Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Fine-grained Access Control scheme for Secure Cloud Storage data

— The pioneering decentralized access control scheme for susceptible data storage in clouds that supports unidentified authentication. In the proposed scheme, the clouds verify the authenticity of the server without knowing the user’s uniqueness before storing data. The proposed design focuses on the full cycle access controlling scheme where in the presented system it was omitted. In order to achieve secure access controlling scheme we proposed fine-gained approach at cloud level , it prevents unauthorized access controlling from users or adversaries effectively our authentication and access control scheme is decentralized and strong, unlike other access control schemes designed for clouds which are centralized. The communication, computation overheads are similar to centralized approaches. More over our proposed design proves that our system has secure crypto mechanism towards achieving data integrity and security.
The estimation of transmitted drug resistance mutation strains probability in the treatment of HIV using the beta-binomial model.

The human immunodeficiency virus (HIV) is a viral infection that destroys the human immune system resulting in acquired immunodeficiency syndrome (AIDS). The Zambia HIV prevalence rate (11.3%) remains among the highest in the sub-Saharan Africa. In the treatment of HIV naïve patients, a problem which relates to the Transmitted Drug Resistance Mutation strains (TDRMs) occurs in the administration of antiretroviral (ARV) drugs. To address this problem, we propose the use of transition probabilities when prescribing a switch from the first-line to the second-line or to the third-line regimen on the ARV drugs combination. We formulate a statistical technique to determine an optimal ARV drugs combination. To compute a transition probability matrix chart on ARV drugs combinations of the first-line and second-line regimens, we apply a beta-binomial hierarchical model on HIV data. The transition probability matrices corresponding to the ARV drugs combinations TDF+ETC+NVP, TDF+FTC+EFV, AZT+3TC+NVP, AZT+3TC+EFV, D4T+3TC+NVP and D4T+3TC+EFV provide an upper triangular matrix of probabilities. We observe a higher probability of remaining in the same regimen state than moving to another state. A transition probability chart provides information on the most effective combination to prescribe to a patient in the presence of TDRM test results. The transmission probabilities play a major role in aiding the physicians make an informed decision to prescribe an optimal ARV drugs combination. We suggest a TDRM test to be done to all newly diagnosed HIV individuals before prescribing any of the ARV drugs combination.
AUTOMATIC DETECTION OF HUMAN FACE BY USING NORMALIZED GABOR FILTERS IN VIDEO SEQUENCES

-Machine analysis for detection of the human face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and the scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Normalized Gabor Features. For each frame, normalization technique is formulated. The frame is divided into overlapping blocks. Gabor features are extracted in each block by using Gabor filter with selected scale and orientations. TheseGabor filter features are skewed and then passed toAdaBoost classifier with the exemplar of face and non-face signature of the images for detecting thehuman face.The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.
A Bibliometric Analysis of Deep Web Research during 1997 to 2019

This study provides a bibliometric account of global deep web research published from 1997 to 2019. A total of 1995 records were imported from the Scopus database in a bibtex file. The bibliometrix package in RStudio was used for analyses. Publication Growth, Citations, Authorship, Country and Affiliations has been analysed. It was found that deep web research had a slow growth rate. In the last four years it has seen a recovery in the growth rate. Furthermore, this study shows the distribution of highly cited papers in the field over 23 years. It shows the country and institutional affiliation pattern of prolific authors. It also presents the most preferred sources, search terms and preferred medium of research communication. It is found that deep web research had a slow growth rate, but since 2016 it is picking up. China is the leading contributor of publications followed by the United States of America, Japan, and the United Kingdom. India is the fifth largest contributor. Contribution of citable publications has been led by Canada and USA with 81.9 per cent of efficiency followed by Australia (79.7 %), France (73.4 %) and Spain (73.1 %). It is also found that most of the prolific authors (by number of publications) do not appear in highly cited publications’ list. Deep web researchers mostly preferred using conference publications to communicate their findings. ‘Machine Learning’ and ‘cryptomarkets’ are two contemporarily popular terms being used by deep web researchers also, which indicates interest towards these topics.
Data independent acquisition-mass spectrometry (DIA-MS)-based comprehensive profiling of bone metastatic cancers revealed molecular fingerprints to assist clinical classifications for bone metastasis of unknown primary (BMUP)

