Miracle-dz/UniFAR-FTUs huggingface dataset:
xQTL workbench: a scalable web environment for multi-level QTL analysis

Summary: xQTL workbench is a scalable web platform for the mapping of quantitative trait loci (QTLs) at multiple levels: for example gene expression (eQTL), protein abundance (pQTL), metabolite abundance (mQTL) and phenotype (phQTL) data. Popular QTL mapping methods for model organism and human populations are accessible via the web user interface. Large calculations scale easily on to multi-core computers, clusters and Cloud. All data involved can be uploaded and queried online: markers, genotypes, microarrays, NGS, LC-MS, GC-MS, NMR, etc. When new data types come available, xQTL workbench is quickly customized using the Molgenis software generator. Availability: xQTL workbench runs on all common platforms, including Linux, Mac OS X and Windows. An online demo system, installation guide, tutorials, software and source code are available under the LGPL3 license from http://www.xqtl. org. Contact: m.a.swertz@rug.nl
Towards a Portfolio of FLOSS Project Success Measures

Project success is one of the most widely used dependent variables in information systems research. However, conventional measures of project success are difficult to apply to Free/Libre Open Source Software projects. In this paper, we present an analysis of four measures of success applied to SourceForge projects: number of members of the extended development community, project activity, bug fixing time and number of downloads. We argue that these four measures provide different insights into the collaboration and control mechanisms of the projects.
TORINO Repository ISTITUZIONALE

The continuous growth of modern cities and the request for better quality of life, coupled with the increased availability of computing resources, lead to an increased attention to smart city services. Smart cities promise to deliver a better life to their inhabitants while simultaneously reducing resource requirements and pollution. They are thus perceived as a key enabler to sustainable growth. Out of many other issues, one of the major concerns for most cities in the world is traffic, which leads to a huge waste of time and energy, and to increased pollution. To optimize traffic in cities, one of the first steps is to get accurate information in real time about the traffic flows in the city. This can be achieved through the application of automated video analytics to the video streams provided by a set of cameras distributed throughout the city. Image sequence processing can be performed both peripherally and centrally. In this paper, we argue that, since centralized processing has several advantages in terms of availability, maintainability and cost, it is a very promising strategy to enable effective traffic management even in large cities. However, the computational costs are enormous, and thus require an energy-efficient High-Performance Computing approach. Field Programmable Gate Arrays (FPGAs) provide comparable computational resources to CPUs and GPUs, yet require much lower amounts of energy per operation (around 6 × and 10 × for the application considered in this case study). They are thus preferred resources to reduce both energy supply and cooling costs in the huge datacenters that will be needed by Smart Cities. In this paper, we describe efficient implementations of high-performance algorithms that can process traffic camera image sequences to provide traffic flow information in real-time at a low energy and power
A New Secure Remote User Authentication Scheme with Smart Cards

Remote user authentication scheme is one of the simplest and the most convenient authentication mechanisms to deal with secret data over insecure networks. These types of schemes are applicable to the areas such as computer networks, wireless networks, remote login systems, operation systems and database management systems. The goal of a remote user authentication scheme is to identify a valid card holder as having the rights and privileges indicated by the issuer of the card. In recent years, so many remote user authentication schemes have been proposed to authenticate a legitimate user, but none of them can solve all possible problems and withstand all possible attacks. This paper presents a secure remote user authentication scheme with smart cards. The proposed scheme provides the essential security requirements and achieves particular attributes.
A Graph-to-Sequence Model for Joint Intent Detection and Slot Filling in Task-Oriented Dialogue Systems

Effectively decoding semantic frames in task-001 oriented dialogue systems remains a challenge, 002 which typically includes intent detection and 003 slot ﬁlling. Although RNN-based neural mod-004 els show promising results by jointly learning 005 of these two tasks, dominant RNNs are pri-006 marily focusing on modeling sequential depen-007 dencies. Rich graph structure information hid-008 den in the dialogue context is seldomly ex-009 plored. In this paper, we propose a novel 010 Graph-to-Sequence model to tackle the spoken 011 language understanding problem by modeling 012 both temporal dependencies and structural in-013 formation in a conversation. We introduce a 014 new Graph Convolutional LSTM (GC-LSTM) 015 encoder to learn the semantics contained in 016 the dialogue dependency graph by incorporat-017 ing a powerful graph convolutional operator. 018 Our proposed GC-LSTM can not only capture 019 the spatio-temporal semantic features in a di-020 alogue, but also learn the co-occurrence rela-021 tionship between intent detection and slot ﬁll-022 ing. Furthermore, a LSTM decoder is utilized 023 to perform ﬁnal decoding of both slot ﬁlling 024 and intent detection, which mutually improves 025 both tasks through global optimization. Exper-026 iments on benchmark ATIS and Snips datasets 027 show that our model achieves state-of-the-art 028 performance and outperforms existing models. 029
D ec 2 01 4 Probabilistic low-rank matrix completion on finite alphabets

