Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Regulating Artificial-Free Linear Programming Solver Using a Jump and Simplex Method

An enthusiastic artificial-free linear programming method based on a sequence of jumps and the simplex method is proposed in this paper. It performs in three phases. Starting with phase 1, it guarantees the existence of a feasible point by relaxing all non-acute constraints. With this initial starting feasible point, in phase 2, it sequentially jumps to the improved objective feasible points. The last phase reinstates the rest of the non-acute constraints and uses the dual simplex method to find the optimal point. The computation results show that this method is more efficient than the standard simplex method and the artificial-free simplex algorithm based on the non-acute constraint relaxation for 41 netlib problems and 280 simulated linear programs.
On the Stability with Respect to H-Manifolds for Cohen–Grossberg-Type Bidirectional Associative Memory Neural Networks with Variable Impulsive Perturbations and Time-Varying Delays

The present paper is devoted to Bidirectional Associative Memory (BAM) Cohen–Grossberg-type impulsive neural networks with time-varying delays. Instead of impulsive discontinuities at fixed moments of time, we consider variable impulsive perturbations. The stability with respect to manifolds notion is introduced for the neural network model under consideration. By means of the Lyapunov function method sufficient conditions that guarantee the stability properties of solutions are established. Two examples are presented to show the validity of the proposed stability criteria.
Gamified Information Systems for Assisted Living Facilities - Relevant Design Guidelines, Affordances and Adoption Barriers

Gamification and gamified information systems (GIS) apply video game elements to encourage the work on boring and everyday tasks. Meanwhile, several research works provide evidence that gamification increases efficiency and effectivity of such tasks. The paper at hand investigates the health care sector, which is challenged with cost pressure and suffers in process efficiency. We hypothesize that GIS may improve the efficiency and quality of care processes. By applying an interview-based content analysis, the paper at hand evaluates gamification elements in an assisted living environment and provides three research contributions. First, insights into relevant GIS affordances and application examples for assisted living facilities are given. Second, assisted living experts evaluate GIS design guidelines. Both the relevant affordances and design principles comprise a basis for the development of a GIS for social workers in assisted living facilities. Third, potential adoption barriers and design guidelines for GIS in assisted living are presented.
Establishing Host Identity Protocol Opportunistic Mode with TCP Option

This document specifies an alternative opportunistic mode for the Host Identity Protocol (HIP). The opportunistic mode is initialized by adding a 128 bit Host Identity Tag (HIT) as a TCP option to a TCP SYN packet. The mode allows a TCP connection to be established directly without a timeout delay in the case the peer does not support HIP.
Abdominal Complications after Lung Transplantation-Systematic Review and Meta-Analysis

Background: Lung transplantation is well accepted therapy for end-stage lung disease. Abdominal complications after lung transplantation are well known, have been described in retrospective, descriptive series, and are important causes of morbidity and mortality.Aim: To assess the incidence of abdominal complications in patients after lung transplantation, to estimate the surgical approach and mortality.Methods: Medical literature database searches were performed through 31.1. 2018. Eligible studies were included if they met the following criteria: 1. Published as full articles, 2. Written in English, 3. Being observational studies, 4. Described abdominal complications in patients after lung transplantation. Event rates (ER, 95% confidence intervals (95%CIs)) were calculated by using Der Simonian and Laird method (random effect model). Meta-analysis was performed by utilizing suitable software (Comprehensive Meta-Analysis–Version 2, BIOSTAT INC., Englewood, NJ, USA). Results: There was significant heterogeneity in the included studies (Q=393.928, df (Q) = 11, I2= 97.208 %, p<0.0001). The pooled ERs (95% CI) for abdominal complications = 0.214, (95%CI 0.124- 0.343, test for overall effect Z= -3.013). To assess the mortality, surgery needed to deal with the abdominal complications and the relative contribution of lower and upper gastrointestinal tract, separate meta-analyses were performed and found significant pooled ERs (95%CI): 0.039 (0.024-0.062), 0.096 (0.071-0.128), 0.087 (0.057-0.129) and 0.052 (0.015-0.166), respectively.Conclusion: Abdominal complications, more of the lower gastro-intestinal tract than the upper, are not rare after lung transplantation, and exceed 21%. Up to 10% of the patients may need surgery and about 3.9% will die.
Towards Understanding and Fixing Upstream Merge Induced Conflicts in Divergent Forks: An Industrial Case Study

Divergent forks are a common practice in open-source software development to perform long-term, independent and diverging development on top of a popular source repository. However, keeping such divergent downstream forks in sync with the upstream source evolution poses engineering challenges in terms of frequent merge conflicts. In this paper, we conduct the first industrial case study of the implications of frequent merges from upstream and the resulting merge conflicts, in the context of Microsoft Edge development. The study consists of two parts. First, we describe the nature of merge conflicts that arise due to merges from upstream and classify them into textual conflicts, build breaks, and test failures. Second, we investigate the feasibility of automatically fixing a class of merge conflicts related to build breaks that consume a significant amount of developer time to rootcause and fix. Towards this end, we have implemented a tool MrgBldBrkFixer and evaluate it on three months of real Microsoft Edge Beta development data, and report encouraging results.
A review of the Polyrhachis aculeata species-group of the subgenus Myrma Billberg (Hymenoptera: Formicidae: Formicinae), with keys and descriptions of new species.

