Miracle-dz/UniFAR-FTUs huggingface dataset:
A DEEP LEARNING FRAMEWORK FOR SMALL TRAFFIC LIGHT RECOGNITION IN TRAFFIC SCENE IMAGE

Traffic light recognition plays a crucial role in intelligent transport systems. In traffic scene images, traffic light instances usually occupy a small region. Thus, recent state-of-the-art object detectors such as Faster R-CNN and SSD obtain low accuracy on traffic light recognition in traffic scene images. This paper presents a deep learning framework for traffic light recognition in traffic scene image. Considering that feature maps at shallow layers have higher resolution which will improve small traffic light detection, and feature maps at deep layers contain more discriminative representation which will improve traffic light classification task, this paper designs a feature fusion subnet for feature extraction to solve the problem of small traffic light detection. The feature fusion subnet fuses feature maps at different layers. Thus, the feature fusion subnet not only can preserve the information of small traffic lights but also enhance the semantic information. Furthermore, a detection subnet is designed at the detection prediction stage. The detection subnet includes multiple detection layers, and each layer performs detection predictions with a coarse-to-fine detection strategy. The coarse-to-fine detection strategy is applied to improve the classification performance of the detection network. The proposed approach is evaluated on Bosch Small Traffic Lights dataset. Experimental results show that the proposed approach obtains higher accuracy compared with recent state-of-the-art detectors such as Faster R-CNN and SSD.
Recovery of 15 N labelled rice and soybean residues by maize grown on an inceptisol of Malang, Indonesia

A study was conducted to evaluate the N recovery of 15 N labelled crop residues (rice straw=RS; soybean=SY) with 15 N isotope dilution technique on growth of maize. The 15 N labelled crop residue from the rice and soybean crops that were grown in glass-house under three 15 N concentrations, that is, 0.625 mM N (N1), 2.5 mM N (N2) , and 10 mM N (N3) supplied as CO( 15 NH2)2, in 30 cm diameter plastic pots containing 5 kg of quartz sand. Eight weeks after planting the above-ground biomass was pruned and oven dried at 60°C for 48 h and analyzed for polyphenol, lignin, N, C, C/N and % 15 N-abundance. The seven treatments (six types of crop residues and one treatment with no added residues) were arranged in a completely randomized block design with four replicates. The maize was plant pots containing 10 kg of soil and placed in a glass-house. The varying supply of 15 N concentrations resulted in varying quality of crop residues. The amounts of mineral N in the soil supplied with crop residues were significantly greater than in the control at 2, 4 and 8 weeks. The amounts of mineral N in soils amended with residues of SY and RS with a 10 mM N supply were significantly (P<0.05) greater than those grown with 2.5 mM and 0.625 mM N supplies during the incubation time. The N recovery by maize shoot from residues with 10.0 mM N was greater (80.86% on average) than that from residues with 2.5 mM N (70.06% on average) and that from residues with 0.625 mM N (58.43% on average). On average, the %N recovery of crop residue N by maize estimated by the 15 N method was 10.73% more than that estimated by the difference method.
UvA-DARE (

This paper presents PROV-O-Viz, a Web-based visualization tool for PROV-based provenance traces coming from various sources, that leverages Sankey Diagrams to reﬂect the ﬂow of information through activities. We brieﬂy discuss the advantages of this approach compared to other provenance visualization tools. PROV-O-Viz has already been used to visualize provenance traces generated by very diﬀerent applications.
Effects of Dl-3-n-butylphthalide on Cognitive Functions and Blood-brain Barrier in Chronic Cerebral Hypoperfusion Rats

Purpose Vascular cognitive impairment (VCI) has been one of the major types of cognitive impairment. Blood-brain barrier damage plays an essential part in the pathogenesis of VCI. At present, the treatment to VCI mainly focused on prevention, with no drug clinically approved for the treatment of VCI. This study aimed to investigate the effects of DL-3-n-butylphthalide (NBP) on VCI rats.
Pathological Evaluation of Corneal Regrafts During Ten Years at a Tertiary Referral Center in Tehran, Iran

Purpose To evaluate the histopathological and epidemiologic features of corneal regrafts over ten years. Methods A single-center retrospective analysis was performed on corneal specimens diagnosed as corneal graft failure retrieved from The Farabi Eye Hospital (Tehran, Iran) over a 10-year period. Also, Demographic data including sex, age, laterality, primary diagnosis, type of graft, cause of corneal regraft, associated eye diseases and systemic comorbidities were evaluated. Results
Hippocampal organotypic cultures as ex vivo model for tissue response to upconverting nanoparticles

