Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian dimensionality reduction via identifications of data intrinsic dimensions

Even if they are defined on a space with a large dimension, data points usually lie onto a hypersurface, or manifold with a much smaller intrinsic dimension (ID). The recent TWO-NN method (Facco et al., 2017, Scientific Report) allows estimating the ID when all points lie onto a single manifold. Let us consider the data as a configuration of a Poisson Process (PP) with an intensity proportional to the true density. TWO-NN makes only a weak assumption: locally, on the scale of the second nearest neighbor, the original PP can be well approximated by a homogeneous one. In other words, we assume that in a small neighborhood around each point the intensity of the underlying PP is approximately constant. Under this hypothesis, the ratio of the distances of a point from its first and second neighbor follows a Pareto distribution that depends parametrically only on the ID, allowing for an immediate estimation of the latter. We have extended the TWO-NN model to the case in which the data lie onto several manifolds with different ID. While the idea behind the extension is simple, a non-trivial Bayesian scheme is required for estimating the model and assigning each point to the correct manifold. Applying this method, which we dub Hidalgo (heterogeneous intrinsic dimension algorithm), we uncover a surprising ID variability in several realworld datasets. The Hidalgo model obtains remarkable results, but its limitation consists in fixing a priori the number of components in the mixture. We then let K →∞, adopting a Bayesian Nonparametric approach: we model the data with a Dirichlet Process Mixture Model as an infinite mixture of Pareto distributions. This approach can consider the uncertainty relative to the number of mixture components. Since the
Test Case Generation Assisted by Control Dependence Analysis

—The paper proposes and develops a new test case generation tool named Symbolic Execution & Taint Analysis (SYTA) that can capture implicit information flows by control dependence analysis. When running, SYTA traces execution paths to track constraints on symbolic variables. Some equivalence relationship asserts will be constructed to store the equivalence information among variables for control dependence analysis. If a security sink is reached, SYTA builds a constraint, path conditions and equivalence relationship asserts, which are to be sent to a constraints solver. The test cases will be generated from possible counterexamples in constraint solving. Compared with traditional static analysis tools, SYTA can track implicit information flows, and generate test cases by control dependences analysis effectively.
REAL TIME AUDIO SIGNAL PROCESSING THROUGH DSB-SC USING SIMULINK

: This research work explicates the various simulation results using Simulink and System Generator. Initially the most important analog modulation techniques which is DSB-SC modulation technique is elucidated. Afterwards this modulation technique is constructed and tested with in Simulink. Prior to realization of this design within System Generator the audio signal is imported to the Simulink and the simulation results are investigated.
Routing of Logistics Distribution Vehicles using Cloud Adaptive Mean Particle Swarm Optimization

— Due to its fixed inertia weight, the standard Particle Swarm Optimization (PSO) algorithm is easy to fall into local optimal solution when used to solve vehicle routing problems. To overcome this problem we propose a cloud adaptive adjustment strategy for inertia weights. The particles are divided into three groups according to the fitness of each particle, and different inertia parameter generating methods are used. The inertia weights in the general group is adaptively adjusted by X-conditional cloud generator. The cloud droplet has bias stability and randomness property, this not only preserves the swarm diversity, but also improves the convergence speed of the algorithm. Concurrently, the notion of mean is introduced into the PSO algorithm, and the velocity updating formula of the particle is modified. Finally, a cloud adaptive mean particle swarm optimization (CAMPSO) algorithm is proposed for the vehicle routing problem. The simulation experiments show that the proposed algorithm is greatly better than standard PSO and adaptive PSO in terms of global optimization ability and convergence speed.
Decomposable Common Spatial Patterns: Applying Graphical Model Selection in the Brain-Computer Interface Domain

—When working with Electroencephalography (EEG) in the Brain-Computer Interface (BCI) domain, variability both subject-to-subject and session-to-session, results in the need to acquire labeled calibration data. This process can be time consuming, and as the number of channels grows, so does data requirements due to the curse of dimensionality. We address this problem by using model selection in undirected Gaussian graphical models, to reduce the number of parameters requiring estimation. The model is represented as a sparse precision matrix, where zeros are introduced by model selection, representing conditional independence between channels. We adapt the established method Common Spatial Patterns (CSP) into Decomposable CSP (DCSP) which is derived using precision matrices, instead of covariance matrices. The approach is evaluated on both an existing and a novel dataset, and is compared to CSP. We ﬁnd that DCSP outperforms CSP, with relative improvements of up to 50% in error rates Furthermore, we ﬁnd that simply converting to use precision as features constitutes an improvement, and conjecture this is due to robustness against outliers. The improvement from the reduced number of parameters is more signiﬁcant as the number of channels grow.
An Experimental Analysis of Security Vulnerabilities in Industrial IoT Devices

