hotspots and opportunities for lower carbon trial design.
Methods
10 collaborating Clinical Trial Units (CTUs) self-identified and a trial was selected from their portfolio to represent a variety of designs, health areas and interventions. Trial activity data was collated by trial teams across 10 modules spanning trial set up through to closure, then multiplied by emission factors provided in the guidance to calculate the carbon footprint. User feedback was collected on the process of applying the draft guidance.
Results
Six completed and four ongoing trials (two in follow up, two recruiting) were footprinted across a variety of interventions (6 IMP trials, one nutritional, one surgical, one health surveillance and one complex intervention trial). The carbon footprint of the 10 trials ranged from 15 to 765 tonnes CO2e. Common hotspots were identified as CTU emissions, trial-specific patient assessments and trial team meetings and travel. Hotspots for specific trial designs were also identified. Time taken to collate activity data and complete carbon calculations ranged from 5–60 hours. The draft guidance was updated to include new activities identified from the 10 trials, and in response to user feedback.
Discussion
There are opportunities to reduce the impact of trials across all modules, particularly trial specific meetings and travel, patient assessments and laboratory practice. A trial’s carbon footprint should be considered at the design stage, but work is required to make this common place.
Effects of T3SS-positive Pseudomonas isolates on sugar beet growth stimulation and pathogen resistance
Authors: Nedeljković Marija , Mesaroš Aleksandra , Rašić Vuk , Nikolić Ivan , Stanković Slaviša , Lozo Jelena , Atanasković Iva 
Published: May 2024

Abstract

The intricate interplay between plants and microorganisms in agricultural ecosystems holds immense potential for increasing crop productivity and resilience in the face of climate change and increasing pathogen pressure. This study addresses the function of the type 3 secretion system (T3SS) in nonpathogenic Pseudomonas strains associated with sugar beet (Beta vulgaris L.). We identified T3SS-positive isolates and characterised their genetic diversity and T3SS expression profiles. Our results show that T3SS is widely distributed among sugar beet-associated Pseudomonas, with variations in T3SS gene sequences and expression patterns. Notably, T3SS functionality was demonstrated in one isolate, P. marginalis OL141. In planta experiments with this isolate showed a correlation between T3SS and the growth of sugar beet and resistance to Pseudomonas syringae infections. The T3SS-mediated interactions in P. marginalis OL141 point to a novel mechanism underlying plant-microbe symbiosis and offer promising opportunities for sustainable agriculture. Future research directions include elucidating the mechanistic basis of T3SS-mediated plant-microbe interactions and exploring their broader implications for sustainable agriculture and global food security.
Integrated use of the CA-Markov model and the Trends.Earth module to enhance the assessment of land cover degradation: Application in the Upper Zambezi Basin, southern Africa
Authors: Zimba Henry M. , Banda Kawawa E. , Mbewe Stephen , Nyambe Imasiku A. 
Published: May 2024

Abstract

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata-Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the European Space Agency Climate Change Initiative (ESA CCI) global LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.34 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the ESA CCI global LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of -0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. By leveraging the predictive power of the CA-Markov model and the capabilities of the LCD model, as evidenced in this study, valuable information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.
In Vivo Mapping of the Chemical Exchange Relayed Nuclear Overhauser Effect using Deep Magnetic Resonance Fingerprinting (rNOE-MRF)
Authors: Power Inbal , Rivlin Michal , Zaiss Moritz , Navon Gil , Perlman Or 
Published: May 2024

