Miracle-dz/UniFAR-FTUs huggingface dataset:
TANSIVTx: Time-Accurate Network Simulation Interconnecting VMs with Hardware Virtualization Towards Stealth Analysis

: Security researchers confine malware samples in isolated and controlled environments called sandboxes, where they can be safely analyzed. However some malware analyze their environment to detect the presence of a sandbox and evade the analysis. In this work we focus on malware that are using timing analysis on the network to detect sandbox environments. The goal of the internship is to study an implementation of an analysis environment resistant to network fingerprinting. One key aspect of the approach is the use of hardware virtualization (Intel VT-x) to improve performance and robustness towards evasion techniques. This implementation will indeed extend the current prototype, TANSIV, which relies on QEMU’s emulation mode. In this internship report, we present two different approaches to add hardware virtualization support to TANSIV.
A guide to using nucleic acid aptamers in cell based assays

Aptamers are single stranded nucleic acid molecules that fold into complex three-dimensional conformations. They have been used for a number of years as replacements for monoclonal antibodies by a core group of researchers. However, now that more and more companies are offering aptamers commercially for research and development applications, there is a need for a ‘how to’ guide for researchers who are interested in using aptamers for their own applications. Within this article, we detail the critical concerns for working with aptamers, from what to dilute them in, concentrations and times to use for incubation steps, as well as a suggested protocol. We hope that this guide will prove useful for researchers who are trialling aptamers for cell based assays, and as a starting point for researchers investigating aptamers for other applications.
The Relationship between Loneliness, Anxiety Level and Depressive Symptoms among Elderly

The likelihood of experiencing age-related health problems increase with growing age. Many people experience mental and psychological illnesses such as loneliness, depression, and anxiety in old age. The purpose of this study was to assess the relationship between loneliness, anxiety level and depressive symptoms among elderly. A descriptive correlational design was utilized. The study was conducted at geriatric nursing homes in Berket EL Sabba and Alsadat City. A Convenient sample of 53 elderly who met inclusion and exclusion criteria were selected from geriatric nursing home. Three tools were used to fulfill: Loneliness Scale, Geriatric Anxiety Scale and Patient Health Questionnaire depression scale. The results of this study indicated that nearly half of the sample had high degree of loneliness, half of the sample had mild level of anxiety and one third of the sample had moderate depressive symptoms. There was also a positive correlation between loneliness, anxiety and depressive symptoms. The study concluded that there was a positive correlation between loneliness, anxiety and depressive symptoms. Therefore, it is recommended that, a screening program for early discovering and intervention program for reducing loneliness, anxiety and depressive symptoms among elderly and training programs for increasing the community awareness about needs, problems of elderly, how to adjust with it and and the available community mental health services.
Preparation of poly(methyl methacrylate)/titanium oxide composite particles via in-situ emulsion polymerization

Poly(methyl methacrylate) (PMMA)/Titanium oxide (TiO2) composite particles were prepared via in-situ emulsion polymerization of MMA in the presence of TiO2 particles. Before polymerization, the TiO2 particles was modified by the silane coupling agent, which is crucial to ensure that PMMA reacts with TiO2 via covalent bond bindings. The structure of the obtained PMMA/TiO2 composite particles was characterized using Fourier transform infrared spectra (FTIR) and thermogravimetric analysis (TGA). The results indicate that there are covalent bond bindings between PMMA macromolecules and TiO2 particles. Based on these facts, several factors affecting the resulting PMMA/TiO2 composite system, such as the type of coupling agents, the mass ratio of the MMA to the modified TiO2, the emulsifier concentration, and the initiator concentration, etc., were examined by the measurement of conversion of monomers, the gel content of polymers, the percentage of grafting, and the grafting efficiency, using gravity method or TGA method. As a result, the optimized recipe was achieved, and the percentage of grafting and the grafting efficiency could reach 216.86 and 96.64%, respectively. In addition, the obtained PMMA/TiO2 composite particles were found to a stable colloidal dispersion in good solvent for PMMA. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4056–4063, 2006
RESHAPING THE FINANCIAL SERVICES DELIVERY IN SAUDI ARABIA: THE ROLE OF BLOCKCHAIN TECHNOLOGY