Background Bone metastasis is the third most common metastatic cancers worldwide. It is a group of highly heterogeneous diseases with various potential cancer primaries. Among them, one third was diagnosed as bone metastasis of unknown primary (BMUP) due to lack of indication for the primary tumor even after comprehensive examinations. Thus, the prognosis of BMUP is often very poor since the treatment was largely empirical and untargeted. To assist identification of the primary tumor, a series of molecular markers including traditional tissue-specific histochemistry as well as gene and mRNA markers were developed with moderate to good sensitivity and specificity. Methods In this paper, we carried out a comprehensive expression profiling for fresh-frozen tissue samples of bone metastasis from lung, prostate and liver cancers using high resolution, data-independent-acquisition mass spectrometry (DIA-MS). The proteome variation was analyzed and protein classifiers were prioritized. Results Over 6,000 proteins were quantified from 18 samples, which, to the best of our knowledge, was never achieved before. Further statistical analysis and bioinformatics data mining revealed 4 significant proteins (RFIP1, CK15, ESYT2, and MAL2) with excellent discriminating capabilities with AUCs higher than 0.8. Conclusions The comprehensive proteome map of bone metastases will complement available genomic and transcriptomic data. Newly discovered protein classifiers will expand current diagnostic arsenal for tissue of origin studies in BMUP. Furthermore, the proteome map generated in this study by DIA-MS allows future data re-mining as our knowledge advances to assist investigation of bone metastasis and progression of tumors as well as the development of diagnostic tools and prognosis management for BMUPs.
Development of Cryptographic Algorithm for Secure Communication

RSA algorithm uses Integer Factorization as the backbone of data security while Diffie-Hellman Algorithm uses Discrete Logarithm to provide platform for secure key exchange. In this paper, we provide algorithm to merge both of them to provide a user with even higher level of data security. Actually, our intent is to secure data of smaller as well as larger size by obtaining one randomly chosen key pair from set of RSA keys and one randomly chosen secret key using Diffie-Hellman algorithm and then applying RSA encryption to make even public components of Diffie-Hellman algorithm inaccessible for any eavesdropper freely. We design the encryption method, which uses Diffie-Hellman Secret Key in such a way that it would not only encrypt the data but also does not increase the size of it any further. On top of that, encrypted exchange of public components of Diffie-Hellman part of the system makes it hidden from all other than intended users to see Diffie-Hellman generator and prime modulus. This will make our algorithm M*N times complex to break using even the latest version of Brute Force attack, where M and N are corresponding complexities imposed by the Diffie-Hellman and RSA algorithms respectively.
Efficient Network Recovery Scheme for a node or Link failure using Multiple Standard Routing Configurations

: As the Internet takes an increasingly central role in our communications infrastructure; the slow convergence of routing protocols after a network failure becomes a growing problem. To assure fast recovery from link and node failures in IP networks, we present a new recovery scheme called Multiple Routing Configurations (MRC). Our proposed scheme guarantees recovery in all single failure scenarios, using a single mechanism to handle both link and node failures, and without knowing the root cause of the failure. MRC is strictly connectionless, and assumes only destination based hop-by hop forwarding. MRC is based on keeping additional routing information in the routers, and allows packet forwarding to continue on an alternative output link immediately after the detection of a failure. It can be implemented with only minor changes to existing solutions. In this paper we present MRC, and analyze its performance with respect to scalability, backup path lengths, and load distribution after a failure. We also show how an estimate of the traffic demands in the network can be used to improve the distribution of the recovered traffic, and thus reduce the chances of congestion when MRC is used.
Aiding Drug Design with Deep Neural Networks

An important computational tool in drug design is target prediction where either for a given chemical structure the interacting biomolecules (e.g. proteins) must be identiﬁed. Chemical structures interact with different biomolecules if they have similar 3D structure. Thus, the outputs of the prediction are highly interdependent from each other. Furthermore, we have partially labelled molecules since not all training molecules are measured of being active on each biomolecule. The Merck Kaggle challenge on chemical compound activity was won by Hin-ton’s group with deep networks. This indicates the high potential of deep learning in drug design and attracted the attention of big pharma. However, the unreal-istically small scale of the Kaggle dataset does not allow to assess the value of deep learning in drug target prediction if applied to in-house data of pharmaceutical companies. Even a publicly available drug activity data base like ChEMBL is magnitudes larger than the Kaggle dataset. ChEMBL has 13 M compound descriptors, 1.3 M compounds, and 5 k drug targets, compared to the Kaggle dataset with 11 k descriptors, 164 k compounds, and 15 drug targets. On the ChEMBL database, we compared the performance of deep learning to seven target prediction methods, including two commercial predictors, three predictors deployed by pharma, and machine learning methods that we could scale to this dataset. Deep learning outperformed all other methods with respect to the area under ROC curve and was signiﬁcantly better than all commercial products. Deep learning surpassed the threshold to make virtual compound screening possible and has the potential to become a standard tool in industrial drug design.
Exome sequencing identified six copy number variations as a prediction model for recurrence of primary prostate cancers with distinctive prognosis