The revolutionary development of the Internet of Things has triggered a huge demand for Internet of Things devices. They are extensively applied to various fields of social activities, and concerning manufacturing, they are a key enabling concept for the Industry 4.0 ecosystem. Industrial Internet of Things (IIoT) devices share common vulnerabilities with standard IoT devices, which are increasingly exposed to the attackers. As such, connected industrial devices may become sources of cyber, as well as physical, threats for people and assets in industrial environments. In this work, we examine the attack surfaces of a networked embedded system, composed of devices representative of those typically used in the IIoT field. We carry on an analysis of the current state of the security of IIoT technologies. The analysis guides the identification of a set of attack vectors for the examined networked embedded system. We set up the corresponding concrete attack scenarios to gain control of the system actuators and perform some hazardous operations. In particular, we propose a couple of variations of Mirai attack specifically tailored for attacking industrial environments. Finally, we discuss some possible
OPERATION WANJARRI: OPTIMISING FERAL CAT BAITING STRATEGIES IN THE ARID ZONE

A research program was conducted at an arid zone site where bait acceptance by feral cats was monitored at monthly intervals over a year to highlight possible differences in bait consumption over time with respect to seasonal/rainfall influences. At the same time, the abundance of prey species was monitored to enable comparison of bait uptake with season and prey availability, which would potentially indicate a broad optimum period, or periods, for baiting. Bait acceptance by feral cats across the study period was high, but increased and became more consistent from mid autumn onwards (83 – 100% over the period May 2001-January 2002). There was no significant relationship between bait uptake by cats and season/rainfall or prey abundance. To maximise the effectiveness of baiting campaigns it is suggested that the optimum time to conduct these programs is late autumn/winter. At this time, rainfall which will cause degradation of baits is less likely to occur than during the summer months, and the abundance of all prey types, in particular predator-vulnerable young mammalian prey and reptiles, is at its lowest. The consistently high bait acceptance observed here extends the possibility of predator control to the arid zone. As cats are controlled in target areas, it will be possible to return native animals to their former habitats, allowing fauna recovery programs to proceed that were previously obstructed by predation by feral cats.
Selection of Optimal Path Planning Algorithm for Autonomous Robots in Structured Environment

To determine a collision free path for a robot between start and goal positions in an environment filled with obstacles is a very challenging task in the design of an autonomous robot path planning. This paper aims to select an optimal path planning algorithm for a mobile robot in structured environment. To achieve the goal, comprehensive strengths and weaknesses of different path planning algorithm are discussed and evaluated. A wooden box with some fixed obstacles and robot inside it is basically the environment. Information about the environment is used to build a roadmap or graph of the environment. After getting a convenient representation of the environment, then graph search methods can be used to obtain shortest possible path through this roadmap. It is well known that computing shortest paths for autonomous robots is an important task in many path planning applications. Selecting a suitable algorithm from the various algorithms reported in the literature is a decisive step in many applications including path planning task. A set of three shortest path algorithms that compute optimal path from start to goal location has been identified and these are Uniform Cost Search, Greedy Search and A* algorithm. After comparing execution time and path length of path computed by these three algorithms, A * algorithm proves to be best suited for this particular application of path planning.
Propensity Score Matching Sports Activity of Genesis Diabetes Mellitus Using Logistic Regression

Propensity score is the conditional probability to get a specific treatment based on the observed kovariat. Method is used to reduce the bias in the estimation of treatment effects on the data is the observation due to confounding factors. If the treatment is binary form, logistic regression model is one of the estimation of the value of the propensity score is exactly because easily in the estimation and interpretation. The purpose of this research is to examine the estimates of the propensity score match based on binary logistic regression in the case of diabetes mellitus (DM). The results of the study showed that the Genesis diabetes mellitus influenced by sports activity, triglyceride, hypertension, and obesity. While the sports activity was influenced by the triglyceride, hypertension, and obesity. So sports activity as a variable counfonding, which shows that the patient was cushioned normal triglyceride levels will sports activity of 9.335 times than that have high triglyceride. Furthermore, a person who is not hypertension will sports activity of 4.531 times than that have hypertension, and a person who does not obesity will sports activity of 14.451 times than that of obesity. The test results showed that mathing propensity score sports activity based covariat (triglycerides, hypertension and obesity) influence the genesis of diabetes mellitus.
Comparison of Handball Project Trainees with Non Trainee Students in Physical Fitness and Academic Achievement the Case of Bench Maji Zone; Ethiopia

