Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Supervisor: Method of Reinforcement Learning Fault Elimination by Application of Supervised Learning

Reinforcement Learning (RL) is a popular approach for solving increasing number of problems. However, standard RL approach has many deficiencies. In this paper multiple approaches for addressing those deficiencies by incorporating Supervised Learning are discussed and a new approach, Reinforcement Learning with Adaptive Supervisor, is proposed. In this model, actions chosen by the RL method are rated by the supervisor and may be replaced with safer ones. The supervisor observes the results of each action and on that basis it learns the knowledge about safety of actions in various states. It helps to overcome one of the Reinforcement Learning deficiencies - risk of wrong action execution. The new approach is designed for domains, where failures are very expensive. The architecture was evaluated on a car intersection model. The proposed method eliminated around 50% of failures.
A New Subject-Based Document Retrieval from Digital Libraries Using Vector Space Model

Document retrieval from digital libraries based on user's query is highly affected by the terms appeared in the query. In many cases, there are some documents in the digital libraries that do not share exactly the same terms with the query, but they are related to the user's need. We address this challenge in this paper by introducing a new subject-based retrieval approach in which, apart from ranking documents based on the terms in the query, a new subject-based scoring scheme is defined between the query and a document. We define this score by introducing a new vector space model in which a vectorized subject-based representation is defined for each document and its keywords, and the terms in the query, as well. We have tested the new subject-based scoring scheme on a database of scientific papers obtained from Web of Science. Our Experimental results show that in 83% of times users prefer the proposed scoring scheme with respect to the classic scoring ones.
What makes people click: assessing individual differences in susceptibility to email fraud

Cyber security experts have acknowledged that human users are consistently the most vulnerable part of a computer network, however little psychological research has considered why. This thesis focuses on susceptibility to email fraud, and highlights three core approaches to understanding why some users are more likely to respond than others, using a mixed methods approach across seven experiments. The first approach considers the persuasive techniques employed by the sender to make an email more believable. Qualitative data from Studies 1 and 5 demonstrate that authority, familiarity, and the relevance of a communication are important factors when users are considering the legitimacy of an email. The second approach focuses on the situational factors that may make users more susceptible under specific circumstances. Findings demonstrate that time pressure (Study 3) and a secondary verbal task (Study 6) can impair accuracy in judging email legitimacy. Finally, individual differences in cognitive make-up between users are considered, with two distinct tasks used to measure susceptibility. Using a forced-choice email legitimacy task (Study 3) and an office simulation, in which participants were naive to the purpose of the research (Study 7), cognitive reflection, inhibition, and sensation seeking were found to be influential in the decision-making process. The findings from this thesis outline key influencing factors, which explain some of the variance in individual differences in susceptibility to email fraud. These provide valuable points for consideration in future efforts to educate users on issues surrounding email fraud. Further to this, the development of two lab-based measures of susceptibility, with findings replicated between the two, provides a platform for further research in understanding and reducing susceptibility. Variations upon the email legitimacy task demonstrate how this can be used to assess effects of a number of manipulations, such as different proportions of phishing and legitimate stimuli (Study 4) and dual-task paradigms (Study 6). The incorporation of additional qualitative data analysis in the thesis, from the use of focus group discussions (Study 1) and think-aloud protocols (Study 5), also provides convergent evidence for the quantitative research findings reported.
"Passeport Vacances": an assignment problem with cost balancing

Passeport Vacances is an offer for school-aged children to discover a set of activities during holidays. For more than 30 years, it has been an established social function in several countries, including Germany and Switzerland. Proposed activities might occur several times during the Passeport Vacances. The assignment of activities to children is computed in order to maximize the children’s preferences, as well as to balance each child’s incurred cost, toward an equity goal. There are several sets of constraints associated with the assignment problem: no overlapping activities assigned to the same child, minimal and maximal ages per activity, minimum number of children for opening an activity, maximal size of a group for each activity, no similar activities assigned to the same child, no already assigned ‘lifetime’-activity per child, and at most one activity per period and per child. We propose a binary linear programming model that describes the assignment problem, report CPU computation issues regarding the model implementation, and report numerical results based on a state-of-the-art MIP solver. Tests where conducted with real data from the 2016 edition of Passeport Vacances in Morges.
Classification of Computer Network Users with Convolutional Neural Networks

Automatic detection of abnormal behaviour of computer network users is a desirable and hard to achieve feature. We show that convolutional neural networks can classify users in local computer networks based on features of web pages which were requested by a user (e.g. URL address, URL category, the day of week or time when the web page was visited). We demonstrate our approach on data collected from a firewall over an eight-month period. This network traffic meta-data allowed to achieve satisfactory classification accuracy on unseen, future network traffic data.
Managing configuration history in domestic networks