Background Prostate cancer (PCa) is a common type of malignancy, which represents one of the leading causes of death among men worldwide. Copy number variations (CNVs) and gene fusions play important roles in PCa and may serve as markers for the prognosis of this condition. Methods We have presently conducted an analysis of CNVs and gene fusions in PCa, using whole exome sequencing (WES) data of primary tumors. For this, a cohort of 74 PCa patients, including 30 recurrent and 44 non-recurrent cases, were assessed during 5 years of follow-up. Results We have identified 66 CNVs that were specific to the primary tumor tissues from the recurrent PCa group. Most of duplicated genomic regions were located in 8q2, suggesting that this chromosomal region could be important for the prognosis of PCa. Meanwhile, we have developed a random forest model, using six selected CNVs, with an accuracy near 90% for predicting PCa recurrence according to a 10-fold cross validation. In addition, we have detected 16 recurrent oncogenic gene fusions in PCa. Among these, ALK (ALK receptor tyrosine kinase)-involved fusions were the most common type of gene fusion (n=7). Four of these fusions (i.e., EML4-ALK, STRN-ALK, CLTC-ALK, ETV6-ALK) were previously identified in other cancer types, while the remaining three gene fusions (FRYL-ALK, ABL1-ALK, and BCR-ALK) were here identified. Conclusions Our findings expand the current understanding in regard to prostate carcinogenesis. Current data might be further used for assay development as well as to predict PCa recurrence, using primary tissues.
Bioactivity of Active Extracts of Leaf of Bauhinia Racemosa

Infection diseases are one of the main reasons which cause the death every day. Medicinal plants are used by 80% of the world population as the only available medicines especially in developing countries, the use of medicinal plants is very wide spread in many parts of the world because it is commonly considered that herbal drugs are cheaper and safer as compared to synthetic drugs and may be used without or minimum side effects. Plants used for traditional medicine contain a wide range of substances that can be used to treat chronic as well as infectious diseases (Subhashini et al., 2010). WHO (2001) defines medicinal plant as herbal preparations produced by subjecting plant materials to extraction, fractionation, purification, concentration or other physical or biological processes which may be produced for immediate consumption or as a basis for herbal products. Ojelere et al.,(2014) point out that medicinal plants are plants containing inherent active ingredients used to cure disease or relieve pain.
Haverford College

MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a 6-yr Sloan Digital Sky Survey (SDSS-IV) survey that will obtain spatially resolved spectroscopy from 3600 to 10 300 Å for a representative sample of over 10 000 nearby galaxies. In this paper, we present the analysis of nebular emission-line properties using observations of 14 galaxies obtained with P-MaNGA, a prototype of the MaNGA instrument. By using spatially resolved diagnostic diagrams, we find extended star formation in galaxies that are centrally dominated by Seyfert/LINER-like emission, which illustrates that galaxy characterizations based on single fibre spectra are necessarily incomplete. We observe extended low ionization nuclear emission-line regions (LINER)-like emission (up to 1Re) in the central regions of three galaxies. We make use of the Hα equivalent width [EW(Hα)] to argue that the observed emission is consistent with ionization from hot evolved stars. We derive stellar population indices and demonstrate a clear correlation between Dn(4000) and EW(HδA) and the position in the ionization diagnostic diagram: resolved galactic regions which are ionized by a Seyfert/LINER-like radiation field are also devoid of recent star formation and host older and/or more metal-rich stellar populations. We also detect extraplanar LINER-like emission in two highly inclined galaxies, and identify it with diffuse ionized gas. We investigate spatially resolved metallicities and find a positive correlation between metallicity and star formation rate surface density. We further study the relation between N/O versus O/H on resolved scales. We find that, at given N/O, regions within individual galaxies are spread towards lower metallicities, deviating from the sequence defined by galactic central regions as traced by Sloan 3-arcsec fibre spectra. We suggest that the observed dispersion can be a tracer for gas flows in galaxies: infalls of pristine gas and/or the effect of a galactic fountain.
Katzung and Trevor's Pharmacology Examination and Board Review

MORE THAN 1000 Q&As PREPARE YOU FOR MEDICAL PHARMACOLOGY COURSE EXAMS AND BOARD EXAMINATIONS From the authors of pharmacology's #1 textbook, Katzung & Trevor's Basic & Clinical Pharmacology, Twelfth Edition, this review delivers a clear, concise review of fundamental concepts backed by more than 1,000 review questions and answers. The chapter-based approach facilitates use with course notes or larger texts. Outstanding learning aids include: Short discussion of the major concepts that underliebasic principles or specific drug groups Explanatory figures and tables Review questions followed by answers and explanations Drug trees in drug-oriented chapters that visually organize drug groups A list of high-yield terms and definitions you need to know Skill Keeper Questions that prompt you to review previous material to understand links between related topics A checklist of tasks you should be able to perform upon completion of a chapter Summary tables that list the important drugs and include key information about their mechanisms of action, effects, clinical uses, pharmacokinetics, drug interactions, and toxicities Two comprehensive 100-question examinations followedby the correct answers and rationales Valuable test-taking strategies for improving your test performance
Cooperative Provable Data Possession for Multi-Cloud Server Storage

