Miracle-dz/UniFAR-FTUs huggingface dataset:
Activity Detection in Untrimmed Surveillance Videos

Accurately detecting activities in untrimmed videos is a challenging task as systems need to handle variance in object scales, multiple viewpoints, and multiple types of activities. Furthermore, in a real-world scenario, activity detectors are often required to detect novel kinds of activities when the need arises from end-users. To address these issues, we first build an activity classifier on the known activities using detection-based proposals. Then, we propose a retrieval-based solution that utilizes both visual and textual queries for detecting novel types of activities. For known activity classification, a sequence of object detection, optical flow, and hierarchical clustering are run to obtain spatiotemporal proposals. Then, a multilabel loss is used for optimizing the TSM model. Our trained action classifier demonstrates classification and scene generalization capability by performing competitively on the public MEVA test set and the Known Activity Leaderboards from ActEV Challenge. For the vision-based retrieval, the penultimate features from the trained TSM are extracted on both query and gallery proposals. The averaged features from the query proposals are compared against the pool of gallery proposals to select the top-ranked proposals as detected activity instances. We also explore a language-based retrieval system that can utilize the textual descriptions of the unseen activities. An image-text model called CLIP is used to extract textual and visual features from the given examples. The same retrieval technique from the vision-based approach is applied for final predictions. Our proposed system ranked 1st place on the Surprise Activity Leaderboard from ActEV Challenge. We hope that the proposed system can help facilitate the successful deployment of activity detection in the real world.
Cellulase and Sugar Formation by <italic toggle='yes'>Bacteroides cellulosolvens</italic>, a Newly Isolated Cellulolytic Anaerobe

Some cellulolytic anaerobes possess the unique ability to hydrolyze cellulosic materials to cellobiose, in higher quantities than they utilize, and to glucose, which they are unable to use at all. Consequently, these sugars accumulate during cellulolysis (3). These anaerobes are potentially useful in coculture in the single-step conversion of cellulose to sugars or to other useful end products (1, 7, 19). A study of the cellulase system of these anaerobes and the involvement of this system in the saccharification of cellulose is important for improving sugar yields. During a study of the bacteria involved in a methanogenic culture enriched on a synthetic medium containing cellulose (6), three different mesophilic cellulolytic bacteria that produce sugars from cellulose were isolated (3). Among these isolates, Bacteroides cellulosolvens was shown to have a cellulose-degrading ability that is equal to that of Clostridium thermocellum (3). This paper describes the sugars and cellulolytic enzymes produced by B. cellulosolvens. The isolation and characterization of B. cellulosolvens (NRC 2944) have been described elsewhere (13). In brief, this isolate is a non-spore-forming, mesophilic anaerobe, capable of growing in a synthetic medium containing cellulose or cellobiose as the sole carbon source. It produces large amounts of acetic acid, C02, and H2 and small amounts of ethanol and lactic acid as the end products. It does not require yeast extract for growth and does not utilize glucose or xylose as a carbon source. The organism was maintained in a synthetic medium (10) containing tissue paper as a carbon source. All tests were conducted in 60-ml serum vials containing 10 ml of prereduced medium as described earlier (8), cellulose powder (CF-11; Whatman, Inc., Clifton, N.J.) or delignified ball-milled pulp (Solka-floc, BW 200; Brown Co., Berlin, N.H.) at a 5% (wt/vol) concentration, and CaCO3 at a 2% (wt/vol) concentration as a buffering agent. A 3-day-old culture was used for the inoculum (2 to 5% [vol/vol]). All cultures were incubated under an N2-CO2 (80%-20%) gas atmosphere at 37°C on a shaker operated at 200 rpm. All tests were made in triplicate and on two different occasions. After incubation for a desired length of time, cultures were centrifuged for 20 min at 3,000 x g. The supernatant liquid was assayed for sugars and extracellular cellulolytic activities. The pellet was washed three times with acetate buffer and assayed either for cellulose content (9) or for cell-
Survey on Dynamic Rumour Influence Minimization with User Experience in Social Networks.