In the last few years, rare-earth-doped upconverting nanoparticles (UCNPs) have been widely used in different life sciences due to their unique properties. Nanoparticles have become a multifunctional and promising new approach to neurobiological disorders and have shown extraordinary application potential to overcome problems related to conventional treatment strategies. This study evaluated the internalization mechanisms, bio-distribution, and neurotoxicity of NaYF4:20%Yb3+,2%Er3+ UCNPs in rat organotypic hippocampal slices. TEM results showed that UCNPs were easily internalized by hippocampal cells and co-localized with selected organelles inside neurons and astrocytes. Moreover, we found that UCNPs were taken into the neurons via clathrin-and caveolae-mediated endocytosis. Propidium iodide staining and TEM analysis did not con�rm the adverse effects of UCNPs on hippocampal slice viability and morphology. Therefore, UCNPs may be a potent tool for bio-imaging and testing new therapeutic strategies for brain diseases in the future.
Corporate Governance and Earnings Management in Deposit Money Banks in Nigeria

: This study examines corporate governance and earnings management in deposit money banks in Nigeria. The aim of the study was to examine corporate governance and earnings management in deposit money banks in Nigeria. The study was guided by the following specific objectives; to examine the relationship between size and discretionary loan loss provisions in deposit money banks in Nigeria, to determine the relationship between audit committee independence and discretionary loan loss provisions in deposit money banks in Nigeria and to evaluate the relationship between risk management committee independence and discretionary loan loss provisions in deposit money banks in Nigeria. The ex-post facto research design was adopted. The population and sample size of the study is 12 listed deposit money banks in Nigeria stock exchange. The instrument of the study was secondary data. The study adopted descriptive statistics for the research questions and simple regression analysis for the hypotheses. The findings of the study among others were that there is significant relationship between size and discretionary loan loss provisions in deposit money banks in Nigeria. Meanwhile, there is no significant relationship between audit committee independence and
COMMUNITY-BASED TOURISM AND TECHNOLOGY RELATIONSHIP: A BIBLIOMETRIC ANALYSIS

This study aims to find the direction and objectives of research in the current Community-based tourism (CBT) and relationship with technology domain. We Use the five phases of bibliometric analysis method, we extracted 272 documents from the Scopus database for the period 1991 to 2021 and refined keywords by adding inclusion criteria to produce 176 documents. We analyzed the document metadata with features from the Scopus website, exported it to a RIS-type dataset and then processed it using VosVewer to visually map keywords for further analysis. The result, we found the trend of publications related to this topic by time period, the most influential publications, trends in the main topics and potential topics for future research as well as the relationship between the CBT and IT domains. The Conclusion for this research on this domain has increased publication interest from year to year, but the results visualized that there is no direct relationship between the two and also the density diagram for information technology area is bright, which shows that the novelty level is still high for future research.
CLASSIFICATION MODELS COMPARISON FOR PARKINSON’S DISEASE DETECTION THROUGH SPEECH FEATURES

Prior researches have used speech features to classify Parkinson’s disease. This research was conducted to compare thirteen classification models in order to achieve a better performance in the newly published dataset. The method offered in this paper involves the usage of hyperparameter tuning, feature selection using genetic algorithm, and model stacking with ANN as the final classifier. The final stacked model has achieved an accuracy of 90.13% in the test dataset, with an F1-score of 93.7%. The results indicate that classification accuracy of Parkinson’s disease classification through speech features can be increased by utilizing hyperparameter tuning, feature selection, and ensemble stacking. This improvement also indicated that the usage of feature selection in tandem with ensemble stacking method can yield better result compared to prior state of the art models for Parkinson’s disease detection.
FACTORS AFFECTING PADDY FARMERS IN USING DRONES

In Malaysia, the agriculture industry is the most promising, but it is now facing several issues, one of which is the scarcity of the workforce for farming. Extreme weather conditions, an inadequate supply and ineffective fertilizer application, infections, diseases, allergies, and other health issues brought on by chemical applications (fungicides, pesticides, insecticides, etc.) or insect or animal bites are additional issues or difficulties. Drones have shown to be one of the approaches that enable quick and non-destructive examination of the air quality, physical characteristics of the components of soil, or crop growth to address some of the difficulties faced in agriculture through the use of sustainable ICTs. As a result, farmers are turning to cutting-edge drone technology to solve these issues quickly and effectively. Drones can gather information on various topics, including crop yields, livestock health, soil quality, nutrient assessments, weather patterns, and rainfall totals. However, there is none of the literature focused on the advantages of drone usage among paddy farmers in Malaysia. Therefore, this research aims to identify the factors affecting Malaysian paddy farmers using drones. This research collects data from interviews with ten paddy farmers who own agriculture drones to accomplish this objective. The farmers have been selected from Tanjung Karang to Sabak Bernam, where paddy cultivation is encouraging, especially in Selangor, Malaysia. Government agencies can then utilize the information from this research and local authorities to map out any suggestions to paddy farmers on the advantages of using drones in paddy farming. Other than that, the government also can find out ways to help the y farmers in terms of finances to buy more drones.
FEATURE EXTRACTION OF ALZHEIMER’S DISEASE CLASSIFICATION BASED ON PRINCIPAL COMPONENT AND RANDOM SUBSPACE DISCRIMINANT ANALYSIS

