Miracle-dz/UniFAR-FTUs huggingface dataset:
Software Release and Deployment at Exact : A Case Study Report

textabstractFor vendors of product software it is becoming more and more difficult to manage and control the software configurations of all their users at the customer's site. It is labour intensive and error-prone to (semi)automatically register detailed lists of the software artefacts in use by each customer. To alleviate this problem the Deliver project proposes an Intelligent Software Knowledge Base that contains all facts about all artefacts together with their relevant attributes, relations and constraints. In this way, high-quality software configurations can be calculated automatically from a small set of key parameters. It also becomes possible to pose what-if questions about necessary or future upgrades of a customer's configuration. This document describes a case study performed at Exact Software into the processes of release and deployment. The results of the case study are presented, existing of process descriptions of the development, release and deployment processes at Exact Software, a comparison to the Intelligent Software Knowledge Base, and an analysis of the results
Development of HCNG Blended Fuel Engine with Control of NOx Emissions

With increasing concern about energy shortage and environmental protection, research on reducing exhaust emissions, reducing fuel consumption, reducing engine noise and increasing specific outputs has become the major researching aspect in combustion and engine development. Alternative fuels such as CNG, HCNG, LPG, LNG, Bio-Diesel, Biogas, Hydrogen, Ethanol, Methanol, Di-Methyl Ether, Producer gas, P-series have been tried worldwide. Hydrogen as a future fuel for IC engines is also being considered. But several obstacles have to overcome before commercialization of Hydrogen as an IC engine fuel for automotive sector. Hydrogen and CNG blends (HCNG) may be considered as an automotive fuel without any major modification in the existing CNG engine and infrastructure. A strategy has been worked out for converting the developed CNG engine to run on HCNG. The testing is carried out for the neat CNG and 5% blends of Hydrogen by volume with CNG. It is observed in the experimental work that the HCNG engines are more superior to CNG carbureted engines from fuel economy, power output and emission compliance point of view. The power improvement of 11% and fuel consumption reduction of 8% is observed in HCNG engine than the CNG engine. The addition of hydrogen into natural gas increases the burning velocity of mixture, shortening the combustion duration, increasing the cylinder gas temperature, and this increases the effective thermal efficiency compared to that of natural gas engine under the same lean mixture condition. The HCNG engine increases the H/C ratio of the fuel, which drastically reduces the carbon based emissions such as CO, CO2 and HC. To increase the flame speed of HCNG engines, the ignition timing needs to be retarded; this results in reduction of NOx emissions. It is important to note that 5% blends of hydrogen by volume with CNG the phenomenon of hydrogen embrittlement does not occur with respect to engine components, hence no major change is anticipated in fuel system and engine components. Moreover, it improves the engine efficiency, which lowers fuel consumption and hydrocarbon emissions. The research work is undertaken to demonstrate the viability of HCNG as an automotive fuel. This paper explains how CNG is the best route to ensure an early entry of hydrogen fuel into our energy infrastructure. This research work is carried out at Alternative fuel engine development lab, ARAI, Pune, India.
Multi-focal feature tracking for a human-assisted mobile robot

In the project Autonomous City Explorer, an interactive robot is designed to find its way to a given destination in unknown urban environments by interacting with pedestrians. Considering applications in a human dominated environment, the robot can be sent to a destination by tracking a landmark selected by users and described by 2D image features. To achieve a natural landmark selection from the user perspective and an accurate feature tracking for a safe robot navigation, the robot preselects visual features and presents the users only the image regions providing higher tracking accuracy. Furthermore, a multi-focal camera system is used to extend the sensing range. SIFT, Harris corner and optical flow used for tracking and self-localization are compared and applied to different visual sensors. A coordination strategy is realized, in which the camera with wide field of view is used for robot orientation control and the high-resolution camera is applied for robot forward motion control. The performance is experimentally evaluated.
Injectable biodegradable hydrogels for embryonic stem cell transplantation: improved cardiac remodelling and function of myocardial infarction

In this study, an injectable, biodegradable hydrogel composite of oligo[poly(ethylene glycol) fumarate] (OPF) was investigated as a carrier of mouse embryonic stem cells (mESCs) for the treatment of myocardial infarction (MI). The OPF hydrogels were used to encapsulate mESCs. The cell differentiation in vitro over 14 days was determined via immunohistochemical examination. Then, mESCs encapsulated in OPF hydrogels were injected into the LV wall of a rat MI model. Detailed histological analysis and echocardiography were used to determine the structural and functional consequences after 4 weeks of transplantation. With ascorbic acid induction, mESCs could differentiate into cardiomyocytes and other cell types in all three lineages in the OPF hydrogel. After transplantation, both the 24‐hr cell retention and 4‐week graft size were significantly greater in the OPF + ESC group than that of the PBS + ESC group (P < 0.01). Four weeks after transplantation, OPF hydrogel alone significantly reduced the infarct size and collagen deposition and improved the cardiac function. The heart function and revascularization improved significantly, while the infarct size and fibrotic area decreased significantly in the OPF + ESC group compared with that of the PBS + ESC, OPF and PBS groups (P < 0.01). All treatments had significantly reduced MMP2 and MMP9 protein levels compared to the PBS control group, and the OPF + ESC group decreased most by Western blotting. Transplanted mESCs expressed cardiovascular markers. This study suggests the potential of a method for heart regeneration involving OPF hydrogels for stem cell encapsulation and transplantation.
Evolutionary Optimization for Problem Classes with Lamarckian Inheritance