: With the soaring development of huge scale online social networks, online information sharing is becoming ubiquitous a day. Various Information is propagating through online social networks including both positive and negative. Rumour blocking may be a significant issue in large-scale social networks. Malicious rumors could cause chaos in society and hence got to be blocked as soon as possible after being detected. During this system, during this system specialize in the way to minimize the propagation of negative information like the web rumours. The system is predicated on twitter and therefore the intention of the system is to detect and minimize the spreading of false, during this System posted by the user. Another important aim of the system is to form the users refrain from the habit of spreading rumour. During this system, during the System use a model of dynamic rumour influence minimization with user experience (DRIMUX). The goal is to attenuate the influence of the rumour (i.e., the amount of users that have accepted and sent the rumour) by blocking a particular subset of nodes. Additionally, different from existing problems of influence minimization, during this System take under consideration the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of every user exceeds that threshold, the utility of the network will decrease. Experiments are implemented supported large-scale world networks and validate the effectiveness of the method.
ARCHITECTURE FRAMEWORK AND COMPONENTS FOR BIG DATA SECURITY IN CLOUD COMPUTING.

: In this data-driven society, we are collecting a large amount of knowledge and data from people, their actions, sensors, algorithms, and also the web, and for that, the reason handling “Big Data” has become a significant challenge. Big data is that the next big thing in computing. As this data can not be processed using traditional systems, it poses numerous challenges to the research community. Privacy and security are among the important concerns with data, be it traditional data or big data. The cloud is increasingly getting used to store and process large data. Many researchers are trying to guard big data in a cloud computing environment. Traditional security mechanisms are neither efficient nor suited to the task of protecting big data within the Cloud. Protecting data within the cloud are often the same as protecting data within a conventional data center. Some components are used to secure data in cloud-like Authentication and identity, access control, encryption, secure deletion, integrity checking, and data masking are all data protection methods that have applicability in cloud computing.
Kernel Based Cognitive Architecture for Autonomous Agents

One of the main problems of modern cognitive architectures is an excessively schematic approach to modeling the processes of cognitive activity. It does not allow the creation of a universal architecture that would be capable of reproducing mental functions without using a predetermined set of perceptual patterns. This paper considers an evolutionary approach to creating a cognitive functionality. The basis of our approach is the use of the functional kernel which consistently generates the intellectual functions of an autonomous agent. We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution. Evolution of cognitive abilities of the agent is described on the basis of the theory of constructivism.
An Efficient Fourth Order PDE-Based Nonlinear Filter Adapted to Rician Noise for Restoration and Enhancement of Magnetic Resonance Images

In this manuscript, for the removal of Rician noises from MR images a fourth order PDE (partial differential equation) based nonlinear filter is proposed. The filter implemented contains two terms firstly, the data fidelity term and secondly, prior function term. By minimizing the negative log likelihood of Rician probability density function (pdf) we get the fidelity term. A prior function is introduced because the solution of the first term has ill-posed nature. This prior function is a nonlinear anisotropic diffusion based filter. To maintain the trade off balance between these two terms a regularization parameter is introduced. In terms of PSNR at various noise levels the comparative study and performance analysis of the suggested model with other standard methods is presented for Brain Web dataset. From the results obtained, it is clear that the method implemented is performing better noise removal as compared to other methods.
A Generative-Discriminative Approach to Human Brain Mapping