Alzheimer’s disease (AD) is one of the diseases which brings great influences on the lives of the people. AD classification can serve as a supportive tool to help the doctor to analyze the brain images. One of the important steps in AD classification is feature extraction. Among the feature extraction techniques, Principal Component Analysis (PCA) is a widely used machine learning approach. Nevertheless, it is hard to decide the number of dimensions to be extracted after the transformation. The accuracy of the classification can be greatly affected by the number of dimensions to be chosen. Therefore, this paper has developed a feature extraction method based on principal component and random subspace discriminant analysis (PCRSDA) to extract and select the features. The selection of the number of dimensions was determined by 10-fold cross validation where the features were selected randomly without replacement. The dataset in this paper was collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) database across four time points. The classification results were 81%, 84%, 87% and 87% at time point of 24 months before stable diagnosis, 18 months before stable diagnosis, 12 months before stable diagnosis and at the stable diagnosis time point, respectively.
Middle Pliocene rails from western Mongolia

: The fossil remains of three new species of rails from three Middle Pliocene localities in the Ich Nuuryn Tochom of Western Mongolia are described. These include Palaeoaramides tugarinovi new species, Rallns risillus new species, and Crex zazhigini new species. Rails are practically absent from Ich Nuuryn Tochom today, and the presence of three species of rails in Western Mongolia during the Middle Pliocene indicates that there has been a change in the climatic and ecological conditions found there since that time.
MULTI-LAYER FRAMEWORK FOR SECURITY AND PRIVACY BASED RISK EVALUATION ON E-GOVERNMENT

Security and privacy are an important aspect of e-Government 's success in providing online services to the public. The increase of electronic service usage including e-Gov can cause various risks, safety risks, and user’s privacy risks. The lack of concern of security and privacy gives impact to some ]problems of data and information so as to make the lack of public confidence in e-Gov services. So far the concern is the security aspect, while privacy is less attention. In many cases, the privacy aspect has many violations. This study aims to develop a multi-layer security and privacy framework as a basis for the evaluation of risk-based e-Government risk awareness. The steps in this research are creating the objectives of the security and privacy framework, the identification of requirements and the relevance of requirements, constructing the inclusive security aspect, identifying of the multi-layer framework, developing the development framework, and determining the elements for the risk-based evaluation model. The contribution of this research is the compilation of a multi-layer framework model for security and privacy. The relationship between the security and privacy domains forms a complete element of security and privacy which is the development of the Salman multi-layer framework. The resulting framework can be used as a basis for conducting security based on risk evaluations involving privacy factors.
A NOVEL METHOD TO ENHANCE THE RELIABILITY OF TRANSMISSION OVER SECURED SDWAN OVERLAY

The secure transmission of data over Software-Defined Wide Area Network (SDWAN) overlays is of paramount importance in today's interconnected world. However, ensuring both security and reliability poses significant challenges. In this research paper, we propose a novel method to enhance the reliability of transmission over secured SDWAN overlays. Our approach leverages advanced encryption and authentication techniques to secure the data transmission, while also incorporating best in class Reed Solomon FEC encoding, which perform very well in lossy network conditions. By considering factors such as link quality, latency, and packet loss, our method optimizes the transmission reliability for both TCP and UDP traffic at a minimal overhead. Through extensive simulations and evaluations, we demonstrate the effectiveness of our proposed method in enhancing transmission reliability. The results reveal significant improvements in packet delivery ratio, reduced packet loss and latency, and enhanced overall network performance compared to traditional SDWAN solutions. The findings of this research have implications for various industries, including finance, healthcare, and critical infrastructure, where secure and reliable data transmission is essential. Our novel method contributes to the advancement of SDWAN technology by addressing the critical challenge of maintaining reliability without compromising security.
CLASSIFICATION OF TRAINING ENGINEERING RESULTS USING DIGITAL TOOLS: CASE OF PATIENTS TREATED BY DIALYSIS METHOD

Data classification consists of organizing them into categories according to agreed criteria. Carefully planned classification enables more effective use of critical data and its protection across the enterprise; it also participates in risk management and legal knowledge and compliance processes. There is no universal approach to data classification. However, the classification process can be broken down into four key steps, which you can customize to meet your specific business needs when developing your data protection strategy. In this article we have tried to classify the results of training engineering by taking into consideration professional training carried out with health care personnel on dialyse. The subject focuses on the classification of training engineering results using numerical tools, focusing on patients treated by the dialysis method. The aim is to categorize outcomes related to training using digital technologies, based on data from patients undergoing dialysis treatment. This approach aims to identify patterns and trends in outcomes for a better understanding of patient needs and progress. The use of digital tools makes it possible to efficiently collect and analyze medical, laboratory and quality of life data from patients, in order to classify the results according to their health status, their response to treatment and their evolution over time. This methodology offers advantages for personalizing care, anticipating patient needs and improving the overall management of patients on dialysis.
Advanced scheduling algorithm for multi resource scheduling with minimum time consumption