The combination of evolution and learning as two natural principles of optimization, which work on different time scales, has been shown to be very efficient for optimization tasks. We investigate how this combination should be organized to achieve networks that can fast and reliably switch between related problems from one and the same class of problems during operation time. We analyze different methods to evaluate the network’s performance during optimization, namely a time averaged evaluation and a more direct averaging over different problems in each generation. In particular, we show that a specific kind of Lamarckian inheritance can be beneficial for the evolutionary optimization of neural networks even for dynamic environments like problem classes. Soo-Young Lee (Ed.), volume 2, pages 897-902, Taejon, Korea, 2000
Obstacles and challenges of opinion mining of customers reviews

The customer’s reviews and opinion mining become one of the wealthily areas in data mining. Nowadays, as the vast developments of using web applications and spreading of sites that provides a good platform for the customers to express their opinions directly on online shopping and companies web sites like Amazon.com, Cnet.com, customers opinion becomes helpful tools to manufactories to evaluation and measure satisfying ratio and weakness of the products [3]. Recently, there are many researcher works on this area of opinion mining, using different techniques, the developed techniques are good but also still there are many challenges and obstacles are still found. In this paper, we try to review some of the feature base opinion mining base concepts and techniques that are used now, and the challenges that face the opinion mining, and what the future works need to solve is.
ROC Analysis of a Linguistic Decision Tree Merging Algorithm

ROC analysis is an important tool for evaluation and comparison of classiﬁers in imprecise environments (i.e., class distribution and cost parameters are un-known). Area Under the Curve of ROC (AUC) is increasingly being recognized as a better measure for evaluating algorithm performance than accuracy. A big-ger AUC value implies a better ranking performance for a classiﬁer. Linguistic decision tree (LDT) is a model based on a random set framework of modelling with words. Classiﬁcation is made based on probability estimates from all leaves of the tree. In this paper, a branch merging algorithm for LDT model is proposed to generate more compact trees and no signiﬁcant reduction in AUC values is found.
Quality estimation for asr k-best list rescoring in spoken language translation

Spoken language translation (SLT) combines automatic speech recognition (ASR) and machine translation (MT). During the decoding stage, the best hypothesis produced by the ASR system may not be the best input candidate to the MT system, but making use of multiple sub-optimal ASR results in SLT has been shown to be too complex computationally. This paper presents a method to rescore the k-best ASR output such as to improve translation quality. A translation quality estimation model is trained on a large number of features which aim to capture complementary information from both ASR and MT on translation difficulty and adequacy, as well as syntactic properties of the SLT inputs and outputs. Based on the predicted quality score, the ASR hypotheses are rescored before they are fed to the MT system. ASR confidence is found to be crucial in guiding the rescoring step. In an English-to-French speech-to-text translation task, the coupling of ASR and MT systems led to an increase of 0.5 BLEU points in translation quality.
Effective retrieval of polyphonic audio with polyphonic symbolic queries

Accurately finding audio recordings in response to symbolic queries is one of the key challenges in the field of music information retrieval. Pitch is one of the main features of music; in this paper we propose and evaluate approaches for using pitch information in polyphonic symbolic queries to retrieve full tracks of audio recordings. The audio data is first converted into symbolic data, using an automated transcription process. This is a noisy process, adding up to three times as many notes to the transcription than are actually present. Nevertheless, recordings can be accurately retrieved by manually-constructed queries (either in full or truncated) using the longest common subsequence algorithm (and a sliding window if the queries are truncated). Precision at 1 of about 80% was achieved, and around 85% of queries return correct answers in the top 10 from a collection of 1808 recordings. Truncated queries are as effective as untruncated queries for retrieving correct answers in the first rank position. Thus, the burden on users is reduced as they only need to produce a small fraction of a song as a query.
Colored Mosaic Matrix: Visualization Technique for High-Dimensional Data