Abstract

Noninvasive magnetic resonance imaging (MRI) of the relayed nuclear Overhauser effect (rNOE) constitutes a promising approach for gaining biological insights into various pathologies, including brain cancer, kidney injury, ischemic stroke, and liver disease. However, rNOE imaging is time-consuming and prone to biases stemming from the water T1 and the semisolid magnetization transfer (MT) contrasts. Here, we developed a rapid rNOE quantification approach, combining magnetic resonance fingerprinting (MRF) acquisition with deep-learning-based reconstruction. The method was systematically validated using tissue-mimicking phantoms, wild-type mice (n=7), and healthy human volunteers (n=5). In vitro rNOE parameter maps were highly correlated with ground truth (r>0.98, p<0.001). Simultaneous mapping of the rNOE and the semisolid MT exchange parameters in mice and humans were in agreement with previously reported literature values. Whole-brain 3D parameter mapping in humans took less than 5 minutes (282 sec for acquisition and less than 2 sec for reconstruction). With its demonstrated ability to rapidly extract quantitative molecular maps, deep rNOE-MRF can potentially serve as a valuable tool for the characterization and detection of molecular abnormalities in vivo.
Six IL-8 gene polymorphisms and the entire cancer susceptibility according to a comprehensive analysis especially in prostate cancer
Authors: Zhang Xiao , Sun Jian , Ding Xiqi 
Published: May 2024

Abstract

Background: Interleukin-8 (IL-8) primarily regulates cytotoxicity- and local immune response, antibody production, cellular immunity, and delayed hypersensitive inflammation. Emerging evidences suggest that IL-8 polymorphisms are strongly linked to various cancer risks, however, above correlation requires further confirmation.
Methods and Results: We screened and analyzed the aforementioned relationship from relevant published sources including Embase, PubMed, Chinese database, Google Scholar, and Web of Science till Jun 25, 2023. Associated strength analysis was employed through odds ratios and 95% confidence intervals. In addition, serum IL-8 expression about prostate cancer patients was detected by ELISA method. In all, we reviewed 104 case-control investigations, involving 26,029 cancer incidences and 31,577 healthy controls. Firstly, we demonstrated a strong correlation between the +2767 polymorphism and augmented cancer risk. Additionally, the +781 polymorphism elicited a strong increase in cancer risk among Caucasians. However, based on our cancer type subgroup analysis, there had markedly reduced association for hepatocellular carcinoma. Then, enhanced correlation was observed in all cancer samples for -251 polymorphism. Besides, we also revealed comparable results among in the Mixed/Asian populations, gastric cancer, lung cancer, hospital-based subgroup, and genotype method subgroup. Finally, -251 individuals carried AA/AC genotype had higher expression of IL-8 in serum than carrying CC individuals from prostate cancer patients.
Conclusion: Three IL-8 polymorphisms (+2767, +781 and -251) were intricately linked to cancer risk. Additional, -251 polymorphism may influence the expression of IL-8 in prostate cancer patients. Therefore, these polymorphisms may be excellent candidates for tumor biomarkers in the future.
Frustratingly Easy Jailbreak of Large Language Models via Output Prefix Attacks
Authors: Wang Yiwei , Chen Muhao , Peng Nanyun , Chang Kai-Wei 
Published: May 2024

Abstract

Recent research finds that even carefully aligned large language models (LLMs) can be manipulated with malicious intent, leading to unintended behaviors, known as "jailbreaks". Being aware of the LLMs' misalignments under specific jailbreak attacks helps us to build safer LLMs. Previous work on jailbreak attacks primarily focused on optimizing adversarial prompts through costly training or improving decoding configurations via parameter search. Both kinds of jailbreak attacks are complicated and time-consuming. Different from them, in this work, we propose two output prefix attack-based jailbreak approaches that can effectively disrupt model alignment: OPRA and OPRATEA. OPRA enforces the output prefix of LLMs to follow a "fuse" and the user's target. Additionally, OPRATEA conceals the malicious target within the input prompt to circumvent the "Maginot Line", a standalone module in the LLM system that focuses on detecting malicious inputs. Both methods are incredibly simple: they do not require any training or parameter search; the setting up of our attack on any LLM only requires a single inference; the attack with any input only requires a string replacement. OPRA and OPRATEA increase the misalignment rate of LLAMA2-7B-CHAT, LLAMA2-13B-CHAT, LLAMA3-8B-INSTRUCT, and GPT-3.5-TURBO, outperforming the state-of-the-art attack with 1000 x lower computational cost.