In respond to the call by the subject matter of this book on management in a context of digital transformation with particular focus on the role of blockchain technology in effective financial services delivery in Saudi Arabia. This chapter examined reshaping the financial services delivery in Saudi Arabia with particular focus on the role of blockchain technology. The financial sector has in the past experience major changes due to the effect of technology and digital era. The new era is geared towards replacing the traditional financial methods in the delivery of financial services, where services are almost done manually. Due to traditional nature of financial methods, service delivery is therefore considered not effective enough to achieve the desire customer satisfactions. Therefore, a new technology has emerged to reshape the financial industry landscape drastically forcing the financial firms to transform and retain their financial stronghold. The study covered 39 employees of 8 banks in Riyadh district, Saudi Arabia through a cross-sectional survey research design with a quantitative survey questionnaire approach. An email survey procedure was employed to distribute and retrieve the copies of questionnaire distributed to the respondents. In all only 39 copies of questionnaire among those returned were usable. A SmartPls analysis technique that deals with reflective constructs was used to analyse the data collected whereby the finding among others revealed overall support on the relationship between blockchain technology and effective financial service delivery (p<0.001 with t value of 13.465). In other words, it shows that blockchain technology affects the financial service delivery, suggesting that effective service delivery in the banking industry is a function of blockchain technology. It draws a conclusion that blockchain technology enhances financial service delivery in the financial industry.
Hierarchical Temporal Logic Guided Reinforcement Learning

We present a framework that allows speciﬁcation of tasks in terms of temporal logic (TL). Particularly, we allow tasks to be speciﬁed at multiple levels of spatio-temporal abstractions. Using the transformation between a TL formula and a ﬁnite state automaton, our method constructs a reward function from the hierarchical task speciﬁcation along with a hierarchical policy that can be trained end-to-end. We show that our method is able to learn complex logical behaviors in both discrete and continuous state and action spaces.
Molecular Docking and Physicochemical Analysis of the Active Compounds of Soursop (Annona muricata Linn) for an Anti-Breast Cancer Agent

Breast cancer cases continue to increase every year. One plant that potentially has the antibreast-cancer activity is soursop. Some compounds in soursop (Annona muricata Linn) have been reported to inhibit COX-2 enzyme (PDB code: 3LN1) activity. However, each of these test compounds' inhibition potential has not been known really well and still needs to be explored. In this research, the molecular docking simulation and the physicochemical and pharmacochemical descriptor analysis (using SwissADME server) were used to explore the potential of compounds contained in soursop as a COX-2 inhibitor for an anti-breast cancer agent. The results have shown that xylopine can inhibit the COX-2 enzyme activity with a binding energy of -11.9 kcal/mol. Its physicochemical and pharmacochemical descriptors are still within the range of oral drug bioavailability. Molecular interaction analysis has also revealed Val335, Leu338, Ser339, Trp373, Phe504, Val509, Gly512, Ala513, Ser516 amino acids always appear in ligand-COX-2 interaction and predicted to play an important role in the COX-2 inhibition mechanism.
ANALYSIS OF AWARENESS OF HIV/AIDS-AS A GROWING EPIDEMIC IN PAKISTAN

: HIV belongs to Lentivirus , which are also known as "slow virus". The name indicates there mode of action as they enters into body and remain in it for longer period of time. They have unique property of being inserting the information into the DNA of host cell and also have the ability to replicate in non-dividing cells. Two types of HIV has been characterized; HIV-1 and HIV-2. HIV-1 is the most virulent and pathogenic strain. Worldwide, the predominant virus is HIV-1, and generally when people refer to HIV without specifying the type of virus they will be referring to HIV-1. The basic aim of the study is to analyze the prevalence and reasons of growing epidemics of HIV/AIDS in a Pakistani population. This study was basically conducted in a Pakistani population by designing a questionnaire. This was done during 2017 to 2018 with the help of different hospitals and different other departments of universities and hospitals. Basically we design a questionnaire to find out the reasons and prevalence of HIV/AIDS in Pakistani nation. The prevalence of STDs is alarmingly high in Pakistan especially in male individuals. Currently, the STD rate in men in urban areas of Pakistan is estimated to be8.5% in Karachi, 5.3% in Lahore, 4.0% in Faisalabad, 4.3% in Quetta, 2.5% in Rawalpindi, and 2.0% in Peshawar. This is the common thought in the minds of Pakistani people that as an Islamic Republic, Pakistan is protected from HIV/AIDS. This is true that Islam is against pre-marital sex or extra-marital
Crystal structure, optical and magnetic properties of PrFeO3 nanoparticles prepared by modified co-precipitation method

In this work, PrFeO3 nanoparticles were synthesized by modified co-precipitation method and annealed at different temperatures up to 850? C. The annealed PrFeO3 nanoparticles have single phase orthorhombic structure and the average particle size of 25-30 nm. Due to the very small particle size the prepared PrFeO3 nanoparticles are capable of being used as photocatalyst materials thanks to their strong adsorption bands at 230-400 nm and 400-800 nm observed from the UV-Vis spectra. Additionally, the PrFeO3 nanoparticles are paramagnetic materials with Hc ~ 10Oe and Mr ~ 0. These findings demonstrate their potential use not only as photocatalysts, but also as magnetic materials.
Effect of Dry Giant Mimosa Leaves Pellet on Digestibility Efficiency and Skin Color in Ornamental Fish