The 'Polyrhachis aculeata' species-group of the subgenus 'Myrma' Billberg is reviewed. Seventeen species of the group are recognised, including nine previously described: 'Polyrhachis aculeata' Mayr, 'P. cybele' Wheeler, 'P. diana' Wheeler, 'P. gibbosa' Forel, 'P. hemiopticoides' Mukerjee, 'P. latona' Wheeler, 'P. murina' Emery, 'P. numeria' Fr. Smith and 'P. pubescens' Mayr. Two former subspecies, 'Polyrhachis murina selecta' Forel and 'P. pubescens alatisquamis' Forel, are raised to specific status and six species are described as new: 'Polyrhachis dimoculata', 'P. kebunraya', 'P. noonananti', 'P. palawanensis', 'P. starri' and 'P. watanasiti'. 'Polyrhachis alatisquamis' is redescribed and the previously unknown queens of 'P. cybele', 'P. latona' and 'P. pubescens' are described. A neotype of 'Polyrhachis pubescens' Mayr and a lectotype of 'P. murina' Emery are designated. A key to the species of the group is included. All species are illustrated and their known distributions summarised.
Group-wise attention fusion network for choroid segmentation in OCT images

The choroid is an important structure of the eye and choroid thickness distribution estimated from optical coherence tomography (OCT) images plays a vital role in analysis of many retinal diseases. This paper proposes a novel group-wise attention fusion network (referred to as GAF-Net) to segment the choroid layer, which can effectively work for both normal and pathological myopia retina. Currently, most networks perform unified processing of all feature maps in the same layer, which leads to not satisfactory choroid segmentation results. In order to improve this , GAF-Net proposes a group-wise channel module (GCM) and a group-wise spatial module (GSM) to fuse group-wise information. The GCM uses channel information to guide the fusion of group-wise context information, while the GSM uses spatial information to guide the fusion of group-wise context information. Furthermore, we adopt a joint loss to solve the problem of data imbalance and the uneven choroid target area. Experimental evaluations on a dataset composed of 1650 clinically obtained B-scans show that the proposed GAF-Net can achieve a Dice similarity coefficient of 95.21±0.73%.
A new measure of conservation value combining rarity and ecological diversity: a case study with light trap collected caddisflies (Insecta: Trichoptera)

The objective of the present study was to analyse the conservation importance of streams, rivers and lakes for maintaining caddistly assemblages of Hungarian localities. Light traps ensured comparable catches of caddisflies from different aquatic habitats. A total of 245,363 individuals belonging to 152 species collected from 23 localities over the flight period were included in the analysis. Conservation value of caddistly assemblages was evaluated on the basis of a newly developed Rarity and Ecological Diversity (RED)-index expressing ecological diversity and the average rarity of caddistlies in Hungarian localities. The results showed that streams were the most suitable habitats for maintaining rare caddisfly species in diverse assemblages, while rivers had the lowest conservation importance.
Graphical Algorithms for Finding the Nucleolus of Binary-Valued Matching Games

In cooperative game theory, the class of assignment games models a hypothetical market with two groups of people (for instance, buyers and sellers, or men and women). When a player from one group pairs up with a player from the other group, they receive a payoff, which is specified for each potential pair. The players work together to maximize the total payoff for the market. But then the real problem arises, which is how to split that payoff amongst everyone. One popular solution for cooperative games, and especially for assignment games, is the nucleolus. This is a way of splitting the payoff which maximizes everyone's satisfaction, and minimizes possible incentive for deviating from the group. In this thesis, we expand upon previous work by Solymosi and Raghavan, which gives an algorithm for calculating the nucleolus of assignment games. We focus on a special case, in which all pairs' payoffs are either 0 or 1. This can be interpreted as a measure of compatibility for the pair, and the way each pair splits their payoff can be viewed as their balance of power. In this special case, the previous algorithm can be recontextualized and streamlined using purely graphical methods. We also provide an algorithm for a more general class of games, called matching games. These are defined similarly to assignment games, but without the restriction that the market must be bipartite. This gives us deeper insight into societal applications: for instance, the matching game can be used to model a marriage market in our modern society, while the assignment game would only apply to societies where same-sex marriage is not allowed.
Machine learning applications in finance: some case studies