The task of reconstructing a matrix given a sample of observe d entries is known as thematrix completion problem. It arises in a wide range of problems, including recommender systems, collaborative filtering, dim ensionality reduction, image processing, quantum physics or multi-class classific ation to name a few. Most works have focused on recovering an unknown real-value d ow-rank matrix from randomly sub-sampling its entries. Here, we inves tigate the case where the observations take a finite number of values, correspondi ng for examples to ratings in recommender systems or labels in multi-class cla sification. We also consider a general sampling scheme (not necessarily unifor m) ver the matrix entries. The performance of a nuclear-norm penalized estim ator is analyzed theoretically. More precisely, we derive bounds for the Kullba ck-Leibler divergence between the true and estimated distributions. In practice, we have also proposed an efficient algorithm based on lifted coordinate gradient d escent in order to tackle potentially high dimensional settings.
Elasticity of Orthoenstatite at High Pressure and Temperature: Implications for the Origin of Low VP/VS Zones in the Mantle Wedge

Orthopyroxene (opx) is an important mineral in petrologic models for the upper mantle. Its elastic properties are fundamental for understanding the chemical composition and geodynamics of the upper mantle. Here we calculate the elastic properties of orthoenstatite (MgSiO3), the Mg end‐member orthopyroxene under upper mantle pressure and temperature conditions using first principle calculations with local density approximation. Bulk and shear moduli increase nonlinearly with pressure at mantle temperatures, but the shear modulus and VS show very weak pressure dependence in comparison with VP. Compared to other major minerals in the upper mantle, orthoenstatite has the lowest compressional velocities (VP), shear velocities (VS), and VP/VS ratio down to the depth of approximately 300 km. The enrichment of opx in the upper mantle can cause the unusually low VP/VS observed in the mantle wedge.
4. Multilayer Perceptrons and Back- Propagation 4.1 the Back-propagation Algorithm

Multilayer feed-forward networks, or multilayer perceptrons (MLPs) have one or several " hidden " layers of nodes. This implies that they have two or more layers of weights. The limitations of simple perceptrons do not apply to MLPs. In fact, as we will see later, a network with just one hidden layer can represent any Boolean function (including the XOR which is, as we saw, not linearly separable). Although the power of MLPs to represent functions has been recognized a long time ago, only since a learning algorithm for MLPs, backpropagation, has become available, have these kinds of networks attracted a lot of attention. The back-propagation algorithm is central to much current work on learning in neural networks. It was independently invented several times (e.
Analysis of influence of chemical composition of Al-Si-Cu casting alloy on formation of casting defects

Purpose: A methodology of the computer-aided determining relationship between chemical composition of aluminum alloy and castings quality was presented in the paper. Design/methodology/approach: To resolve the problem artificial neural networks were used. Classification problems were evaluated by the consideration mainly the values of mistakes and correct answers of networks for test data. On the basis of data analyzed by the neural network, which has the best quality classification of chemical composition of tested material, the concentration of alloying elements range, which have an effect on formation casting defects, were developed to eliminate them in the future. Findings: Combining of all methods making use of chemical composition of aluminium alloy and neural networks will make it possible to achieve a better casting quality. Research limitations/implications: The presented issues may be use, among others, for manufacturers of car subassemblies from light alloys, where meeting the stringent quality requirements ensures the demanded service life of the manufactured products. Originality/value: The correctly specified number of chemical composition of aluminum alloy enables such technological process control where the number of castings defects can be reduced by means of the proper correction of the process.
Present Glaciers of Tavan Bogd Mountain Massif and Their Changes Since the LIA

The study is based on the results of long-term field studies, satellite and aerial data analysis. In the maximum of the Little Ice Age (LIA) 243 glaciers with a total area of 353.4 km2 were reconstructed. By the results of interpretation of Corona images by 1968 the number of glaciers increased (236), the total area reduced to 242 km2. In 2010 glaciation was represented by 237 glaciers with a total area of 201 km2. Thus, from the maximum of the LIA, the glaciation of the Tavan-Bogd mountains decreased by 43%, which is somewhat less than the neighboring glacial centers (Ikh-Turgen, Tsambagarav, Tsengel-Khayrkhan and Mongun-Taiga mountains). The probable cause is the predominance of larger glaciers relatively resistant to warming and higher altitudes. The effect of glacier size on their stability is supported by differences in the relative shrinkage of glaciers in different parts of the Tavan-Bogdo-Ola massif: the smallest decline occurred in the basins of the Tsagan-Gol (31.7%) and Sangadir (36.4%) rivers where the largest glaciers are located. On the contrary, on the lower periphery of the massif, where small glaciers predominate, the relative reduction was large (74-79%). On the background of the general retreat trend large valleys glaciers retreated faster in 1968-1977 and after 2010. In 1990-s the retreat was slow. After 2010 the glacial retreat was extremely fast. The retreat of glaciers in the last 50-60 years was caused by a trend towards a decrease in the amount of precipitation until the mid-1970s and a sharp warming in the 1990s and early 2000s.
CzEng 0.9: Large Parallel Treebank with Rich Annotation

