Miracle-dz/UniFAR-FTUs huggingface dataset:
Cancer Treatment and Pharmacogenomics

In cancer, we cannot pick and trial medicines, we only choose medicines after doing genetic studies. Because due to metabolic deficiency, a drug can be toxic to someone. Just like the deficiency of glucose-6-phosphate dehydrogenase. In normal cases, its deficiency is not noticeable because its function is performed by any other enzyme in the body. But if a person with this deficiency takes anti-malarian or some kind of food, RBCs start to lyse, this can result in many complications like kidney failure. Also, this is the only pathway in humans to remove oxidative damage in red blood cells. If oxidative stress occurs in RBCs, oxidation of haemoglobin occurs which results in its functional loss and it attaches with the cell membrane or in the worst cases RBC lyse.
Stress and Sleep in Relation to Severity of Building Related Symptoms

The results from this study has implications for the treatment of persons affected by building related symptoms, since not only the environment needs to be treated, but also the possible consequences such as burnout or sleep problems. Objective This study investigates different aspects of stress and sleep in medically examined individuals with varying severity of building-related symptoms (BRS). Methods Three questionnaires were used to assess acute and long-term stress and sleep (Perceived Stress Scale; Shirom Melamed Burnout Questionnaire, and Karolinska Sleep Questionnaire). Results Individuals with BRS, regardless of severity, did not differ in level of perceived stress (indicator of short-term stress). The indicators of long-term stress differed between the groups where an increased severity was associated with higher levels of burnout and sleep problems. Conclusions The study suggests an association between symptom severity and measures of long-term stress and sleep quality. This has implications for the treatment of persons affected by BRS, because not only the environment needs to be treated, but also the concurrent signs of distress, such as burnout or sleep problems.
A study on the existence of tolerance to certain code smells and of implicit gender bias in Code Reviews

Code review is an activity with the goal of guaranteeing code quality, in which the identiﬁcation of code smells is an important practice. In addition to detecting code smells, another aspect of code review that is being increasingly studied is gender bias in pull request reviews, which intensiﬁes prejudice against women on software development when PRs from women are rejected because of the author’s gender. This research aims to analyze these two aspects of code reviews through a survey questionnaire with 180 participants, where they were asked to simulate that they were reviewing code snippets and they had to choose only one to approve in each pair. We were able to classify 8 different types of code smells in terms of detection difﬁculty and to analyze their code choices in terms of code smells rejection rate and dependence on the demographic aspects of the participant. Typo was clas-siﬁed as the most difﬁcult code smell to detect, and, in fact, it was by far the one that was chosen the most as the snippet that they felt most comfortable in ap-proving, corroborating our hypothesis that the more difﬁcult the detection, the lower the rejection. Using Chi-Squared statistical tests, the participant’s demographic aspects and their code choices were considered independent variables, therefore the participant’s choice does not depend on their gender, for example.
Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge Systems via Auctions

Cloud-edge systems are important Emergency Demand Response (EDR) participants that help maintain power grid stability and demand-supply balance. However, as users are increasingly executing artificial intelligence (AI) workloads in cloud-edge systems, existing EDR management has not been designed for AI workloads and thus faces the critical challenges of the complex trade-offs between energy consumption and AI model accuracy, the degradation of model accuracy due to AI model quantization, the restriction of AI training deadlines, and the uncertainty of AI task arrivals. In this paper, targeting Federated Learning (FL), we design an auction-based approach to overcome all these challenges. We firstly formulate a nonlinear mixed-integer program for the long-term social welfare optimization. We then propose a novel algorithmic approach that generates candidate training schedules, reformulates the original problem into a new schedule selection problem, and solves this new problem using an online primal-dual-based algorithm, with a carefully embedded payment design. We further rigorously prove that our approach achieves truthfulness and individual rationality, and leads to a constant competitive ratio for the long-term social welfare. Via extensive evaluations with real-world data and settings, we have validated the superior practical performance of our approach over multiple alternative methods.
Minimum Unorthodox Measure of Entropy for Prescribed Harmonic Mean and Arithmetic Mean

Maximum entropy probability distribution has been obtained by using different measures. But minimum entropy probability distribution should be obtain for complete information of probability distribution. Entropy is concave function so its minimization is more difficult than maximization. In the present paper, We use Unorthodox measure of entropy to obtain minimum entropy for given Harmonic mean and Arithmetic mean.
Approximating Dense ReLU Networks Using Linear Regression