During everyday behaviours, the brain shows complex spatial patterns of activity. These activity maps are very replicable within an individual, but vary significantly across individuals, even though they are evoked by the same behaviour. It is unknown how differences in these spatial patterns relate to differences in behavior or function. More fundamentally, the structural, developmental, and genetic factors that determine the spatial organisation of these brain maps in each individual are unclear. Here we propose a new quantitative approach for uncovering the basic principles by which functional brain maps are organized. We propose to take an generative-discriminative approach to human brain mapping, with the fundamental idea that, if we understand the underlying principles that organises brain activity maps, we should be able to generate sets of artificial maps that are indistinguishable from real ones. Different generative models are tested by a series of adversarial, classifier models, ranging from linear classifiers based on specific marginal statistics, to a full convolutional neural network. We apply our new framework to a collection of measured finger activity maps measures with fMRI in the human sensori-motor cortex (N=50). To account for characteristics of the brain maps that depend on the specific measurement process (spatial resolution of fMRI, signal-to-noise, etc) we supplemented the generative process with a measurement model. Initial results clearly demonstrate that the matching of simple marginal statistics (covariance and smoothness of activity pattern) is sufficient to fool the human eye, but not a more systematic machine learning approach. The proposed evaluation framework therefore opens up a pathway for discovering specific characteristics of brain activity maps that are important to explain function or individual differences.
An empirical study on Resource Description Framework reification for trustworthiness in knowledge graphs

Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. RDF reification increases the magnitude of data as several statements are required to represent a single fact. Another limitation for applications that uses provenance data like in the medical domain and in cyber security is that not all facts in these knowledge graphs are annotated with provenance data. In this paper, we have provided an overview of prominent reification approaches together with the analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. However, facts in Wikidata and YAGO4 can be fetched without using reification to cater for applications that do not require metadata. To the best of our knowledge, this is the first paper that investigates the method and the extent of metadata covered by two prominent KGs, Wikidata and YAGO4.
Genome-wide identification of DCL, AGO and RDR gene families and their associated functional regulatory elements analyses in banana (Musa acuminata)

RNA silencing is mediated through RNA interference (RNAi) pathway gene families, i.e., Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) and their cis-acting regulatory elements. The RNAi pathway is also directly connected with the post-transcriptional gene silencing (PTGS) mechanism, and the pathway controls eukaryotic gene regulation during growth, development, and stress response. Nevertheless, genome-wide identification of RNAi pathway gene families such as DCL, AGO, and RDR and their regulatory network analyses related to transcription factors have not been studied in many fruit crop species, including banana (Musa acuminata). In this study, we studied in silico genome-wide identification and characterization of DCL, AGO, and RDR genes in bananas thoroughly via integrated bioinformatics approaches. A genome-wide analysis identified 3 MaDCL, 13 MaAGO, and 5 MaRDR candidate genes based on multiple sequence alignment and phylogenetic tree related to the RNAi pathway in banana genomes. These genes correspond to the Arabidopsis thaliana RNAi silencing genes. The analysis of the conserved domain, motif, and gene structure (exon-intron numbers) for MaDCL, MaAGO, and MaRDR genes showed higher homogeneity within the same gene family. The Gene Ontology (GO) enrichment analysis exhibited that the identified RNAi genes could be involved in RNA silencing and associated metabolic pathways. A number of important transcription factors (TFs), e.g., ERF, Dof, C2H2, TCP, GATA and MIKC_MADS families, were identified by network and sub-network analyses between TFs and candidate RNAi gene families. Furthermore, the cis-acting regulatory elements related to light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and other activities (OT) functions were identified in candidate MaDCL, MaAGO, and MaRDR genes. These genome-wide analyses of these RNAi gene families provide valuable information related to RNA silencing, which would shed light on further characterization of RNAi genes, their regulatory elements, and functional roles, which might be helpful for banana improvement in the breeding program.
Bruton’s Tyrosine Kinase Inhibition for the Treatment of Rheumatoid Arthritis