Digestibility efficiency of dry giant mimosa leaves pellet and skin colour were investigated in fancy carp and goldfish. The feed were included the basal feed (0 g. of giant mimosa; T1) and mixed with giant mimosa leaves of 50 g (T2), 100 g (T3), and 150 g (T4). Fancy carp and goldfish with an initial weight of 26.54±6.39 and 20.69±0.26 g were used for those experiments. Two groups’ fish were separated into the 70 litter aquarium glasses with 5 fish/aquaria and triplicate per treatment. Digestibility efficiency (DE) of trial feeds were investigated by indirect method. Skin colour of that fish was measured by the colour reader (Konica Minolta model CR10). The result showed that different consequence DE in fancy carp and goldfish. Fancy carp displayed that significant difference (p<0.05) in DE of protein, lipid, fiber and NFE. DE of protein and lipid had high value in T3 as 68.32±9.1 and 63.40±8.4%, respectively. DE of fiber and NFE were high in T1 as 7.08±0.6 and 45.11±3.3%, respectively. The data of color parameters (L*, lightness, a* and b* chromaticity) of fancy carp and goldfish were not significant difference (p>0.05). However, the b* value showed the difference in the final time. That fish fed with T3 had higher value than the other with 37.71±0.28. The chromaticities of that fish were slight changed from initial to final time. These researches concluded that dry giant mimosa leaves meal can use in fancy fish and goldfish up to 100 g kg -1 with not affect to DE. Moreover, the skin colour in term of L*, a* and b* showed high value in that fish fed with 100 g kg dry giant mimosa leaves.
Master research Internship Internship report Hierarchical Bandits for "Black Box" Optimization

Heuristics like Monte-Carlo Tree Search (MCTS), which trades oﬀ well exploration and exploitation , are widely used for sequential global optimization problems, and has led to some great success especially in game AI designing. In many cases, the exploration phase follows the famous optimism in the face of uncertainty principle, which is encountered in the so-called multi-armed bandit problem. However, recent studies on these models shows that they are not optimal for the optimization purpose, and that methods based on best arm identiﬁcation are preferred. During this internship, some approaches based on these new statistic tools are investigated both in a practical and theoretical way.
An Asynchronous Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

This paper studies a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communication with only local neighbors. An asynchronous multi-agent actor-critic algorithm is proposed for possibly unidirectional communication relationships depicted by a directed graph. Each agent independently updates its variables at “event times” determined by its own clock. It is not assumed that the agents’ clocks are synchronized or that the event times are evenly spaced. It is shown that the algorithm can solve the problem for any strongly connected graph in the presence of communication and computation delays
ECONOMIC AND DEMOGRAPHIC DETERMINANTS OF THE DEMAND FOR LIFE

The aim of this paper is to summarize a large number of economic and demographic determinants that are used to predict the demand for life insurance into a smaller number of component variables (components) and to determine which component has a stronger influence on demand for life insurance. Data are collected for 150 countries during the period 2005-2010. Final cross-country database is consisted of six-year average values on variables for selected countries. The methodology includes techniques of multivariate analysis: principal component analysis (PCA) and multiple linear regression. Results show that initial determinants of demand for life insurance can be summarized into two components: economic and demographic. Both components have a statistically significant positive influence on the demand for life insurance. On the basis of standardized regression coefficients it can be concluded that economic component is stronger determinant of demand for life insurance in comparison with demographic.
The Tango Between Blockchain and Deep Learning: An Outlook

Background : Blockchain and deep learning, touted as two revolutionary technologies, have gained great success in their respective fields. Quite a lot of efforts have been made to develop various algorithms and applications. However, few studies have considered on how to combine these two technologies together and improve their respective performance. Methods : In this paper, we propose to address this issue by answering two questions:
A Framework of Critical Factors to Knowledge Workers’ Adoption of Inter-organizational Knowledge Sharing Systems