—With increasing model sizes and more complex problems, training of Neural network models has become a signiﬁcantly resource intensive task taking up to hundreds of hours. In this paper, we propose a new efﬁcient method to train dense ReLU-based neural networks. Our method estimates a layer’s weight matrix using linear regression. We evaluate our models on 3 regression tasks using the UCI Machine Learning Repository’s Auto MPG Data Set and Concrete Compressive Strength Data Set [1] and a synthetic dataset generated with y = sin ( x ) + 1 function. We compare our results to traditional gradient-based optimization methods. In both shallow (2-layers) and wide (10 hidden neurons) networks, this method signiﬁcantly outperforms traditional gradient descent methods like AdaGrad in terms of speed, generalization, and accuracy.
Less than 4% of Archived Instagram Account Pages for the Disinformation Dozen are Replayable

We examined the archived Instagram account pages of the “Disinformation Dozen”, a group of anti-vaccine conspiracy theorists, along with account pages of a set of health authorities. In the Internet Archive, we found that a majority of the archived web pages, or mementos, are actually redirections to the Instagram login page, and of the remaining replayable mementos, many are missing post images. In our combined dataset, over 83% of the archived account pages redirected to the Instagram login page, due to Instagram's treatment of access by non-logged in users. For the Disinformation Dozen, 96.13% of their account page mementos redirect to the login page, and only 27.16% of the remaining replayable mementos contain every post image that was requested. Combined, these results reveal that merely 1.05% of mementos for the Disinformation Dozen account pages are replayable with complete post images. Furthermore, we found that the percentage of replayable mementos is decreasing over time, with a particular lack of replayable mementos for the years 2021 and 2022.
Towards Visual Social Navigation in Photo-realistic Indoor Scenes

—As reinforcement learning (RL) has shown great potential in games and robotics, real-world robotic services are drawing more and more attention, such as home-serving robots. For home service, robots should move in a socially compliant way around human beings. However, current state-of-the-art RL-based visual navigation methods for static environments cannot be directly applied to the social setting, which will lead to a high risk of dangerous collisions. In this paper, we use an end-to-end model to address the visual social navigation (VSN) task. We tackle VSN as a non-communication multi-agent problem for which we allow the robot to have the ability of safe navigation through centralized training. To enhance spatial perception, we further apply a goal-aware geometric mapping module before the visual encoder. Besides, to allow our method to adapt to variational environmental settings, we propose a potential function-based self-tuning method from the perspective of uncertainty. We conduct extensive experiments to compare our method with existing baselines, both planning-based and learning-based. The results demonstrate that our agent can surpass baselines on both point-goal navigation and visual social navigation and show better robustness.
Noninvasive Prenatal Testing to Determine Fetal Aneuploidies, Microdeletions, Single-Gene Disorders, and Twin Zygosity

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Empirical Study on Work Pressure, Regional Differences and Audit Quality Based on Big Data

As an important monitoring mechanism for company management, the quality of audit affects the achievement of audit business objectives. In this paper, we selected A-share listed companies in China from 2015–2019 as the research sample, used the analysis method of multiple linear regression, and conducted correlation analysis and robustness test by stata 16 software to empirically study the impact of work pressure and regional differences on audit quality. The main ﬁndings are as follows: (1) Work pressure of signing CPA has negative impact on audit quality. (2) Regional differences in signing CPA have negative impact on audit quality. (3) Regional differences in signing CPA enhance the negative impact of work stress on audit quality. Finally, the paper makes recommendations from three aspects: accounting ﬁrms, government at all levels and audited entities.
Big Data: Quasi-public Goods Correlating with National Security and Social Stability

IntheInternetera,dataisoccupyinganincreasinglyimportantposition, and some important data may even affect the country. More and more people demand that big data correlating with national security and social stability be treated as public goods which hold the characteristics of non-excludable and non-rivalrous. However, due to the economic attribute of big data, we proposes that this type of big data is excludable and non-rivalrous, and should be treated as quasi-public goods. To support this point of view, we quoted two examples, DiDi Chuxing and Cambridge analytics, and give solutions respectively. We believes that the government, as a public service provider, should supervises the use of big data rather than the content of big data. It not only protects the private rights of enterprises to data, but also avoids big data leaks that threaten national security.
Multimodal glosses enhance learning of Arabic vocabulary

