Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithms and Security Concern in Blockchain Technology: A Brief Review

The Blockchain is an ingenious and remarkable invention. This technology created the backbone of a new type of internet. It was originally designed for the digital currency, Bitcoin, in which you can send digital money to anyone, even a stranger. Originally, it transmits bitcoin. It is considered as a revolutionary technology. Blockchain helps in creating the record whose authenticity can be verified by the entire community. To make centralized and distributed systems it stores the data over a network of computers, which anyone can use this system, no central organization or person owns a system. It contains a number of blocks that contain data. The database of Blockchain is not stored at a single location, it is an open source database which can be accessed by anyone and it is easily verifiable. No one owns the blockchain technology. As the block chain’s nature is distributed, there must be a simple way by which all the nodes reach an agreement; Consensus agreement is a way by which all the participants agree on the contents of the blockchain. Here we will tell you about the basic principles and characteristics of the Consensus Algorithms so that you can decide which of the algorithm the best one to work with is. In this paper, we will research on the number of technical aspects of Blockchain technology, the pace in which this technology is booming and most importantly its implementation on the Bitcoin cryptocurrency which has totally revolutionized the financial infrastructure of the World. Along with this, we will also consider a number of security concerns, challenges and other technical vulnerabilities associated with the Bitcoin technology. The decentralized mechanism, distributed mechanism, scripted mechanism and the mechanism of the password related to Blockchain has opened a new view on the rapidly developing internet technology. There is no need for participants or any third party who are needed to be known to each other. The responsibility is included in the recording, transmission and the activities regarding transferring the storage by distributed technology. That is how the assurance is guaranteed by keeping these aloof from tampering and forged. With the assistance of asymmetric cryptographic algorithm, each and every participant can reach towards consensus on the information of the blockchain. The blockchain technology has the potential to play a key role in case of the information security technology. Hence, in this paper we will also cover the impact of blockchain will ensure the expansion in the sphere of information security.
Studies on the biosynthesis of antibiotics mupirocinand thiomarinol

Biosynthetic steps in the mupirocin (pseudomonic acids) biosynthetic pathway of \(Pseudomonas\) \(fluorescens\) NCIMB 10586 have already been deduced. Putative functions of most of the genes of \(mup\) cluster have been assigned although exact sequence of steps in the pathway and their timings are not yet known. Thiomarinols are another group of anti-bacterials produced by \(Pseudoalteromonas\) \(sp\). SANK 73390. Very little is known about the biosynthesis of thiomarinols that share striking structural similarity with pseudomonic acids in their polyketide and fatty acid moieties. This similarity is reflected at genetic level as significant similarity in amino acid identity between the products of at least 27 ORFs in these biosynthetic clusters. This project aimed to learn more of biosynthetic steps in the biosynthesis of mupirocin and thiomarinol antibiotics by testing for functional cross-complementation between pair of genes or a group of genes whose products show significant homology. Surprisingly, only two genes \(tmlJ\) and \(tmlS\) out of nine studied showed complementation in the \(mup\) system. Findings suggested protein-protein interactions limited interchangeability of equivalent functions between two biosynthetic systems. It was shown by expressing related genes as groups for complementation in the \(mup\) system that it was possible to confirm specificities of such interactions.
Reinsurance Demand and Liquidity Creation: A Search for Bi-Causality

We analyze the relationship between insurers’ liquidity creation and reinsurance demand. Early theoretical contributions on liquidity creation propose that financial institutions enhance economic growth by creating liquidity in the economy. Liquidity creation means financing relatively illiquid assets with relatively liquid liabilities. However, liquidity creation exposes insurers to financial risks. There is a trade-off between getting higher returns on risky investments and being able to compensate clients at a low cost when unexpected claims happen. Unexpected claims can be protected by reinsurance, which introduces a second trade-off between reinsurance demand and liquidity creation. This trade-off can be more important for insurers that have fewer diversification opportunities. Our main empirical results, from regularized GMM and ML-SME methods of estimation, show similar positive bi-causal effects between liquidity creation and reinsurance demand for small insurers (22% of insurance activity). The link between the two activities is not significant for large insurers (60% of insurance activity). We obtain mixed results for medium insurers. In all estimations, the standard GMM model is rejected.
Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators

The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has high qualifications in the statistical analysis of insulation status of electric generators. However, although the IEEE 1434 standard has well-established standards, it will not always be simple to classify signals obtained in the measurement of the hydro generator coupler due to variations in the same type of PD incidence that may occur as a result of the uniqueness of each machine subject to staff evaluation. In order to streamline the machine diagnostic process, a tool is suggested in this article that will provide this signal classification feature. These measurements will be established in groups that represent each known form of partial discharge established by the literature. It was combined with supervised and unsupervised techniques to create a hybrid method that identified the patterns and classified the measurement signals, with a high degree of precision. This paper proposes the use of data-mining techniques based on clustering to group the characteristic patterns of PD in hydro generators, defined in standards. Then, random forest decision trees were trained to classify cases from new measurements. A comparative analysis was performed among eight clustering algorithms and random forest for choosing which is the superior combination to make a better classification of the equipment diagnosis. R2 was used for assessing the data trend.
Geometry Effects in Eulerian/Granular Simulation of a Turbulent FCC Riser with a (kg-?g)-KTGF Model

Three-dimensional, transient turbulent particulate flow in an FCC riser is modeled using an Eulerian/Granular approach. The turbulence in the gas phase is described by a modified realizable (kg-? g) closure model and the kinetic theory of granular flow (KTGF) is employed for the particulate phase. Separate simulations are conducted for a rectangular and a cylindrical riser with similar dimensions. The model predictions are validated against experimental data of Sommerfeld et al (2002) and also compared with the previously reported LES-KTGF simulations of Hansen et al (2003) for the rectangular riser. The (kg-? g)-KTGF model does not perform as well as the LES-KTGF model for the riser with a rectangular cross section. This is because, unlike the more elaborate LES-KTGF model, the simpler (kg-? g)-KTGF model cannot capture the large scale secondary circulations induced by anisotropic turbulence at the corners of the rectangular riser. In the cylindrical geometry, however, the (kg-? g)-KTGF model gives good prediction of the data and is a viable alternative to the more complex LES-KTGF model. This is not surprising as the circulations in the riser with a circular cross section are due to the curvature of the walls and not due to the presence of sharp corners.
Defense for adversarial videos by self-adaptive JPEG compression and optical texture

Despite demonstrated outstanding effectiveness in various computer vision tasks, Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples. Nowadays, adversarial attacks as well as their defenses w.r. t. DNNs in image domain have been intensively studied, and there are some recent works starting to explore adversarial attacks w.r. t. DNNs in video domain. However, the corresponding defense is rarely studied. In this paper, we propose a new two-stage framework for defending video adversarial attack. It contains two main components, namely self-adaptive Joint Photographic Experts Group (JPEG) compression defense and optical texture based defense (OTD). In self-adaptive JPEG compression defense, we propose to adaptively choose an appropriate JPEG quality based on an estimation of moving foreground object, such that the JPEG compression could depress most impact of adversarial noise without losing too much video quality. In OTD, we generate "optical texture" containing high-frequency information based on the optical flow map, and use it to edit Y channel (in YCrCb color space) of input frames, thus further reducing the influence of adversarial perturbation. Experimental results on a benchmark dataset demonstrate the effectiveness of our framework in recovering the classification performance on perturbed videos.
Miniature FSCV Devices: A Review

Fast-scan cyclic voltammetry (FSCV) is an electrochemical sensing method used for measuring the concentration of neurotransmitters within the brain. FSCV is typically conducted using benchtop equipment, providing accurate and high temporal resolution measurements. However, the large size and high cost of benchtop FSCV systems pose significant limitations in widespread preclinical use of FSCV. Recent technological improvements have facilitated the miniaturization of FSCV equipment, creating systems that can be mounted on the subject as they are small and lightweight. One key remaining bottleneck in the emerging miniaturized FSCV systems is that their data typically needs to be transmitted to an external computer for analysis. This paper presents a review on the current miniature FSCV devices. It discusses the latest improvements in voltage scan waveforms that help increase the sensitivity of measuring specific analytes, for use in miniaturized FSCV. It also tabulates analytes that have been detected using FSCV. It also details current issues with FSCV and the latest achievements in post-processing of FSCV data to better identify target analytes. Furthermore, the emerging miniature FSCV devices that have integrated on-board data processing capabilities to mitigate external data processing are discussed. These advances can accelerate neurochemistry research, reducing time spent analysing data on external computers, and allowing for investigation into questions on neurotransmitter dynamics in target brain regions.
A note on the complexity of S4.2