Owing to a limited display resolution, it may be difficult to obtain an overview of high-dimensional data in the display area used for visualization. In this paper, we aimed to obtain an overview of high-dimensional data in a limited screen area. We developed Colored Mosaic Matrix as a method to obtain a data overview. Colored Mosaic Matrix is a visualization method for high-dimensional categorical data that uses a color representation of the features. By representing quantitative data in category units, the proposed method enables the visualization of data containing a large number of records. As a result of an experimental investigation of its readability, we found our method to be useful in obtaining a data overview.
FlyTF: improved annotation and enhanced functionality of the Drosophila transcription factor database

FlyTF (http://www.flytf.org) is a database of computationally predicted and/or experimentally verified site-specific transcription factors (TFs) in the fruit fly Drosophila melanogaster. The manual classification of TFs in the initial version of FlyTF that concentrated primarily on the DNA-binding characteristics of the proteins has now been extended to a more fine-grained annotation of both DNA binding and regulatory properties in the new release. Furthermore, experimental evidence from the literature was classified into a defined vocabulary, and in collaboration with FlyBase, translated into Gene Ontology (GO) annotation. While our GO annotations will also be available through FlyBase as they will be incorporated into the genes’ official GO annotation in the future, the entire evidence used for classification including computational predictions and quotes from the literature can be accessed through FlyTF. The FlyTF website now builds upon the InterMine framework, which provides experimental and computational biologists with powerful search and filter functionality, list management tools and access to genomic information associated with the TFs.
Quantum Query Complexity of Some Graph Problems

Quantum algorithms for graph problems are considered, both in the adjacency matrix model and in an adjacency list-like array model. We give almost tight lower and upper bounds for the bounded error quantum query complexity of Connectivity, Strong Connectivity, Minimum Spanning Tree, and Single Source Shortest Paths. For example we show that the query complexity of Minimum Spanning Tree is in Θ(n 3/2) in the matrix model and in \(\Theta(\sqrt{nm})\) in the array model, while the complexity of Connectivity is also in Θ(n 3/2) in the matrix model, but in Θ(n) in the array model. The upper bounds utilize search procedures for finding minima of functions under various conditions.
Negotiating in networks: unleashing the power of options

University of Bath Suppliers in business markets are faced with a relatively small number of significant customers with which they must negotiate about numerous issues surrounding complex offerings. Each of these negotiations does not take place in isolation. Each is part of a complex and continuing relationship between the companies and each affects and is affected by the other relationships and negotiations of the companies in the surrounding network. This paper moves beyond the tradition of examining dyadic processes in negotiation to investigate negotiations in networks of organisations, as a way of contributing to an understanding of customer behaviour. This is based on the idea that a network view can provide a more complete understanding of negotiations and conversely, that an understanding of the process of negotiations can improve our understanding of networks. Adopting this alternate perspective provides a new intellectual lens that allows managers to “unleash the power of options” inherent in networks of organisations. Instead of providing prescriptions for deal making, we draw from the research experience of the IMP group to develop a description that explores and explains some aspects of the practice of inter-organisational negotiations.
Semantic integration of IEC 60870 into CIM

The transition from distribution and transport power networks into the so-called smart grid vision must cope with many problems, one of them being the large amount of related standard protocols. Bringing them to work together in an standard fashion is the key to this goal. We focus here on the IEC 60870, the Spanish standard for high-voltage meter control and management. Applying the Semantic Web paradigm, we have mapped the IEC 60870 services to CIM in order to achieve interoperability. Moreover, we have enabled SPARQL (SPARQL Protocol and RDF Query Language) queries to retrieve the information from CIM-compliant meters and, as a proof-of-case we have implemented it with an IEC 60870 meter.
A feature subset selection method based on symmetric uncertainty and Ant Colony Optimization

Feature subset selection is one of the key problems in the area of pattern recognition and machine learning. Feature subset selection refers to the problem of selecting only those features that are useful in predicting a target concept i.e. class. Data acquired through different sources are not particularly screened for any specific task e.g. classification, clustering, anomaly detection, etc. When the data are fed to a learning algorithm, its results deteriorate. The proposed method is a pure filter based feature subset selection technique which incurs less computational cost and highly efficient in terms of classification accuracy. Moreover, along with high accuracy the proposed method requires less number of features in most of the cases. In the proposed method the issue of feature ranking and threshold value selection is addressed. The proposed method adaptively selects number of features as per the worth of an individual feature in the dataset. An extensive experimentation is performed, comprised of a number of benchmark datasets over three well known classification algorithms. Empirical results endorse efficiency and effectiveness of the proposed method.
Pattern-Based Retrieval of Cultural Heritage Images

