Miracle-dz/UniFAR-FTUs huggingface dataset:
ACCEPTANCE SAMPLING FOR NETWORK INTRUSION DETECTION

Network Intrusion Detection System (NIDS) is to prevent entry of anomalous network flows into networks. Hundred percent inspections of all the fragments of network flows for detecting malicious fragments and thereof anomalous flows are highly prohibitive. The Selective Sampling Method (SSM) considers only network flows of small size not exceeding 80 fragments. Further, it is applicable for detecting port scan and host scan attacks only. This study proposes a novel NIDS adapting acceptance sampling method, referred to as ASNID. It is applicable to detect Land, Xmass, Nestea, Rose, Winnuke, NULL Scan, Teardrop, Fraggle, Port scan, Host scan. A randomly chosen sample of fragments from a network flow is inspected for detecting whether it is anomalous or not. It reduces the computational effort by a factor of 0<k<1 where k is the ratio of sample size to total fragments of a network flow. It is proved experimentally that the GMAI, performance metric of ASNID tends to one as the sample size increases to 60%. It is also proved that as the percentage of anomalous flows increases GMAI increases. Hence, ASNID would of immense use in network intrusion detection.
Albumin and IgG Removal by Cellulose Biosorbents

DOI: 10.21276/sjams.2017. 5.11. 51 Abstract: Proteomic analysis is a great and effective biomarker discovery tool that is being used to discover and identify various diseases biomarkers. The complexity of the plasma content exceeds the existing methods to determine lower abundance proteins that may prove to be informative biomarkers. The aim of this study was to investigate the removal efficiency of albumin and IgG (immunoglobulin G) by cellulose biosorbents in batch wise at room temperature. Polysaccharide based cellulose biosorbents were prepared and stabilized by solvent remove/solvent evaporation method in chitosan media. 2% chitosan amount, 1400 rpm stirring rate, 40 o C solvent evaporation temperature were determined as optimal conditions in our previous study for polysaccharide based cellulose biosorbents. The prepared spherical biosorbents were then studied for albumin and IgG removal by cellulose biosorbents. 976,6 mg albumin/g polymer and 209 mg IgG/g capturing capacity was obtained with cellulose biosorbents.
Gamification Elements and Their Perception by Different Gamer Types - A Case Study for a Project Management Software

Gamification is being applied more and more to information systems. Many developers apply similar strategies for the gamification of tasks. A benchmark conducted in 22 mobile apps containing gamification elements showed reward points as the most frequent design element. In a pilot user experiment, we analyzed the user perception of three gamification elements including reward points in the context of project management. Reward points are seen as motivating; in contrast a leader board was viewed more critically. It was shown that the judgment of elements depends on the game personality type. The gamer type Killer had a medium and significant correlation with a positive evaluation of the gaming elements. Only the gamer type Achiever had a positive and significant correlation with a positive judgment of rewards points.
Principal Component Regression for Egyptian Stock Market Prediction

ARTICLE INFO Financial markets are very rich with information and variables. In contradiction to the Efficient Market Hypothesis, much research has been conducted to predict asset prices with promising accuracy. However, ensuring good models requires extracting important information from given data sets. This paper investigates the main Egyptian Stock Exchange index (EGX 30) and constructs some alternative portfolios by identifying important linear combinations of EGX 30 constituents. This could be approached by a dimensionality reduction technique, which is performed following the principal components analysis (PCA). The results show that the first three Principal Article history: Received 29 nov.2020 Accepted 4 feb 2021
ULTIMATE OWNERSHIP STRUCTURE AND CAPITAL STRUCTURE: EVIDENCE FROM CHINESE LISTED COMPANIES

This study investigates the impact of the ultimate corporate ownership structure, particularly the divergence of ultimate controlling shareholder’s control rights and cash flow rights, on the capital structure decisions among firms listed in Chinese market where the legal protection for creditors and minority shareholders is weak. I find that firms with a wider divergence between the ultimate controlling shareholder’s control rights and cash flow rights have significantly higher leverage level of capital structure. I also identify factors that affect this relation, including state ownership, institutional ownership, the presence of large tradable shareholders and NTS reform. My results suggest that leverage-increasing motivation of ultimate controlling shareholders with the risk of expropriation dominates in Chinese market and raising debt is a tool for them to maintain control over resources and corporate decisions to facilitate their selfdealing expropriation.
Thermal noise and coating optimization in multilayer dielectric mirrors