Abstract Bruton’s tyrosine kinase (BTK) inhibitors are an emerging class of drugs that inhibit B cell receptor activation, FC-γ receptor signaling, and osteoclast proliferation. Following on approval for treatment of hematologic malignancies, BTK inhibitors are now under investigation to treat a number of different autoimmune diseases, including rheumatoid arthritis (RA). While the results of BTK inhibitors in RA animal models have been promising, the ensuing human clinical trial outcomes have been rather equivocal. This review will outline the mechanisms of BTK inhibition and its potential impact on immune mediated disease, the types of BTK inhibitors being studied for RA, the findings from both preclinical and clinical trials of BTK inhibitors in RA, and directions for future research.
Alterations of Ocular Surface Parameters in Type 2 Diabetic Patients

Objective The purpose of the study was to evaluate the changes in the ocular surface parameters of type 2 diabetic patients. Methods This study is a cross-sectional observational study. It included 76 eyes of 38 patients with type 2 diabetes and 183 eyes of 92 non-diabetic controls. The diabetic subjects were divided into two groups based on diabetic duration. Ocular surface parameters were assessed for all participants. Results The diabetic group had a significantly thinner lipid layer thickness (LLT) (p = 0.008) than non-diabetic controls. LLT in the subgroup with diabetic duration ≥10 years was significantly thinner than that in the subgroup with diabetic duration <10 years (p = 0.006). Multivariate linear regression analysis showed that LLT was associated with the duration of diabetes (β = −1.497, p = 0.009) and Schirmer I test (SIT) score (β = 0.852, p = 0.04) in diabetic. Diabetic duration was significantly negatively associated with LLT (β = −0.062, p = 0.004) and significantly positively associated with ocular surface disease index (OSDI) (β = 0.087, p = 0.039). Conclusion Diabetic patients exhibit significantly reduced LLT and this trend becomes more pronounced with increased duration of diabetes.
High-Sensitivity Cardiac Troponin T Is a Risk Factor for Major Adverse Cardiovascular Events and All-Cause Mortality: A 9.5-Year Follow-Up Study

Background The relationship between high-sensitivity cardiac troponin T (hs-cTnT) and different cardiovascular events has been observed in several large community studies, and the results have been controversial. However, there is currently no cross-sectional or longitudinal follow-up study on hs-cTnT in the Chinese population. Methods We analyzed the association of plasma hs-cTnT levels with major adverse cardiovascular events (MACEs) and all-cause mortality in 1325 subjects from a longitudinal follow-up community-based population in Beijing, China. Results In the Cox proportional hazards models analysis, the risk of MACEs increased with the increase of hs-cTnT levels (HR, 1.223, 95% CI, 1.054–1.418, P=0.008). Increased hs-cTnT levels were associated with coronary events (HR, 1.391, 95% CI, 1.106–1.749, P=0.005) in Model 4. Cox proportional risk regression model analysis revealed that increased hs-cTnT levels were associated with an increased risk of mortality (HR, 1.763, 95% CI, 1.224–2.540, P=0.002), even after adjusting hs-CRP and NT-proBNP. The area under the ROC curve for predicting MACEs was 0.559 (95% CI, 0.523–0.595, P=0.001). The areas under the ROC curve for predicting coronary events and mortality were 0.629 (95% CI, 0.580–0.678, P < 0.001) and 0.644 (95% CI, 0.564–0.725, P < 0.001), respectively. Conclusions Our findings in the Chinese cohort support that hs-cTnT is a risk factor for major adverse cardiovascular events and all-cause mortality.
What is the risk of a deadly adenovirus pandemic?

Many of us had refresher courses in virology, immunology, and epidemiology in 2020, and we were reminded of the fact that Homo sapiens, the wiliest predator on the planet, has been hunting everything that moves for millennia. These repeated interspecies contacts inherently lead to recurrent zoonosis (nonhuman to human) and anthroponosis (human to nonhuman). Given the accelerating changes in our ecosystems since the neolithic revolution, it was not surprising to see a virus that spreads via aerosolization and liquid droplets cause a pandemic in a few months. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic begs the question—which viruses could cause a global threat? In this Opinion, the characteristics that make adenoviruses a risk, which include efficient intra- and interspecies transmission, thermostable particles, persistent/latent infections in diverse hosts, and the ability to readily recombine and escape herd immunity, are discussed.
Major Depressive Disorder and Difference between Genders