Positive association between physical fitness and academic achievement in youth students has been suggested yet the causal effect of physical fitness on academic achievement remains unclear. The aim of this study is to compare the physical fitness and academic achievement of handball project trainees and non-trainees students of Bench Maji zone, SNNPRS, Ethiopia. To achieve the innate objective of the study cross sectional research design with quantitative approach is employed. In so doing purposefully twenty five Mizan primary and secondary school students whose member of handball project with the age of 15.9+0.7 and twenty five students from grade 9, 15.7+. 6 years old were selected randomly. Physical fitness is measured by 60 meter shuttle; 12 meter cooper test and Illinois agility test whereas the academic achievement is measured by 2017/18 transcript. The data is analyzed through descriptive statistics, independent T-test and Pearson correlation coefficient. The statistical analysis revel that, academic achievement of handball train students is 53.6 +5.8 and 52.4+5.8, whereas non-train students are 61.32+1 and 60.6+ 11.9 respectively. The above mean value shows that as handball train students are significantly less than non-train students (P= 0.03, 0.04) in both semester. However in 60m shuttle (speed) the handball trainees are significantly greater than non-trainees (P=0.00) with the mean value of 7+ 0.96 and 6.09+0.53 respectively. In the 12’ copper test handball trainees score mean value is 2009.7+339.5 whereas non trainees 2436.9 + 372, this statistical value shows as handball trainees’ students are significantly greater than non-trainees in endurance performance. However result of Illinois agility test indicates as there is no significant difference among the group (P=0.3). The person correlation shows as there is no any strong relation between Physical fitness variables and academic achievement of 1 st semester (r=0.06 P=.75, r= .04 P=.81, r=.17 P=.405) and 2 nd semester (r =.2 P =.32, r =.15 P =.46 and r=.08 P=.65). Overall in the physical fitness the handball trainee students are greater, inversely in the academic achievement the non-hand ball trainees are greater, however the correlation value indicates as there is no any strong relation between academic achievement and physical fitness.
Interregional Migration and Housing Vacancy: Theory and Empirics

We examine homeowner vacancy rate interdependencies over time and space through the channel of migration. Our theoretical analysis extends the Wheaton (1990) search and matching model for housing by incorporating interregional spillovers due to some households’ desires to migrate between regions and by allowing for regime-switching behavior. Our empirical analysis of vacancy rates for the entire U.S. and for Census regions provides visual evidence for the possibility of regime-switching behavior. We explicitly test our model by estimating basic Vector Autoregression (VAR) and Markov-Switching Vector Autoregression (MS-VAR) models using data on all four Census regions’ vacancy rates and migration dynamics among regions. Our preferred models accurately predict vacancy rates across our sample period. This regime switching approach provides unique insights for housing market and migration dynamics during the financial crisis period and its aftermath. For example, the Northeast returned to its low-vacancy state relatively quickly, while the South has remained in its high-vacancy state. Our theoretical model indicates the anticipated sign of the relationship between vacancy rates and migration measures and also the possibility of interdependencies across regions. Empirical results differ across regions. For example, comparing regions, the West is the least connected. Also, the South and Midwest are the regions that show significant evidence of higher vacancy rates when residents in other regions do not desire to migrate.
National Social Investment Programme (NSIP) and Sustainable Poverty Reduction in Nigeria: Challenges and Prospects

It is estimated that 62.6 percent of Nigerians live below the international poverty line (PPP US$1.25 per day). This translates to about 100 million Nigerians. Against this backdrop, the Buhari Administration launched the National Social Investment Programme (NSIP) in 2016 to tackle the country‟s high rate of poverty and vulnerability. The programme comprises: the N-Power designed to assist young graduates to acquire and develop life-long skills; the Conditional Cash Transfer (CCT) for the support of those within the lowest poverty brackets; the Government Enterprise and Empowerment Programme (GEEP) which is a micro–lending intervention for traders, farmers, women, etc, and the National Home Grown School Feeding Programme (NHGSF) which aims to deliver school meals to young children. After more than two years in operation, this article examines the extent of the implementation, the challenges facing the programme and the prospects of NSIP achieving sustainable poverty reduction in Nigeria. Data were gathered through official publications, text books, online journals, periodicals, newspapers and key informant interviews (KII). The prerequisites for NSIP achieving sustainable poverty reduction were discussed. The contents of the programme were considered adequate but there are challenges with regard to operational procedures and effective implementation. These include inadequate funding, lack of transparency and accountability, poor information management and poor infrastructure. Measures to mitigate these problems were proffered.
Improvised Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data