In second language acquisition, a popular method of introducing new vocabulary is by embedding the words in a natural text. Supplementary information (e.g., definitions, illustrations, synonyms, etc.), or glosses, can be included in the margins of the texts to highlight and improve retention of the new words. Previous studies suggest multimodal glosses facilitate learning, although the question of which glosses are most effective remains an active research topic. In the present study, we focused on a sample of university students studying Arabic as a second language. In two experiments, we found that (a) recognition and recall of target vocabulary words were superior when target words were accompanied by a combination of text and picture glosses compared to text-only or picture-only; and (b) including information about the Arabic root word from which the target word was derived in the glosses provided an additional memory benefit. Overall, this study adds further support for glosses as a teaching tool by generalizing to the case of Arabic. Our results also show how glosses that highlight root information can be useful for improving recognition and recall of Arabic vocabulary specifically.
Two-level Graph Caching for Expediting Distributed GNN Training

Graph Neural Networks (GNNs) are increasingly popular due to excellent performance on learning graph-structured data in various domains. With fast expanding graph sizes and feature dimensions, distributed GNN training has been adopted, with multiple concurrent workers learning on different portions of a large graph. It has been observed that a main bottleneck in distributed GNN training lies in graph feature fetching across servers, which dominates the training time of each training iteration at each worker. This paper studies efficient feature caching on each worker to minimize feature fetching overhead, in order to expedite distributed GNN training. Current distributed GNN training systems largely adopt static caching of fixed neighbor nodes. We propose a novel two-level dynamic cache design exploiting both GPU memory and host memory at each worker, and design efficient two-level dynamic caching algorithms based on online optimization and a lookahead batching mechanism. Our dynamic caching algorithms consider node requesting probabilities and heterogeneous feature fetching costs from different servers, achieving an O(log3 k) competitive ratio in terms of overall feature-fetching communication cost (where k is the cache capacity). We evaluate practical performance of our caching design with testbed experiments, and show that our design achieves up to 5.4x convergence speed-up.
Research on Metadata in the Era of Big Data Based on Bibliometric Analysis by CiteSpace

Metadata is an indispensable element for the development of big data. In order to explore the construction of metadata in the context of big data and to provide reference for future metadata research, this paper uses 989 papers under this topic included in the Web of Science core database between 2001 and 2021 as the data source, and examines the external characteristics and content characteristics of the literature with the help of bibliometric methods and the visual analysis software CiteSpace. Through the study, this paper concludes that the current cooperation is relatively fragmented and there is less cross-regional cooperation; the research heat of metadata is on the rise and will still continue for some time, and the hot spots of metadata research in recent years are in the ﬁelds of data mining and machine learning, and there is a trend of developing to ﬁner branches such text mining.
Searching the ACL Anthology with Math Formulas and Text

Mathematical notation is a key analytical resource for science and technology. Unfortunately, current math-aware search engines require LATEX or template palettes to construct formulas, which can be challenging for non-experts. Also, their indexed collections are primarily web pages where formulas are represented explicitly in machine-readable formats (e.g., LATEX, Presentation MathML). The new MathDeck system searches PDF documents in a portion of the ACL Anthology using both formulas and text, and shows matched words and formulas along with other extracted formulas in-context. In PDF, formulas are not demarcated: a new indexing module extracts formulas using PDF vector graphics information and computer vision techniques. For non-expert users and visual editing, a central design feature of MathDeck's interface is formula 'chips' usable in formula creation, search, reuse, and annotation with titles and descriptions in cards. For experts, LATEX is supported in the text query box and the visual formula editor. MathDeck is open-source, and our demo is available online.
Japanese Mainstream Media’s Stance on China’s Efforts in Poverty Alleviation Based on Big Data An Informatization Study of News Report

Big data technology has a huge impact on the patterns and structures of international relations as well as triggering changes in traditional research methods. Poverty governance is an important part of global governance and an essential step in promoting human development. In recent years, China has made remark-able achievements in poverty-eradication and has increasingly become the back-bone strength in global anti-poverty campaign. The international press plays an essential role in actively contributing to the outreach of poverty alleviation expe-riences. This study selected the mainstream Japanese media Asahi Shinbun and collected China-related reports on the era of poverty alleviation. Based on the theoretical framework of critical discourse analysis and the comprehensiveness, authenticity and timeliness of big data technology in data analysis, the scientiﬁc research method of corpus was applied to explore Japan’s position on China’s efforts in poverty alleviation and to make proposals on Sino-Japan cooperation in poverty-eradication.
Moving Object Re-identification from True-Color and Grayscale Videos