Background: Depression is a common mental disorder and represents a global mental health concern. Presents with depressed mood, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, low energy and poor concentration. Epidemiologic research have found clear genders differences in the prevalence of Major depressive disorders (MDD). Objective: The aim of this study was to find the difference in the symptoms of Major depressive disorder (MDD) between genders. Methods: It was analyzed 92 subjects from Health Center Zivinice in the period from September 2019 to May 2021, of which 57 (62 %) are women and 35 (38%) are men. The study identified and measured the severity of 25 different symptoms of depressive disorders in the analyzed subjects. The average age of women is 56 years ± 8.88, and the average age of men is 52 years ± 11.03. Statistical data were analyzed in SPSS statistical program. Results: Comparing the results t tests revealed significant difference between genders in symptoms like depressed mood, lack of energy, psychomotor retardation, pessimistic attitude towards the future. Symptoms such as angry outbursts, irritability from frustration, even over small matters, frequent or recurrent thoughts of death, suicidal thoughts suicide attempts and impulsive reaction, risky behaviour statistical significance in men in relation to women. Conclusion: In this study it was confirmed that MDD is more often diagnosed in women and showing different ways of experiencing, expressing and dealing with the symptoms of MDD. Women complained more about the typical symptoms of depressive disorder according, while men complained more about anger, irritability, waking up early in the morning and alcohol abuse.
Value-added chemicals from lignin and hemicellulose fractions of sugarcane bagasse

Sugarcane bagasse was analyzed for its chemical composition and was found to contain cellulose, hemicellulose, lignin 42.11, 28.42, 19.20%, and glucan, xylan, arabinan, acid insoluble lignin, acid soluble lignin, ash 39.65, 21.46, 2.75, 15.10, 3.31, 3.96%, respectively. Maximum lignin extraction (17.60%) was found to be with 10% NaOH which was further confirmed by scanning electron microscopy images. Lignin was chemically modified by oxidation, acetylation, epoxidation and hydroxymethylation and characterized using FT-IR, 1 H, 13 CNMR spectra. HPLC analysis revealed p-coumaric acid as major phenolic acid in bagasse lignin (14.76 mg g -1 bagasse) followed by ferulic, vanillic and syringic acid. p-Coumaric and ferulic acid were isolated, derivatized into esters (reactions with alcohols) and amides (reactions with amines, amide linking reagent EDC-HCl (N-(3dimethylaminopropyl)-N‟-ethylcarbodiimide) and 1-hydroxybenzotriazole, HOBt) and characterized by FT-IR, 1 H, 13 CNMR spectra and elemental analyses. Lignin, modified lignins, phenolic acids and their derivatives were evaluated for antioxidant (DPPH radical scavenging) activity and antibacterial activity against Bacillus aryabhattai, Acinetobacter sp. and Klebsiella sp. Lignin and its oxidized and hydroxymethyl derivative were observed to have higher antioxidant activity than 3,5-di-tert-butyl-4-hydroxytoluene (BHT) and lower than 3-tert-butyl-4-hydroxyanisole (BHA). Phenolic compounds and their derivatives were effective at lower concentrations for antibacterial activity than lignin and modified lignins. Xylooligosaccharides (XOS) were produced from bagasse by xylan extraction (5, 10, 15% KOH and NaOH) followed by acid hydrolysis (0.25 and 0.50MH2SO4; 20, 40, 60min) and analyzed by HPTLC. The best treatment for xylan hydrolysis leading to increased production of XOS and reduced concentration of xylose was found to be 0.25MH2SO4 for 20 min. XOS were found to be better prebiotics for three probiotic bacteria viz. Lactobacillus acidophilus, Lactobacillus brevis and Lactobacillus viridescens than glucose and standard fructooligosaccharides.