The first part of this thesis discusses the application of artificial intelligence to stock price prediction and the building of another artificial intelligence model for stock price prediction during mergers and acquisitions. It is demonstrated that a combination of Long Short-Term Memory/Recurrent Neural Network + Convolutional Neural Network + Attention (LSTM/RNN+CNN+Attention) trained on dailyand minute-stock price and volume data are able to predict future stock prices and stock price changes three times better than traditional non-deep learning methods. Further research has been done on stock price prediction during Mergers and Acquisitions for technology unicorns, finding that when more information on M&A has been added into the input dataset, the accuracy can be improved by about 2%. It is the first time that a deep learning algorithm has been applied to modelling volume and price/price change, and to M&A circumstances. In the second part of this thesis, a Support Vector Machine (SVM) method is proposed, which enables adverting agencies to assess the effectiveness of their advertisement rapidly, quantitatively and without bias. This is crucial work for business/equity trading because when the product/service of a company sells well, then the corresponding company stock prices rise. The success of an advertising agency in their ability to produce compelling advertisement is therefore related to stock price prediction. This research explores models that outperform existing research; even the basic machine learning model improves the accuracy of a prediction to purchase from 70% up to 80%. The last part of this thesis incorporates a deep learning method to encrypt the data going between input nodes and the rest of the network. This method has the advantage of simultaneously preventing sensitive data being intercepted during the transmission from the network edge over insecure transports, and also anonymize incoming data in a way that could help satisfy privacy laws such as General Data Protection Regulation (GDPR). This research has provided a new data privacy and security structure that is more cost efficient than existed deep learning model done solely locally or remotely.
Prediction of University Network Traffic Using Deep Learning Method

The paper goes over what would happen when deep learning methods are implemented for University Network Traffic. In order to predict the outcome, the paper will compare the network before the implementation of deep learning and after the implementation of deep learning. If the results show an increase in data transfer speed after the implementation of deep learning, it suggests that the implementation of deep learning in any network system will most likely improve the data transfer speed. The paper first defines what deep learning is. It then utilizes different methods of deep learning in order to train the system. The system will go through the training phase, testing phase, and the prediction phase in order to familiarize it with the current network system. Once it understands the network system, it will find the optimized network system in order to improve the speed of network connection.
Psoriasis. Diagnostic and therapeutic recommendations of the Polish Dermatological Society. Part II: Moderate to severe psoriasis

The second part of diagnostic and therapeutic guidelines of Polish Dermatological Society focuses on moderate and severe psoriasis, in which phototherapy or systemic treatment should be initiated. Currently, it is recommended to treat moderate to severe plaque psoriasis with either classic anti-psoriatic drugs (methotrexate, cyclosporine, acitretin), TNF-a-inhibitors (adalimumab, etanercept, infliximab), interleukin 12/23 blocking agent (ustekinumab) and new drugs – interleukin-17 inhibitors (secukinumab, ixekizumab) or interleukin 23 inhibitors (guselkumab, tildrakizumab), as well as the new low-molecular weight agents (apremilast, dimethyl fumarate). In some patients, UV-based therapy may be advisable. These recommendations summarize the principles of the use of these agents in patients with psoriasis in dermatological practice. The efficacy and safety of therapy is discussed and available methods to prevent adverse events are summarized. Special article/Praca sPecjalna
Non-Orthogonal Multiple Access in the Presence of Phase Noise

In the present letter, we investigate the impact of phase noise on multi-carrier wireless systems that employ non-orthogonal multiple access (NOMA). Specifically, we extract novel closed-form expressions for the outage probability. These expressions can quantify and evaluate the system performance for the general case in which different levels of inter-carrier interference (ICI) is experienced by neighbor carriers as well as the special case, where the neighbor carriers creates the same ICI. The results reveal the importance of taking into consideration the influence of local oscillator imperfections, when assessing the performance of NOMA networks.
“Tell Me Something Good”: Testing the Longitudinal Effects of Constructive News Using the Google Assistant

Americans say that reading, watching, or listening to the news is one of their leading causes of stress. And indeed, research has shown that negative news can negatively impact people’s attitudes, behaviors, and mental health. To combat the unwelcome effects of negative news, some have suggested that reporters practice more constructive or solution-oriented journalism by reporting stories that highlight societal progress. Drawing on cognitive appraisal theory and the broaden-and-build theory of positive emotions, this study tested the impact of constructive news. In a mixed-design quasi-experiment, participants received access to a Google Assistant feature in which they could prompt the assistant to summarize a constructive news story. After 2 weeks, those who used the feature were more likely, between pretest and posttest, than those who did not to feel positive while consuming traditional news, suggesting constructive news could mitigate the effects of more typical, negative news.
Understanding User’s Behavior by Social Media Data Clustering