is the modal logic of directed partial pre-orders and/or the modal logic of reflexive and transitive relational frames with a final cluster. It holds a distinguished position in philosophical logic, where it has been advocated as the ‘correct’ logic of knowledge; it has also found interesting applications in the temporal logic of relativistic spacetime and the metamathematics of forcing in set theory. The satisfiability problem for is -complete: this is a result established in an AiML 2004 paper of Shapirovsky [(2004). On PSPACE-decidability in transitive modal logic. In R. A. Schmidt, I. Pratt-Hartmann, M. Reynolds, & H. Wansing (Eds.), Advances in modal logic 5 (pp. 269–287). King's College Publications] where the complexity classification of emerges as a consequence of a very general method for constructing decision procedures for transitive modal logics. We provide here a ‘classical’ proof in the standard Halpern-Moses style of Halpern and Moses [(1992). A guide to completeness and complexity for modal logics of knowledge and belief. Artificial Intelligence, 54(2), 319–379]. With little extra work, it is shown that the -completeness result extends to , the multimodal version of . We prove that the -completeness characterisation of monomodal persists even if we restrict ourselves to fragments with bounded modal depth, but the problem is -complete when it is restricted to formulae with modal depth at most one. The complexity of satisfiability for the fragment of the language with a finite number of propositional variables (but unbounded modal depth) remains -hard. For a finite language and bounded modal depth, -satisfiability can be checked in linear time.
Semi-Supervised Temporal Segmentation of Manufacturing Work Video by Automatically Building a Hierarchical Tree of Category Labels

Nowadays, many industrial companies visually record workers’ activities for the purposes of streamlining their work processes. However, since untrimmed raw videos are hard to use, it is desired to automatically divide the videos into segments and recognize which kind of operation is performed on each segment. This task is called temporal video segmentation. We propose a method for achieving it, particularly targeting videos of manufacturing work with a specialized vehicle such as a hydraulic excavator. To make the performance of temporal video segmentation high, it is quite essential to extract good visual features from input videos. This can be hardly achieved by unsupervised methods, whereas supervised methods have another drawback that collecting a sufficient amount of training data is difficult due to its labor-intensiveness. To overcome these drawbacks, the proposed method employs a semi-supervised approach. We assume that a set of weakly-labeled videos whose frames only sparsely have a category label are given as input, where the labeled frames are used as training data to train a desirable feature extractor. Under this assumption, the proposed method first divides the input videos into short segments called primitive segments having the fixed length and then clusters them using visual features extracted by the above feature extractor. To achieve higher performance, we also use a hierarchical tree of the category labels and recursively perform the above process at each branch in the tree, where the tree is automatically built by the proposed method. In our experiments, we achieved a segmentation performance of 0.947 on the F-measure, even when only 1.25% of all the frames in the input videos are labeled.
SEC-NoSQL: Towards Implementing High Performance Security-as-a-Service for NoSQL Databases

During the last few years, the explosion of Big Data has prompted cloud infrastructures to provide cloud-based database services as cost effective, efficient and scalable solutions to store and process large volume of data. Hence, NoSQL databases became more and more popular because of their inherent features of better performance and high scalability compared to other relational databases. However, with this deployment architecture where the information is stored in a public cloud, protection against the sensitive data is still being a major concern. Since the data owner does not have the full control over his sensitive data in a cloud-based database solution, many organizations are reluctant to move forward with Database-as-a-Service (DBaaS) solutions. Some of the recent work addressed this issue by introducing additional layers to provide encryption mechanisms to encrypt data, however, these approaches are more application specific and they need to be properly evaluated to ensure whether they can achieve high performance with the scalability when it comes to large volume of data in a cloud-based production environment. This paper proposes a practical system design and implementation to provide Security-as-a-Service for NoSQL databases (SEC-NoSQL) while supporting the execution of query over encrypted data with guaranteed level of system performance. Several different models of implementations are proposed, and their performance is evaluated using YCSB benchmark considering large number of clients processing simultaneously. Experimental results show that our design fits well on encrypted data while maintaining the high performance and scalability. Moreover, to deploy our solution as a cloud-based service, a practical guide establishing Service Level Agreement (SLA) is also included.
$\ell^p\!-\!\ell^q$-Norm Minimization for Joint Precoding and Peak-to-Average-Power Ratio Reduction

Wireless communication systems that rely on orthogonal frequency-division multiplexing (OFDM) suffer from a high peak-to-average (power) ratio (PAR), which necessitates power-inefficient radio-frequency (RF) chains to avoid an increase in error-vector magnitude (EVM) and out-of-band (OOB) emissions. The situation is further aggravated in massive multiuser (MU) multiple-input multiple-output (MIMO) systems that would require hundreds of linear RF chains. In this paper, we present a novel approach to joint precoding and PAR reduction that builds upon a novel $\ell^p\! -\! \ell^q$-norm formulation, which is able to find minimum PAR solutions while suppressing MU interference. We provide a theoretical underpinning of our approach and provide simulation results for a massive MU-MIMO-OFDM system that demonstrate significant reductions in PAR at low complexity, without causing an increase in EVM or OOB emissions.
Performance measurement practices in manufacturing firms: an empirical investigation