: Surveillance is the monitoring of behavior or activities of a person or an object. It can be done by using electronic equipment such as closed-circuit television (CCTV). Re-identification is an important for surveillance system. Video surveillance is one of the most active and relevant research topics in computer vision. It is well suited for a broad range of applications such as an efficient management of traffic, public safety, and military security. Moving object detection and classification are important and challenging tasks in many computer vision systems. The main application areas of object re-identification are person re-identification and vehicle re-identification. Person re-identification (Re-ID) focus on matching persons captured by different camera views. Most existing person re-identification methods focus on matching pedestrians captured by different true-color cameras. The captured pedestrian videos may be grayscale in some cases for saving storage space. In these cases, the person re-identification between true color and grayscale pedestrian videos will be needed. The proposed system is focused on moving object re-identification from true color and grayscale videos. And this is the first method for matching objects from true-color and grayscale videos. In the proposed system an efficient existing Oriented FAST and rotated BRIEF algorithm (ORB) is used for object detection. The image similarity check will be done by using Brute-Force matching algorithm.
Creating Synergy for Knowledge Infusion in Deep Learning for Question Answering: A Survey

LLMs often lack efficient domain-specific understanding, which is particularly crucial in specialized fields such as aviation. This necessitates the integration of knowledge graphs with deep learning which is thriving in improving the performance of various natural language processing (NLP) tasks. Knowledge graphs have emerged as a powerful framework for representing structured knowledge and capturing the relationships between entities in various domains. They provide a comprehensive and interconnected view of information, enabling efficient reasoning, search, and data integration. Language models are trained on a lot of data and perform well on tasks from the general domain. This survey paper presents an overview of the knowledge graph, deep learning, and the synergy of both knowledge graph and language models for question answering.
Comparative Evaluation of Curcumn Nano Particles of Casein and Chitosan

Curcumin is a poly phenolic compound derived from turmeric possesses anti-inflammatory, antioxidant, ant proliferative and antigenic activities but exhibits poor bioavailability. Curcumin exhibits poor bioavailability, the reason for this was poor absorption, rapid metabolism and rapid systemic elimination. The main objective of the study is to overcome the above limitations by curcumin delivery through nanotechnology to formulate curcumin nanoparticles by using casein and chitosan and to carry out its comparative evaluation. The curcumin casein nanoparticles are prepared by using PH conservation method and curcumin chitosan nanoparticles are prepared by using ionic gelation method. In the present study 10 formulations were formulated. Five of curcumin and chitosan (CCH1- CCH5) five of curcumin and casein (CCN1-CCN5) by keeping the drug constant and by varying the polymer ratio. The prepared nanoparticles are smooth and free flowing and are subjected to evaluation like FT-IR, Particle size, Zeta potential, Surface morphology, Drug content, Entrapment efficiency and In-vitro release. The prepared nanoparticles possess zeta potential ranging from -16.3± 0.4mV to -39.07 ± 0.2mV for (CCN) and -11.14± 0.4mV to -32.75± 0.53mV for (CCH) and particle size ranging from 110±5.04nm to 248±10.7nm (CCN) and 121±8.2nm to 242±4.3nm (CCH), Drug content ranging from 75.0±0.706% to 94.5±0.706%(CCN) and 85.0±0.706% to 92.5±0.706%(CCH), In-vitro release studies were conducted for curcumin nanoparticles exhibited drug release in the range of 76.98 to 92.78 (CCN) and 83.42 to 96.75 (CCH). The present study conclusively demonstrates that curcumin nanoparticles prepared by using chitosan are more stable feasible and effective than prepared by using casein. Hence chitosan encapsulating curcumin nanoparticles form potential controlled release drug delivery system.
Prospective Teachers’ Perceptions, Reliance, and Barriers to ICT Integration in Mathematics Learning

Prospective teachers’ (PTs’) ICT integration (ICTi) for learning math (LM) has been studied in the global context, however, it lacks in the local context. This study served to bridge the gap by exploring PTs’: perceptions toward ICT competence and integration; reasons for relying on technology; and the challenges encountered while LM. The methodology was grounded in interpretivism, induction, mono-method-qualitative, case study, and cross-sectional components. The participants’ selection was based on convenient and purposive techniques. Data were collected through semi-structured interviews (SSI) and focus group discussions (FGD) and were analyzed using thematic data analysis. The findings informed positive perceptions of PTs toward ICTi where the purpose of relying on technology varied across participants. Though ICTi was found important, still there are inherent challenges associated with its integration, for example, scanning online material, encoding math language, and limited awareness of mathematics application software. The recommendations necessitate the development of special programs to offer hands-on learning experiences along with mitigating challenges for the prospective teachers to integrate ICT for LM. Future researchers may focus on variables through the blended model of teaching and learning that impact the academic performance of students in a math course.