Optical multilayer coatings of high-reflective mirrors significantly determine properties of Fabry-Perot resonators. Thermal (Brownian) noise in these coatings produce excess phase noise which can seriously degrade the sensitivity of high-precision measurements using these cavities. In particular, it is one of the main limiting factors at the current stage in laser gravitational-wave detectors (for example, project LIGO). We present a method to calculate this effect accurately and analyze different strategies to diminish it by optimizing the coating. Traditionally, the effect of the Brownian noise is calculated as if the beam is reflected from the very surface of the mirror's coating. However, the beam penetrates the coating, and Brownian expansion of the layers leads to dephasing of interference in the coating and consequently to an additional change in the reflected amplitude and phase. Fluctuations in the thickness of a layer change the strain in the medium and hence, due to a photoelastic effect, change the refractive index of this layer. This additional effect should also be considered. It is possible to reduce the noise by changing the total number and thicknesses of high and low refractive layers preserving the reflectivity. We show how an optimized coating may be constructed analytically rather than numerically as before. We also check the possibility of using internal resonant layers, an optimized cap layer, and double mirrors to decrease the thermal noise.
Spelling Error Assessment Module for Answer Evaluation Process

This paper discusses methods and algorithm to find various types of spelling errors occurs during writing a word. The examples included in this paper are based on the analysis of writing samples collected for the answer evaluation and other researchers discussed on their papers. Algorithm used in the model works on the different methods for matching the string with list of known word and reassemble word as string with gapes between letter when there is no exact match for best matched word. The module assesses the spelling error through the process of finding gaps in the string of letters.
On a New Method for Derivative Free Optimization

A new derivative-free optimization method for unconstrained optimization of partially separable functions is presented. Using average curvature information compute d from sampled function values the method generates an averag e Hessian-like matrix and uses its eigenvectors as new search directions. Numerical experiments demonstrate that this new derivative free optimization method has the very desirable property of avoiding saddle points. This is illustrated on two test functions and compared to other well known derivative free methods. Further, we compare the efficiency of the new method with two classical derivative methods using a class o f testproblems. Keywords-Generating Set Search, Derivative-Free Optimization, Saddle points, Sparsity.
How convergent are regional house prices in the United Kingdom? Some new evidence from panel data unit root testing

Using a variety of econometric methods, existing studies have failed to reach a consensus on whether or not UK regional house prices are engaged in longrun equilibrium relationships with each other. Using data for the 1973-2005 study period, this study offers a novel approach to this debate through the application of unit root testing within a seemingly unrelated regression framework. It is argued that there exist significant advantages in this approach over and above existing univariate and panel data unit root testing procedures. The results indicate that the majority of UK regions exhibit regional house price convergence. However, there is an eastwest split in terms of whether regional house prices have a tendency towards longrun equilibrium relationship with UK prices as whole. There is also evidence of considerable heterogeneity in the regional speeds of adjustment towards long-run equilibrium. JEL Classification Codes: C5, R0.
Convex clustering via `1 fusion penalization

We study the large sample behavior of a convex clustering framework, which minimizes the sample within cluster sum of squares under an `1 fusion constraint on the cluster centroids. This recently proposed approach has been gaining in popularity, however, its asymptotic properties have remained mostly unknown. Our analysis is based on a novel representation of the sample clustering procedure as a sequence of cluster splits determined by a sequence of maximization problems. We use this representation to provide a simple and intuitive formulation for the population clustering procedure. We then demonstrate that the sample procedure consistently estimates its population analog, and derive the corresponding rates of convergence. The proof conducts a careful simultaneous analysis of a collection of M-estimation problems, whose cardinality grows together with the sample size. Based on the new perspectives gained from the asymptotic investigation, we propose a key post-processing modification of the original clustering framework. We show, both theoretically and empirically, that the resulting approach can be successfully used to estimate the number of clusters in the population. Using simulated data, we compare the proposed method with existing number of clusters and modality assessment approaches, and obtain encouraging results. We also demonstrate the applicability of our clustering method for the detection of cellular subpopulations in a single-cell virology study. Some key words: Convex Clustering; Fusion Penalties; Number of Clusters; Rates of Convergence
Removal by Denoising Autoencoder in Speech Recognition

Correspondence: wangdong99@cslt.riit. tsinghua.edu. cn CSLT, RIIT, Tsinghua University, 100084 Beijing, China Full list of author information is available at the end of the article Abstract Music embedding often causes significant performance degradation in automatic speech recognition (ASR). This paper proposes a music-removal method based on the denoising autoencoder (DAE) that learns and removes music from music-embedded speech signals. Our study shows that the DAE model can learn patterns of music in different genres and the DAE-based music removal offers significant performance improvement for ASR. Furthermore, involving convolutional feature extraction offers additional performance gains. Finally, we demonstrate that the music-removal DAE is largely language independent, which means that a model trained with data in one language can be applied to remove music from speech in another language, and models trained with multilingual data may lead to better performance.
Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme

Computer networks are facing threats of ever-increasing frequency and sophistication. Encryption is becoming the norm in both legitimate and malicious network traffic. Therefore, intrusion detection systems (IDSs) are now required to work efficiently regardless of the encryption. In this study, we propose two new methods to improve the efficacy of the Cisco Cognitive Threat Analytics (CTA) system. In the first method, the efficacy of CTA is improved by sharing of intelligence information across a large number of enterprise networks. In the second method, a four variant-based global reputation model (GRM) is designed by employing an outlier ensemble normalization algorithm in the presence of missing data. Intelligence sharing provides additional information in the intrusion detection process, which is much needed, particularly for analysis of encrypted traffic with inherently low information content. Robustness of the novel outlier ensemble normalization algorithm is also demonstrated. These improvements are measured using both encrypted and non-encrypted network traffic. Results show that the proposed information sharing methods greatly improve the anomaly detection efficacy of malicious network behavior with bad base-line detection efficacy and slightly improve upon the average case.
Phospholipid Adsorption Polymeric Materials for Detection of Xylazine and Metabolite in Blood and Urine

Polymers have been used in different areas. Recently, polymeric material is favored in analytical area due to its high performance and high consistency, which was used in sample pretreatment in this study. Xylazine poisoning is often seen in body fluid samples obtained from various accidents or suicides. However, the content of xylazine is difficult to detect precisely due to matrix effect in testing practices. In this paper, a method application for phospholipid adsorption polymeric materials to determine xylazine in blood and urine samples was proposed, developed, and validated. Compared with existing method, this method using polymeric pretreatment has a wider linear range of 2.0–2000.0 ng/mL for xylazine and its metabolite 2,6-dimethylaniline in both blood and urine and lower detection limits of 0.3 ng/mL for 2,6-dimethylaniline and xylazine in blood and 0.2 ng/mL for 2,6-dimethylaniline and xylazine in urine. Therefore, this method is suggested to be applied in testing practices by academic groups and commercial organizations.
Nexus Between Uptake of Courses and Finance Availability on Success of Expansion Strategies in Public Training Institutions: A Case Study of Kenya Medical Training College

This study investigates the influence of uptake of courses and finance availability on the expansion strategies in public training institutions with reference to the Kenya Medical Training College. The specific objectives of the study are to: reveal the influence of uptake of courses on the success of expansion strategies in the public training institutions and; assess the influence of finances on the success of expansion strategies in the public training institutions. This study is based on the Ansoff Growth Matrix (AGM) and the theory of customer responsiveness. It adopts the descriptive survey design. The study focuses on 2393 KMTC officers from the 73 KMTC campuses in Kenya, 42 ministries of health officials in the 42 counties that have KMTC campuses and 42 local leaders from those counties that have KMTC campuses. The total targeted study participants were thus 2477. From these, 10% (248 persons) were sampled. The study used purposive and stratified random sampling techniques to obtain the study sample. Primary data was collected using structured questionnaires and interviews. Data from questionnaires were analysed using the Statistical Package for the Social Sciences (SPSS) version 24. Descriptive statistics (frequencies, percentages, and means) were used to describe the central tendencies of the data. In addition, inferential statistics (Pearson Correlation and Multiple Regression Analysis) were also used. Data from key informants were analysed using content analysis techniques. The findings show that the two study variables influenced the success of expansion strategies at KMTC campuses. In this regard, Pearson correlation shows that there was statistical significant relationship between uptake of courses (r=.308, p<0.001) and; finance availability (r=0.673, p<0.001) and success of expansion strategies. These findings show that finance availability was the strongest factor that influenced the success of expansion strategies. As such, the success of expansion strategies can only be successful if the college put in place strategies aimed at ensuring that there were sufficient finances. This could go on to enhance the development of facilities and equipping them. An effort aimed at ensuring that new campuses had a high intake of students could also contribute to the level of success of expansion strategies at KMTC. High intakes could enhance the revenue generation, further enhancing the funding and success of new KMTC campuses.
The infrared luminosity function of AKARI 90 μm galaxies in the local Universe

Local infrared (IR) luminosity functions (LFs) are necessary benchmarks for high-redshift IR galaxy evolution studies. Any accurate IR LF evolution studies require accordingly accurate local IR LFs. We present infrared galaxy LFs at redshifts redshifts of $z \leq 0.3$ from AKARI space telescope, which performed an all-sky survey in six IR bands (9, 18, 65, 90, 140 and 160 micron) with 10 times better sensitivity than its precursor IRAS. Availability of 160 micron filter is critically important in accurately measuring total IR luminosity of galaxies, covering across the peak of the dust emission. By combining data from Wide-field Infrared Survey Explorer (WISE), Sloan Digital Sky Survey (SDSS) Data Release 13 (DR13), 6-degree Field Galaxy Survey (6dFGS) and the 2MASS Redshift Survey (2MRS), we created a sample of 15,638 local IR galaxies with spectroscopic redshifts, factor of 7 larger compared to previously studied AKARI -SDSS sample. After carefully correcting for volume effects in both IR and optical, the obtained IR LFs agree well with previous studies, but comes with much smaller errors. Measured local IR luminosity density is $\Omega_{IR}=$ 1.19$\pm$0.05 $\times 10^{8}$ L$_{\odot}$ Mpc$^{-3}$. The contributions from luminous infrared galaxies and ultra luminous infrared galaxies to IR are very small, 9.3 per cent and 0.9 per cent, respectively. There exists no future all sky survey in far-infrared wavelengths in the foreseeable future. The IR LFs obtained in this work will therefore remain an important benchmark for high-redshift studies for decades.
Profitability prediction model for dairy farms using the random forest method