Conventional Content-Based Image Retrieval (CBIR) systems make use of similarity measures estimated directly from low-level image features, involving multidimensional and exhaustive, nearest neighbor searching. In this paper we present an image retrieval methodology suited for efficient search in cultural heritage images that utilizes similarity measures defined over higher-level patterns associated with clusters of low-level image features. The similarity between two patterns is estimated as a function of the similarity between both the structure and the measure components of the patterns. We evaluate our system using cultural heritage images derived from the repository of the Foundation of Hellenic World (FHW). For the experiments we use Local Binary Pattern (LBP) and contrast distributions as image features and the results show that the proposed pattern-based approach efficiently retrieves images compared to commonly employed methods.
An efficient implementation of phonological rules using finite-state transducers

Context-dependent phonological rules are used to model the mapping from phonemes to their varied phonetic surface realizations. Others, most notably Kaplan and Kay, have described how to compile general context-dependent phonological rewrite rules into finite-state transducers. Such rules are very powerful, but their compilation is complex and can result in very large nondeterministic automata. In this paper we present a simplified rewrite rule system and a technique to efficiently compile such a system into finite-state transducers.
Understanding how people interact with web search results that change in real-time using implicit feedback

The way a searcher interacts with query results can reveal a lot about what is being sought. Considerable research has gone into using implicit relevance feedback to identify relevant con-tent in real-time, but little is known about how to best present this newly identified relevant content to users. In this paper we compare a traditional search interface with one that dynamical-ly re-ranks and recommends search results as the user interacts with it in order to build a picture of how and when users should be offered dynamically identified relevant content. We present several studies that compare logged behavior for hun-dreds of thousands of users and millions of queries as well as self-reported measures of success across the two interaction models. Compared to traditional web search, users presented with dynamically ranked results exhibit higher engagement and find information faster, particularly during exploratory tasks. These findings have implications for how search engines might best exploit implicit feedback in real-time in order to help users identify the most relevant results as quickly as possible.
Gauss–Bonnet cosmologies: crossing the phantom divide and the transition from matter dominance to dark energy

Dark energy cosmologies with an equation of state parameter w less than −1 are often found to violate the null energy condition and show unstable behaviour. A solution to this problem may require the existence of a consistent effective theory that violates the null energy condition only momentarily and does not develop any instabilities or other pathological features for a late time cosmology. A model which incorporates a dynamical scalar field φ coupled to the quadratic Riemann invariant of the Gauss–Bonnet form is a viable proposal. Such an effective theory is shown to admit non-singular cosmological evolutions for a wide range of scalar–Gauss–Bonnet coupling. We discuss the conditions for which our model yields observationally supported spectra of scalar and tensor fluctuations, under cosmological perturbations. The model can provide a reasonable explanation for the transition from matter dominance to dark energy regime and the late time cosmic acceleration, offering an interesting testing ground for investigations of the cosmological modified gravity.
Eicosapentaenoic Acid and Docosahexaenoic Acid in Whole Blood Are Differentially and Sex-Specifically Associated with Cardiometabolic Risk Markers in 8–11-Year-Old Danish Children

n-3 long-chain polyunsaturated fatty acids improve cardiovascular risk markers in adults. These effects may differ between eicosapentaenoic acid (EPA, 20∶5n-3) and docosahexaenoic acid (DHA, 22∶6n-3), but we lack evidence in children. Using baseline data from the OPUS School Meal Study we 1) investigated associations between EPA and DHA in whole blood and early cardiometabolic risk markers in 713 children aged 8–11 years and 2) explored potential mediation through waist circumference and physical activity and potential dietary confounding. We collected data on parental education, pubertal stage, 7-day dietary records, physical activity by accelerometry and measured anthropometry, blood pressure, and heart rate. Blood samples were analyzed for whole blood fatty acid composition, cholesterols, triacylglycerol, insulin resistance by the homeostatic model of assessment (HOMA-IR), and inflammatory markers. Whole blood EPA was associated with a 2.7 mmHg (95% CI 0.4; 5.1) higher diastolic blood pressure per weight% EPA, but only in boys. Heart rate was negatively associated with both EPA and DHA status (P = 0.02 and P = 0.002, respectively). Whole blood EPA was negatively associated with triacylglycerol (P = 0.003) and positively with total cholesterol, low density and high density lipoprotein (HDL) cholesterol and HDL:triacylglycerol (all P<0.01) whereas DHA was negatively associated with insulin and HOMA-IR (P = 0.003) and tended to be negatively associated with a metabolic syndrome-score (P = 0.05). Adjustment for waist circumference and physical activity did not change the associations. The association between DHA and HOMA-IR was attenuated but remained after adjustment for fiber intake and none of the other associations were confounded by dietary fat, protein, fiber or energy intake. This study showed that EPA status was negatively associated with triacylglycerol and positively with cholesterols whereas DHA was negatively associated with insulin resistance, and both were inversely associated with heart rate in children. The sex-specific associations with blood pressure confirm our previous findings and warrant further investigation.