Domestic Networks are gaining in complexity, with an increasing number and variety of devices. Increasing complexity results in greater difficulty managing configuration and troubleshooting when problems occur. This thesis presents strategies to assist users in managing the complexity of their networks. The work is split into several parts. First, configuration changes are tracked and users are presented with a timeline of changes to their network. Provision of a selective undo system is the second feature. The undo facility is designed to allow any change to be undone independently of any other. Users are also given the option of reverting to an earlier point, either before a specific change, or to a specific timestamp. The next feature is use of notifications. Any changes that require further actions can be broadcast to users directly. Changing Wi-Fi configuration is one example. The range of devices in use makes changing Wi-Fi configuration (and the subsequent reconfiguration of devices) a challenge, because the devices affected may be part of the infrastructure of a home (lights or thermostat for example). Because these devices have unique methods of network setup, restoring connectivity to every device can be challenging. This thesis also presents a method of changing Wi-Fi configuration which allows users a grace period to reconnect all their devices. Each of these features was assessed by a user study, the results of which are also discussed.
High Performance Recommendation and Adaptation Platform for Enterprise Applications

Modern enterprise applications have high degree of complexity. Usage recommendations help to address this complexity from the user perspective. The paper uses recommendations to guide business process execution in enterprise applications. To provide timely support, the recommendations should be generated in near real-time without compromising performance. That is achieved by developing a platform integrating data streams and generating recommendations autonomously of the enterprise application. The platform is shown capable of processing high volume data streams and generate recommendations in near real-time. Keyword : Software development, performance testing, load testing
Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction

The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increased computational burden owing to the concurrent learning of multiple labels. A straightforward approach to address this issue is feature selection; however, it may be insufficient to satisfy the practical constraints because the computational cost for feature selection can be impractical when the number of labels is large. In this study, we propose an efficient multilabel feature selection method to achieve scalable multilabel learning when the number of labels is large. The empirical experiments on several multilabel datasets show that the multilabel learning process can be boosted without deteriorating the discriminating power of the multilabel classifier.
An ensemble of Deep Convolutional Neural Networks for Marking Hair Follicles on Microscopic Images

This paper presents an application of a Convolutional Neural Network as a solution for a task associated with ESENSEI Challenge: Marking Hair Follicles on Microscopic Images. As we show in this paper quality of classification results could be improved not only by changing architecture but also by ensemble networks. In this paper, we present two solutions for the task, the first one based on benchmark convolutional neural network, and the second one, an ensemble of VGG-16 networks. Presented models took first and third places in the final competition leaderboard.
Image Clustering Method Based on Particle Swarm Optimization

To implement efficient computer vision mechanisms, efficient image clustering methods are important. The paper elaborates a clustering method based on particle swarm optimization (PSO) which provides automatic establishment of clustering parameters. The developed PSO based clustering method was tested on 860 images for a car vision system and its results and contribution to the pattern recognition quality improvement were assessed in comparison with fuzzy C-means and k-means. The results do not differ significantly, but distinction in average time of work for these methods was noted. The PSO clustering method is faster than k-means and slower than fuzzy C-means. However, fuzzy C-means method does not guarantee correct results during the further analysis, so the PSO clustering method can be more efficient for implementation in computer vision systems.
Evaluation of a bovine visual pregnancy test for the detection of pregnancy‐associated glycoproteins in sheep

As sheep produce similar pregnancy-associated glycoproteins (PAGs) to cattle, a commercially available bovine visual pregnancy test based on the detection of PAGs (visual-PAG-test) was evaluated in sheep. The test was performed with whole blood (WhB), plasma (P) and serum (S) of 163 pregnant and 153 non-pregnant ewes. Additionally, 11 pregnant ewes were tested weekly from day 14 to 49 of gestation and monthly from day 60 to day 149. Ten ewes were sampled weekly from the date of lambing until day 63 post-partum (p.p.). The sensitivity in mid-pregnancy (n = 163) was 98.16% (WhB), 99.39% (P) and 99.39% (S) compared to transabdominal ultrasonography as the gold standard while the specificity (n = 153) was 94.12% (WhB), 76.47% (P) and 93.46% (S), respectively. During early pregnancy, all 11 ewes were correctly identified as pregnant on day 42 (100%); however, the test sensitivity decreased to 54.6% (WhB) and 63.6% (S and P, respectively) at day 49. The ewes were again consistently identified as pregnant on day 63 (P) or on day 119 (S, WhB). The test was consistently negative from day 42 p.p. onwards in eight out of ten ewes. Two ewes remained consistently positive until the last sample on day 63 p.p. In conclusion, the test could be used to accurately select pregnant ewes at day 42 with a drop in sensitivity at day 49. The sensitivity of the visual-PAG-test was good in mid to late pregnancy, and early detection of pregnancy was possible in individual animals. In some ewes, the PAGs were however detectable for more than 63 days p.p.-the previous breeding history should therefore always be taken into account for correct interpretation of the test results.
Virtualising the real: a virtual reality contemporary sculpture park for children