: Cloud computing enables individuals and organizations to run large, scalable computing to support large data applications in the domain, such as healthcare and scientific research. As a result, data owners are involved in outsourcing their data on cloud servers for data availability. However, data sets such as client files and records in electronic documents often contain sensitive information, which raises concerns about personal information, if the document is published or shared with some unreliable third party in the cloud. The most widely used and practical technique for keeping personal information is encrypting data before outsourcing to a cloud server. This reduces the utility of data and enables traditional data analysis operators. Fetching documents on k using outdated keywords. In this article, we will investigate the top-k search queries for multiple keywords for large data encodings to protect privacy, and attempt to identify effective and secure solutions for this problem. Particularly for privacy concerns about query data, we've created a special tree-like index structure and designed a randomized search algorithm that makes even the same search query create routes. Different views on the index can also be maintained. Under the stronger privacy. For query performance improvement, we have proposed a multi-layered top-k search scheme based on the concept of a partition, which contains a cluster of indexes based on the tree created for all documents. Finally, we combine these methods together as a powerful and secure way of identifying similar top-k searches. Our experimental results in real-life data sets show that the guidelines we offer can be improved. The ability to protect privacy, scalability, and efficiency in query processing is greatly enhanced by modern methods.
STUDY OF SHAPE EFFECT IN WOUNDED SANDWICH GIANT MAGNETOIMPEDANCE

Giant Magneto Impedance (GMI) sensors use impedance variation provoked by an external magnetic field. The purpose of this paper is to study the shape effect in wounded GMI sandwich by means of demagnetizing field coefficient computation and his contribution on intrinsic sensitivity magnitude. We first discuss analytic expression of wounded GMI impedance next, and then we derive analytic modelling of intrinsic sensitivity. Finally we compare the theoretical modelling with experimental results taking into account shape effect.
Wage Cyclicality of New and Continuing Jobs

This paper provides evidence of the effect of aggregate macro conditions on individual wages, using administrative data for the universe of wage earners and firms in Chile between 2005 and 2016. Our data allows us to distinguish direct job-to-job transitions from hires out of nonemployment and, among the latter, first-time entrants and job movers that went through a nonemployment spell. Moreover, we identify the precise duration of the non-employment spell. In line with most of the literature, we find that wages of newly created jobs are significantly more sensitive to aggregate unemployment than those of continuing jobs. Crucially, we find that the highest wage procyclicality holds for new hires from short-duration non-employment, which suggests that wage cyclicality not only reflects the cyclicality of match quality for job-to-job transitions, but rather actual wage procyclicality across different types of new hires. Our results are robust to the use of alternative business cycle indicators, and the inclusion/exclusion of different sets of fixed effects, including match-specific fixed effects. We find that wage procyclicality of the different job types is practically symmetric over the business cycle, and is mainly concentrated in higher-income and older workers in large firms. JEL classification: E24, E32, J64
Free Riders and the High Cost of Energy-Efficiency Subsidies

Economists have long argued that many recipients of energy-efficiency subsidies may be “free riders,” getting paid to do what they would have done anyway. Demonstrating this empirically has been difficult, however, because of endogeneity concerns and other challenges. In this paper we use a regression discontinuity analysis to examine participation in a large-scale appliance replacement program in Mexico. Comparing behavior just on either side of several eligibility thresholds, we find that program participation increases with larger subsidy amounts, but that the magnitude of the increase is small. For example, when an air-conditioner subsidy increases from $110 to $170, the number of participants increases by only 21%. The large fraction of inframarginal households means that larger subsidy amounts are almost certainly not cost-effective. Overall, we find that accounting for free riders decreases the cost-effectiveness of the program by about 50%.
NUCLEAR ARCHITECTURE: A Genetic Approach

■ Abstract Eukaryotic genomes are distributed on linear chromosomes that are grouped together in the nucleus, an organelle separated from the cytoplasm by a characteristic double membrane studded with large proteinaceous pores. The chromatin within chromosomes has an as yet poorly characterized higher-order structure, but in addition to this, chromosomes and specific subchromosomal domains are nonrandomly positioned in nuclei. This review examines functional implications of the long-range organization of the genome in interphase nuclei. A rigorous test of the physiological importance of nuclear architecture is achieved by introducing mutations that compromise both structure and function. Focussing on such genetic approaches, we address general concepts of interphase nuclear order, the role of the nuclear envelope (NE) and lamins, and finally the importance of spatial organization for DNA replication and heritable gene expression.
MODIFIED AHP TO SELECT NEW SUPPLIERS IN THE INDONESIAN STEEL PIPE INDUSTRY

The analytical hierarchy process (AHP) is not as effective and efficient as the pairwise comparison (PWC) matrix. The aim of this research is to tackle the weaknesses of the AHP and then applied them to a real-life case of new supplier selection in the Indonesian steel pipe industry. Some criteria are identified that are relevant in the Indonesian steel pipe companies to select new suppliers. A total score is calculated for each supplier and this ranking is used to identify the best one. In this research, the decision maker created the sorting of the criteria used in supplier selection. The matrix of PWC was constructed based on the sorting. Then, the weight of each criterion was calculated using AHP. The result of the method was an improvement over previous methods, because the value of the consistency ratio (CR) was zero, indicating a high degree of validity. This modified method was an improvement on the AHP, with a consistent solution without the need to repeat the calculation.