We applied an ensemble learning method known as random forests, which is widely used in supervised machine learning, to predict the profitability ratio of dairy farms based on financial and production related variables. The predictive model was implemented as a web service to enable farmers to calculate the profitability of their business. Hereby, farmers can better assess the sustainability of their business over time, or in comparison to other farms in the sector.
A social network approach to flexing the environment in organizational fit

This developmental paper looks at how organizational fit has een assessed, principally, from the perspective of the ndividual person(P). It goes on to suggest that the influence of the environment (E) – through the development of informal social networks - may be more salient to individuals in the workplace and have greater influence for those individuals over outcomes such as job satisfaction and organizational commitment. The paper suggests that it may be that individuals form social networks outside of the usual organizational constraints/structures of, for example, the geographic location of where the employee works. This paper contends that value congruence amongst disparate individuals in the workplace may be the basis for developing a social network. If so, this would suggest that it could be environmental factors which encourage or discourage the formation of informal social networks which may be more salient to individuals in the workplace over and above any sense of organizational fit. Finally, the paper suggests how the hypothesis might be tested.
Generative network models for simulating urban networks, the case of inter-city transport network in Southeast Asia

This paper examines the driving forces of urban network formation through the simulation of inter-city transport networks in Southeast Asia. We present a generative network model (GNM) considering geographical and topological effects, thus combining factors commonly analysed through traditional spatial simulation models (e.g., gravity models) and topological simulation models (e.g., actor-oriented stochastic models) in a single framework. In our GNM, it is assumed that the probability of connections between cities emerges from competing forces. Stimulating factors are a measure of city size (i.e., population) and a topological rule favouring the formation of connections between cities sharing nearest neighbours (i.e., transitive effects). The hampering factors are physical distance between two cities as well as institutional distance (i.e., border effects). We discuss the model in the context of on-going engagements between urban-geographical research and the network science literature, and validate the credence of the model against empirical data on the transport networks connecting 51 major cities in Southeast Asia. Our results show that (1) the generated networks approximate the observed ones in terms of average path length, clustering, modularity, efficiency and quadratic assignment procedure (QAP) correlation between the observed composite network and the generated one, and that (2) GNM performs best when topographical and topological factors are considered simultaneously. Each factor contributes differently to network formation, with transitive effects playing the most important role.
SINAI at Affect Task in MediaEval 2010

In this paper, a basic approach to establish the boredom score of several videos using their speech transcriptions is presented. We carried out an analysis of the development data provided trying to establish a correlation between the terms of the transcriptions and their boredom scores predicted for each video. For the development data, we analyzed dierent weighting schemes such as TF, TF IDF, binary or correlation weights, due to the small size of the texts involved. We also studied how the use of stopper and stemmer inuences the system. Finally, we concluded that apply stopper and stemmer is interesting in this task and TF and correlation weights obtained the best results. Therefore, two experiments were run, using TF and correlation weights as weighting schemes, always preprocessing the transcriptions of the videos. The experiment that used correlation weights obtained the best result for the test videos provided using the Kendall tau distance as evaluation metric.
An Analysis of Music and Its Influence on Adult Lifestyle Choices and Behavioral Tendencies

This thesis uses a secondary analysis to examine music and its influence on the behavior and lifestyle choices of adults. Data were obtained from the 1993 General Social Survey, in which information was collected from a national representative sample of 1,500 respondents. The research question poses whether the enjoyment of listening to certain genres of music was related to different lifestyle choices and behaviors of adults. The variables in the hypotheses include demographic, behavioral, cultural, and criminal justice related variables. The findings indicate that a variety of relationships exist between music and various lifestyle choices and behaviors of the participants. From the findings, one can see how a relationship did not exist between musical preference and gender, while a relationship did exist between musical preference and age, musical preference and attitudes over various criminal justice issues, and musical preference and certain behaviors/activities. Strengths and limitations of the thesis as well as suggestions for future research are also presented and discussed to conclude this thesis.