The objective of this research is to outline the critical factors to knowledge workers’ adoption of “public-good” Inter-organizational knowledge sharing systems (IOKSS). Public-good IOKSS is one that is open to all firms in a specific sector. Public-good IOKSS is valuable and critical for effective social and economic development of any sector, especially knowledge-based sectors. However, the deployment of inter-organizational knowledge networks and collaborations may incur costs, challenges and risks to organizations and their individuals. To deploy a sustained interorganizational electronic knowledge sharing, these obstacles must be overcome. Prior knowledge sharing research has mostly focused on the within-firm context. Prior research on interorganizational systems (IOS) has focused on organizational adoption, and most empirical studies have mainly focused on supply-chain organizations, those that are vertically-linked. Based on KM and IOS literature, the critical factors to knowledge workers’ adoption of public good IOKSS can be related to individual factors, their relationships with peers, the organization, the proposed IOKSS system, and the sector.
STORABILITY AND SEED QUALITY ASSESSMENT OF NIGER (GUIZOTIA ABYSSINICA) SEEDS STORED IN AMBIENT CONDITIONS

The present study investigates the effect of storage on seed quality parameters of niger (Guizotia abyssinica), a marginal oilseed crop of Central India. The studied quality parameters were: percentage of vital molecules (oil content, protein and sugars), markers of seed deterioration (lipid peroxides, proline content) and activity of antioxidative enzymes (peroxidase, catalase) in the seeds stored for a period of seven years under ambient conditions. The results revealed that during storage, oil content reduced by 13%, protein by 18.4%, total sugar by 23.4% and reducing sugars by 45.6%. The seed deterioration markers increased as storage time increased. Proline content increased by 20.22% while malonaldehyde (lipid peroxide) increased by 32.6%. The antioxidative enzyme activity decreased by 45.5% for peroxidase and 36.2% for catalase respectively. Statistical analysis shows that niger seeds can be stored for one year without any significant loss of quality and upto 4 years with marginal loss of quality parameters.
Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

Abstract A long‐standing question in cognitive science is how high‐level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top‐down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target phoneme (e.g., the initial phoneme of a stimulus from a tapes‐capes continuum), consistent with the influence of an unambiguous context phoneme in the same position. Because this phoneme‐to‐phoneme compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would constitute strong support for top–down interaction. However, results from previous LCfC studies have been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous context‐final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for coarticulation for a subsequent target phoneme. We observed robust LCfC in a well‐powered, preregistered experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40). These results provide strong evidence in favor of computational models of spoken word recognition that include top–down feedback.
NORDISK TIDSKRIFT FÖR BARN- OCH UNGDOMSPSYKOTERAPI NORDIC JOURNAL OF CHILD AND ADOLESCENT PSYCHOTHERAPY

The author analyzes the relationship of siblings in connection with the problem of the neglect of children’s developmental needs. The dynamics of the family and especially the problem of role reversal or parentification are elaborated. The dilemma: external vs. internal and the problem of the bodymind connection are discussed and illustrated by two fairy tales: Cinderella and Hansel and Gretel. The author proposes the hypothesis that in families where the neglect of children’ s developmental needs (e.g. parentification) occurs, one of the consequences of the disturbed family dynamics could be the way in which the child perceives and feels his or her body. The theoretical discourse is enriched by a clinical example. The author discusses the attitude of different schools in psychoanalysis in terms of their coexistence and rivalry. The metaphor of siblings is used in this context.
Transfer and Residual Learning for Plant Disease Detection

— The study about diseases in plants is paramount to alleviate the issue of food security in all around the world. The foremost necessary step to mitigate the matter is correct as well as the timely detection of the disease in the plant. The primary and initial step in the identification of a plant disease is performing a visual inspection. This large scale problem and also shortage of professionals demand an automated and correct visual investigation technique. The recent progress in the popular fields of machine learning and computer vision, mainly with the help of methods like the use of famous convolutional neural networks (CNN) and deep learning has been successfully generated spectacular leads to the sector of both object recognition and image classification. Through this paper, we try to address to solve the matter of identifying diseases in plants using the images of plant leaves using transfer and residual learning. The Plant Village dataset is used which comprises of 54,309 images of 14 crops across 39 classes (healthy or a specific disease image). The images of leaves are of top quality which were taken manually and under acceptable lighting conditions. With the Plant Village dataset, the model is successful in achieving mean accuracy of 96% using transfer and residual learning.
Judgment Prediction Based on Case Life Cycle

Legal judgment prediction(LJP) is an essential task for legal AI. While prior methods employed the static judge-summarized case narrative as the only input, neglecting critical case life-cycle information could threaten the case logic representation quality and prediction correctness. In this paper, we approach to predict the legal judgment in a reasonably encyclopedic manner by leveraging the genuine input of the case – plaintiff’s claims and court debate data with comprehensively understanding the multi-role dialogues of the court debate, and then learnt to discriminate the claims so as to reach the final judgment through multi-task learning. An extensive set of experiments with a large civil trial data set shows that the proposed model can more accurately characterize the interactions among claims, judgments and debate for legal judgment prediction against several alternatives while the neural predictions can also be interpretable and easily observed.