CzEng 0.9: Large Parallel Treebank with Rich Annotation We describe our ongoing efforts in collecting a Czech-English parallel corpus CzEng. The paper provides full details on the current version 0.9 and focuses on its new features: (1) data from new sources were added, most importantly a few hundred electronically available books, technical documentation and also some parallel web pages, (2) the full corpus has been automatically annotated up to the tectogrammatical layer (surface and deep syntactic analysis), (3) sentence segmentation has been refined, and (4) several heuristic filters to improve corpus quality were implemented. In total, we provide a sentence-aligned automatic parallel treebank of about 8.0 million sentences, 93 million English and 82 million Czech words. CzEng 0.9 is freely available for non-commercial research purposes.
AIS Electronic

The problem of credit-risk evaluation is a very challenging and important financial analysis problem. Recently, researchers have found that neural networks perform very well for this complex and unstructured problem when compared to more traditional statistical approaches. A major drawback associated with the use of neural networks for decision making is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the reasoning behind how they reach their decisions is not readily available. In this paper, we present the results from analyzing two real life credit-risk evaluation data sets using neural network rule extraction techniques. Clarifying the neural network decisions by explanatory rules that capture the learned knowledge embedded in the networks can help the human experts in explaining why a particular decision is made. Furthermore, we also discuss how these rules can be visualized as a decision table in a compact and intuitive graphical format. Hence, extracting rules from trained neural networks and representing these rules as a decision table may offer a viable and valuable alternative for building credit-risk evaluation expert systems.
Enzymatic processes and enzyme development in biorefining

Abstract: Improvements in enzyme cocktails for converting biomass have driven down costs for production of bioproducts and have allowed commercialization of the first cellulosic biorefineries. Innovations in biochemical engineering have resulted in cocktails that require dramatically less enzyme for a given level of biomass conversion than were required just a few years ago. Developments in the enzymes themselves must be accompanied by optimization of the biochemical conversion process to allow for the lowest possible product costs to be achieved. This chapter describes both enzyme innovations and critical themes in process optimization.
Human vision system-based structural similarity model for evaluating seismic image quality

The quality of a seismic section is generally evaluated based on relevant qualitative and quantitative criteria. We have developed a new quantitative model to evaluate seismic sections, i.e., a full-reference human visual system based on seismic image structural similarity. With this model, we compared and analyzed two seismic sections before and after data processing in terms of energy intensity, contrast, and seismic reflection configuration similarity measures. We evaluated the quality of the seismic section in combination with the three measures, and their changing trends before and after data processing are represented with relevant quantitative indicators. The numerical simulation used algorithms and actual data, and the results indicate that compared with conventional evaluation methods, our method is easier to understand and simpler to calculate. Our method can also highlight the differences between changing seismic sections and improve the consistency of the objective evaluation results of seismic images with the corresponding human subjective perception.
Bulletin of Electrical Engineering and Informatics

Received Aug 5, 2021 Revised Dec 20, 2021 Accepted Feb 5, 2022 Web application has become an essential part of daily activities to provide easy accessibility that ensures better performance. It is a platform where sensitive information such as username, password, credit card details, operating system and software version. is stored that attracts intruders to generate most of their attacks. Intruders can steal valuable data by compromising web application security flaws; cross site scripting (XSS) vulnerability is one of these. Several studies have been conducted in order to prevent the XSS vulnerability. In this research, we searched Scopus Indexed articles published in the last 11 years (between 2008 and 2020) using two keywords (“XSS attack prevention” and “XSS prevention”). The purpose of this study was to conduct a literature review on XSS prevention techniques e.g., strengths and weaknesses, including structural issues and real-time deployment location in order to extract valuable information. This review identified 14 articles among the 25 selected articles that provided various suitable prevention techniques for XSS attacks. Seven articles are based on tools that have been implemented and take into account design, coding, testing, and integrating validation processes, six articles are about server site solutions, and one is about automatic mitigation solutions. As a result, this research will be invaluable in guiding the advancement of XSS prevention techniques.
Identifying Similarities : Sequence Comparison by Means of Alignments