This paper discusses a virtual reality experience for a contemporary sculpture park, Jupiter Artland, developed in Minecraft targeting 9-11-year-old children. Issues of fidelity, realism and authenticity are considered, examining the use of Minecraft to create visual representations loosely coupled with reality that generate affective responses. We discuss the build of our virtual sculpture park outlining our approach to creating a facsimile of the sculpture park through adopting practices from the Minecraft community and refining and improving the artworks and experience informed by a class of 9-10 year old children. Using the real and virtual sculpture parks we explore the impact of engaging with the real before the virtual Jupiter Artland and vice versa, with the results from a three class study outlined in this paper.
EA Management in the German Public Sector: An Initial Perspective on Priorities

Worldwide, Enterprise Architectures (EAs) have been gaining popularity due to benefits, like, e.g., increased transparency of dependencies between business and technology, reduction of efforts for system customization or optimizing integrated and enterprise-wide processes and matching actions. Private businesses and the public sector show significant differences in the use of EA. Thus, the application of reference architectures originating from the private sector for purposes in the public sector creates substantial challenges. For a better understanding of the challenges, it is necessary to identify the most affected EA layers and the insufficiently defined, modeled or described elements of EA models in general. Based on an investigation in the Germany, this paper identifies weaknesses of EA in the public sector, like an incompletely modeled technology layer, and also strengths, such as existing definitions, descriptions and models of enterprise objectives. These findings are the foundation for deriving actions and recommendations to strengthening the existing structure and turning it into a coherent overall architecture for federal and state agencies in Germany.
Enhanced production of tanshinone IIA in endophytic fungi Emericella foeniculicola by genome shuffling

Abstract Context: Tanshinone IIA, commercially produced from Salvia miltiorrhiza Bunge (C.Y.Wu) (Labiatae), has various biological benefits. Currently, this compound is mainly extracted from plants. However, because of the long growth cycle and the unstable quality of plants, the market demands can barely be satisfied. Objective: The genomic shuffling technology is applied to screen the high-yield tanshinone IIA strain, which could be used to replace the plant S. miltiorrhiza for the production of tanshinone IIA. The change in the production of tanshinone IIA is clarified by comparing it with the original strain. Materials and methods: Tanshinone IIA was extracted from Strains cells, which was prepared through 0.5 mL protoplast samples by using hypertonic solution I from two different strains. Then, it was analyzed by high-performance liquid chromatography at 30 °C and UV 270 nm. Total DNA from the strains was extracted for RAPD amplification and electrophoresis to isolate the product. Results: In this study, a high-yield tanshinone IIA strain F-3.4 was screened and the yield of tanshinone IIA was increased by 387.56 ± 0.02 mg/g, 11.07 times higher than that of the original strain TR21. Discussion: This study shows that the genetic basis of high-yield strains is achieved through genome shuffling, which proves that genome shuffling can shorten the breeding cycle and improve the mutagenesis efficiency in obtaining the strains with good traits and it is a useful method for the molecular breeding of industrial strains.
Impaired Mitochondrial Fatty Acid Synthesis Leads to Neurodegeneration in Mice

There has been a growing interest toward mitochondrial fatty acid synthesis (mtFAS) since the recent discovery of a neurodegenerative human disorder termed MEPAN (mitochondrial enoyl reductase protein associated neurodegeneration), which is caused by mutations in the mitochondrial enoyl-CoA/ACP (acyl carrier protein) reductase (MECR) carrying out the last step of mtFAS. We show here that MECR protein is highly expressed in mouse Purkinje cells (PCs). To elucidate mtFAS function in neural tissue, here, we generated a mouse line with a PC-specific knock-out (KO) of Mecr, leading to inactivation of mtFAS confined to this cell type. Both sexes were studied. The mitochondria in KO PCs displayed abnormal morphology, loss of protein lipoylation, and reduced respiratory chain enzymatic activities by the time these mice were 6 months of age, followed by nearly complete loss of PCs by 9 months of age. These animals exhibited balancing difficulties ∼7 months of age and ataxic symptoms were evident from 8–9 months of age on. Our data show that impairment of mtFAS results in functional and ultrastructural changes in mitochondria followed by death of PCs, mimicking aspects of the clinical phenotype. This KO mouse represents a new model for impaired mitochondrial lipid metabolism and cerebellar ataxia with a distinct and well trackable cellular phenotype. This mouse model will allow the future investigation of the feasibility of metabolite supplementation approaches toward the prevention of neurodegeneration due to dysfunctional mtFAS. SIGNIFICANCE STATEMENT We have recently reported a novel neurodegenerative disorder in humans termed MEPAN (mitochondrial enoyl reductase protein associated neurodegeneration) (Heimer et al., 2016). The cause of neuron degeneration in MEPAN patients is the dysfunction of the highly conserved mitochondrial fatty acid synthesis (mtFAS) pathway due to mutations in MECR, encoding mitochondrial 2-enoyl-CoA/ACP reductase. The report presented here describes the analysis of the first mouse model suffering from mtFAS-defect-induced neurodegenerative changes due to specific disruption of the Mecr gene in Purkinje cells. Our work sheds a light on the mechanisms of neurodegeneration caused by mtFAS deficiency and provides a test bed for future treatment approaches.
Assessing Effectiveness of Recommendations to Requirements-Related Problems Through Interviews with Experts