The scheduling of appropriate resources for cloud workloads is a di�cult task, as it depends on the quality of service needs of cloud applications. Due to their limited data storage and energy capabilities, IoT applications demand high-speed data transfer and low latency. Many IoT devices generate data continuously and want to store it on the cloud quickly and e�ciently. Dynamic virtual machine (VM) allocation in cloud data centers (DCs) is taking advantage of the cloud computing paradigm. Each VM request is characterized by four parameters: CPU, RAM, disk, and bandwidth. Allocators are designed to accept as many VM requests as possible, considering the power consumption of the IoT device's network. Resource scheduling and time consumption is the two most signi�cant problems in cloud computing. To overcome this problem, in this paper, the author has extended CloudSim with a multi-resource scheduling and minimum time consumption model that allows a more accurate valuation of time consumption in dynamic multi-resource scheduling. The author proposes a new scheduling algorithm advance scheduling algorithm(ASA), which provides a better solution to other scheduling algorithms like Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Arti�cial Bee Colony(ABC). also tries to reduce energy consumption and time to give a task to the VM.
CORE Scholar CORE

Industry and researchers have identiﬁed numerous ways to monetize microblogs for personalization and recommendation. A common challenge across these diﬀerent works is the identiﬁcation of user interests. Although techniques have been developed to address this challenge, a ﬂexible approach that spans multiple levels of granularity in user interests has not been forthcoming. In this work, we focus on exploiting hierarchical semantics of concepts to infer richer user interests expressed as a Hierarchical Interest Graph . To create such graphs, we utilize users’ tweets to ﬁrst ground potential user interests to structured background knowledge such as Wikipedia Category Graph. We then adapt spreading activation theory to assign user interest score to each category in the hierarchy. The Hierarchical Interest Graph not only comprises of users’ explicitly mentioned interests determined from Twitter, but also their implicit interest categories inferred from the background knowledge source.
Imagerie c´er´ebrale : vers une connaissance plus approfondie du syst`eme nerveux central

– L’imagerie fonctionnelle a permis de progresser de fa¸con signiﬁcative dans la connaissance du cerveau. Elle permet d’appr´ehender le fonctionnement c´er´ebral tant dans des conditions physiologiques normales que pathologiques. Nous avons sou-hait´e faire une mise au point sur les concepts et les approches d’analyse d’images qui permettent d’aborder l’interconnectivit´e c´er´ebrale, en ayant pris soin de red´eﬁnir au pr´ealable et le plus rigoureusement possible les notions essentielles de la biologie du neurone qui constituent le socle des hypoth`eses et des mod`eles test´es en neuroimage-rie. L’´etude de circuits sp´eciﬁques en r´eseaux va permettre de cerner de fa¸con plus r´ealiste le fonctionnement dynamique du SNC qui sous-tend de nombreuses fonctions c´er´ebrales. Deux m´ethodes de mod´elisation de l’activit´e et de l’interactivit´e c´er´ebrales mesur´ees par IRMf sont pr´esent´ees. Mots cl´es : Imagerie / syst`eme nerveux central / connectivit´e c´er´ebrale / mod´elisation / IRMf Abstract – Brain imaging: towards a more detailed knowledge of the central nervous system. Functional imaging allowed the knowledge of the brain to progress signiﬁcantly. Recent ﬁndings about modeling of the activity and the interactivity measured by functional imaging are presented, they allow to apprehend brain functioning and brain plasticity both in normal and pathophysiological conditions. The study of speciﬁc circuits in networks should make it possible to deﬁne more realistically the dynamic functioning of the central nervous system which underlies numerous brain functions.
THE SECURITY CONCERNS AND SOLUTIONS FOR CLOUD-BASED IOT SYSTEM

From recent times our day to day life has significantly started to depend on the technological advancements of the Internet of Things. IoT has made it possible to converge the abilities of almost all types of devices ranging from small hand held devices to massive machines. Any system based on IoT infrastructure needs to deal with the issues of collection, storage and analysis of humongous data. Using external cloud servers is more convenient than the other alternative of being responsible for onsite storage but the issue of security and privacy need to be addressed in this case. This paper primarily focuses on the analysis of possible security threats for cloud based IoT systems. The authors have classified the security challenges for such a system and have presented a detailed survey of the techniques of cryptographic solutions to address the identified challenges. The authors have also suggested the security techniques and tools that can be used at each layer of the cloud service provider.