Mining Social Media data is an important data mining problem. There are a couple tools used for these purposes and document clustering is one of them. Clustering of documents is the automatic division of text documents into a group of clusters in such a manner that the documents within a cluster have high similarity values, but dissimilar to documents in the other clusters. Document clustering is quite popular among the database community and NLP people and it has been studied by a host of researches of diverse fields. It has wide range of application areas like search engines, web mining, information retrieval and topological analysis. Recently, it is used in analysis of Social Media data to understand the Social Human behavior of users. In this article we propose a clustering algorithm for categorizing the users and hence understanding their behaviour. Our proposed algorithm is a hierarchical agglomerative one which uses fuzzy concept, where each cluster is viewed as a fuzzy set over some finite universal set. A Canberra metric based similarity measure is used to measure inter-cluster similarity. The efficiency of the algorithm has been established with the help of complexity analysis
Social Work Education in Europe: A Retrospective View

The introduction of European funding in the 1980s played an important part in the establishment of networks contributing to the learning opportunities of social work students in the latter part of the twentieth century and more recently. While the number of British social work schools participating were in the minority, membership of networks contributed to the growth in knowledge about European frameworks and comparative social work and professional education. This has subsequently became formalised through edited (comparative) texts and research projects. Use of the term ‘social professionals’ highlights both the central importance of ‘the social’ and the diversity of educational and professional awards and occupational groups engaged in this broad field. The experience in networks introduced staff and students alike to different ways of thinking about the roles of the state and the family and of organising work in relation to a range of often similar social problems. British participation was sometimes hindered by language limitations, but European experience introduced new vocabulary such as subsidiarity, social pedagogy and social exclusion. It has also served to illustrate the narrowing of British social work; the high degree of regulation and the dichotomy between education and training evident in the UK.
Cost-minimization analysis of community pharmacy-based point-of-care testing for strep throat in 5 Canadian provinces

Background: Strep throat point-of-care (POC) testing in community pharmacies will enable pharmacist-based care for this condition. Our objective was to conduct an economic evaluation of treating severe sore throat when this service was offered in pharmacies in 5 Canadian provinces. Methods: We conducted 5 separate cost-minimization analyses for the provinces of Alberta, British Columbia, Nova Scotia, Ontario and Saskatchewan, from the public payer perspective, to estimate mean cost per patient of treating severe sore throat in 2 scenarios: 1) physician-based usual care in a family physician’s office, a walk-in clinic or an emergency room (ER) and 2) a new scenario where patients received care described above or in a pharmacy offering strep throat POC testing. One-way sensitivity analyses were conducted to account for model uncertainty. Results: Mean cost per patient for each pathway in the base-case analyses for the 5 provinces ranged from 1) $37.55 to $61.57 for family physician, 2) $37.55 to $61.57 for walk-in clinic, 3) $38.88 to $57.56 for ER and 4) $19.12 to $21.83 for pharmacy, representing savings ranging from $12.47 to $24.36 per patient for the new scenario. Approximate total cost savings range from $1.3 million to $2.6 million per year across the 5 provinces. All sensitivity analyses yielded cost savings for the new scenario. Discussion: Across 5 provinces, strep throat POC testing in pharmacies was cost saving compared to physician-based care. Sensitivity analyses demonstrated the robustness of these results. Conclusions: Funding strep throat POC testing in community pharmacies in these 5 provinces would lead to public health system cost savings and potentially improve patients’ access to care for severe sore throat.
VIBRODIAGNOSTICS OF ROLLING BALL BEARINGS CONNECTED WITH PROCESSING, RESULT´S COMPARISON AND PREDICTION OF SERVICE LIFE

This article deals with vibration analysis and the processing of the vibration signal obtained during the experimental measurement on a rotary shaft at start-up, constant speed and run-out, with rolling ball bearings. The next step of the research is the introduction and application of mathematical and numerical methods for the processing of input and measured data using diagnostic and mathematical software. The principle of measurement and processing was to compare the results of oscillation of an undamaged bearing with several types of damaged bearings with corresponding deformations in the orbits/raceways. The purpose of this research and comparison was to achieve optimal results (in terms of amplitude values, the occurrence of eigenvalue vibrations and the resonance phenomenon) of individual bearings with corresponding deformations in the raceways and to obtain calibration diagrams as a tool for possible prediction of bearings in operation.