Purpose – The objective of this study is to shed some light on performance measurement issues relevant to current practices.Design/methodology/approach – The performance measurement practices in terms of utilization, relevance, and availability of information are studied for a sample of 92 Portuguese manufacturing executives. Several statistical instruments were used namely multiple regression analysis, cluster analysis and gap analysis.Findings – The results of this study underscore consistent patterns pointing to a lack of a broad perspective on manufacturing performance measurement. Conclusions and their implications to the theory and art of performance measurement are presented.Research limitations/implications – The sample used in this study is specific in nature (Portuguese executives). Thus, the results should be interpreted accordingly. Future research should test the applicability of the obtained results using other sample frames.Practical implications – This study provides practicing managers wi.
Performance Enhancement Schemes and Effective Incentives for Federated Learning

The advent of artificial intelligence applications demands for massive amount of data to supplement the training of machine learning models. Traditional machine learning schemes require central processing of large volumes of data that may contain sensitive patterns such as user location, personal information, or transactions history. Federated learning (FL) has been proposed to complement the traditional centralized methods where multiple local models are trained and aggregated over a centralized cloud server. However, the performance of FL needs to be further improved, since its accuracy is not on par with traditional centralized machine learning approaches. Furthermore, due to the possibility of privacy information leakage, there are not enough clients willing to participate in FL training process. Common practice for the uploaded local models is an evenly weighted aggregation, assuming that each node of the network contributes to advancing the global model equally, which is unfair with higher contribution model owners. This thesis focuses on three aspects of improving a whole federated learning pipeline: client selection; reputation enabled weight aggregation; and incentive mechanism. For client selection, a reputation score consists of evaluation metrics is introduced to eliminate poor performing model contributions. This scheme enhances the original implementation by up to 10% for non-IID datasets. We also reduce the training time of selection scheme by roughly 27.7% compared to the baseline implementation. Then, a reputation-enabled weighted aggregation of the local models for distributed learning is proposed. Thus, the contribution of a local model and its aggregation weight is evaluated and determined by its reputation score, which is formulated as same above. Numerical comparison of the proposed methodology that assigns different aggregation weights based on the accuracy of each model to a baseline that utilizes standard average aggregation weight shows an accuracy improvement of 17.175% over the standard baseline for not independent and identically distributed (non-IID) scenarios for an FL network of 100 participants. Last but not least, for incentive mechanism, we can reward participants based on data quality, data quantity, reputation and resource allocation of participants.In this thesis, we adopt a reputation-aware reverse auction that was earlier proposed to recruit dependable participants for mobile crowdsensing campaigns, and modify that incentive to adapt it to a FL setting where user utility is defined as a function of the assigned payment from the central server and the user’s service cost, such as battery and processor usage. Through numerical results, we show that: 1) the proposed incentive can improve the user utilities when compared to the baseline approaches, 2) platform utility can be maintained at a
EXPERIMENTAL RESEARCH ON CONCRETE STIFFENED STEEL PLATE SHEAR WALL

Steel plate shear wall (SPSW) and concrete stiffened steel plate shear wall (CSPSW) are manipulated as ductile structures and appropriate lateral-load resisting systems. CSPSW consists of an infill steel plate and a reinforced concrete panel attached to one side of the infill steel plate. In CSPSW, beams and columns are considered as boundary elements which develop pure shear of the infill steel plate. Although CSPSW is known as a good solution for tall buildings in a region of high seismic hazard, limited research has been fulfilled on this kind of shear wall so far. Hence, more experimental and numerical investigations are demanded to grasp the complicated behaviour of this system. In this study, an experimental investigation into ductile concrete stiffened steel plate shear wall is conducted at Road, Housing & Urban Development Research Center, Tehran, Iran, and the test results and observations are discussed. In accordance with experimental results, the CSPSW specimen provides a stable cyclic manner and can reach inter-story drift of 6%. By proper design of CSPSW, it can be able to show an acceptable failure mode. INTRODUCTION Steel plate shear wall (SPSW) is a system which resists story shear and overturning moment in highrise buildings. The system consists of an infill steel plate and boundary elements, beams and columns. SPSW carries lateral loads by developing diagonal tension field; however, owing to compression field, the infill steel plate buckles. By buckling of the infill steel plate, stiffness of SPSW decreases markedly. For preventing buckling of diagonal compression, a reinforced concrete panel is attached to one side of the infill steel plate by shear connectors, bolts. This system is called concrete stiffened steel plate shear wall (CSPSW) (Astaneh-Asl, 2001 and 2002).