Outline Bioinformatics is the study of biological information using computational approaches. It depends on knowledge of both the underlying biology and physical chemical information. It is important for the microbiologist to understand the basic methodologies used in bioinformatics, in order to be able to successfully apply available tools and correctly interpret results from the lab. Of these tools, sequence alignment methods, such as BLAST, are of crucial importance. This chapter will focus mainly on commonly used alignment tools for sequence-based methods of comparison. The chapter is clearly not an introduction to bioinformatics in general, as many aspects of the field are ignored. Instead, we provide here the basics that are tailored for use in later chapters, where bioinformatic tools are applied to the comparison of bacterial genomes.
Quality prediction and improvement in Lesotho commercial wheat (Triticum aestivum L.) cultivars

Thirty bread wheat cultivars grown in Lesotho were analysed using sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). High and low molecular weight gluten in subunits (HMW-GS and LMW-GS) and gliadins were used to determine genetic variability and relationships between cultivars. The HMW-GS could not distinguish the cultivars, while both LMW-GS and gliadins could distinguish all cultivars. Cluster analysis performed using gliadin bands alone and in combination with LMW-GS, generated dendrograms which segregated cultivars according to genetic distance. Dendrograms were different from each other showing main groups and sub-groupings with different composition of cultivars. However, the genetic distance between cultivars were so close that it could be concluded that they are from the same gene-pool and have been used several times in breeding programmes.
MODIS Retrievals of Cloud Effective Radius in Marine Stratocumulus Exhibit No Significant Bias

Satellite retrievals of cloud effective radius re are frequently validated using aircraft in situ measurements. Past intercomparisons have found a significant bias toward larger remotely sensed re. Explanations for this bias have focused on retrieval algorithms and large‐scale heterogeneity, with in situ measurement uncertainty regarded as a minor factor. We compare Moderate Resolution Imaging Spectroradiometer re with in situ observations of marine stratocumulus clouds from three aircraft campaigns using a phase Doppler interferometer probe. Retrieved and in situ re typically agree within uncertainty in both nonprecipitating and drizzling conditions with no apparent systematic bias (mean bias of −0.22 μm, mean relative bias 3%). Agreement depends on the choice of in situ probe as well as microphysical context. We demonstrate that probes must adequately characterize the width of the drop size distribution to avoid systematic underestimation of re.
Disease Prediction using Symptoms based on Machine Learning Algorithms

People are currently suffering from a variety of diseases. Many people are unsure if the symptoms they are experiencing are indicative of a certain disease, and hence they are unable to take the required safeguards. Anticipating the disease during prodromal stage lowers the likelihood of complications. People will not be able to visit a doctor every time they experience a symptom. It may sometimes become a serious ailment if not treated. A model is suggested that uses a variety of symptoms as input to predict the illness. For disease prediction, the suggested method utilizes Decision trees, Naive Bayes, and Random forest classifiers. The ultimate result will be the mode of all these machine learning models. Users will be given a graphical user interface (GUI) to choose their symptoms. The final result will be shown on the interface using all three machine learning techniques, and feature extraction will be done depending on their symptoms. Four modules make up the proposed methodology. Preprocessing will be done on the dataset in the first module. The decision tree classifier is used to generate a prediction model in the second module. The Random forest method is used for forecast the illness in the third module, and the Naive Bayes technique is utilized in the fourth model, with the mode of the outputs from all the three models taken into account.
Energy Aware Resource Management of Cloud Data Centers

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Virtualization technology forms a key concept for new cloud computing architectures. The data centers are used to provide cloud services burdening a significant cost due to high energy consumption. Data centers are provisioned to accommodate peak demand rather than average demand and cloud applications consume much more electrical energy than they need. Thus, it necessitates that cloud computing solutions not only minimize operational costs, but also reduce the power consumption. In this paper, we investigate load balancing and power saving methods in virtualized cloud infrastructures. Imbalanced distribution of workloads across resources can lead to performance degradation and much electrical power consumption in such data centers. We present an architectural framework and principles for energy-efficient cloud computing environments. Resource provisioning and allocation algorithms, named Load-Power-aware, are proposed in this architecture. The algorithm employs a heuristic to dynamically improve the energy efficiency in data center, while guarantees the Quality of Service (QoS). The efficiency of the proposed approach is evaluated by using the most common cloud computing simulation toolkit, CloudSim. The performance modeling and simulation results are depicted the proposed approach significantly improves the energy efficiency in a given dynamic scenario, while a small amount of service level agreements (SLA) is missed. doi: 10.5829/ije.2017. 30.11b. 14