Requirements Engineering and Business Analysis are known as very important to software project outcome but also difficult activities. , coping with many problems and challenges. The work reported in this paper was preceded by a survey which revealed most common requirements-related problems in Polish IT industry. We addressed ten most frequently reported problems by reviewing the literature for recommendations how to cope with those problems. The resulting set of recommendations is included in the paper. Next. , we conducted interviews with three experienced IT analysts asking them to assess effectiveness of particular recommendations. , based on their experience. The results show significant differences in assessments and indicate that effectiveness is dependent on contextual factors to a large extent. Our conclusion is that a follow-up work is required to document more recommendations and to annotate them with guidelines about applicability. , intended context of use and possible pitfalls.
Rice Bran Oil Containing Gamma-Oryzanol Improves Lipid Profiles and Antioxidant Status in Hyperlipidemic Subjects: A Randomized Double-Blind Controlled Trial.

OBJECTIVES Rice bran oil (RBO) is a major source of monounsaturated fatty acid and gamma-oryzanol, which may assist in lowering blood lipids and oxidative stress. This study examined the effects of RBO containing different amounts of gamma-oryzanol on blood lipid, antioxidant, and inflammatory markers. MATERIALS AND METHODS A total of 59 hyperlipidemic subjects completed the study. They were divided into four groups: RBO1 (4,000 ppm gamma-oryzanol, n = 14), RBO2 (8,000 ppm gamma-oryzanol, n = 15), RBO3 (11,000 ppm gamma-oryzanol, n = 15), and control (soybean oil, n = 15). The assigned oil (30 mL) was incorporated into three cooked meals each day for 4 weeks. Anthropometrical measurements and blood samples were taken to evaluate body weight, body composition, lipid parameters, antioxidant status, and inflammatory markers before and after the intervention. RESULTS Compared with the control, consumption of RBO significantly decreased low-density lipoprotein cholesterol (LDL-C) levels (percentage change: -0.8% [control]; -8% [RBO1]; -11.8% [RBO2]; and -12.2% [RBO3], p = 0.012) with the greatest change found in RBO2 and RBO3. In addition, antioxidant status also improved significantly. Levels of oxygen radical absorbance capacity (ORAC) and ferric reducing antioxidant power (FRAP) increased after consumption of a diet with RBO compared with consumption of a diet with soybean oil (ORAC: -2.7% [control]; 4.1% [RBO1]; 8.6% [RBO2]; and 10.1% [RBO3], p < 0.001; FRAP: -4.4% [control]; 4.7% [RBO1]; 7.4% [RBO2]; and 7.6% [RBO3], p < 0.001). CONCLUSIONS RBO with gamma-oryzanol could decrease LDL-C levels and increase antioxidant capacity in hyperlipidemic subjects. Therefore, RBO consumption may reduce cardiovascular disease risk factors.
Candidate Biomarkers of Liver Fibrosis: A Concise, Pathophysiology-oriented Review

Abstract Repair of sustained liver injury results in fibrosis (i.e. the accumulation of extracellular matrix proteins), and ultimately the complete distortion of parenchymal architecture of the liver, which we call cirrhosis. Detecting and staging of fibrosis is thus a mainstay in the management of chronic liver diseases, since many clinically relevant decisions, such as starting treatment and/or monitoring for complications including hepatocellular carcinoma, may depend on it. The gold standard for fibrosis staging is liver biopsy, the role of which, however, is questioned nowadays because of cost, hazards and poor acceptance by patients. On the other hand, imaging techniques and/or measurement of direct and indirect serum markers have not proved to be completely satisfactory under all circumstances as alternatives to liver biopsy. Making progress in this field is now more crucial than ever, since treatments for established fibrosis appear on the horizon. Fine dissection of the pathways involved in the pathophysiology of liver diseases has put forward several novel candidate biomarkers of liver fibrosis, such as growth arrest-specific6, Mac-2-binding protein, osteopontin, placental growth factor, growth/differentiation factor 15 and hepatocyte growth factor. All molecules have been suggested to have potential to complement or substitute methods currently used to stage liver diseases. Here, we review the pros and cons for their use in this setting.