Provable data possession (PDP) is a strategy for guaranteeing the trustworthiness of information away outsourcing. In this paper, we address the construction of a proficient PDP scheme for disseminated distributed storage to bolster the versatility of administration and information migration, in which we consider the presence of different cloud administration providers to agreeably store and keep up the customers' information. We display an cooperative PDP (CPDP) scheme based on homomorphic verifiable response and hash file indexing order. We demonstrate the security of our scheme based on multi-proven zeroknowledge proof system, which can fulfill culmination, information soundness, and zero-information properties. In addition, we verbalize execution optimization instruments for our scheme, and specifically exhibit a proficient technique for selecting ideal parameter qualities to minimize the computation expenses of customers and capacity administration providers. Our examinations demonstrate that our solution presents lower computation and communication overheads in comparison with non-helpful methodologies
Improving care for cancer survivors

ABSTRACT Introduction: Adult cancer survivors may experience a range of consequences from their disease and its treatment. There is a need to improve the current model of post-treatment care, which is neither sufficiently effective nor sustainable in the context of a burgeoning population of cancer survivors. Areas covered: We review the current state of post-treatment care, including its strengths and limitations. Guiding principles in the design of optimal survivorship care are discussed, including longitudinal risk stratification, survivorship care plans, incorporating patient preferences and remote monitoring. Alternative models of survivorship care are reviewed which seek to address individual patient needs and preferences. Other strategies for improving survivor outcomes are reviewed. Future research priorities are outlined, including exploring the lived experience of survivors and appropriate models of care. Expert commentary: The challenge of providing optimal survivorship care is shared internationally and there is an opportunity to learn from the experiences of others. Despite high quality evidence for at least some cancer types, there has been limited adoption of alternative models of care and more work is required to translate these findings into routine practice.
Commercial Sexual Exploitation of Adolescents: Gender- Specific and Trauma-Informed Care Implications

The commercial sexual exploitation of adolescents is a critical social injustice that calls for extensive care and sensitivity in practice. Understanding the intricacies of the broad, yet disturbing, dynamics of trauma triggers, coupled with realities of sex trafficking, escalates the level of professional concern. Social work professionals need enhanced knowledge of the challenges involved in working with female adolescents who experience prolonged adverse reactions that result from this form of oppression. Services inclusive of gender-specific and trauma-informed strategies are essential for this form of complex trauma. This article highlights the interplay of sex trafficking, life stage, and service implications through an ethical lens.
Collapse of the Nation's Iron Ore Deposit: A Case of Itakpe Iron Ore Deposit, Kogi State

NIOMCO (National Iron Ore Mining Company), Nigeria's iron ore mining company has been under the captivity of bureaucracy over years of its existence resulting to her zero performance. This paper identifies some crucial aspects or factors surrounding the company's zero existence and suggesting a reverse of these factors as a recipe for reviving the company, knowing the capacity of the mining sector in strengthening the nation's industrial base as exposed in some literatures. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 30-09-2017 Date of acceptance: 16-12-2017 -------------------------------------------------------------------------------------------------------------------------------------
Underwater Gesture Recognition Using Classical Computer Vision and Deep Learning Techniques

Underwater Gesture Recognition is a challenging task since conditions which are normally not an issue in gesture recognition on land must be considered. Such issues include low visibility, low contrast, and unequal spectral propagation. In this work, we explore the underwater gesture recognition problem by taking on the recently released Cognitive Autonomous Diving Buddy Underwater Gestures dataset. The contributions of this paper are as follows: (1) Use traditional computer vision techniques along with classical machine learning to perform gesture recognition on the CADDY dataset; (2) Apply deep learning using a convolutional neural network to solve the same problem; (3) Perform confusion matrix analysis to determine the types of gestures that are relatively difficult to recognize and understand why; (4) Compare the performance of the methods above in terms of accuracy and inference speed. We achieve up to 97.06% accuracy with our CNN. To the best of our knowledge, our work is one of the earliest attempts, if not the first, to apply computer vision and machine learning techniques for gesture recognition on the said dataset. As such, we hope this work will serve as a benchmark for future work on the CADDY dataset.